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+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"toc_visible":true,"authorship_tag":"ABX9TyNhJHP7WxQ7d+Oy8alOoMPg"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"_u851roakTYV"},"outputs":[],"source":["import matplotlib.pyplot as plt\n","import pandas as pd\n","import torch\n","import torch.nn as nn\n","from keras.optimizers import Adam\n","import numpy as np\n","import torch.optim as optim\n","import torch.utils.data as data\n","from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import MinMaxScaler, LabelEncoder\n","import ast\n","import seaborn as sns\n","from torch.utils.data import DataLoader, TensorDataset\n","from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n","import matplotlib.pyplot as plt\n","\n","from keras.models import Sequential\n","from keras.layers import LSTM, Dense, Dropout\n","from keras.callbacks import EarlyStopping\n"]},{"cell_type":"code","source":["dataset = pd.read_csv('merged_dataset.csv')"],"metadata":{"id":"uNjvJuH1aJJJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def is_float(num):\n","    try:\n","        float(num)\n","        return True\n","    except ValueError:\n","        return False"],"metadata":{"id":"fqmz0Ym6aNdP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataset['landmarks'] = dataset['landmarks'].apply(lambda arr: np.array([float(n) for n in arr.split() if is_float(n)]))"],"metadata":{"id":"YC4WIebyaN2r"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataset['Label'].value_counts()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1gBiAjQ7aPfx","executionInfo":{"status":"ok","timestamp":1693268298394,"user_tz":300,"elapsed":6,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"2afb6955-f6e6-42ef-9c15-a647747ecb3e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["good    8593\n","bad     7051\n","Name: Label, dtype: int64"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["# Group the data by 'video' and 'group'\n","grouped_data = dataset.groupby(['video', 'group'])\n","\n","# Define the sequence length\n","sequence_length = 10\n","\n","# Create lists to store the sequences and labels\n","sequences = []\n","labels = []\n","\n","# Iterate over each group\n","for group, data in grouped_data:\n","    landmarks = data['landmarks'].tolist()\n","    group_labels = data['Label'].tolist()\n","\n","    # Create sequences of landmarks\n","    for i in range(len(landmarks) - sequence_length + 1):\n","        sequence = landmarks[i:i+sequence_length]\n","        sequences.append(sequence)\n","        labels.append(group_labels[i+sequence_length-1])"],"metadata":{"id":"xvOuYs5maQ_f"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["sequences = np.array(sequences)\n","\n","scaler = MinMaxScaler()\n","normalized_sequences = np.zeros_like(sequences)\n","\n","for i in range(sequences.shape[0]):\n","    for j in range(sequences.shape[1]):\n","        # Flatten the landmarks for each set within the sequence\n","        landmarks_flattened = np.reshape(sequences[i, j], (-1, 1))\n","        # Normalize the landmarks\n","        landmarks_normalized = scaler.fit_transform(landmarks_flattened)\n","        # Reshape the normalized landmarks back to the original shape\n","        normalized_landmarks = np.reshape(landmarks_normalized, sequences[i, j].shape)\n","        # Update the normalized landmarks in the sequences array\n","        normalized_sequences[i, j] = normalized_landmarks"],"metadata":{"id":"ZiBVV-MUadD1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["label_encoder = LabelEncoder()\n","labels_encoded = label_encoder.fit_transform(labels)"],"metadata":{"id":"Y9rHRobSaejd"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_X, test_X, train_y, test_y = train_test_split(normalized_sequences, labels_encoded, test_size=0.2, shuffle=True)"],"metadata":{"id":"pUKrvhFhagFW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(train_X.shape)\n","print(train_y.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ALPRQJbhahmX","executionInfo":{"status":"ok","timestamp":1693268302775,"user_tz":300,"elapsed":8,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"3e81f0b3-cb38-4471-b695-cfbe1d137653"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(1188, 10, 50)\n","(1188,)\n"]}]},{"cell_type":"code","source":["train_X_tensor = torch.Tensor(train_X)\n","train_y_tensor = torch.Tensor(train_y)"],"metadata":{"id":"NyUzmLW3ajDS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_dataset = TensorDataset(train_X_tensor, train_y_tensor)\n","train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n","num_features = normalized_sequences.shape[2]"],"metadata":{"id":"vVAnlex_akRj"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# First Model Working"],"metadata":{"id":"zFxG3XpR9tr8"}},{"cell_type":"code","source":["\n","# Define the LSTM model\n","model_ex0 = Sequential()\n","model_ex0.add(LSTM(units=32, input_shape=(sequence_length, num_features), return_sequences=True))\n","model_ex0.add(Dropout(0.7))\n","model_ex0.add(LSTM(units=64))\n","model_ex0.add(Dropout(0.5))\n","model_ex0.add(Dense(units=1, activation='sigmoid'))\n","\n","custom_learning_rate = 0.0001\n","optimizer = Adam(learning_rate=custom_learning_rate)\n","\n","# Compile the model\n","model_ex0.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n","\n","# Print the model summary\n","model_ex0.summary()\n","\n","# Train the model\n","batch_size = 32\n","epochs = 200\n","early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n","history_ex0 = model_ex0.fit(train_X, train_y, batch_size=batch_size, epochs=epochs, validation_split=0.2, verbose=1, callbacks=[early_stopping])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oujTczcldo_O","executionInfo":{"status":"ok","timestamp":1693268652924,"user_tz":300,"elapsed":94260,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"c6294544-02fe-4f5e-c58b-7729a2ec78c4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," lstm (LSTM)                 (None, 10, 32)            10624     \n","                                                                 \n"," dropout (Dropout)           (None, 10, 32)            0         \n","                                                                 \n"," lstm_1 (LSTM)               (None, 64)                24832     \n","                                                                 \n"," dropout_1 (Dropout)         (None, 64)                0         \n","                                                                 \n"," dense (Dense)               (None, 1)                 65        \n","                                                                 \n","=================================================================\n","Total params: 35,521\n","Trainable params: 35,521\n","Non-trainable params: 0\n","_________________________________________________________________\n","Epoch 1/200\n","30/30 [==============================] - 6s 67ms/step - loss: 0.7008 - accuracy: 0.4832 - val_loss: 0.6909 - val_accuracy: 0.5336\n","Epoch 2/200\n","30/30 [==============================] - 1s 26ms/step - loss: 0.6873 - accuracy: 0.5368 - val_loss: 0.6891 - val_accuracy: 0.5336\n","Epoch 3/200\n","30/30 [==============================] - 1s 21ms/step - loss: 0.6942 - accuracy: 0.5358 - val_loss: 0.6878 - val_accuracy: 0.5336\n","Epoch 4/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.6907 - accuracy: 0.5453 - val_loss: 0.6860 - val_accuracy: 0.5336\n","Epoch 5/200\n","30/30 [==============================] - 1s 18ms/step - loss: 0.6901 - accuracy: 0.5432 - val_loss: 0.6845 - val_accuracy: 0.5336\n","Epoch 6/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.6848 - accuracy: 0.5611 - val_loss: 0.6824 - val_accuracy: 0.5336\n","Epoch 7/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.6861 - accuracy: 0.5621 - val_loss: 0.6801 - val_accuracy: 0.5336\n","Epoch 8/200\n","30/30 [==============================] - 1s 18ms/step - loss: 0.6796 - accuracy: 0.5747 - val_loss: 0.6770 - val_accuracy: 0.5336\n","Epoch 9/200\n","30/30 [==============================] - 0s 17ms/step - loss: 0.6725 - accuracy: 0.5768 - val_loss: 0.6725 - val_accuracy: 0.5462\n","Epoch 10/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.6693 - accuracy: 0.5884 - val_loss: 0.6663 - val_accuracy: 0.5714\n","Epoch 11/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.6630 - accuracy: 0.6284 - val_loss: 0.6567 - val_accuracy: 0.6134\n","Epoch 12/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.6537 - accuracy: 0.6368 - val_loss: 0.6449 - val_accuracy: 0.6639\n","Epoch 13/200\n","30/30 [==============================] - 1s 25ms/step - loss: 0.6389 - accuracy: 0.6547 - val_loss: 0.6289 - val_accuracy: 0.6807\n","Epoch 14/200\n","30/30 [==============================] - 1s 34ms/step - loss: 0.6165 - accuracy: 0.7021 - val_loss: 0.6128 - val_accuracy: 0.6807\n","Epoch 15/200\n","30/30 [==============================] - 1s 41ms/step - loss: 0.5983 - accuracy: 0.6937 - val_loss: 0.5923 - val_accuracy: 0.7017\n","Epoch 16/200\n","30/30 [==============================] - 1s 37ms/step - loss: 0.5871 - accuracy: 0.7074 - val_loss: 0.5806 - val_accuracy: 0.7101\n","Epoch 17/200\n","30/30 [==============================] - 1s 31ms/step - loss: 0.5640 - accuracy: 0.7284 - val_loss: 0.5735 - val_accuracy: 0.7143\n","Epoch 18/200\n","30/30 [==============================] - 2s 53ms/step - loss: 0.5697 - accuracy: 0.7242 - val_loss: 0.5656 - val_accuracy: 0.7143\n","Epoch 19/200\n","30/30 [==============================] - 1s 45ms/step - loss: 0.5535 - accuracy: 0.7368 - val_loss: 0.5657 - val_accuracy: 0.7269\n","Epoch 20/200\n","30/30 [==============================] - 1s 45ms/step - loss: 0.5449 - accuracy: 0.7411 - val_loss: 0.5566 - val_accuracy: 0.7227\n","Epoch 21/200\n","30/30 [==============================] - 1s 49ms/step - loss: 0.5516 - accuracy: 0.7316 - val_loss: 0.5529 - val_accuracy: 0.7269\n","Epoch 22/200\n","30/30 [==============================] - 2s 63ms/step - loss: 0.5554 - accuracy: 0.7442 - val_loss: 0.5527 - val_accuracy: 0.7143\n","Epoch 23/200\n","30/30 [==============================] - 1s 40ms/step - loss: 0.5314 - accuracy: 0.7579 - val_loss: 0.5484 - val_accuracy: 0.7395\n","Epoch 24/200\n","30/30 [==============================] - 1s 37ms/step - loss: 0.5409 - accuracy: 0.7358 - val_loss: 0.5476 - val_accuracy: 0.7395\n","Epoch 25/200\n","30/30 [==============================] - 1s 37ms/step - loss: 0.5404 - accuracy: 0.7474 - val_loss: 0.5450 - val_accuracy: 0.7437\n","Epoch 26/200\n","30/30 [==============================] - 1s 46ms/step - loss: 0.5367 - accuracy: 0.7537 - val_loss: 0.5418 - val_accuracy: 0.7395\n","Epoch 27/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.5280 - accuracy: 0.7558 - val_loss: 0.5399 - val_accuracy: 0.7437\n","Epoch 28/200\n","30/30 [==============================] - 1s 38ms/step - loss: 0.5341 - accuracy: 0.7621 - val_loss: 0.5426 - val_accuracy: 0.7353\n","Epoch 29/200\n","30/30 [==============================] - 1s 34ms/step - loss: 0.5274 - accuracy: 0.7611 - val_loss: 0.5352 - val_accuracy: 0.7437\n","Epoch 30/200\n","30/30 [==============================] - 1s 46ms/step - loss: 0.5252 - accuracy: 0.7589 - val_loss: 0.5349 - val_accuracy: 0.7395\n","Epoch 31/200\n","30/30 [==============================] - 1s 37ms/step - loss: 0.5169 - accuracy: 0.7495 - val_loss: 0.5454 - val_accuracy: 0.7437\n","Epoch 32/200\n","30/30 [==============================] - 1s 29ms/step - loss: 0.5210 - accuracy: 0.7600 - val_loss: 0.5303 - val_accuracy: 0.7311\n","Epoch 33/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.5136 - accuracy: 0.7663 - val_loss: 0.5294 - val_accuracy: 0.7353\n","Epoch 34/200\n","30/30 [==============================] - 1s 25ms/step - loss: 0.5118 - accuracy: 0.7653 - val_loss: 0.5339 - val_accuracy: 0.7437\n","Epoch 35/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.5273 - accuracy: 0.7558 - val_loss: 0.5274 - val_accuracy: 0.7353\n","Epoch 36/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.5008 - accuracy: 0.7684 - val_loss: 0.5242 - val_accuracy: 0.7353\n","Epoch 37/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.5125 - accuracy: 0.7684 - val_loss: 0.5239 - val_accuracy: 0.7395\n","Epoch 38/200\n","30/30 [==============================] - 1s 18ms/step - loss: 0.5132 - accuracy: 0.7611 - val_loss: 0.5231 - val_accuracy: 0.7353\n","Epoch 39/200\n","30/30 [==============================] - 1s 18ms/step - loss: 0.4959 - accuracy: 0.7747 - val_loss: 0.5346 - val_accuracy: 0.7395\n","Epoch 40/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.5050 - accuracy: 0.7632 - val_loss: 0.5201 - val_accuracy: 0.7395\n","Epoch 41/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4881 - accuracy: 0.7737 - val_loss: 0.5177 - val_accuracy: 0.7395\n","Epoch 42/200\n","30/30 [==============================] - 0s 17ms/step - loss: 0.4944 - accuracy: 0.7737 - val_loss: 0.5222 - val_accuracy: 0.7521\n","Epoch 43/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4987 - accuracy: 0.7737 - val_loss: 0.5234 - val_accuracy: 0.7479\n","Epoch 44/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.4955 - accuracy: 0.7800 - val_loss: 0.5111 - val_accuracy: 0.7479\n","Epoch 45/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4840 - accuracy: 0.7737 - val_loss: 0.5088 - val_accuracy: 0.7395\n","Epoch 46/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4863 - accuracy: 0.7811 - val_loss: 0.5140 - val_accuracy: 0.7479\n","Epoch 47/200\n","30/30 [==============================] - 1s 25ms/step - loss: 0.4781 - accuracy: 0.7832 - val_loss: 0.5089 - val_accuracy: 0.7437\n","Epoch 48/200\n","30/30 [==============================] - 1s 28ms/step - loss: 0.4773 - accuracy: 0.7811 - val_loss: 0.5065 - val_accuracy: 0.7563\n","Epoch 49/200\n","30/30 [==============================] - 1s 25ms/step - loss: 0.4771 - accuracy: 0.7842 - val_loss: 0.5002 - val_accuracy: 0.7479\n","Epoch 50/200\n","30/30 [==============================] - 1s 22ms/step - loss: 0.4689 - accuracy: 0.7811 - val_loss: 0.4989 - val_accuracy: 0.7605\n","Epoch 51/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.4729 - accuracy: 0.7821 - val_loss: 0.4966 - val_accuracy: 0.7605\n","Epoch 52/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.4711 - accuracy: 0.7968 - val_loss: 0.5061 - val_accuracy: 0.7521\n","Epoch 53/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4643 - accuracy: 0.7874 - val_loss: 0.4927 - val_accuracy: 0.7647\n","Epoch 54/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.4682 - accuracy: 0.7947 - val_loss: 0.5087 - val_accuracy: 0.7521\n","Epoch 55/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.4624 - accuracy: 0.7853 - val_loss: 0.4902 - val_accuracy: 0.7563\n","Epoch 56/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4516 - accuracy: 0.8063 - val_loss: 0.4855 - val_accuracy: 0.7689\n","Epoch 57/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4469 - accuracy: 0.8105 - val_loss: 0.4879 - val_accuracy: 0.7773\n","Epoch 58/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.4531 - accuracy: 0.8011 - val_loss: 0.4897 - val_accuracy: 0.7605\n","Epoch 59/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.4454 - accuracy: 0.7989 - val_loss: 0.4926 - val_accuracy: 0.7605\n","Epoch 60/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4471 - accuracy: 0.8032 - val_loss: 0.4817 - val_accuracy: 0.7731\n","Epoch 61/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.4440 - accuracy: 0.8105 - val_loss: 0.4910 - val_accuracy: 0.7689\n","Epoch 62/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4428 - accuracy: 0.8053 - val_loss: 0.4866 - val_accuracy: 0.7689\n","Epoch 63/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.4405 - accuracy: 0.8084 - val_loss: 0.4772 - val_accuracy: 0.7689\n","Epoch 64/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4246 - accuracy: 0.8084 - val_loss: 0.4748 - val_accuracy: 0.7857\n","Epoch 65/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4371 - accuracy: 0.8147 - val_loss: 0.4826 - val_accuracy: 0.7689\n","Epoch 66/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4218 - accuracy: 0.8179 - val_loss: 0.4774 - val_accuracy: 0.7731\n","Epoch 67/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.4539 - accuracy: 0.7947 - val_loss: 0.4763 - val_accuracy: 0.7731\n","Epoch 68/200\n","30/30 [==============================] - 1s 18ms/step - loss: 0.4271 - accuracy: 0.8263 - val_loss: 0.4688 - val_accuracy: 0.7899\n","Epoch 69/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.4161 - accuracy: 0.8253 - val_loss: 0.4692 - val_accuracy: 0.7941\n","Epoch 70/200\n","30/30 [==============================] - 1s 20ms/step - loss: 0.4182 - accuracy: 0.8189 - val_loss: 0.4689 - val_accuracy: 0.7815\n","Epoch 71/200\n","30/30 [==============================] - 1s 24ms/step - loss: 0.4207 - accuracy: 0.8253 - val_loss: 0.4706 - val_accuracy: 0.8025\n","Epoch 72/200\n","30/30 [==============================] - 1s 24ms/step - loss: 0.4280 - accuracy: 0.8232 - val_loss: 0.4703 - val_accuracy: 0.7647\n","Epoch 73/200\n","30/30 [==============================] - 1s 25ms/step - loss: 0.4077 - accuracy: 0.8168 - val_loss: 0.4804 - val_accuracy: 0.7899\n","Epoch 74/200\n","30/30 [==============================] - 1s 22ms/step - loss: 0.4142 - accuracy: 0.8232 - val_loss: 0.4748 - val_accuracy: 0.7899\n","Epoch 75/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4234 - accuracy: 0.8147 - val_loss: 0.4724 - val_accuracy: 0.7857\n","Epoch 76/200\n","30/30 [==============================] - 1s 18ms/step - loss: 0.4077 - accuracy: 0.8284 - val_loss: 0.4741 - val_accuracy: 0.7815\n","Epoch 77/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4122 - accuracy: 0.8211 - val_loss: 0.4679 - val_accuracy: 0.7857\n","Epoch 78/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4055 - accuracy: 0.8242 - val_loss: 0.4651 - val_accuracy: 0.7983\n","Epoch 79/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3998 - accuracy: 0.8316 - val_loss: 0.4684 - val_accuracy: 0.8025\n","Epoch 80/200\n","30/30 [==============================] - 1s 18ms/step - loss: 0.4148 - accuracy: 0.8232 - val_loss: 0.4679 - val_accuracy: 0.8067\n","Epoch 81/200\n","30/30 [==============================] - 0s 17ms/step - loss: 0.4081 - accuracy: 0.8263 - val_loss: 0.4772 - val_accuracy: 0.7983\n","Epoch 82/200\n","30/30 [==============================] - 1s 20ms/step - loss: 0.4035 - accuracy: 0.8326 - val_loss: 0.4708 - val_accuracy: 0.7983\n","Epoch 83/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.4004 - accuracy: 0.8274 - val_loss: 0.4629 - val_accuracy: 0.8193\n","Epoch 84/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4076 - accuracy: 0.8316 - val_loss: 0.4705 - val_accuracy: 0.8025\n","Epoch 85/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4057 - accuracy: 0.8326 - val_loss: 0.4877 - val_accuracy: 0.7773\n","Epoch 86/200\n","30/30 [==============================] - 1s 18ms/step - loss: 0.4213 - accuracy: 0.8316 - val_loss: 0.4622 - val_accuracy: 0.8025\n","Epoch 87/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4044 - accuracy: 0.8389 - val_loss: 0.4577 - val_accuracy: 0.8025\n","Epoch 88/200\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3940 - accuracy: 0.8411 - val_loss: 0.4575 - val_accuracy: 0.8109\n","Epoch 89/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4019 - accuracy: 0.8326 - val_loss: 0.4991 - val_accuracy: 0.7941\n","Epoch 90/200\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3893 - accuracy: 0.8389 - val_loss: 0.4541 - val_accuracy: 0.8109\n","Epoch 91/200\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3898 - accuracy: 0.8432 - val_loss: 0.4662 - val_accuracy: 0.7941\n","Epoch 92/200\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3928 - accuracy: 0.8432 - val_loss: 0.4508 - val_accuracy: 0.8193\n","Epoch 93/200\n","30/30 [==============================] - 1s 21ms/step - loss: 0.3911 - accuracy: 0.8337 - val_loss: 0.4738 - val_accuracy: 0.8109\n","Epoch 94/200\n","30/30 [==============================] - 1s 27ms/step - loss: 0.3968 - accuracy: 0.8442 - val_loss: 0.4720 - val_accuracy: 0.7773\n","Epoch 95/200\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3877 - accuracy: 0.8389 - val_loss: 0.4605 - val_accuracy: 0.7941\n","Epoch 96/200\n","30/30 [==============================] - 1s 24ms/step - loss: 0.3805 - accuracy: 0.8484 - val_loss: 0.4530 - val_accuracy: 0.8067\n","Epoch 97/200\n","30/30 [==============================] - 1s 21ms/step - loss: 0.3848 - accuracy: 0.8389 - val_loss: 0.4551 - val_accuracy: 0.8025\n","Epoch 98/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3867 - accuracy: 0.8505 - val_loss: 0.4643 - val_accuracy: 0.8151\n","Epoch 99/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.3959 - accuracy: 0.8316 - val_loss: 0.4481 - val_accuracy: 0.8067\n","Epoch 100/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.3738 - accuracy: 0.8547 - val_loss: 0.4499 - val_accuracy: 0.8067\n","Epoch 101/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3781 - accuracy: 0.8432 - val_loss: 0.4563 - val_accuracy: 0.8025\n","Epoch 102/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.3737 - accuracy: 0.8432 - val_loss: 0.4619 - val_accuracy: 0.8151\n","Epoch 103/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3899 - accuracy: 0.8368 - val_loss: 0.4570 - val_accuracy: 0.8025\n","Epoch 104/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3734 - accuracy: 0.8474 - val_loss: 0.4429 - val_accuracy: 0.8193\n","Epoch 105/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3768 - accuracy: 0.8474 - val_loss: 0.4523 - val_accuracy: 0.8109\n","Epoch 106/200\n","30/30 [==============================] - 0s 17ms/step - loss: 0.3756 - accuracy: 0.8400 - val_loss: 0.4437 - val_accuracy: 0.8193\n","Epoch 107/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.3712 - accuracy: 0.8453 - val_loss: 0.4481 - val_accuracy: 0.8067\n","Epoch 108/200\n","30/30 [==============================] - 0s 17ms/step - loss: 0.3712 - accuracy: 0.8547 - val_loss: 0.4472 - val_accuracy: 0.8151\n","Epoch 109/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3655 - accuracy: 0.8516 - val_loss: 0.4814 - val_accuracy: 0.7983\n","Epoch 110/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3874 - accuracy: 0.8368 - val_loss: 0.4444 - val_accuracy: 0.8193\n","Epoch 111/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.3699 - accuracy: 0.8547 - val_loss: 0.4487 - val_accuracy: 0.8151\n","Epoch 112/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3856 - accuracy: 0.8453 - val_loss: 0.4634 - val_accuracy: 0.8151\n","Epoch 113/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3748 - accuracy: 0.8442 - val_loss: 0.4389 - val_accuracy: 0.8193\n","Epoch 114/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3690 - accuracy: 0.8526 - val_loss: 0.4358 - val_accuracy: 0.8235\n","Epoch 115/200\n","30/30 [==============================] - 0s 16ms/step - loss: 0.3683 - accuracy: 0.8495 - val_loss: 0.4400 - val_accuracy: 0.8151\n","Epoch 116/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3723 - accuracy: 0.8589 - val_loss: 0.4403 - val_accuracy: 0.8235\n","Epoch 117/200\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3851 - accuracy: 0.8421 - val_loss: 0.4446 - val_accuracy: 0.8193\n","Epoch 118/200\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3732 - accuracy: 0.8463 - val_loss: 0.4424 - val_accuracy: 0.8151\n","Epoch 119/200\n","30/30 [==============================] - 1s 24ms/step - loss: 0.3722 - accuracy: 0.8474 - val_loss: 0.4347 - val_accuracy: 0.8361\n","Epoch 120/200\n","30/30 [==============================] - 1s 24ms/step - loss: 0.3632 - accuracy: 0.8589 - val_loss: 0.4421 - val_accuracy: 0.8151\n","Epoch 121/200\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3620 - accuracy: 0.8558 - val_loss: 0.4398 - val_accuracy: 0.8193\n","Epoch 122/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3732 - accuracy: 0.8474 - val_loss: 0.4555 - val_accuracy: 0.8025\n","Epoch 123/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3619 - accuracy: 0.8621 - val_loss: 0.4289 - val_accuracy: 0.8403\n","Epoch 124/200\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3630 - accuracy: 0.8547 - val_loss: 0.4332 - val_accuracy: 0.8193\n","Epoch 125/200\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3576 - accuracy: 0.8484 - val_loss: 0.4359 - val_accuracy: 0.8151\n","Epoch 126/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3620 - accuracy: 0.8611 - val_loss: 0.4545 - val_accuracy: 0.8109\n","Epoch 127/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3694 - accuracy: 0.8579 - val_loss: 0.4377 - val_accuracy: 0.8277\n","Epoch 128/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3672 - accuracy: 0.8526 - val_loss: 0.4326 - val_accuracy: 0.8235\n","Epoch 129/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3679 - accuracy: 0.8474 - val_loss: 0.4319 - val_accuracy: 0.8193\n","Epoch 130/200\n","30/30 [==============================] - 0s 17ms/step - loss: 0.3626 - accuracy: 0.8568 - val_loss: 0.4357 - val_accuracy: 0.8319\n","Epoch 131/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3625 - accuracy: 0.8547 - val_loss: 0.4324 - val_accuracy: 0.8277\n","Epoch 132/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3570 - accuracy: 0.8705 - val_loss: 0.4330 - val_accuracy: 0.8235\n","Epoch 133/200\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3466 - accuracy: 0.8663 - val_loss: 0.4402 - val_accuracy: 0.8277\n"]}]},{"cell_type":"code","source":["# Evaluate the model\n","test_predictions = model_ex0.predict(test_X)\n","test_predictions_binary = (test_predictions > 0.7).astype(int)\n","\n","test_f1 = f1_score(test_y, test_predictions_binary)\n","test_recall = recall_score(test_y, test_predictions_binary)\n","\n","test_loss, test_accuracy = model_ex0.evaluate(test_X, test_y)\n","print(\"Test Loss:\", test_loss)\n","print(\"Test Accuracy:\", test_accuracy)\n","print(\"Test F1-Score:\", test_f1)\n","print(\"Test Recall:\", test_recall)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pDCJ8OpUhZEE","executionInfo":{"status":"ok","timestamp":1693268971722,"user_tz":300,"elapsed":610,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"1b2af6df-714e-42f1-9bc9-15ac5a062eb5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["10/10 [==============================] - 0s 5ms/step\n","10/10 [==============================] - 0s 5ms/step - loss: 0.4104 - accuracy: 0.8456\n","Test Loss: 0.4104210138320923\n","Test Accuracy: 0.8456375598907471\n","Test F1-Score: 0.8359133126934984\n","Test Recall: 0.84375\n"]}]},{"cell_type":"code","source":["# Plot training and validation metrics\n","plt.figure(figsize=(10, 4))\n","plt.subplot(1, 2, 1)\n","plt.plot(history_ex0.history['loss'], label='Training Loss')\n","plt.plot(history_ex0.history['val_loss'], label='Validation Loss')\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.legend()\n","\n","plt.subplot(1, 2, 2)\n","plt.plot(history_ex0.history['accuracy'], label='Training Accuracy')\n","plt.plot(history_ex0.history['val_accuracy'], label='Validation Accuracy')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":407},"id":"SYZd6nHoha86","executionInfo":{"status":"ok","timestamp":1693268807782,"user_tz":300,"elapsed":1399,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"bebbb290-95cc-4937-f7f8-c6294d4d622a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x400 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# Predict probabilities on test data\n","predictions = model_ex0.predict(test_X)\n","\n","# Convert probabilities to binary classes using a threshold (e.g., 0.5)\n","predicted_classes = (predictions > 0.7).astype(int)\n","\n","# Calculate the confusion matrix\n","confusion_mtx = confusion_matrix(test_y, predicted_classes)\n","\n","# Plot the confusion matrix\n","plt.figure(figsize=(8, 6))\n","sns.heatmap(confusion_mtx, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False)\n","plt.xlabel('Predicted')\n","plt.ylabel('True')\n","plt.title('Confusion Matrix')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":581},"id":"eIp3UliQhtYk","executionInfo":{"status":"ok","timestamp":1693268808826,"user_tz":300,"elapsed":512,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"1771a934-d41c-431e-9730-a19a050aab96"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["10/10 [==============================] - 0s 6ms/step\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["model_ex0.save('lstm_model_ex0.h5')"],"metadata":{"id":"kfC-zPBxkcyN"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Experiments"],"metadata":{"id":"IyDTJ7q89yAq"}},{"cell_type":"markdown","source":["## Experiment 1\n","Test Loss: 0.3311097323894501\n","\n","Test Accuracy: 0.8691275119781494"],"metadata":{"id":"FU0Ah5WF91S4"}},{"cell_type":"code","source":["model_ex1 = Sequential()\n","model_ex1.add(LSTM(units=128, input_shape=(sequence_length, num_features), return_sequences=True))\n","model_ex1.add(Dropout(0.3))\n","model_ex1.add(LSTM(units=64))\n","model_ex1.add(Dropout(0.5))\n","model_ex1.add(Dense(units=1, activation='sigmoid'))\n","\n","optimizer = Adam(learning_rate=0.001)\n","model_ex1.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n","\n","batch_size = 32\n","epochs = 100\n","# early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n","history_ex1 = model_ex1.fit(train_X, train_y, batch_size=batch_size, epochs=epochs, validation_split=0.2, verbose=1)"],"metadata":{"id":"ulq4xmqP-bNr","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1693268971161,"user_tz":300,"elapsed":131345,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"eb34be97-8e39-40dc-a341-86ab10d85f22"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","30/30 [==============================] - 12s 175ms/step - loss: 0.6823 - accuracy: 0.5421 - val_loss: 0.6466 - val_accuracy: 0.6218\n","Epoch 2/100\n","30/30 [==============================] - 2s 82ms/step - loss: 0.6111 - accuracy: 0.6926 - val_loss: 0.6143 - val_accuracy: 0.6849\n","Epoch 3/100\n","30/30 [==============================] - 3s 87ms/step - loss: 0.5567 - accuracy: 0.7389 - val_loss: 0.6673 - val_accuracy: 0.6471\n","Epoch 4/100\n","30/30 [==============================] - 3s 102ms/step - loss: 0.5535 - accuracy: 0.7284 - val_loss: 0.5442 - val_accuracy: 0.7311\n","Epoch 5/100\n","30/30 [==============================] - 3s 87ms/step - loss: 0.5302 - accuracy: 0.7358 - val_loss: 0.5354 - val_accuracy: 0.7353\n","Epoch 6/100\n","30/30 [==============================] - 2s 59ms/step - loss: 0.4987 - accuracy: 0.7611 - val_loss: 0.5295 - val_accuracy: 0.7521\n","Epoch 7/100\n","30/30 [==============================] - 2s 65ms/step - loss: 0.4902 - accuracy: 0.7726 - val_loss: 0.5128 - val_accuracy: 0.7521\n","Epoch 8/100\n","30/30 [==============================] - 2s 73ms/step - loss: 0.5016 - accuracy: 0.7547 - val_loss: 0.5606 - val_accuracy: 0.6975\n","Epoch 9/100\n","30/30 [==============================] - 2s 68ms/step - loss: 0.4902 - accuracy: 0.7642 - val_loss: 0.5182 - val_accuracy: 0.7227\n","Epoch 10/100\n","30/30 [==============================] - 3s 96ms/step - loss: 0.4901 - accuracy: 0.7695 - val_loss: 0.5050 - val_accuracy: 0.7647\n","Epoch 11/100\n","30/30 [==============================] - 2s 71ms/step - loss: 0.4700 - accuracy: 0.7895 - val_loss: 0.4869 - val_accuracy: 0.7899\n","Epoch 12/100\n","30/30 [==============================] - 2s 70ms/step - loss: 0.5421 - accuracy: 0.7347 - val_loss: 0.5276 - val_accuracy: 0.7521\n","Epoch 13/100\n","30/30 [==============================] - 2s 51ms/step - loss: 0.5093 - accuracy: 0.7463 - val_loss: 0.5252 - val_accuracy: 0.7605\n","Epoch 14/100\n","30/30 [==============================] - 1s 29ms/step - loss: 0.5001 - accuracy: 0.7516 - val_loss: 0.5091 - val_accuracy: 0.7437\n","Epoch 15/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.5040 - accuracy: 0.7684 - val_loss: 0.4969 - val_accuracy: 0.7605\n","Epoch 16/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.4742 - accuracy: 0.7705 - val_loss: 0.5560 - val_accuracy: 0.7605\n","Epoch 17/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.4399 - accuracy: 0.8032 - val_loss: 0.5216 - val_accuracy: 0.7563\n","Epoch 18/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.4922 - accuracy: 0.7684 - val_loss: 0.5071 - val_accuracy: 0.7563\n","Epoch 19/100\n","30/30 [==============================] - 2s 53ms/step - loss: 0.4767 - accuracy: 0.7916 - val_loss: 0.4804 - val_accuracy: 0.7857\n","Epoch 20/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.4364 - accuracy: 0.8063 - val_loss: 0.4524 - val_accuracy: 0.8109\n","Epoch 21/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.4053 - accuracy: 0.8253 - val_loss: 0.4192 - val_accuracy: 0.8151\n","Epoch 22/100\n","30/30 [==============================] - 1s 33ms/step - loss: 0.4179 - accuracy: 0.8179 - val_loss: 0.4362 - val_accuracy: 0.8235\n","Epoch 23/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.4167 - accuracy: 0.8211 - val_loss: 0.4494 - val_accuracy: 0.7815\n","Epoch 24/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.4003 - accuracy: 0.8263 - val_loss: 0.4285 - val_accuracy: 0.8109\n","Epoch 25/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.4196 - accuracy: 0.8116 - val_loss: 0.4315 - val_accuracy: 0.8067\n","Epoch 26/100\n","30/30 [==============================] - 1s 34ms/step - loss: 0.4077 - accuracy: 0.8179 - val_loss: 0.4988 - val_accuracy: 0.7605\n","Epoch 27/100\n","30/30 [==============================] - 1s 33ms/step - loss: 0.3817 - accuracy: 0.8421 - val_loss: 0.4113 - val_accuracy: 0.8151\n","Epoch 28/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3819 - accuracy: 0.8411 - val_loss: 0.4684 - val_accuracy: 0.8109\n","Epoch 29/100\n","30/30 [==============================] - 1s 33ms/step - loss: 0.3965 - accuracy: 0.8221 - val_loss: 0.4555 - val_accuracy: 0.7983\n","Epoch 30/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3719 - accuracy: 0.8474 - val_loss: 0.4035 - val_accuracy: 0.8319\n","Epoch 31/100\n","30/30 [==============================] - 2s 52ms/step - loss: 0.4010 - accuracy: 0.8232 - val_loss: 0.4294 - val_accuracy: 0.7983\n","Epoch 32/100\n","30/30 [==============================] - 1s 42ms/step - loss: 0.4044 - accuracy: 0.8074 - val_loss: 0.4903 - val_accuracy: 0.7815\n","Epoch 33/100\n","30/30 [==============================] - 1s 29ms/step - loss: 0.3953 - accuracy: 0.8347 - val_loss: 0.3902 - val_accuracy: 0.8193\n","Epoch 34/100\n","30/30 [==============================] - 1s 33ms/step - loss: 0.3820 - accuracy: 0.8505 - val_loss: 0.4052 - val_accuracy: 0.8445\n","Epoch 35/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3736 - accuracy: 0.8389 - val_loss: 0.3821 - val_accuracy: 0.8445\n","Epoch 36/100\n","30/30 [==============================] - 1s 33ms/step - loss: 0.3453 - accuracy: 0.8547 - val_loss: 0.5203 - val_accuracy: 0.7605\n","Epoch 37/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.4220 - accuracy: 0.8126 - val_loss: 0.4013 - val_accuracy: 0.8109\n","Epoch 38/100\n","30/30 [==============================] - 1s 31ms/step - loss: 0.3640 - accuracy: 0.8516 - val_loss: 0.4247 - val_accuracy: 0.8151\n","Epoch 39/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3727 - accuracy: 0.8421 - val_loss: 0.4599 - val_accuracy: 0.7941\n","Epoch 40/100\n","30/30 [==============================] - 1s 32ms/step - loss: 0.3445 - accuracy: 0.8600 - val_loss: 0.3828 - val_accuracy: 0.8403\n","Epoch 41/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3740 - accuracy: 0.8453 - val_loss: 0.3984 - val_accuracy: 0.8319\n","Epoch 42/100\n","30/30 [==============================] - 1s 33ms/step - loss: 0.3344 - accuracy: 0.8653 - val_loss: 0.3811 - val_accuracy: 0.8403\n","Epoch 43/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.3351 - accuracy: 0.8674 - val_loss: 0.3675 - val_accuracy: 0.8487\n","Epoch 44/100\n","30/30 [==============================] - 2s 50ms/step - loss: 0.3340 - accuracy: 0.8600 - val_loss: 0.3981 - val_accuracy: 0.8319\n","Epoch 45/100\n","30/30 [==============================] - 1s 37ms/step - loss: 0.3525 - accuracy: 0.8632 - val_loss: 0.4237 - val_accuracy: 0.7983\n","Epoch 46/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3540 - accuracy: 0.8579 - val_loss: 0.4159 - val_accuracy: 0.8109\n","Epoch 47/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3713 - accuracy: 0.8432 - val_loss: 0.3991 - val_accuracy: 0.8319\n","Epoch 48/100\n","30/30 [==============================] - 1s 33ms/step - loss: 0.3733 - accuracy: 0.8421 - val_loss: 0.4484 - val_accuracy: 0.8067\n","Epoch 49/100\n","30/30 [==============================] - 1s 33ms/step - loss: 0.3761 - accuracy: 0.8358 - val_loss: 0.4096 - val_accuracy: 0.8361\n","Epoch 50/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3455 - accuracy: 0.8547 - val_loss: 0.3839 - val_accuracy: 0.8235\n","Epoch 51/100\n","30/30 [==============================] - 1s 33ms/step - loss: 0.3621 - accuracy: 0.8442 - val_loss: 0.3942 - val_accuracy: 0.8235\n","Epoch 52/100\n","30/30 [==============================] - 1s 31ms/step - loss: 0.3434 - accuracy: 0.8611 - val_loss: 0.3775 - val_accuracy: 0.8361\n","Epoch 53/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3302 - accuracy: 0.8632 - val_loss: 0.3493 - val_accuracy: 0.8571\n","Epoch 54/100\n","30/30 [==============================] - 1s 33ms/step - loss: 0.3603 - accuracy: 0.8411 - val_loss: 0.3917 - val_accuracy: 0.8445\n","Epoch 55/100\n","30/30 [==============================] - 1s 33ms/step - loss: 0.3232 - accuracy: 0.8684 - val_loss: 0.3439 - val_accuracy: 0.8445\n","Epoch 56/100\n","30/30 [==============================] - 2s 51ms/step - loss: 0.3687 - accuracy: 0.8474 - val_loss: 0.4065 - val_accuracy: 0.8193\n","Epoch 57/100\n","30/30 [==============================] - 1s 50ms/step - loss: 0.3435 - accuracy: 0.8632 - val_loss: 0.3622 - val_accuracy: 0.8529\n","Epoch 58/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3206 - accuracy: 0.8684 - val_loss: 0.4875 - val_accuracy: 0.8109\n","Epoch 59/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3539 - accuracy: 0.8547 - val_loss: 0.3839 - val_accuracy: 0.8361\n","Epoch 60/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3182 - accuracy: 0.8674 - val_loss: 0.4173 - val_accuracy: 0.8361\n","Epoch 61/100\n","30/30 [==============================] - 1s 32ms/step - loss: 0.3365 - accuracy: 0.8663 - val_loss: 0.4702 - val_accuracy: 0.7899\n","Epoch 62/100\n","30/30 [==============================] - 1s 31ms/step - loss: 0.3288 - accuracy: 0.8705 - val_loss: 0.4280 - val_accuracy: 0.8193\n","Epoch 63/100\n","30/30 [==============================] - 1s 31ms/step - loss: 0.3395 - accuracy: 0.8611 - val_loss: 0.3571 - val_accuracy: 0.8445\n","Epoch 64/100\n","30/30 [==============================] - 1s 33ms/step - loss: 0.3161 - accuracy: 0.8747 - val_loss: 0.3744 - val_accuracy: 0.8403\n","Epoch 65/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3255 - accuracy: 0.8653 - val_loss: 0.4689 - val_accuracy: 0.8025\n","Epoch 66/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3594 - accuracy: 0.8368 - val_loss: 0.4007 - val_accuracy: 0.7941\n","Epoch 67/100\n","30/30 [==============================] - 1s 31ms/step - loss: 0.3623 - accuracy: 0.8474 - val_loss: 0.4035 - val_accuracy: 0.8025\n","Epoch 68/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3443 - accuracy: 0.8684 - val_loss: 0.3991 - val_accuracy: 0.8277\n","Epoch 69/100\n","30/30 [==============================] - 2s 52ms/step - loss: 0.3073 - accuracy: 0.8779 - val_loss: 0.3416 - val_accuracy: 0.8445\n","Epoch 70/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3182 - accuracy: 0.8642 - val_loss: 0.3531 - val_accuracy: 0.8445\n","Epoch 71/100\n","30/30 [==============================] - 1s 34ms/step - loss: 0.3206 - accuracy: 0.8684 - val_loss: 0.3858 - val_accuracy: 0.8235\n","Epoch 72/100\n","30/30 [==============================] - 1s 31ms/step - loss: 0.3011 - accuracy: 0.8779 - val_loss: 0.4307 - val_accuracy: 0.8235\n","Epoch 73/100\n","30/30 [==============================] - 1s 31ms/step - loss: 0.2996 - accuracy: 0.8716 - val_loss: 0.3603 - val_accuracy: 0.8487\n","Epoch 74/100\n","30/30 [==============================] - 1s 34ms/step - loss: 0.2817 - accuracy: 0.8926 - val_loss: 0.3889 - val_accuracy: 0.8277\n","Epoch 75/100\n","30/30 [==============================] - 1s 31ms/step - loss: 0.3054 - accuracy: 0.8737 - val_loss: 0.3578 - val_accuracy: 0.8529\n","Epoch 76/100\n","30/30 [==============================] - 1s 33ms/step - loss: 0.3179 - accuracy: 0.8642 - val_loss: 0.3637 - val_accuracy: 0.8445\n","Epoch 77/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3043 - accuracy: 0.8758 - val_loss: 0.3454 - val_accuracy: 0.8529\n","Epoch 78/100\n","30/30 [==============================] - 1s 32ms/step - loss: 0.2826 - accuracy: 0.8832 - val_loss: 0.3621 - val_accuracy: 0.8529\n","Epoch 79/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2684 - accuracy: 0.8874 - val_loss: 0.3439 - val_accuracy: 0.8655\n","Epoch 80/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2843 - accuracy: 0.8895 - val_loss: 0.3393 - val_accuracy: 0.8529\n","Epoch 81/100\n","30/30 [==============================] - 1s 50ms/step - loss: 0.2607 - accuracy: 0.9000 - val_loss: 0.4605 - val_accuracy: 0.8067\n","Epoch 82/100\n","30/30 [==============================] - 2s 52ms/step - loss: 0.3399 - accuracy: 0.8547 - val_loss: 0.4559 - val_accuracy: 0.8067\n","Epoch 83/100\n","30/30 [==============================] - 1s 29ms/step - loss: 0.3305 - accuracy: 0.8684 - val_loss: 0.3451 - val_accuracy: 0.8487\n","Epoch 84/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3077 - accuracy: 0.8800 - val_loss: 0.3908 - val_accuracy: 0.8613\n","Epoch 85/100\n","30/30 [==============================] - 1s 32ms/step - loss: 0.2858 - accuracy: 0.8800 - val_loss: 0.4830 - val_accuracy: 0.8235\n","Epoch 86/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3234 - accuracy: 0.8568 - val_loss: 0.3674 - val_accuracy: 0.8403\n","Epoch 87/100\n","30/30 [==============================] - 1s 34ms/step - loss: 0.3101 - accuracy: 0.8758 - val_loss: 0.3605 - val_accuracy: 0.8571\n","Epoch 88/100\n","30/30 [==============================] - 1s 32ms/step - loss: 0.2771 - accuracy: 0.8863 - val_loss: 0.3411 - val_accuracy: 0.8487\n","Epoch 89/100\n","30/30 [==============================] - 1s 33ms/step - loss: 0.2981 - accuracy: 0.8705 - val_loss: 0.3625 - val_accuracy: 0.8571\n","Epoch 90/100\n","30/30 [==============================] - 1s 32ms/step - loss: 0.3012 - accuracy: 0.8768 - val_loss: 0.3947 - val_accuracy: 0.8571\n","Epoch 91/100\n","30/30 [==============================] - 1s 31ms/step - loss: 0.2980 - accuracy: 0.8853 - val_loss: 0.3588 - val_accuracy: 0.8529\n","Epoch 92/100\n","30/30 [==============================] - 2s 58ms/step - loss: 0.2697 - accuracy: 0.8905 - val_loss: 0.4435 - val_accuracy: 0.8193\n","Epoch 93/100\n","30/30 [==============================] - 1s 49ms/step - loss: 0.2825 - accuracy: 0.8947 - val_loss: 0.3317 - val_accuracy: 0.8655\n","Epoch 94/100\n","30/30 [==============================] - 1s 49ms/step - loss: 0.2612 - accuracy: 0.8926 - val_loss: 0.3434 - val_accuracy: 0.8782\n","Epoch 95/100\n","30/30 [==============================] - 1s 33ms/step - loss: 0.2637 - accuracy: 0.8989 - val_loss: 0.3238 - val_accuracy: 0.8697\n","Epoch 96/100\n","30/30 [==============================] - 1s 33ms/step - loss: 0.2467 - accuracy: 0.9053 - val_loss: 0.3746 - val_accuracy: 0.8529\n","Epoch 97/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2722 - accuracy: 0.8905 - val_loss: 0.3704 - val_accuracy: 0.8361\n","Epoch 98/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2802 - accuracy: 0.8842 - val_loss: 0.3456 - val_accuracy: 0.8571\n","Epoch 99/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2566 - accuracy: 0.8958 - val_loss: 0.3815 - val_accuracy: 0.8529\n","Epoch 100/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2638 - accuracy: 0.8968 - val_loss: 0.3565 - val_accuracy: 0.8277\n"]}]},{"cell_type":"code","source":["test_predictions = model_ex1.predict(test_X)\n","test_predictions_binary = (test_predictions > 0.7).astype(int)\n","\n","test_f1 = f1_score(test_y, test_predictions_binary)\n","test_recall = recall_score(test_y, test_predictions_binary)\n","\n","test_loss, test_accuracy = model_ex1.evaluate(test_X, test_y)\n","print(\"Test Loss:\", test_loss)\n","print(\"Test Accuracy:\", test_accuracy)\n","print(\"Test F1-Score:\", test_f1)\n","print(\"Test Recall:\", test_recall)\n","\n","test_loss, test_accuracy= model_ex1.evaluate(test_X, test_y)\n","print(\"Test Loss:\", test_loss)\n","print(\"Test Accuracy:\", test_accuracy)\n","print(\"Test F1-Score:\", test_f1)\n","print(\"Test Recall:\", test_recall)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oLhkBYbY_POj","executionInfo":{"status":"ok","timestamp":1693269016405,"user_tz":300,"elapsed":1061,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"fd7a9b42-f73a-47f0-99b0-2346303ca24f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["10/10 [==============================] - 0s 13ms/step\n","10/10 [==============================] - 0s 18ms/step - loss: 0.3826 - accuracy: 0.8490\n","Test Loss: 0.38260623812675476\n","Test Accuracy: 0.8489933013916016\n","Test F1-Score: 0.8862275449101797\n","Test Recall: 0.925\n","10/10 [==============================] - 0s 18ms/step - loss: 0.3826 - accuracy: 0.8490\n","Test Loss: 0.38260623812675476\n","Test Accuracy: 0.8489933013916016\n","Test F1-Score: 0.8862275449101797\n","Test Recall: 0.925\n"]}]},{"cell_type":"code","source":["# Plot training and validation metrics\n","plt.figure(figsize=(10, 4))\n","plt.subplot(1, 2, 1)\n","plt.plot(history_ex1.history['loss'], label='Training Loss')\n","plt.plot(history_ex1.history['val_loss'], label='Validation Loss')\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.legend()\n","\n","plt.subplot(1, 2, 2)\n","plt.plot(history_ex1.history['accuracy'], label='Training Accuracy')\n","plt.plot(history_ex1.history['val_accuracy'], label='Validation Accuracy')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":406},"id":"QJFi-Ml7AZeP","executionInfo":{"status":"ok","timestamp":1693269023107,"user_tz":300,"elapsed":1504,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"ffaa4b11-d61d-49f6-d19b-52a2869dc1cd"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x400 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# Predict probabilities on test data\n","predictions = model_ex1.predict(test_X)\n","\n","# Convert probabilities to binary classes using a threshold (e.g., 0.5)\n","predicted_classes = (predictions > 0.7).astype(int)\n","\n","# Calculate the confusion matrix\n","confusion_mtx = confusion_matrix(test_y, predicted_classes)\n","\n","# Plot the confusion matrix\n","plt.figure(figsize=(8, 6))\n","sns.heatmap(confusion_mtx, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False)\n","plt.xlabel('Predicted')\n","plt.ylabel('True')\n","plt.title('Confusion Matrix')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":581},"id":"wRW9xbPtAaCL","executionInfo":{"status":"ok","timestamp":1693269074337,"user_tz":300,"elapsed":468,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"5ceece7a-7384-4a96-918d-6b8737e243df"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["10/10 [==============================] - 0s 9ms/step\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["model_ex1.save('lstm_model_ex1.h5')"],"metadata":{"id":"e1WOVNd1FCux"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Experiment 1.1 (6)\n","\n","Test Loss: 0.40347662568092346\n","\n","Test Accuracy: 0.8892617225646973\n","\n"],"metadata":{"id":"UE5FG5pgPB8i"}},{"cell_type":"code","source":["model_ex1v2 = Sequential()\n","model_ex1v2.add(LSTM(units=128, input_shape=(sequence_length, num_features), return_sequences=True))\n","model_ex1v2.add(Dropout(0.5))\n","model_ex1v2.add(LSTM(units=64))\n","model_ex1v2.add(Dropout(0.7))\n","model_ex1v2.add(Dense(units=1, activation='sigmoid'))\n","\n","optimizer = Adam(learning_rate=0.001)\n","model_ex1v2.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n","\n","batch_size = 32\n","epochs = 200\n","# early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n","history_ex6 = model_ex1v2.fit(train_X, train_y, batch_size=batch_size, epochs=epochs, validation_split=0.2, verbose=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2LtTWT2pPEES","executionInfo":{"status":"ok","timestamp":1693269413179,"user_tz":300,"elapsed":272575,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"1f654495-ef45-4185-b690-bc2cd3418198"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/200\n","30/30 [==============================] - 13s 103ms/step - loss: 0.6953 - accuracy: 0.5516 - val_loss: 0.6576 - val_accuracy: 0.6261\n","Epoch 2/200\n","30/30 [==============================] - 2s 52ms/step - loss: 0.6330 - accuracy: 0.6537 - val_loss: 0.6707 - val_accuracy: 0.5882\n","Epoch 3/200\n","30/30 [==============================] - 1s 48ms/step - loss: 0.5649 - accuracy: 0.7274 - val_loss: 0.5511 - val_accuracy: 0.7395\n","Epoch 4/200\n","30/30 [==============================] - 2s 70ms/step - loss: 0.5780 - accuracy: 0.7042 - val_loss: 0.5561 - val_accuracy: 0.7185\n","Epoch 5/200\n","30/30 [==============================] - 2s 53ms/step - loss: 0.5705 - accuracy: 0.7042 - val_loss: 0.5551 - val_accuracy: 0.7185\n","Epoch 6/200\n","30/30 [==============================] - 1s 50ms/step - loss: 0.5637 - accuracy: 0.7337 - val_loss: 0.5610 - val_accuracy: 0.7059\n","Epoch 7/200\n","30/30 [==============================] - 2s 55ms/step - loss: 0.5211 - accuracy: 0.7526 - val_loss: 0.5236 - val_accuracy: 0.7353\n","Epoch 8/200\n","30/30 [==============================] - 1s 31ms/step - loss: 0.5107 - accuracy: 0.7663 - val_loss: 0.5406 - val_accuracy: 0.7521\n","Epoch 9/200\n","30/30 [==============================] - 2s 53ms/step - loss: 0.5217 - accuracy: 0.7516 - val_loss: 0.5126 - val_accuracy: 0.7605\n","Epoch 10/200\n","30/30 [==============================] - 1s 49ms/step - loss: 0.5159 - accuracy: 0.7558 - val_loss: 0.5640 - val_accuracy: 0.7101\n","Epoch 11/200\n","30/30 [==============================] - 2s 64ms/step - loss: 0.5017 - accuracy: 0.7705 - val_loss: 0.5695 - val_accuracy: 0.7353\n","Epoch 12/200\n","30/30 [==============================] - 2s 81ms/step - loss: 0.5058 - accuracy: 0.7768 - val_loss: 0.5775 - val_accuracy: 0.7059\n","Epoch 13/200\n","30/30 [==============================] - 2s 80ms/step - loss: 0.5047 - accuracy: 0.7505 - val_loss: 0.4912 - val_accuracy: 0.7647\n","Epoch 14/200\n","30/30 [==============================] - 2s 79ms/step - loss: 0.4869 - accuracy: 0.7779 - val_loss: 0.5013 - val_accuracy: 0.7605\n","Epoch 15/200\n","30/30 [==============================] - 2s 66ms/step - loss: 0.5274 - accuracy: 0.7516 - val_loss: 0.6289 - val_accuracy: 0.6471\n","Epoch 16/200\n","30/30 [==============================] - 2s 60ms/step - loss: 0.4899 - accuracy: 0.7621 - val_loss: 0.5030 - val_accuracy: 0.7563\n","Epoch 17/200\n","30/30 [==============================] - 2s 56ms/step - loss: 0.4664 - accuracy: 0.7821 - val_loss: 0.4830 - val_accuracy: 0.7731\n","Epoch 18/200\n","30/30 [==============================] - 2s 67ms/step - loss: 0.4555 - accuracy: 0.7926 - val_loss: 0.4903 - val_accuracy: 0.7731\n","Epoch 19/200\n","30/30 [==============================] - 2s 73ms/step - loss: 0.4411 - accuracy: 0.8021 - val_loss: 0.4969 - val_accuracy: 0.7689\n","Epoch 20/200\n","30/30 [==============================] - 2s 61ms/step - loss: 0.4673 - accuracy: 0.7874 - val_loss: 0.4599 - val_accuracy: 0.7815\n","Epoch 21/200\n","30/30 [==============================] - 2s 51ms/step - loss: 0.4166 - accuracy: 0.8211 - val_loss: 0.4665 - val_accuracy: 0.8235\n","Epoch 22/200\n","30/30 [==============================] - 2s 55ms/step - loss: 0.4103 - accuracy: 0.8347 - val_loss: 0.4197 - val_accuracy: 0.8025\n","Epoch 23/200\n","30/30 [==============================] - 2s 51ms/step - loss: 0.4083 - accuracy: 0.8284 - val_loss: 0.4604 - val_accuracy: 0.8109\n","Epoch 24/200\n","30/30 [==============================] - 1s 50ms/step - loss: 0.4280 - accuracy: 0.8074 - val_loss: 0.4800 - val_accuracy: 0.7983\n","Epoch 25/200\n","30/30 [==============================] - 1s 32ms/step - loss: 0.3954 - accuracy: 0.8305 - val_loss: 0.4434 - val_accuracy: 0.7983\n","Epoch 26/200\n","30/30 [==============================] - 1s 37ms/step - loss: 0.4059 - accuracy: 0.8168 - val_loss: 0.5141 - val_accuracy: 0.7815\n","Epoch 27/200\n","30/30 [==============================] - 1s 50ms/step - loss: 0.4131 - accuracy: 0.8305 - val_loss: 0.4236 - val_accuracy: 0.8151\n","Epoch 28/200\n","30/30 [==============================] - 1s 44ms/step - loss: 0.3835 - accuracy: 0.8474 - val_loss: 0.4168 - val_accuracy: 0.8235\n","Epoch 29/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3653 - accuracy: 0.8516 - val_loss: 0.4212 - val_accuracy: 0.8151\n","Epoch 30/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.3717 - accuracy: 0.8579 - val_loss: 0.4407 - val_accuracy: 0.8151\n","Epoch 31/200\n","30/30 [==============================] - 1s 44ms/step - loss: 0.4303 - accuracy: 0.8074 - val_loss: 0.4341 - val_accuracy: 0.7983\n","Epoch 32/200\n","30/30 [==============================] - 2s 53ms/step - loss: 0.4236 - accuracy: 0.8200 - val_loss: 0.4205 - val_accuracy: 0.8109\n","Epoch 33/200\n","30/30 [==============================] - 2s 56ms/step - loss: 0.3904 - accuracy: 0.8389 - val_loss: 0.4387 - val_accuracy: 0.8025\n","Epoch 34/200\n","30/30 [==============================] - 1s 42ms/step - loss: 0.3621 - accuracy: 0.8568 - val_loss: 0.3569 - val_accuracy: 0.8655\n","Epoch 35/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.4649 - accuracy: 0.7884 - val_loss: 0.4692 - val_accuracy: 0.7857\n","Epoch 36/200\n","30/30 [==============================] - 2s 54ms/step - loss: 0.4217 - accuracy: 0.8032 - val_loss: 0.4391 - val_accuracy: 0.8025\n","Epoch 37/200\n","30/30 [==============================] - 2s 68ms/step - loss: 0.3689 - accuracy: 0.8611 - val_loss: 0.3781 - val_accuracy: 0.8487\n","Epoch 38/200\n","30/30 [==============================] - 2s 56ms/step - loss: 0.3755 - accuracy: 0.8589 - val_loss: 0.4249 - val_accuracy: 0.8067\n","Epoch 39/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3954 - accuracy: 0.8432 - val_loss: 0.3814 - val_accuracy: 0.8445\n","Epoch 40/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3756 - accuracy: 0.8379 - val_loss: 0.4478 - val_accuracy: 0.7815\n","Epoch 41/200\n","30/30 [==============================] - 1s 29ms/step - loss: 0.3627 - accuracy: 0.8516 - val_loss: 0.5008 - val_accuracy: 0.7521\n","Epoch 42/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3678 - accuracy: 0.8463 - val_loss: 0.3908 - val_accuracy: 0.8109\n","Epoch 43/200\n","30/30 [==============================] - 1s 28ms/step - loss: 0.3551 - accuracy: 0.8463 - val_loss: 0.3971 - val_accuracy: 0.8403\n","Epoch 44/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.3658 - accuracy: 0.8526 - val_loss: 0.4171 - val_accuracy: 0.8235\n","Epoch 45/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3577 - accuracy: 0.8632 - val_loss: 0.3926 - val_accuracy: 0.8235\n","Epoch 46/200\n","30/30 [==============================] - 1s 32ms/step - loss: 0.3414 - accuracy: 0.8600 - val_loss: 0.4810 - val_accuracy: 0.7899\n","Epoch 47/200\n","30/30 [==============================] - 1s 32ms/step - loss: 0.3619 - accuracy: 0.8474 - val_loss: 0.3912 - val_accuracy: 0.8109\n","Epoch 48/200\n","30/30 [==============================] - 1s 34ms/step - loss: 0.3605 - accuracy: 0.8674 - val_loss: 0.4306 - val_accuracy: 0.7983\n","Epoch 49/200\n","30/30 [==============================] - 2s 51ms/step - loss: 0.3551 - accuracy: 0.8547 - val_loss: 0.3815 - val_accuracy: 0.8403\n","Epoch 50/200\n","30/30 [==============================] - 1s 47ms/step - loss: 0.3412 - accuracy: 0.8611 - val_loss: 0.4150 - val_accuracy: 0.8361\n","Epoch 51/200\n","30/30 [==============================] - 1s 29ms/step - loss: 0.3351 - accuracy: 0.8600 - val_loss: 0.3671 - val_accuracy: 0.8403\n","Epoch 52/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3625 - accuracy: 0.8474 - val_loss: 0.3789 - val_accuracy: 0.8487\n","Epoch 53/200\n","30/30 [==============================] - 1s 31ms/step - loss: 0.3750 - accuracy: 0.8389 - val_loss: 0.3642 - val_accuracy: 0.8529\n","Epoch 54/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3346 - accuracy: 0.8779 - val_loss: 0.4787 - val_accuracy: 0.8193\n","Epoch 55/200\n","30/30 [==============================] - 2s 52ms/step - loss: 0.3271 - accuracy: 0.8663 - val_loss: 0.3950 - val_accuracy: 0.8487\n","Epoch 56/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3482 - accuracy: 0.8737 - val_loss: 0.4748 - val_accuracy: 0.8025\n","Epoch 57/200\n","30/30 [==============================] - 1s 29ms/step - loss: 0.3575 - accuracy: 0.8621 - val_loss: 0.3928 - val_accuracy: 0.7983\n","Epoch 58/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3699 - accuracy: 0.8600 - val_loss: 0.3674 - val_accuracy: 0.8529\n","Epoch 59/200\n","30/30 [==============================] - 1s 29ms/step - loss: 0.3244 - accuracy: 0.8758 - val_loss: 0.3995 - val_accuracy: 0.8529\n","Epoch 60/200\n","30/30 [==============================] - 1s 31ms/step - loss: 0.3352 - accuracy: 0.8716 - val_loss: 0.3947 - val_accuracy: 0.8445\n","Epoch 61/200\n","30/30 [==============================] - 1s 49ms/step - loss: 0.3292 - accuracy: 0.8726 - val_loss: 0.3573 - val_accuracy: 0.8487\n","Epoch 62/200\n","30/30 [==============================] - 2s 52ms/step - loss: 0.3247 - accuracy: 0.8674 - val_loss: 0.4124 - val_accuracy: 0.8361\n","Epoch 63/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3298 - accuracy: 0.8663 - val_loss: 0.3424 - val_accuracy: 0.8487\n","Epoch 64/200\n","30/30 [==============================] - 1s 32ms/step - loss: 0.3108 - accuracy: 0.8789 - val_loss: 0.3949 - val_accuracy: 0.8361\n","Epoch 65/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3280 - accuracy: 0.8716 - val_loss: 0.3750 - val_accuracy: 0.8529\n","Epoch 66/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3160 - accuracy: 0.8695 - val_loss: 0.7733 - val_accuracy: 0.7479\n","Epoch 67/200\n","30/30 [==============================] - 1s 29ms/step - loss: 0.3756 - accuracy: 0.8400 - val_loss: 0.4301 - val_accuracy: 0.8277\n","Epoch 68/200\n","30/30 [==============================] - 1s 29ms/step - loss: 0.3447 - accuracy: 0.8642 - val_loss: 0.3622 - val_accuracy: 0.8277\n","Epoch 69/200\n","30/30 [==============================] - 1s 29ms/step - loss: 0.3312 - accuracy: 0.8653 - val_loss: 0.4853 - val_accuracy: 0.7815\n","Epoch 70/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3393 - accuracy: 0.8642 - val_loss: 0.3473 - val_accuracy: 0.8445\n","Epoch 71/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3322 - accuracy: 0.8537 - val_loss: 0.4148 - val_accuracy: 0.8151\n","Epoch 72/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.3281 - accuracy: 0.8737 - val_loss: 0.3968 - val_accuracy: 0.8235\n","Epoch 73/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3087 - accuracy: 0.8874 - val_loss: 0.3844 - val_accuracy: 0.8445\n","Epoch 74/200\n","30/30 [==============================] - 2s 50ms/step - loss: 0.3253 - accuracy: 0.8674 - val_loss: 0.3732 - val_accuracy: 0.8151\n","Epoch 75/200\n","30/30 [==============================] - 1s 49ms/step - loss: 0.3431 - accuracy: 0.8589 - val_loss: 0.3669 - val_accuracy: 0.8403\n","Epoch 76/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3119 - accuracy: 0.8768 - val_loss: 0.3715 - val_accuracy: 0.8445\n","Epoch 77/200\n","30/30 [==============================] - 1s 29ms/step - loss: 0.4078 - accuracy: 0.8316 - val_loss: 0.4107 - val_accuracy: 0.8193\n","Epoch 78/200\n","30/30 [==============================] - 1s 32ms/step - loss: 0.3460 - accuracy: 0.8600 - val_loss: 0.4546 - val_accuracy: 0.8025\n","Epoch 79/200\n","30/30 [==============================] - 1s 29ms/step - loss: 0.3138 - accuracy: 0.8800 - val_loss: 0.3568 - val_accuracy: 0.8361\n","Epoch 80/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3092 - accuracy: 0.8779 - val_loss: 0.3962 - val_accuracy: 0.8067\n","Epoch 81/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3093 - accuracy: 0.8789 - val_loss: 0.3734 - val_accuracy: 0.8613\n","Epoch 82/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3042 - accuracy: 0.8832 - val_loss: 0.3776 - val_accuracy: 0.8445\n","Epoch 83/200\n","30/30 [==============================] - 1s 32ms/step - loss: 0.2862 - accuracy: 0.8853 - val_loss: 0.3637 - val_accuracy: 0.8487\n","Epoch 84/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2828 - accuracy: 0.8863 - val_loss: 0.4002 - val_accuracy: 0.8529\n","Epoch 85/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3194 - accuracy: 0.8695 - val_loss: 0.3202 - val_accuracy: 0.8571\n","Epoch 86/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3051 - accuracy: 0.8842 - val_loss: 0.3730 - val_accuracy: 0.8445\n","Epoch 87/200\n","30/30 [==============================] - 1s 48ms/step - loss: 0.3247 - accuracy: 0.8642 - val_loss: 0.4208 - val_accuracy: 0.8445\n","Epoch 88/200\n","30/30 [==============================] - 1s 50ms/step - loss: 0.3160 - accuracy: 0.8632 - val_loss: 0.4351 - val_accuracy: 0.8403\n","Epoch 89/200\n","30/30 [==============================] - 1s 31ms/step - loss: 0.3133 - accuracy: 0.8705 - val_loss: 0.3672 - val_accuracy: 0.8571\n","Epoch 90/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3137 - accuracy: 0.8705 - val_loss: 0.3848 - val_accuracy: 0.8361\n","Epoch 91/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3056 - accuracy: 0.8853 - val_loss: 0.4478 - val_accuracy: 0.8235\n","Epoch 92/200\n","30/30 [==============================] - 1s 32ms/step - loss: 0.3369 - accuracy: 0.8516 - val_loss: 0.3646 - val_accuracy: 0.8361\n","Epoch 93/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2700 - accuracy: 0.9011 - val_loss: 0.4030 - val_accuracy: 0.8571\n","Epoch 94/200\n","30/30 [==============================] - 1s 32ms/step - loss: 0.3476 - accuracy: 0.8600 - val_loss: 0.4018 - val_accuracy: 0.8109\n","Epoch 95/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.3185 - accuracy: 0.8800 - val_loss: 0.4275 - val_accuracy: 0.8403\n","Epoch 96/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3041 - accuracy: 0.8779 - val_loss: 0.4468 - val_accuracy: 0.8487\n","Epoch 97/200\n","30/30 [==============================] - 1s 29ms/step - loss: 0.2898 - accuracy: 0.8853 - val_loss: 0.4350 - val_accuracy: 0.8361\n","Epoch 98/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3144 - accuracy: 0.8695 - val_loss: 0.4222 - val_accuracy: 0.8403\n","Epoch 99/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3165 - accuracy: 0.8863 - val_loss: 0.3148 - val_accuracy: 0.8739\n","Epoch 100/200\n","30/30 [==============================] - 1s 48ms/step - loss: 0.2895 - accuracy: 0.8937 - val_loss: 0.3711 - val_accuracy: 0.8529\n","Epoch 101/200\n","30/30 [==============================] - 2s 52ms/step - loss: 0.2779 - accuracy: 0.8895 - val_loss: 0.4742 - val_accuracy: 0.8403\n","Epoch 102/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3029 - accuracy: 0.8747 - val_loss: 0.3881 - val_accuracy: 0.8151\n","Epoch 103/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2786 - accuracy: 0.8926 - val_loss: 0.4320 - val_accuracy: 0.8445\n","Epoch 104/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2847 - accuracy: 0.8916 - val_loss: 0.3810 - val_accuracy: 0.8529\n","Epoch 105/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2668 - accuracy: 0.8916 - val_loss: 0.3646 - val_accuracy: 0.8487\n","Epoch 106/200\n","30/30 [==============================] - 1s 32ms/step - loss: 0.2886 - accuracy: 0.9011 - val_loss: 0.3832 - val_accuracy: 0.8529\n","Epoch 107/200\n","30/30 [==============================] - 1s 42ms/step - loss: 0.2858 - accuracy: 0.8884 - val_loss: 0.3785 - val_accuracy: 0.8571\n","Epoch 108/200\n","30/30 [==============================] - 2s 50ms/step - loss: 0.2908 - accuracy: 0.8737 - val_loss: 0.3788 - val_accuracy: 0.8655\n","Epoch 109/200\n","30/30 [==============================] - 1s 39ms/step - loss: 0.2949 - accuracy: 0.8832 - val_loss: 0.3921 - val_accuracy: 0.8571\n","Epoch 110/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2888 - accuracy: 0.8789 - val_loss: 0.3947 - val_accuracy: 0.8613\n","Epoch 111/200\n","30/30 [==============================] - 1s 35ms/step - loss: 0.2623 - accuracy: 0.8979 - val_loss: 0.3693 - val_accuracy: 0.8403\n","Epoch 112/200\n","30/30 [==============================] - 2s 51ms/step - loss: 0.2666 - accuracy: 0.9095 - val_loss: 0.4017 - val_accuracy: 0.8361\n","Epoch 113/200\n","30/30 [==============================] - 1s 44ms/step - loss: 0.2926 - accuracy: 0.8863 - val_loss: 0.3988 - val_accuracy: 0.8361\n","Epoch 114/200\n","30/30 [==============================] - 1s 29ms/step - loss: 0.2791 - accuracy: 0.8800 - val_loss: 0.4470 - val_accuracy: 0.8613\n","Epoch 115/200\n","30/30 [==============================] - 1s 31ms/step - loss: 0.2765 - accuracy: 0.8842 - val_loss: 0.4453 - val_accuracy: 0.8277\n","Epoch 116/200\n","30/30 [==============================] - 1s 34ms/step - loss: 0.2947 - accuracy: 0.8853 - val_loss: 0.4190 - val_accuracy: 0.8445\n","Epoch 117/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2935 - accuracy: 0.8937 - val_loss: 0.3946 - val_accuracy: 0.8571\n","Epoch 118/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.2803 - accuracy: 0.8926 - val_loss: 0.3927 - val_accuracy: 0.8487\n","Epoch 119/200\n","30/30 [==============================] - 1s 29ms/step - loss: 0.2612 - accuracy: 0.8989 - val_loss: 0.3350 - val_accuracy: 0.8739\n","Epoch 120/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2912 - accuracy: 0.8842 - val_loss: 0.4146 - val_accuracy: 0.8571\n","Epoch 121/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.2895 - accuracy: 0.8811 - val_loss: 0.3569 - val_accuracy: 0.8866\n","Epoch 122/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3080 - accuracy: 0.8726 - val_loss: 0.3729 - val_accuracy: 0.8571\n","Epoch 123/200\n","30/30 [==============================] - 1s 29ms/step - loss: 0.2831 - accuracy: 0.8884 - val_loss: 0.3380 - val_accuracy: 0.8697\n","Epoch 124/200\n","30/30 [==============================] - 1s 39ms/step - loss: 0.2912 - accuracy: 0.8758 - val_loss: 0.3550 - val_accuracy: 0.8866\n","Epoch 125/200\n","30/30 [==============================] - 2s 51ms/step - loss: 0.2614 - accuracy: 0.9084 - val_loss: 0.3820 - val_accuracy: 0.8613\n","Epoch 126/200\n","30/30 [==============================] - 1s 40ms/step - loss: 0.2673 - accuracy: 0.9011 - val_loss: 0.5508 - val_accuracy: 0.8445\n","Epoch 127/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2850 - accuracy: 0.8926 - val_loss: 0.3749 - val_accuracy: 0.8782\n","Epoch 128/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.2661 - accuracy: 0.9021 - val_loss: 0.4358 - val_accuracy: 0.8655\n","Epoch 129/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2464 - accuracy: 0.9095 - val_loss: 0.3865 - val_accuracy: 0.8655\n","Epoch 130/200\n","30/30 [==============================] - 1s 31ms/step - loss: 0.2681 - accuracy: 0.8926 - val_loss: 0.4056 - val_accuracy: 0.8697\n","Epoch 131/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.2472 - accuracy: 0.9021 - val_loss: 0.3317 - val_accuracy: 0.8613\n","Epoch 132/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2441 - accuracy: 0.8979 - val_loss: 0.4057 - val_accuracy: 0.8277\n","Epoch 133/200\n","30/30 [==============================] - 1s 29ms/step - loss: 0.2828 - accuracy: 0.8947 - val_loss: 0.4169 - val_accuracy: 0.8445\n","Epoch 134/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2741 - accuracy: 0.8937 - val_loss: 0.3877 - val_accuracy: 0.8613\n","Epoch 135/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2734 - accuracy: 0.8916 - val_loss: 0.3676 - val_accuracy: 0.8782\n","Epoch 136/200\n","30/30 [==============================] - 2s 62ms/step - loss: 0.2574 - accuracy: 0.9000 - val_loss: 0.3777 - val_accuracy: 0.8613\n","Epoch 137/200\n","30/30 [==============================] - 1s 50ms/step - loss: 0.3095 - accuracy: 0.8747 - val_loss: 0.3623 - val_accuracy: 0.8529\n","Epoch 138/200\n","30/30 [==============================] - 1s 44ms/step - loss: 0.2980 - accuracy: 0.8832 - val_loss: 0.4204 - val_accuracy: 0.8319\n","Epoch 139/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2675 - accuracy: 0.8895 - val_loss: 0.3901 - val_accuracy: 0.8739\n","Epoch 140/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.2746 - accuracy: 0.8926 - val_loss: 0.4245 - val_accuracy: 0.8487\n","Epoch 141/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2385 - accuracy: 0.9063 - val_loss: 0.5029 - val_accuracy: 0.8782\n","Epoch 142/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2345 - accuracy: 0.9084 - val_loss: 0.4608 - val_accuracy: 0.8697\n","Epoch 143/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.2421 - accuracy: 0.9147 - val_loss: 0.3833 - val_accuracy: 0.8571\n","Epoch 144/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2478 - accuracy: 0.9011 - val_loss: 0.4109 - val_accuracy: 0.8697\n","Epoch 145/200\n","30/30 [==============================] - 1s 31ms/step - loss: 0.2407 - accuracy: 0.9011 - val_loss: 0.4339 - val_accuracy: 0.8739\n","Epoch 146/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2462 - accuracy: 0.9032 - val_loss: 0.3967 - val_accuracy: 0.8697\n","Epoch 147/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2383 - accuracy: 0.9074 - val_loss: 0.4148 - val_accuracy: 0.8739\n","Epoch 148/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2206 - accuracy: 0.9105 - val_loss: 0.3774 - val_accuracy: 0.8739\n","Epoch 149/200\n","30/30 [==============================] - 1s 39ms/step - loss: 0.2529 - accuracy: 0.9021 - val_loss: 0.4815 - val_accuracy: 0.8403\n","Epoch 150/200\n","30/30 [==============================] - 2s 51ms/step - loss: 0.2453 - accuracy: 0.8989 - val_loss: 0.4087 - val_accuracy: 0.8697\n","Epoch 151/200\n","30/30 [==============================] - 1s 40ms/step - loss: 0.2323 - accuracy: 0.9116 - val_loss: 0.3940 - val_accuracy: 0.8487\n","Epoch 152/200\n","30/30 [==============================] - 1s 29ms/step - loss: 0.2501 - accuracy: 0.9084 - val_loss: 0.3796 - val_accuracy: 0.8782\n","Epoch 153/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2318 - accuracy: 0.9116 - val_loss: 0.4486 - val_accuracy: 0.8445\n","Epoch 154/200\n","30/30 [==============================] - 1s 32ms/step - loss: 0.2146 - accuracy: 0.9221 - val_loss: 0.3644 - val_accuracy: 0.8950\n","Epoch 155/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.2488 - accuracy: 0.9063 - val_loss: 0.4043 - val_accuracy: 0.8739\n","Epoch 156/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.2667 - accuracy: 0.8916 - val_loss: 0.4639 - val_accuracy: 0.8697\n","Epoch 157/200\n","30/30 [==============================] - 1s 34ms/step - loss: 0.2103 - accuracy: 0.9221 - val_loss: 0.4104 - val_accuracy: 0.8697\n","Epoch 158/200\n","30/30 [==============================] - 1s 32ms/step - loss: 0.2258 - accuracy: 0.9168 - val_loss: 0.4078 - val_accuracy: 0.8782\n","Epoch 159/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2232 - accuracy: 0.9168 - val_loss: 0.4552 - val_accuracy: 0.8866\n","Epoch 160/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2474 - accuracy: 0.9063 - val_loss: 0.4135 - val_accuracy: 0.8361\n","Epoch 161/200\n","30/30 [==============================] - 1s 31ms/step - loss: 0.2692 - accuracy: 0.8979 - val_loss: 0.4701 - val_accuracy: 0.8613\n","Epoch 162/200\n","30/30 [==============================] - 1s 48ms/step - loss: 0.2339 - accuracy: 0.9095 - val_loss: 0.4638 - val_accuracy: 0.8697\n","Epoch 163/200\n","30/30 [==============================] - 1s 50ms/step - loss: 0.2157 - accuracy: 0.9179 - val_loss: 0.4123 - val_accuracy: 0.8487\n","Epoch 164/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.2460 - accuracy: 0.9053 - val_loss: 0.4503 - val_accuracy: 0.8824\n","Epoch 165/200\n","30/30 [==============================] - 1s 29ms/step - loss: 0.2521 - accuracy: 0.8968 - val_loss: 0.5145 - val_accuracy: 0.8697\n","Epoch 166/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2244 - accuracy: 0.9189 - val_loss: 0.4494 - val_accuracy: 0.8655\n","Epoch 167/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.1915 - accuracy: 0.9263 - val_loss: 0.4030 - val_accuracy: 0.8697\n","Epoch 168/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2176 - accuracy: 0.9147 - val_loss: 0.4478 - val_accuracy: 0.8403\n","Epoch 169/200\n","30/30 [==============================] - 1s 31ms/step - loss: 0.2469 - accuracy: 0.9116 - val_loss: 0.4887 - val_accuracy: 0.8782\n","Epoch 170/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.2325 - accuracy: 0.9074 - val_loss: 0.3971 - val_accuracy: 0.8655\n","Epoch 171/200\n","30/30 [==============================] - 1s 31ms/step - loss: 0.2121 - accuracy: 0.9105 - val_loss: 0.4598 - val_accuracy: 0.8655\n","Epoch 172/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2485 - accuracy: 0.9042 - val_loss: 0.3460 - val_accuracy: 0.8782\n","Epoch 173/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2487 - accuracy: 0.9116 - val_loss: 0.3650 - val_accuracy: 0.8655\n","Epoch 174/200\n","30/30 [==============================] - 1s 32ms/step - loss: 0.2049 - accuracy: 0.9253 - val_loss: 0.4298 - val_accuracy: 0.8866\n","Epoch 175/200\n","30/30 [==============================] - 1s 50ms/step - loss: 0.1995 - accuracy: 0.9221 - val_loss: 0.4404 - val_accuracy: 0.8866\n","Epoch 176/200\n","30/30 [==============================] - 1s 49ms/step - loss: 0.2199 - accuracy: 0.9147 - val_loss: 0.3519 - val_accuracy: 0.8697\n","Epoch 177/200\n","30/30 [==============================] - 1s 35ms/step - loss: 0.2085 - accuracy: 0.9168 - val_loss: 0.4043 - val_accuracy: 0.8908\n","Epoch 178/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.2055 - accuracy: 0.9137 - val_loss: 0.4958 - val_accuracy: 0.8529\n","Epoch 179/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.2714 - accuracy: 0.9000 - val_loss: 0.3996 - val_accuracy: 0.8739\n","Epoch 180/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2182 - accuracy: 0.9242 - val_loss: 0.3700 - val_accuracy: 0.8782\n","Epoch 181/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.1953 - accuracy: 0.9242 - val_loss: 0.5285 - val_accuracy: 0.8571\n","Epoch 182/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2263 - accuracy: 0.9084 - val_loss: 0.4338 - val_accuracy: 0.8739\n","Epoch 183/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2115 - accuracy: 0.9242 - val_loss: 0.3828 - val_accuracy: 0.8824\n","Epoch 184/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.2011 - accuracy: 0.9263 - val_loss: 0.4162 - val_accuracy: 0.8782\n","Epoch 185/200\n","30/30 [==============================] - 1s 31ms/step - loss: 0.2016 - accuracy: 0.9263 - val_loss: 0.4286 - val_accuracy: 0.8739\n","Epoch 186/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.1719 - accuracy: 0.9347 - val_loss: 0.4310 - val_accuracy: 0.8529\n","Epoch 187/200\n","30/30 [==============================] - 1s 36ms/step - loss: 0.2455 - accuracy: 0.9042 - val_loss: 0.4591 - val_accuracy: 0.8655\n","Epoch 188/200\n","30/30 [==============================] - 1s 49ms/step - loss: 0.2394 - accuracy: 0.9158 - val_loss: 0.3707 - val_accuracy: 0.8655\n","Epoch 189/200\n","30/30 [==============================] - 1s 47ms/step - loss: 0.2205 - accuracy: 0.9126 - val_loss: 0.4016 - val_accuracy: 0.8824\n","Epoch 190/200\n","30/30 [==============================] - 1s 31ms/step - loss: 0.2400 - accuracy: 0.9011 - val_loss: 0.4063 - val_accuracy: 0.8571\n","Epoch 191/200\n","30/30 [==============================] - 1s 31ms/step - loss: 0.1910 - accuracy: 0.9221 - val_loss: 0.3857 - val_accuracy: 0.8697\n","Epoch 192/200\n","30/30 [==============================] - 1s 31ms/step - loss: 0.2097 - accuracy: 0.9168 - val_loss: 0.4319 - val_accuracy: 0.8739\n","Epoch 193/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.1915 - accuracy: 0.9326 - val_loss: 0.4881 - val_accuracy: 0.8866\n","Epoch 194/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.1940 - accuracy: 0.9232 - val_loss: 0.3159 - val_accuracy: 0.8908\n","Epoch 195/200\n","30/30 [==============================] - 1s 31ms/step - loss: 0.1910 - accuracy: 0.9337 - val_loss: 0.3700 - val_accuracy: 0.8782\n","Epoch 196/200\n","30/30 [==============================] - 1s 31ms/step - loss: 0.1837 - accuracy: 0.9295 - val_loss: 0.3351 - val_accuracy: 0.8992\n","Epoch 197/200\n","30/30 [==============================] - 1s 30ms/step - loss: 0.1687 - accuracy: 0.9411 - val_loss: 0.4043 - val_accuracy: 0.8992\n","Epoch 198/200\n","30/30 [==============================] - 1s 31ms/step - loss: 0.1922 - accuracy: 0.9295 - val_loss: 0.3854 - val_accuracy: 0.8950\n","Epoch 199/200\n","30/30 [==============================] - 1s 33ms/step - loss: 0.2021 - accuracy: 0.9200 - val_loss: 0.3483 - val_accuracy: 0.8908\n","Epoch 200/200\n","30/30 [==============================] - 1s 42ms/step - loss: 0.1862 - accuracy: 0.9263 - val_loss: 0.6028 - val_accuracy: 0.8487\n"]}]},{"cell_type":"code","source":["test_predictions = model_ex1v2.predict(test_X)\n","test_predictions_binary = (test_predictions > 0.7).astype(int)\n","\n","test_f1 = f1_score(test_y, test_predictions_binary)\n","test_recall = recall_score(test_y, test_predictions_binary)\n","\n","test_loss, test_accuracy = model_ex1v2.evaluate(test_X, test_y)\n","\n","print(\"Test Loss:\", test_loss)\n","print(\"Test Accuracy:\", test_accuracy)\n","print(\"Test F1-Score:\", test_f1)\n","print(\"Test Recall:\", test_recall)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AhowXghbPSpT","executionInfo":{"status":"ok","timestamp":1693269506412,"user_tz":300,"elapsed":3208,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"fb8b36a5-1847-48cc-b2fb-1b9fdcda78e8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["10/10 [==============================] - 2s 16ms/step\n","10/10 [==============================] - 0s 15ms/step - loss: 0.5672 - accuracy: 0.8356\n","Test Loss: 0.567160427570343\n","Test Accuracy: 0.8355704545974731\n","Test F1-Score: 0.85\n","Test Recall: 0.85\n"]}]},{"cell_type":"code","source":["# Plot training and validation metrics\n","plt.figure(figsize=(10, 4))\n","plt.subplot(1, 2, 1)\n","plt.plot(history_ex6.history['loss'], label='Training Loss')\n","plt.plot(history_ex6.history['val_loss'], label='Validation Loss')\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.legend()\n","\n","plt.subplot(1, 2, 2)\n","plt.plot(history_ex6.history['accuracy'], label='Training Accuracy')\n","plt.plot(history_ex6.history['val_accuracy'], label='Validation Accuracy')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":406},"id":"ZnZ5STQ9PT-F","executionInfo":{"status":"ok","timestamp":1693269588109,"user_tz":300,"elapsed":863,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"9735fccd-b05d-4dc5-800a-892de4091c6d"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x400 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["model_ex1v2.save('lstm_model_ex1v2.h5')"],"metadata":{"id":"EMT5vG_YucDa"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Experiment 2\n","\n","Test Loss: 0.3368649184703827\n","\n","Test Accuracy: 0.8758389353752136"],"metadata":{"id":"DUf_8GpNCZl7"}},{"cell_type":"code","source":["model_ex2 = Sequential()\n","model_ex2.add(LSTM(units=64, input_shape=(sequence_length, num_features), return_sequences=True))\n","model_ex2.add(Dropout(0.7))\n","model_ex2.add(LSTM(units=64, return_sequences=True))\n","model_ex2.add(LSTM(units=32))\n","model_ex2.add(Dropout(0.7))\n","model_ex2.add(Dense(units=1, activation='sigmoid'))\n","\n","optimizer = Adam(learning_rate=0.001)\n","model_ex2.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n","\n","batch_size = 32\n","epochs = 100\n","early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n","history_ex2 = model_ex2.fit(train_X, train_y, batch_size=batch_size, epochs=epochs, validation_split=0.2, verbose=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UXq0ReljCKLY","executionInfo":{"status":"ok","timestamp":1693269923152,"user_tz":300,"elapsed":149147,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"e4e274c9-bc93-4b1c-dee9-634c62959e9c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","30/30 [==============================] - 10s 116ms/step - loss: 0.6920 - accuracy: 0.5558 - val_loss: 0.6839 - val_accuracy: 0.5294\n","Epoch 2/100\n","30/30 [==============================] - 1s 27ms/step - loss: 0.6669 - accuracy: 0.5874 - val_loss: 0.6495 - val_accuracy: 0.6092\n","Epoch 3/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.6214 - accuracy: 0.6695 - val_loss: 0.6308 - val_accuracy: 0.6218\n","Epoch 4/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.5722 - accuracy: 0.7232 - val_loss: 0.5519 - val_accuracy: 0.7311\n","Epoch 5/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.6052 - accuracy: 0.6979 - val_loss: 0.5732 - val_accuracy: 0.7395\n","Epoch 6/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.5831 - accuracy: 0.7074 - val_loss: 0.5535 - val_accuracy: 0.7269\n","Epoch 7/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.5679 - accuracy: 0.7274 - val_loss: 0.5529 - val_accuracy: 0.7101\n","Epoch 8/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.5558 - accuracy: 0.7389 - val_loss: 0.5623 - val_accuracy: 0.7017\n","Epoch 9/100\n","30/30 [==============================] - 1s 27ms/step - loss: 0.5267 - accuracy: 0.7621 - val_loss: 0.5504 - val_accuracy: 0.7395\n","Epoch 10/100\n","30/30 [==============================] - 1s 40ms/step - loss: 0.5206 - accuracy: 0.7537 - val_loss: 0.5263 - val_accuracy: 0.7479\n","Epoch 11/100\n","30/30 [==============================] - 1s 40ms/step - loss: 0.5532 - accuracy: 0.7432 - val_loss: 0.5452 - val_accuracy: 0.7353\n","Epoch 12/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.5588 - accuracy: 0.7232 - val_loss: 0.5581 - val_accuracy: 0.7311\n","Epoch 13/100\n","30/30 [==============================] - 1s 27ms/step - loss: 0.5466 - accuracy: 0.7421 - val_loss: 0.5272 - val_accuracy: 0.7353\n","Epoch 14/100\n","30/30 [==============================] - 1s 27ms/step - loss: 0.4954 - accuracy: 0.7589 - val_loss: 0.5459 - val_accuracy: 0.7479\n","Epoch 15/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.5074 - accuracy: 0.7653 - val_loss: 0.5420 - val_accuracy: 0.7353\n","Epoch 16/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.5096 - accuracy: 0.7642 - val_loss: 0.5684 - val_accuracy: 0.6849\n","Epoch 17/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.4905 - accuracy: 0.7726 - val_loss: 0.5352 - val_accuracy: 0.7311\n","Epoch 18/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.5071 - accuracy: 0.7495 - val_loss: 0.5155 - val_accuracy: 0.7437\n","Epoch 19/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.4862 - accuracy: 0.7674 - val_loss: 0.5411 - val_accuracy: 0.7521\n","Epoch 20/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.5121 - accuracy: 0.7558 - val_loss: 0.5562 - val_accuracy: 0.7353\n","Epoch 21/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.4732 - accuracy: 0.7789 - val_loss: 0.5097 - val_accuracy: 0.7521\n","Epoch 22/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.4635 - accuracy: 0.7884 - val_loss: 0.4790 - val_accuracy: 0.7857\n","Epoch 23/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.4779 - accuracy: 0.7842 - val_loss: 0.5197 - val_accuracy: 0.7605\n","Epoch 24/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.4902 - accuracy: 0.7558 - val_loss: 0.5460 - val_accuracy: 0.7647\n","Epoch 25/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.4773 - accuracy: 0.7811 - val_loss: 0.5184 - val_accuracy: 0.7395\n","Epoch 26/100\n","30/30 [==============================] - 1s 42ms/step - loss: 0.4824 - accuracy: 0.7874 - val_loss: 0.5458 - val_accuracy: 0.7227\n","Epoch 27/100\n","30/30 [==============================] - 1s 32ms/step - loss: 0.4880 - accuracy: 0.7821 - val_loss: 0.5019 - val_accuracy: 0.7521\n","Epoch 28/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.4554 - accuracy: 0.7947 - val_loss: 0.4720 - val_accuracy: 0.8025\n","Epoch 29/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.4567 - accuracy: 0.7968 - val_loss: 0.4705 - val_accuracy: 0.7857\n","Epoch 30/100\n","30/30 [==============================] - 1s 48ms/step - loss: 0.4462 - accuracy: 0.8053 - val_loss: 0.4476 - val_accuracy: 0.8067\n","Epoch 31/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.4752 - accuracy: 0.7832 - val_loss: 0.4927 - val_accuracy: 0.7815\n","Epoch 32/100\n","30/30 [==============================] - 1s 24ms/step - loss: 0.4458 - accuracy: 0.7989 - val_loss: 0.5153 - val_accuracy: 0.7647\n","Epoch 33/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.4296 - accuracy: 0.8137 - val_loss: 0.4362 - val_accuracy: 0.8151\n","Epoch 34/100\n","30/30 [==============================] - 1s 27ms/step - loss: 0.4182 - accuracy: 0.8179 - val_loss: 0.4749 - val_accuracy: 0.7521\n","Epoch 35/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.4439 - accuracy: 0.8147 - val_loss: 0.4344 - val_accuracy: 0.8361\n","Epoch 36/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.4235 - accuracy: 0.8168 - val_loss: 0.4502 - val_accuracy: 0.8109\n","Epoch 37/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.4115 - accuracy: 0.8295 - val_loss: 0.4882 - val_accuracy: 0.7605\n","Epoch 38/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3942 - accuracy: 0.8284 - val_loss: 0.4179 - val_accuracy: 0.8277\n","Epoch 39/100\n","30/30 [==============================] - 1s 34ms/step - loss: 0.4057 - accuracy: 0.8284 - val_loss: 0.4736 - val_accuracy: 0.7941\n","Epoch 40/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.4025 - accuracy: 0.8326 - val_loss: 0.4241 - val_accuracy: 0.8277\n","Epoch 41/100\n","30/30 [==============================] - 1s 42ms/step - loss: 0.4431 - accuracy: 0.8063 - val_loss: 0.4440 - val_accuracy: 0.8025\n","Epoch 42/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3976 - accuracy: 0.8326 - val_loss: 0.3920 - val_accuracy: 0.8403\n","Epoch 43/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3848 - accuracy: 0.8326 - val_loss: 0.3895 - val_accuracy: 0.8403\n","Epoch 44/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.4319 - accuracy: 0.8095 - val_loss: 0.4728 - val_accuracy: 0.7689\n","Epoch 45/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3972 - accuracy: 0.8389 - val_loss: 0.3946 - val_accuracy: 0.8487\n","Epoch 46/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3891 - accuracy: 0.8389 - val_loss: 0.3951 - val_accuracy: 0.8403\n","Epoch 47/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.4305 - accuracy: 0.8158 - val_loss: 0.4234 - val_accuracy: 0.7899\n","Epoch 48/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3888 - accuracy: 0.8337 - val_loss: 0.3999 - val_accuracy: 0.8445\n","Epoch 49/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3718 - accuracy: 0.8516 - val_loss: 0.4015 - val_accuracy: 0.8403\n","Epoch 50/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3680 - accuracy: 0.8389 - val_loss: 0.4780 - val_accuracy: 0.7899\n","Epoch 51/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3631 - accuracy: 0.8589 - val_loss: 0.4040 - val_accuracy: 0.8403\n","Epoch 52/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3833 - accuracy: 0.8442 - val_loss: 0.3833 - val_accuracy: 0.8445\n","Epoch 53/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3776 - accuracy: 0.8495 - val_loss: 0.4076 - val_accuracy: 0.8277\n","Epoch 54/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3336 - accuracy: 0.8579 - val_loss: 0.4306 - val_accuracy: 0.8403\n","Epoch 55/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3660 - accuracy: 0.8505 - val_loss: 0.4731 - val_accuracy: 0.7647\n","Epoch 56/100\n","30/30 [==============================] - 1s 42ms/step - loss: 0.3799 - accuracy: 0.8453 - val_loss: 0.3847 - val_accuracy: 0.8445\n","Epoch 57/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3466 - accuracy: 0.8726 - val_loss: 0.4441 - val_accuracy: 0.7857\n","Epoch 58/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3528 - accuracy: 0.8600 - val_loss: 0.3780 - val_accuracy: 0.8361\n","Epoch 59/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3783 - accuracy: 0.8526 - val_loss: 0.4301 - val_accuracy: 0.8403\n","Epoch 60/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3825 - accuracy: 0.8400 - val_loss: 0.4483 - val_accuracy: 0.7899\n","Epoch 61/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3669 - accuracy: 0.8442 - val_loss: 0.6091 - val_accuracy: 0.7731\n","Epoch 62/100\n","30/30 [==============================] - 1s 24ms/step - loss: 0.3645 - accuracy: 0.8505 - val_loss: 0.3666 - val_accuracy: 0.8445\n","Epoch 63/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3372 - accuracy: 0.8695 - val_loss: 0.3939 - val_accuracy: 0.8403\n","Epoch 64/100\n","30/30 [==============================] - 1s 27ms/step - loss: 0.3349 - accuracy: 0.8632 - val_loss: 0.3669 - val_accuracy: 0.8277\n","Epoch 65/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3529 - accuracy: 0.8600 - val_loss: 0.3921 - val_accuracy: 0.8151\n","Epoch 66/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3673 - accuracy: 0.8442 - val_loss: 0.4003 - val_accuracy: 0.8235\n","Epoch 67/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3793 - accuracy: 0.8516 - val_loss: 0.4232 - val_accuracy: 0.8193\n","Epoch 68/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3443 - accuracy: 0.8621 - val_loss: 0.3768 - val_accuracy: 0.8403\n","Epoch 69/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3435 - accuracy: 0.8684 - val_loss: 0.3968 - val_accuracy: 0.8487\n","Epoch 70/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3233 - accuracy: 0.8705 - val_loss: 0.3765 - val_accuracy: 0.8319\n","Epoch 71/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3364 - accuracy: 0.8695 - val_loss: 0.4532 - val_accuracy: 0.7983\n","Epoch 72/100\n","30/30 [==============================] - 1s 33ms/step - loss: 0.3501 - accuracy: 0.8642 - val_loss: 0.3891 - val_accuracy: 0.8361\n","Epoch 73/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3769 - accuracy: 0.8463 - val_loss: 0.4541 - val_accuracy: 0.8151\n","Epoch 74/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3584 - accuracy: 0.8600 - val_loss: 0.3807 - val_accuracy: 0.8277\n","Epoch 75/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3226 - accuracy: 0.8737 - val_loss: 0.4336 - val_accuracy: 0.8109\n","Epoch 76/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3348 - accuracy: 0.8674 - val_loss: 0.3991 - val_accuracy: 0.8277\n","Epoch 77/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3219 - accuracy: 0.8779 - val_loss: 0.3819 - val_accuracy: 0.8445\n","Epoch 78/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3207 - accuracy: 0.8705 - val_loss: 0.3527 - val_accuracy: 0.8487\n","Epoch 79/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3087 - accuracy: 0.8800 - val_loss: 0.3805 - val_accuracy: 0.8403\n","Epoch 80/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3175 - accuracy: 0.8800 - val_loss: 0.3724 - val_accuracy: 0.8487\n","Epoch 81/100\n","30/30 [==============================] - 1s 28ms/step - loss: 0.3314 - accuracy: 0.8653 - val_loss: 0.3652 - val_accuracy: 0.8487\n","Epoch 82/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3408 - accuracy: 0.8705 - val_loss: 0.3688 - val_accuracy: 0.8445\n","Epoch 83/100\n","30/30 [==============================] - 1s 42ms/step - loss: 0.3529 - accuracy: 0.8684 - val_loss: 0.3750 - val_accuracy: 0.8571\n","Epoch 84/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3226 - accuracy: 0.8737 - val_loss: 0.3397 - val_accuracy: 0.8571\n","Epoch 85/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3174 - accuracy: 0.8716 - val_loss: 0.3352 - val_accuracy: 0.8655\n","Epoch 86/100\n","30/30 [==============================] - 1s 36ms/step - loss: 0.3109 - accuracy: 0.8811 - val_loss: 0.3788 - val_accuracy: 0.8445\n","Epoch 87/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3428 - accuracy: 0.8621 - val_loss: 0.3772 - val_accuracy: 0.8571\n","Epoch 88/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3300 - accuracy: 0.8674 - val_loss: 0.3711 - val_accuracy: 0.8487\n","Epoch 89/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3030 - accuracy: 0.8811 - val_loss: 0.3525 - val_accuracy: 0.8571\n","Epoch 90/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3003 - accuracy: 0.8821 - val_loss: 0.3833 - val_accuracy: 0.8277\n","Epoch 91/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3170 - accuracy: 0.8758 - val_loss: 0.3969 - val_accuracy: 0.8487\n","Epoch 92/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3193 - accuracy: 0.8800 - val_loss: 0.3685 - val_accuracy: 0.8361\n","Epoch 93/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3119 - accuracy: 0.8832 - val_loss: 0.3755 - val_accuracy: 0.8445\n","Epoch 94/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3009 - accuracy: 0.8789 - val_loss: 0.4129 - val_accuracy: 0.8235\n","Epoch 95/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3304 - accuracy: 0.8674 - val_loss: 0.3571 - val_accuracy: 0.8319\n","Epoch 96/100\n","30/30 [==============================] - 1s 27ms/step - loss: 0.3013 - accuracy: 0.8747 - val_loss: 0.3829 - val_accuracy: 0.8487\n","Epoch 97/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3208 - accuracy: 0.8716 - val_loss: 0.3531 - val_accuracy: 0.8655\n","Epoch 98/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3169 - accuracy: 0.8863 - val_loss: 0.4120 - val_accuracy: 0.8319\n","Epoch 99/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.2954 - accuracy: 0.8811 - val_loss: 0.4078 - val_accuracy: 0.8445\n","Epoch 100/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3055 - accuracy: 0.8800 - val_loss: 0.3515 - val_accuracy: 0.8613\n"]}]},{"cell_type":"code","source":["test_predictions = model_ex2.predict(test_X)\n","test_predictions_binary = (test_predictions > 0.7).astype(int)\n","\n","test_f1 = f1_score(test_y, test_predictions_binary)\n","test_recall = recall_score(test_y, test_predictions_binary)\n","\n","test_loss, test_accuracy = model_ex2.evaluate(test_X, test_y)\n","\n","print(\"Test Loss:\", test_loss)\n","print(\"Test Accuracy:\", test_accuracy)\n","print(\"Test F1-Score:\", test_f1)\n","print(\"Test Recall:\", test_recall)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IK8K40SOHi7y","executionInfo":{"status":"ok","timestamp":1693269924620,"user_tz":300,"elapsed":1473,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"a3573b1a-ee0a-4c22-a292-a2669a684dcf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["10/10 [==============================] - 1s 8ms/step\n","10/10 [==============================] - 0s 9ms/step - loss: 0.3693 - accuracy: 0.8725\n","Test Loss: 0.3692934811115265\n","Test Accuracy: 0.8724831938743591\n","Test F1-Score: 0.8695652173913043\n","Test Recall: 0.875\n"]}]},{"cell_type":"code","source":["# Plot training and validation metrics\n","plt.figure(figsize=(10, 4))\n","plt.subplot(1, 2, 1)\n","plt.plot(history_ex2.history['loss'], label='Training Loss')\n","plt.plot(history_ex2.history['val_loss'], label='Validation Loss')\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.legend()\n","\n","plt.subplot(1, 2, 2)\n","plt.plot(history_ex2.history['accuracy'], label='Training Accuracy')\n","plt.plot(history_ex2.history['val_accuracy'], label='Validation Accuracy')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":406},"id":"RenvetO9C11I","executionInfo":{"status":"ok","timestamp":1693269933207,"user_tz":300,"elapsed":1591,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"3d18550c-cd4f-47d5-8d3f-6765c922732e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x400 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["model_ex2.save('lstm_model_ex2.h5')"],"metadata":{"id":"8e0SAte7mG0w"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Hyperparameters of Exp 2\n","\n","Test Loss: 0.32892072200775146\n","\n","Test Accuracy: 0.8825503587722778"],"metadata":{"id":"dC7sAJDuyKDr"}},{"cell_type":"code","source":["model_ex2v2 = Sequential()\n","model_ex2v2.add(LSTM(units=128, input_shape=(sequence_length, num_features), return_sequences=True))\n","model_ex2v2.add(Dropout(0.7))\n","model_ex2v2.add(LSTM(units=64, return_sequences=True))\n","model_ex2v2.add(LSTM(units=32))\n","model_ex2v2.add(Dropout(0.7))\n","model_ex2v2.add(Dense(units=1, activation='sigmoid'))\n","\n","optimizer = Adam(learning_rate=0.001)  # Using a different optimizer\n","model_ex2v2.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n","\n","model_ex2v2.summary()\n","\n","batch_size = 32\n","epochs = 100\n","early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n","history_ex2v2 = model_ex2v2.fit(train_X, train_y, batch_size=batch_size, epochs=epochs, validation_split=0.2, verbose=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rwazXmHIyOaV","executionInfo":{"status":"ok","timestamp":1693271036751,"user_tz":300,"elapsed":135408,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"3b2c0a16-7ad2-47b9-aced-45f0837a992f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_7\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," lstm_18 (LSTM)              (None, 10, 128)           91648     \n","                                                                 \n"," dropout_14 (Dropout)        (None, 10, 128)           0         \n","                                                                 \n"," lstm_19 (LSTM)              (None, 10, 64)            49408     \n","                                                                 \n"," lstm_20 (LSTM)              (None, 32)                12416     \n","                                                                 \n"," dropout_15 (Dropout)        (None, 32)                0         \n","                                                                 \n"," dense_7 (Dense)             (None, 1)                 33        \n","                                                                 \n","=================================================================\n","Total params: 153,505\n","Trainable params: 153,505\n","Non-trainable params: 0\n","_________________________________________________________________\n","Epoch 1/100\n","30/30 [==============================] - 8s 78ms/step - loss: 0.6928 - accuracy: 0.5274 - val_loss: 0.6678 - val_accuracy: 0.6092\n","Epoch 2/100\n","30/30 [==============================] - 2s 59ms/step - loss: 0.6434 - accuracy: 0.6663 - val_loss: 0.6107 - val_accuracy: 0.7185\n","Epoch 3/100\n","30/30 [==============================] - 2s 57ms/step - loss: 0.6121 - accuracy: 0.6821 - val_loss: 0.5905 - val_accuracy: 0.6807\n","Epoch 4/100\n","30/30 [==============================] - 1s 37ms/step - loss: 0.5449 - accuracy: 0.7442 - val_loss: 0.5375 - val_accuracy: 0.7395\n","Epoch 5/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.5447 - accuracy: 0.7305 - val_loss: 0.6676 - val_accuracy: 0.6303\n","Epoch 6/100\n","30/30 [==============================] - 1s 37ms/step - loss: 0.5605 - accuracy: 0.7232 - val_loss: 0.5607 - val_accuracy: 0.7143\n","Epoch 7/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.5680 - accuracy: 0.7189 - val_loss: 0.5484 - val_accuracy: 0.7143\n","Epoch 8/100\n","30/30 [==============================] - 1s 37ms/step - loss: 0.5157 - accuracy: 0.7547 - val_loss: 0.5330 - val_accuracy: 0.7185\n","Epoch 9/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.5148 - accuracy: 0.7537 - val_loss: 0.5444 - val_accuracy: 0.7143\n","Epoch 10/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.4939 - accuracy: 0.7716 - val_loss: 0.5285 - val_accuracy: 0.7143\n","Epoch 11/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.5329 - accuracy: 0.7421 - val_loss: 0.6340 - val_accuracy: 0.6597\n","Epoch 12/100\n","30/30 [==============================] - 1s 37ms/step - loss: 0.4965 - accuracy: 0.7632 - val_loss: 0.5504 - val_accuracy: 0.7227\n","Epoch 13/100\n","30/30 [==============================] - 2s 61ms/step - loss: 0.5066 - accuracy: 0.7505 - val_loss: 0.5268 - val_accuracy: 0.7479\n","Epoch 14/100\n","30/30 [==============================] - 2s 51ms/step - loss: 0.5330 - accuracy: 0.7568 - val_loss: 0.5354 - val_accuracy: 0.7353\n","Epoch 15/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.5120 - accuracy: 0.7611 - val_loss: 0.5987 - val_accuracy: 0.6891\n","Epoch 16/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.4898 - accuracy: 0.7811 - val_loss: 0.5161 - val_accuracy: 0.7437\n","Epoch 17/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.4759 - accuracy: 0.7821 - val_loss: 0.4994 - val_accuracy: 0.7521\n","Epoch 18/100\n","30/30 [==============================] - 1s 37ms/step - loss: 0.5138 - accuracy: 0.7589 - val_loss: 0.5872 - val_accuracy: 0.6555\n","Epoch 19/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.4868 - accuracy: 0.7779 - val_loss: 0.5012 - val_accuracy: 0.7437\n","Epoch 20/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.4770 - accuracy: 0.7821 - val_loss: 0.4997 - val_accuracy: 0.7815\n","Epoch 21/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.4573 - accuracy: 0.7905 - val_loss: 0.4994 - val_accuracy: 0.7731\n","Epoch 22/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.4469 - accuracy: 0.8168 - val_loss: 0.4878 - val_accuracy: 0.7983\n","Epoch 23/100\n","30/30 [==============================] - 1s 42ms/step - loss: 0.4885 - accuracy: 0.7821 - val_loss: 0.6214 - val_accuracy: 0.7311\n","Epoch 24/100\n","30/30 [==============================] - 2s 59ms/step - loss: 0.4454 - accuracy: 0.8042 - val_loss: 0.4524 - val_accuracy: 0.7983\n","Epoch 25/100\n","30/30 [==============================] - 2s 50ms/step - loss: 0.4196 - accuracy: 0.8232 - val_loss: 0.4489 - val_accuracy: 0.7857\n","Epoch 26/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.4265 - accuracy: 0.8253 - val_loss: 0.4648 - val_accuracy: 0.7857\n","Epoch 27/100\n","30/30 [==============================] - 1s 37ms/step - loss: 0.4519 - accuracy: 0.8032 - val_loss: 0.4655 - val_accuracy: 0.7773\n","Epoch 28/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.4432 - accuracy: 0.8105 - val_loss: 0.4587 - val_accuracy: 0.7899\n","Epoch 29/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.4587 - accuracy: 0.8011 - val_loss: 0.4710 - val_accuracy: 0.7857\n","Epoch 30/100\n","30/30 [==============================] - 1s 36ms/step - loss: 0.4222 - accuracy: 0.8263 - val_loss: 0.6268 - val_accuracy: 0.7437\n","Epoch 31/100\n","30/30 [==============================] - 1s 37ms/step - loss: 0.4123 - accuracy: 0.8326 - val_loss: 0.4949 - val_accuracy: 0.7605\n","Epoch 32/100\n","30/30 [==============================] - 1s 37ms/step - loss: 0.3901 - accuracy: 0.8432 - val_loss: 0.4450 - val_accuracy: 0.7983\n","Epoch 33/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.4288 - accuracy: 0.8316 - val_loss: 0.4841 - val_accuracy: 0.7857\n","Epoch 34/100\n","30/30 [==============================] - 2s 53ms/step - loss: 0.4078 - accuracy: 0.8242 - val_loss: 0.5602 - val_accuracy: 0.7227\n","Epoch 35/100\n","30/30 [==============================] - 2s 61ms/step - loss: 0.4110 - accuracy: 0.8358 - val_loss: 0.4435 - val_accuracy: 0.7983\n","Epoch 36/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3836 - accuracy: 0.8379 - val_loss: 0.4857 - val_accuracy: 0.7689\n","Epoch 37/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3897 - accuracy: 0.8505 - val_loss: 0.4286 - val_accuracy: 0.8151\n","Epoch 38/100\n","30/30 [==============================] - 1s 37ms/step - loss: 0.3856 - accuracy: 0.8421 - val_loss: 0.4990 - val_accuracy: 0.7647\n","Epoch 39/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3824 - accuracy: 0.8474 - val_loss: 0.5285 - val_accuracy: 0.7563\n","Epoch 40/100\n","30/30 [==============================] - 1s 36ms/step - loss: 0.4316 - accuracy: 0.8284 - val_loss: 0.4266 - val_accuracy: 0.8109\n","Epoch 41/100\n","30/30 [==============================] - 1s 37ms/step - loss: 0.4461 - accuracy: 0.8095 - val_loss: 0.4352 - val_accuracy: 0.7941\n","Epoch 42/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3864 - accuracy: 0.8432 - val_loss: 0.3982 - val_accuracy: 0.8319\n","Epoch 43/100\n","30/30 [==============================] - 2s 55ms/step - loss: 0.4220 - accuracy: 0.8274 - val_loss: 0.4654 - val_accuracy: 0.7899\n","Epoch 44/100\n","30/30 [==============================] - 2s 69ms/step - loss: 0.3713 - accuracy: 0.8505 - val_loss: 0.4130 - val_accuracy: 0.8109\n","Epoch 45/100\n","30/30 [==============================] - 2s 72ms/step - loss: 0.3672 - accuracy: 0.8537 - val_loss: 0.4328 - val_accuracy: 0.8235\n","Epoch 46/100\n","30/30 [==============================] - 2s 71ms/step - loss: 0.3961 - accuracy: 0.8274 - val_loss: 0.5296 - val_accuracy: 0.7395\n","Epoch 47/100\n","30/30 [==============================] - 2s 51ms/step - loss: 0.4020 - accuracy: 0.8400 - val_loss: 0.4036 - val_accuracy: 0.8319\n","Epoch 48/100\n","30/30 [==============================] - 1s 37ms/step - loss: 0.3666 - accuracy: 0.8579 - val_loss: 0.3796 - val_accuracy: 0.8445\n","Epoch 49/100\n","30/30 [==============================] - 1s 37ms/step - loss: 0.4002 - accuracy: 0.8368 - val_loss: 0.4071 - val_accuracy: 0.8109\n","Epoch 50/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3778 - accuracy: 0.8484 - val_loss: 0.4046 - val_accuracy: 0.8529\n","Epoch 51/100\n","30/30 [==============================] - 1s 37ms/step - loss: 0.3799 - accuracy: 0.8558 - val_loss: 0.3958 - val_accuracy: 0.8193\n","Epoch 52/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3509 - accuracy: 0.8611 - val_loss: 0.4610 - val_accuracy: 0.7731\n","Epoch 53/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3852 - accuracy: 0.8453 - val_loss: 0.3896 - val_accuracy: 0.8403\n","Epoch 54/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.3548 - accuracy: 0.8653 - val_loss: 0.3992 - val_accuracy: 0.8193\n","Epoch 55/100\n","30/30 [==============================] - 1s 48ms/step - loss: 0.3715 - accuracy: 0.8432 - val_loss: 0.4169 - val_accuracy: 0.8277\n","Epoch 56/100\n","30/30 [==============================] - 2s 62ms/step - loss: 0.3886 - accuracy: 0.8495 - val_loss: 0.3986 - val_accuracy: 0.8445\n","Epoch 57/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3689 - accuracy: 0.8568 - val_loss: 0.3679 - val_accuracy: 0.8109\n","Epoch 58/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3616 - accuracy: 0.8526 - val_loss: 0.3872 - val_accuracy: 0.8235\n","Epoch 59/100\n","30/30 [==============================] - 1s 36ms/step - loss: 0.3503 - accuracy: 0.8653 - val_loss: 0.4006 - val_accuracy: 0.8319\n","Epoch 60/100\n","30/30 [==============================] - 1s 36ms/step - loss: 0.3451 - accuracy: 0.8663 - val_loss: 0.6802 - val_accuracy: 0.7605\n","Epoch 61/100\n","30/30 [==============================] - 1s 36ms/step - loss: 0.4068 - accuracy: 0.8189 - val_loss: 0.3930 - val_accuracy: 0.8319\n","Epoch 62/100\n","30/30 [==============================] - 1s 36ms/step - loss: 0.3681 - accuracy: 0.8505 - val_loss: 0.3998 - val_accuracy: 0.8151\n","Epoch 63/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3343 - accuracy: 0.8632 - val_loss: 0.4488 - val_accuracy: 0.7815\n","Epoch 64/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3957 - accuracy: 0.8358 - val_loss: 0.4124 - val_accuracy: 0.8361\n","Epoch 65/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3299 - accuracy: 0.8800 - val_loss: 0.3783 - val_accuracy: 0.8403\n","Epoch 66/100\n","30/30 [==============================] - 2s 60ms/step - loss: 0.3396 - accuracy: 0.8632 - val_loss: 0.4231 - val_accuracy: 0.8277\n","Epoch 67/100\n","30/30 [==============================] - 2s 58ms/step - loss: 0.3858 - accuracy: 0.8526 - val_loss: 0.4447 - val_accuracy: 0.7773\n","Epoch 68/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3585 - accuracy: 0.8537 - val_loss: 0.4134 - val_accuracy: 0.8193\n","Epoch 69/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3319 - accuracy: 0.8726 - val_loss: 0.3929 - val_accuracy: 0.8151\n","Epoch 70/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3641 - accuracy: 0.8611 - val_loss: 0.4233 - val_accuracy: 0.7983\n","Epoch 71/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3338 - accuracy: 0.8632 - val_loss: 0.3828 - val_accuracy: 0.8361\n","Epoch 72/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3508 - accuracy: 0.8632 - val_loss: 0.3798 - val_accuracy: 0.8487\n","Epoch 73/100\n","30/30 [==============================] - 1s 37ms/step - loss: 0.3484 - accuracy: 0.8589 - val_loss: 0.4305 - val_accuracy: 0.8277\n","Epoch 74/100\n","30/30 [==============================] - 1s 36ms/step - loss: 0.3406 - accuracy: 0.8716 - val_loss: 0.4395 - val_accuracy: 0.8067\n","Epoch 75/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3329 - accuracy: 0.8726 - val_loss: 0.3925 - val_accuracy: 0.8403\n","Epoch 76/100\n","30/30 [==============================] - 1s 50ms/step - loss: 0.3496 - accuracy: 0.8705 - val_loss: 0.5579 - val_accuracy: 0.7521\n","Epoch 77/100\n","30/30 [==============================] - 2s 61ms/step - loss: 0.3565 - accuracy: 0.8568 - val_loss: 0.3537 - val_accuracy: 0.8655\n","Epoch 78/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3417 - accuracy: 0.8621 - val_loss: 0.3831 - val_accuracy: 0.8193\n","Epoch 79/100\n","30/30 [==============================] - 1s 36ms/step - loss: 0.3624 - accuracy: 0.8558 - val_loss: 0.4250 - val_accuracy: 0.8067\n","Epoch 80/100\n","30/30 [==============================] - 1s 37ms/step - loss: 0.3372 - accuracy: 0.8611 - val_loss: 0.3841 - val_accuracy: 0.8109\n","Epoch 81/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.3540 - accuracy: 0.8632 - val_loss: 0.3715 - val_accuracy: 0.8529\n","Epoch 82/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.3923 - accuracy: 0.8411 - val_loss: 0.4434 - val_accuracy: 0.8193\n","Epoch 83/100\n","30/30 [==============================] - 1s 37ms/step - loss: 0.3512 - accuracy: 0.8558 - val_loss: 0.4082 - val_accuracy: 0.8361\n","Epoch 84/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.3193 - accuracy: 0.8811 - val_loss: 0.3773 - val_accuracy: 0.8529\n","Epoch 85/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3395 - accuracy: 0.8600 - val_loss: 0.3751 - val_accuracy: 0.8571\n","Epoch 86/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.3195 - accuracy: 0.8768 - val_loss: 0.3929 - val_accuracy: 0.8277\n","Epoch 87/100\n","30/30 [==============================] - 2s 67ms/step - loss: 0.3216 - accuracy: 0.8737 - val_loss: 0.5281 - val_accuracy: 0.7689\n","Epoch 88/100\n","30/30 [==============================] - 2s 71ms/step - loss: 0.3537 - accuracy: 0.8526 - val_loss: 0.3900 - val_accuracy: 0.8235\n","Epoch 89/100\n","30/30 [==============================] - 2s 53ms/step - loss: 0.3331 - accuracy: 0.8547 - val_loss: 0.3755 - val_accuracy: 0.8277\n","Epoch 90/100\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3128 - accuracy: 0.8747 - val_loss: 0.4831 - val_accuracy: 0.7899\n","Epoch 91/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.3429 - accuracy: 0.8663 - val_loss: 0.3859 - val_accuracy: 0.8403\n","Epoch 92/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3149 - accuracy: 0.8747 - val_loss: 0.4614 - val_accuracy: 0.8235\n","Epoch 93/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3688 - accuracy: 0.8484 - val_loss: 0.3712 - val_accuracy: 0.8277\n","Epoch 94/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3167 - accuracy: 0.8789 - val_loss: 0.3648 - val_accuracy: 0.8571\n","Epoch 95/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.3054 - accuracy: 0.8758 - val_loss: 0.3896 - val_accuracy: 0.8445\n","Epoch 96/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.3119 - accuracy: 0.8705 - val_loss: 0.4443 - val_accuracy: 0.8445\n","Epoch 97/100\n","30/30 [==============================] - 1s 37ms/step - loss: 0.3377 - accuracy: 0.8589 - val_loss: 0.3364 - val_accuracy: 0.8445\n","Epoch 98/100\n","30/30 [==============================] - 1s 48ms/step - loss: 0.3102 - accuracy: 0.8726 - val_loss: 0.3838 - val_accuracy: 0.8403\n","Epoch 99/100\n","30/30 [==============================] - 2s 59ms/step - loss: 0.3173 - accuracy: 0.8768 - val_loss: 0.3525 - val_accuracy: 0.8487\n","Epoch 100/100\n","30/30 [==============================] - 1s 44ms/step - loss: 0.3416 - accuracy: 0.8589 - val_loss: 0.3898 - val_accuracy: 0.8151\n"]}]},{"cell_type":"code","source":["test_predictions = model_ex2v2.predict(test_X)\n","test_predictions_binary = (test_predictions > 0.7).astype(int)\n","\n","test_f1 = f1_score(test_y, test_predictions_binary)\n","test_recall = recall_score(test_y, test_predictions_binary)\n","\n","test_loss, test_accuracy = model_ex2v2.evaluate(test_X, test_y)\n","\n","print(\"Test Loss:\", test_loss)\n","print(\"Test Accuracy:\", test_accuracy)\n","print(\"Test F1-Score:\", test_f1)\n","print(\"Test Recall:\", test_recall)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zuJghJ4yyanx","executionInfo":{"status":"ok","timestamp":1693273318735,"user_tz":300,"elapsed":597,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"a4e9eda7-1b23-4663-ef34-4a6f40f1289b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["10/10 [==============================] - 0s 12ms/step\n","10/10 [==============================] - 0s 11ms/step - loss: 0.4451 - accuracy: 0.7919\n","Test Loss: 0.44506028294563293\n","Test Accuracy: 0.791946291923523\n","Test F1-Score: 0.8472622478386167\n","Test Recall: 0.91875\n"]}]},{"cell_type":"code","source":["model_ex2v2.save('lstm_model_ex2v2.h5')"],"metadata":{"id":"NS7_y0oFzzVt"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Plot training and validation metrics\n","plt.figure(figsize=(10, 4))\n","plt.subplot(1, 2, 1)\n","plt.plot(history_ex2v2.history['loss'], label='Training Loss')\n","plt.plot(history_ex2v2.history['val_loss'], label='Validation Loss')\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.legend()\n","\n","plt.subplot(1, 2, 2)\n","plt.plot(history_ex2v2.history['accuracy'], label='Training Accuracy')\n","plt.plot(history_ex2v2.history['val_accuracy'], label='Validation Accuracy')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":407},"id":"fkAO1F_yzhLl","executionInfo":{"status":"ok","timestamp":1693273339636,"user_tz":300,"elapsed":1199,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"b58c4ee8-caf2-4613-876f-3a6654ac0abf"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x400 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# Predict probabilities on test data\n","predictions = model_ex2v2.predict(test_X)\n","\n","# Convert probabilities to binary classes using a threshold (e.g., 0.5)\n","predicted_classes = (predictions > 0.7).astype(int)\n","\n","# Calculate the confusion matrix\n","confusion_mtx = confusion_matrix(test_y, predicted_classes)\n","\n","# Plot the confusion matrix\n","plt.figure(figsize=(8, 6))\n","sns.heatmap(confusion_mtx, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False)\n","plt.xlabel('Predicted')\n","plt.ylabel('True')\n","plt.title('Confusion Matrix')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":581},"id":"a8rhgjfv4f4W","executionInfo":{"status":"ok","timestamp":1693273345318,"user_tz":300,"elapsed":641,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"a9869d3b-6c73-4bcc-90c6-4c682dcfbfe9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["10/10 [==============================] - 0s 24ms/step\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["### Hyperparameters of Exp 2 v3"],"metadata":{"id":"U7OrNUeM0PuX"}},{"cell_type":"code","source":["model_ex2v3 = Sequential()\n","model_ex2v3.add(LSTM(units=128, input_shape=(sequence_length, num_features), return_sequences=True))\n","model_ex2v3.add(Dropout(0.7))\n","model_ex2v3.add(LSTM(units=64, return_sequences=True))\n","model_ex2v3.add(LSTM(units=32, return_sequences=True))\n","model_ex2v3.add(Dropout(0.7))\n","model_ex2v3.add(LSTM(units=16))\n","model_ex2v3.add(Dense(units=1, activation='sigmoid'))\n","\n","optimizer = Adam(learning_rate=0.001)  # Using a different optimizer\n","model_ex2v3.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n","\n","model_ex2v3.summary()\n","\n","batch_size = 32\n","epochs = 100\n","early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n","history_ex2v3 = model_ex2v3.fit(train_X, train_y, batch_size=batch_size, epochs=epochs, validation_split=0.2, verbose=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ri4UNpiI0UhR","executionInfo":{"status":"ok","timestamp":1693273552033,"user_tz":300,"elapsed":195970,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"8f29ebed-1be0-4c19-e20a-81f19d17fba3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_8\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," lstm_21 (LSTM)              (None, 10, 128)           91648     \n","                                                                 \n"," dropout_16 (Dropout)        (None, 10, 128)           0         \n","                                                                 \n"," lstm_22 (LSTM)              (None, 10, 64)            49408     \n","                                                                 \n"," lstm_23 (LSTM)              (None, 10, 32)            12416     \n","                                                                 \n"," dropout_17 (Dropout)        (None, 10, 32)            0         \n","                                                                 \n"," lstm_24 (LSTM)              (None, 16)                3136      \n","                                                                 \n"," dense_8 (Dense)             (None, 1)                 17        \n","                                                                 \n","=================================================================\n","Total params: 156,625\n","Trainable params: 156,625\n","Non-trainable params: 0\n","_________________________________________________________________\n","Epoch 1/100\n","30/30 [==============================] - 12s 102ms/step - loss: 0.6882 - accuracy: 0.5400 - val_loss: 0.6806 - val_accuracy: 0.5336\n","Epoch 2/100\n","30/30 [==============================] - 2s 62ms/step - loss: 0.6455 - accuracy: 0.6642 - val_loss: 0.6025 - val_accuracy: 0.7101\n","Epoch 3/100\n","30/30 [==============================] - 2s 67ms/step - loss: 0.5754 - accuracy: 0.7284 - val_loss: 0.5769 - val_accuracy: 0.7185\n","Epoch 4/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.5604 - accuracy: 0.7274 - val_loss: 0.5600 - val_accuracy: 0.7437\n","Epoch 5/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.5481 - accuracy: 0.7484 - val_loss: 0.5673 - val_accuracy: 0.7101\n","Epoch 6/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.5242 - accuracy: 0.7663 - val_loss: 0.5576 - val_accuracy: 0.7395\n","Epoch 7/100\n","30/30 [==============================] - 1s 43ms/step - loss: 0.5849 - accuracy: 0.7000 - val_loss: 0.5510 - val_accuracy: 0.7395\n","Epoch 8/100\n","30/30 [==============================] - 1s 44ms/step - loss: 0.5504 - accuracy: 0.7295 - val_loss: 0.5788 - val_accuracy: 0.7017\n","Epoch 9/100\n","30/30 [==============================] - 1s 47ms/step - loss: 0.5686 - accuracy: 0.7032 - val_loss: 0.6186 - val_accuracy: 0.6387\n","Epoch 10/100\n","30/30 [==============================] - 1s 44ms/step - loss: 0.5315 - accuracy: 0.7505 - val_loss: 0.5439 - val_accuracy: 0.7269\n","Epoch 11/100\n","30/30 [==============================] - 2s 70ms/step - loss: 0.5059 - accuracy: 0.7695 - val_loss: 0.5511 - val_accuracy: 0.7563\n","Epoch 12/100\n","30/30 [==============================] - 2s 75ms/step - loss: 0.5119 - accuracy: 0.7684 - val_loss: 0.5687 - val_accuracy: 0.7101\n","Epoch 13/100\n","30/30 [==============================] - 2s 81ms/step - loss: 0.5017 - accuracy: 0.7589 - val_loss: 0.5167 - val_accuracy: 0.7521\n","Epoch 14/100\n","30/30 [==============================] - 3s 89ms/step - loss: 0.5030 - accuracy: 0.7579 - val_loss: 0.5395 - val_accuracy: 0.7269\n","Epoch 15/100\n","30/30 [==============================] - 3s 91ms/step - loss: 0.5064 - accuracy: 0.7537 - val_loss: 0.5416 - val_accuracy: 0.7479\n","Epoch 16/100\n","30/30 [==============================] - 4s 124ms/step - loss: 0.5023 - accuracy: 0.7589 - val_loss: 0.5505 - val_accuracy: 0.7605\n","Epoch 17/100\n","30/30 [==============================] - 3s 97ms/step - loss: 0.4774 - accuracy: 0.7853 - val_loss: 0.5505 - val_accuracy: 0.7269\n","Epoch 18/100\n","30/30 [==============================] - 3s 108ms/step - loss: 0.5145 - accuracy: 0.7611 - val_loss: 0.5147 - val_accuracy: 0.7731\n","Epoch 19/100\n","30/30 [==============================] - 3s 92ms/step - loss: 0.4651 - accuracy: 0.7926 - val_loss: 0.5094 - val_accuracy: 0.7647\n","Epoch 20/100\n","30/30 [==============================] - 2s 69ms/step - loss: 0.4711 - accuracy: 0.7811 - val_loss: 0.5496 - val_accuracy: 0.7143\n","Epoch 21/100\n","30/30 [==============================] - 2s 73ms/step - loss: 0.4771 - accuracy: 0.7789 - val_loss: 0.5067 - val_accuracy: 0.7563\n","Epoch 22/100\n","30/30 [==============================] - 2s 54ms/step - loss: 0.4529 - accuracy: 0.8011 - val_loss: 0.4881 - val_accuracy: 0.7815\n","Epoch 23/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.4534 - accuracy: 0.8011 - val_loss: 0.5034 - val_accuracy: 0.7605\n","Epoch 24/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.4790 - accuracy: 0.7811 - val_loss: 0.5392 - val_accuracy: 0.7185\n","Epoch 25/100\n","30/30 [==============================] - 1s 44ms/step - loss: 0.4847 - accuracy: 0.7705 - val_loss: 0.5177 - val_accuracy: 0.7521\n","Epoch 26/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.4780 - accuracy: 0.7737 - val_loss: 0.5501 - val_accuracy: 0.7185\n","Epoch 27/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.4608 - accuracy: 0.7832 - val_loss: 0.5296 - val_accuracy: 0.7815\n","Epoch 28/100\n","30/30 [==============================] - 1s 44ms/step - loss: 0.4571 - accuracy: 0.7863 - val_loss: 0.5059 - val_accuracy: 0.7815\n","Epoch 29/100\n","30/30 [==============================] - 2s 51ms/step - loss: 0.4471 - accuracy: 0.8000 - val_loss: 0.5158 - val_accuracy: 0.7563\n","Epoch 30/100\n","30/30 [==============================] - 2s 74ms/step - loss: 0.4255 - accuracy: 0.8158 - val_loss: 0.5491 - val_accuracy: 0.7731\n","Epoch 31/100\n","30/30 [==============================] - 1s 48ms/step - loss: 0.4832 - accuracy: 0.7853 - val_loss: 0.5754 - val_accuracy: 0.7395\n","Epoch 32/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.4474 - accuracy: 0.8021 - val_loss: 0.4695 - val_accuracy: 0.7689\n","Epoch 33/100\n","30/30 [==============================] - 1s 44ms/step - loss: 0.4290 - accuracy: 0.8126 - val_loss: 0.4935 - val_accuracy: 0.7815\n","Epoch 34/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.4009 - accuracy: 0.8316 - val_loss: 0.4488 - val_accuracy: 0.8067\n","Epoch 35/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.4904 - accuracy: 0.7789 - val_loss: 0.4963 - val_accuracy: 0.7521\n","Epoch 36/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.4445 - accuracy: 0.8084 - val_loss: 0.4705 - val_accuracy: 0.7689\n","Epoch 37/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.4353 - accuracy: 0.8116 - val_loss: 0.4913 - val_accuracy: 0.7731\n","Epoch 38/100\n","30/30 [==============================] - 2s 66ms/step - loss: 0.4522 - accuracy: 0.8074 - val_loss: 0.5650 - val_accuracy: 0.7059\n","Epoch 39/100\n","30/30 [==============================] - 3s 95ms/step - loss: 0.5148 - accuracy: 0.7589 - val_loss: 0.5135 - val_accuracy: 0.7647\n","Epoch 40/100\n","30/30 [==============================] - 2s 62ms/step - loss: 0.4466 - accuracy: 0.8137 - val_loss: 0.5245 - val_accuracy: 0.7815\n","Epoch 41/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.4151 - accuracy: 0.8242 - val_loss: 0.4531 - val_accuracy: 0.8193\n","Epoch 42/100\n","30/30 [==============================] - 1s 44ms/step - loss: 0.4036 - accuracy: 0.8305 - val_loss: 0.4972 - val_accuracy: 0.7983\n","Epoch 43/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.4383 - accuracy: 0.8105 - val_loss: 0.4439 - val_accuracy: 0.7983\n","Epoch 44/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.3757 - accuracy: 0.8495 - val_loss: 0.3969 - val_accuracy: 0.8319\n","Epoch 45/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.3998 - accuracy: 0.8389 - val_loss: 0.4358 - val_accuracy: 0.8193\n","Epoch 46/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.3977 - accuracy: 0.8316 - val_loss: 0.4662 - val_accuracy: 0.8067\n","Epoch 47/100\n","30/30 [==============================] - 2s 66ms/step - loss: 0.3995 - accuracy: 0.8284 - val_loss: 0.4207 - val_accuracy: 0.8151\n","Epoch 48/100\n","30/30 [==============================] - 2s 65ms/step - loss: 0.3558 - accuracy: 0.8611 - val_loss: 0.4700 - val_accuracy: 0.8025\n","Epoch 49/100\n","30/30 [==============================] - 1s 48ms/step - loss: 0.4045 - accuracy: 0.8242 - val_loss: 0.4186 - val_accuracy: 0.8025\n","Epoch 50/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.3893 - accuracy: 0.8368 - val_loss: 0.3909 - val_accuracy: 0.8319\n","Epoch 51/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.3678 - accuracy: 0.8484 - val_loss: 0.4751 - val_accuracy: 0.7941\n","Epoch 52/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.3607 - accuracy: 0.8589 - val_loss: 0.4044 - val_accuracy: 0.8445\n","Epoch 53/100\n","30/30 [==============================] - 1s 44ms/step - loss: 0.3496 - accuracy: 0.8695 - val_loss: 0.4795 - val_accuracy: 0.7899\n","Epoch 54/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.3771 - accuracy: 0.8379 - val_loss: 0.4209 - val_accuracy: 0.8151\n","Epoch 55/100\n","30/30 [==============================] - 2s 52ms/step - loss: 0.3525 - accuracy: 0.8568 - val_loss: 0.3934 - val_accuracy: 0.8193\n","Epoch 56/100\n","30/30 [==============================] - 2s 71ms/step - loss: 0.3715 - accuracy: 0.8379 - val_loss: 0.5091 - val_accuracy: 0.7689\n","Epoch 57/100\n","30/30 [==============================] - 2s 52ms/step - loss: 0.4080 - accuracy: 0.8221 - val_loss: 0.4168 - val_accuracy: 0.8109\n","Epoch 58/100\n","30/30 [==============================] - 1s 44ms/step - loss: 0.3709 - accuracy: 0.8547 - val_loss: 0.4263 - val_accuracy: 0.8151\n","Epoch 59/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.3464 - accuracy: 0.8632 - val_loss: 0.4091 - val_accuracy: 0.8151\n","Epoch 60/100\n","30/30 [==============================] - 1s 43ms/step - loss: 0.3780 - accuracy: 0.8453 - val_loss: 0.4529 - val_accuracy: 0.7899\n","Epoch 61/100\n","30/30 [==============================] - 1s 44ms/step - loss: 0.3446 - accuracy: 0.8684 - val_loss: 0.4516 - val_accuracy: 0.8067\n","Epoch 62/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.3403 - accuracy: 0.8695 - val_loss: 0.4258 - val_accuracy: 0.8193\n","Epoch 63/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.3487 - accuracy: 0.8642 - val_loss: 0.4479 - val_accuracy: 0.8151\n","Epoch 64/100\n","30/30 [==============================] - 2s 53ms/step - loss: 0.3632 - accuracy: 0.8568 - val_loss: 0.4440 - val_accuracy: 0.8235\n","Epoch 65/100\n","30/30 [==============================] - 2s 73ms/step - loss: 0.3849 - accuracy: 0.8337 - val_loss: 0.5320 - val_accuracy: 0.7227\n","Epoch 66/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.3571 - accuracy: 0.8516 - val_loss: 0.4149 - val_accuracy: 0.8403\n","Epoch 67/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.3526 - accuracy: 0.8505 - val_loss: 0.3868 - val_accuracy: 0.8193\n","Epoch 68/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.3379 - accuracy: 0.8558 - val_loss: 0.4859 - val_accuracy: 0.8025\n","Epoch 69/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.3358 - accuracy: 0.8737 - val_loss: 0.4521 - val_accuracy: 0.8109\n","Epoch 70/100\n","30/30 [==============================] - 1s 47ms/step - loss: 0.3599 - accuracy: 0.8537 - val_loss: 0.3643 - val_accuracy: 0.8361\n","Epoch 71/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.3360 - accuracy: 0.8674 - val_loss: 0.3901 - val_accuracy: 0.8319\n","Epoch 72/100\n","30/30 [==============================] - 1s 47ms/step - loss: 0.3314 - accuracy: 0.8611 - val_loss: 0.4090 - val_accuracy: 0.8193\n","Epoch 73/100\n","30/30 [==============================] - 2s 65ms/step - loss: 0.3361 - accuracy: 0.8621 - val_loss: 0.4190 - val_accuracy: 0.8403\n","Epoch 74/100\n","30/30 [==============================] - 2s 68ms/step - loss: 0.3512 - accuracy: 0.8547 - val_loss: 0.3955 - val_accuracy: 0.8235\n","Epoch 75/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.3719 - accuracy: 0.8474 - val_loss: 0.4098 - val_accuracy: 0.8193\n","Epoch 76/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.3438 - accuracy: 0.8611 - val_loss: 0.4037 - val_accuracy: 0.8193\n","Epoch 77/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.3169 - accuracy: 0.8789 - val_loss: 0.3908 - val_accuracy: 0.8235\n","Epoch 78/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.3102 - accuracy: 0.8811 - val_loss: 0.4036 - val_accuracy: 0.7983\n","Epoch 79/100\n","30/30 [==============================] - 2s 67ms/step - loss: 0.3525 - accuracy: 0.8421 - val_loss: 0.4911 - val_accuracy: 0.8109\n","Epoch 80/100\n","30/30 [==============================] - 2s 71ms/step - loss: 0.3780 - accuracy: 0.8484 - val_loss: 0.5012 - val_accuracy: 0.7941\n","Epoch 81/100\n","30/30 [==============================] - 3s 105ms/step - loss: 0.3142 - accuracy: 0.8779 - val_loss: 0.3614 - val_accuracy: 0.8235\n","Epoch 82/100\n","30/30 [==============================] - 2s 82ms/step - loss: 0.3147 - accuracy: 0.8779 - val_loss: 0.5110 - val_accuracy: 0.7941\n","Epoch 83/100\n","30/30 [==============================] - 3s 117ms/step - loss: 0.3426 - accuracy: 0.8695 - val_loss: 0.3642 - val_accuracy: 0.8277\n","Epoch 84/100\n","30/30 [==============================] - 3s 89ms/step - loss: 0.3217 - accuracy: 0.8768 - val_loss: 0.3903 - val_accuracy: 0.8319\n","Epoch 85/100\n","30/30 [==============================] - 3s 105ms/step - loss: 0.3090 - accuracy: 0.8811 - val_loss: 0.3908 - val_accuracy: 0.8403\n","Epoch 86/100\n","30/30 [==============================] - 5s 174ms/step - loss: 0.3280 - accuracy: 0.8674 - val_loss: 0.3989 - val_accuracy: 0.8403\n","Epoch 87/100\n","30/30 [==============================] - 4s 125ms/step - loss: 0.3118 - accuracy: 0.8747 - val_loss: 0.4119 - val_accuracy: 0.8151\n","Epoch 88/100\n","30/30 [==============================] - 3s 102ms/step - loss: 0.3350 - accuracy: 0.8684 - val_loss: 0.4097 - val_accuracy: 0.8235\n","Epoch 89/100\n","30/30 [==============================] - 3s 116ms/step - loss: 0.3107 - accuracy: 0.8737 - val_loss: 0.4099 - val_accuracy: 0.8151\n","Epoch 90/100\n","30/30 [==============================] - 3s 113ms/step - loss: 0.3063 - accuracy: 0.8821 - val_loss: 0.3903 - val_accuracy: 0.8403\n","Epoch 91/100\n","30/30 [==============================] - 2s 81ms/step - loss: 0.3036 - accuracy: 0.8874 - val_loss: 0.4063 - val_accuracy: 0.8277\n","Epoch 92/100\n","30/30 [==============================] - 2s 57ms/step - loss: 0.3069 - accuracy: 0.8726 - val_loss: 0.3882 - val_accuracy: 0.8277\n","Epoch 93/100\n","30/30 [==============================] - 1s 44ms/step - loss: 0.3169 - accuracy: 0.8684 - val_loss: 0.4890 - val_accuracy: 0.8025\n","Epoch 94/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.3270 - accuracy: 0.8789 - val_loss: 0.3724 - val_accuracy: 0.8487\n","Epoch 95/100\n","30/30 [==============================] - 1s 48ms/step - loss: 0.3050 - accuracy: 0.8832 - val_loss: 0.4616 - val_accuracy: 0.8277\n","Epoch 96/100\n","30/30 [==============================] - 2s 74ms/step - loss: 0.3152 - accuracy: 0.8747 - val_loss: 0.3898 - val_accuracy: 0.8697\n","Epoch 97/100\n","30/30 [==============================] - 2s 57ms/step - loss: 0.3416 - accuracy: 0.8526 - val_loss: 0.3778 - val_accuracy: 0.8319\n","Epoch 98/100\n","30/30 [==============================] - 1s 44ms/step - loss: 0.3268 - accuracy: 0.8653 - val_loss: 0.5000 - val_accuracy: 0.7605\n","Epoch 99/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.3136 - accuracy: 0.8768 - val_loss: 0.4107 - val_accuracy: 0.8487\n","Epoch 100/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.3013 - accuracy: 0.8832 - val_loss: 0.3955 - val_accuracy: 0.8445\n"]}]},{"cell_type":"code","source":["test_predictions = model_ex2v3.predict(test_X)\n","test_predictions_binary = (test_predictions > 0.7).astype(int)\n","\n","test_f1 = f1_score(test_y, test_predictions_binary)\n","test_recall = recall_score(test_y, test_predictions_binary)\n","\n","test_loss, test_accuracy = model_ex2v3.evaluate(test_X, test_y)\n","\n","print(\"Test Loss:\", test_loss)\n","print(\"Test Accuracy:\", test_accuracy)\n","print(\"Test F1-Score:\", test_f1)\n","print(\"Test Recall:\", test_recall)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"691m-g-90oQu","executionInfo":{"status":"ok","timestamp":1693273560252,"user_tz":300,"elapsed":2806,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"694fdae5-4b3d-4a65-c964-a51de6b348b7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["10/10 [==============================] - 2s 13ms/step\n","10/10 [==============================] - 0s 12ms/step - loss: 0.3702 - accuracy: 0.8624\n","Test Loss: 0.3701843023300171\n","Test Accuracy: 0.8624160885810852\n","Test F1-Score: 0.8507936507936507\n","Test Recall: 0.8375\n"]}]},{"cell_type":"code","source":["# Plot training and validation metrics\n","plt.figure(figsize=(10, 4))\n","plt.subplot(1, 2, 1)\n","plt.plot(history_ex2v3.history['loss'], label='Training Loss')\n","plt.plot(history_ex2v3.history['val_loss'], label='Validation Loss')\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.legend()\n","\n","plt.subplot(1, 2, 2)\n","plt.plot(history_ex2v3.history['accuracy'], label='Training Accuracy')\n","plt.plot(history_ex2v3.history['val_accuracy'], label='Validation Accuracy')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":406},"id":"4hMrMS6E2mF4","executionInfo":{"status":"ok","timestamp":1693273567335,"user_tz":300,"elapsed":1456,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"24c66832-4051-4fc3-e1c9-8b117350b082"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x400 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["model_ex2v3.save('lstm_model_ex2v3.h5')"],"metadata":{"id":"-zAaqo3V9yYa"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Hyperparameters of Exp 2 v4"],"metadata":{"id":"99gzdrrP6e6y"}},{"cell_type":"code","source":["model_ex2v4 = Sequential()\n","model_ex2v4.add(LSTM(units=32, input_shape=(sequence_length, num_features), return_sequences=True))\n","model_ex2v4.add(Dropout(0.7))\n","model_ex2v4.add(LSTM(units=64, return_sequences=True))\n","model_ex2v4.add(LSTM(units=128))\n","model_ex2v4.add(Dropout(0.7))\n","model_ex2v4.add(Dense(units=1, activation='sigmoid'))\n","\n","optimizer = Adam(learning_rate=0.001)\n","model_ex2v4.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n","\n","model_ex2v4.summary()\n","\n","batch_size = 32\n","epochs = 120\n","early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n","history_ex2v4 = model_ex2v4.fit(train_X, train_y, batch_size=batch_size, epochs=epochs, validation_split=0.2, verbose=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"r9EDw9Xh6XaI","executionInfo":{"status":"ok","timestamp":1693274057699,"user_tz":300,"elapsed":208073,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"6a4b47dc-afea-47cc-de5a-7a53b2fe2bfe"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_10\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," lstm_28 (LSTM)              (None, 10, 32)            10624     \n","                                                                 \n"," dropout_20 (Dropout)        (None, 10, 32)            0         \n","                                                                 \n"," lstm_29 (LSTM)              (None, 10, 64)            24832     \n","                                                                 \n"," lstm_30 (LSTM)              (None, 128)               98816     \n","                                                                 \n"," dropout_21 (Dropout)        (None, 128)               0         \n","                                                                 \n"," dense_10 (Dense)            (None, 1)                 129       \n","                                                                 \n","=================================================================\n","Total params: 134,401\n","Trainable params: 134,401\n","Non-trainable params: 0\n","_________________________________________________________________\n","Epoch 1/120\n","30/30 [==============================] - 9s 135ms/step - loss: 0.6954 - accuracy: 0.5295 - val_loss: 0.6884 - val_accuracy: 0.5378\n","Epoch 2/120\n","30/30 [==============================] - 1s 39ms/step - loss: 0.6897 - accuracy: 0.5505 - val_loss: 0.6811 - val_accuracy: 0.5336\n","Epoch 3/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.6652 - accuracy: 0.5874 - val_loss: 0.6346 - val_accuracy: 0.6513\n","Epoch 4/120\n","30/30 [==============================] - 1s 39ms/step - loss: 0.6058 - accuracy: 0.6968 - val_loss: 0.5934 - val_accuracy: 0.7185\n","Epoch 5/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.5597 - accuracy: 0.7337 - val_loss: 0.5801 - val_accuracy: 0.7269\n","Epoch 6/120\n","30/30 [==============================] - 1s 39ms/step - loss: 0.5648 - accuracy: 0.7421 - val_loss: 0.5703 - val_accuracy: 0.7311\n","Epoch 7/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.5636 - accuracy: 0.7453 - val_loss: 0.5580 - val_accuracy: 0.7101\n","Epoch 8/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.5929 - accuracy: 0.6905 - val_loss: 0.5642 - val_accuracy: 0.7269\n","Epoch 9/120\n","30/30 [==============================] - 1s 39ms/step - loss: 0.5332 - accuracy: 0.7442 - val_loss: 0.5561 - val_accuracy: 0.7311\n","Epoch 10/120\n","30/30 [==============================] - 1s 51ms/step - loss: 0.5327 - accuracy: 0.7547 - val_loss: 0.6055 - val_accuracy: 0.6933\n","Epoch 11/120\n","30/30 [==============================] - 2s 63ms/step - loss: 0.5413 - accuracy: 0.7453 - val_loss: 0.5744 - val_accuracy: 0.7059\n","Epoch 12/120\n","30/30 [==============================] - 1s 39ms/step - loss: 0.5099 - accuracy: 0.7705 - val_loss: 0.5485 - val_accuracy: 0.7269\n","Epoch 13/120\n","30/30 [==============================] - 1s 41ms/step - loss: 0.5141 - accuracy: 0.7705 - val_loss: 0.5498 - val_accuracy: 0.7143\n","Epoch 14/120\n","30/30 [==============================] - 1s 39ms/step - loss: 0.5402 - accuracy: 0.7347 - val_loss: 0.5320 - val_accuracy: 0.7269\n","Epoch 15/120\n","30/30 [==============================] - 1s 39ms/step - loss: 0.5458 - accuracy: 0.7295 - val_loss: 0.5514 - val_accuracy: 0.7143\n","Epoch 16/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.5366 - accuracy: 0.7368 - val_loss: 0.5367 - val_accuracy: 0.7269\n","Epoch 17/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.4813 - accuracy: 0.7853 - val_loss: 0.5481 - val_accuracy: 0.7017\n","Epoch 18/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.5229 - accuracy: 0.7516 - val_loss: 0.5373 - val_accuracy: 0.7395\n","Epoch 19/120\n","30/30 [==============================] - 1s 39ms/step - loss: 0.5169 - accuracy: 0.7547 - val_loss: 0.5368 - val_accuracy: 0.7521\n","Epoch 20/120\n","30/30 [==============================] - 1s 44ms/step - loss: 0.4931 - accuracy: 0.7737 - val_loss: 0.5153 - val_accuracy: 0.7605\n","Epoch 21/120\n","30/30 [==============================] - 2s 62ms/step - loss: 0.4806 - accuracy: 0.7968 - val_loss: 0.4997 - val_accuracy: 0.7521\n","Epoch 22/120\n","30/30 [==============================] - 1s 48ms/step - loss: 0.4543 - accuracy: 0.7947 - val_loss: 0.4978 - val_accuracy: 0.7647\n","Epoch 23/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.4657 - accuracy: 0.7895 - val_loss: 0.4855 - val_accuracy: 0.7563\n","Epoch 24/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.4857 - accuracy: 0.7789 - val_loss: 0.5743 - val_accuracy: 0.7185\n","Epoch 25/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.4661 - accuracy: 0.7853 - val_loss: 0.5917 - val_accuracy: 0.7479\n","Epoch 26/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.4832 - accuracy: 0.7758 - val_loss: 0.4803 - val_accuracy: 0.7815\n","Epoch 27/120\n","30/30 [==============================] - 1s 41ms/step - loss: 0.4826 - accuracy: 0.7905 - val_loss: 0.4544 - val_accuracy: 0.7857\n","Epoch 28/120\n","30/30 [==============================] - 1s 39ms/step - loss: 0.4295 - accuracy: 0.8179 - val_loss: 0.5022 - val_accuracy: 0.7815\n","Epoch 29/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.4423 - accuracy: 0.7979 - val_loss: 0.4664 - val_accuracy: 0.7899\n","Epoch 30/120\n","30/30 [==============================] - 1s 42ms/step - loss: 0.4366 - accuracy: 0.8042 - val_loss: 0.4766 - val_accuracy: 0.7731\n","Epoch 31/120\n","30/30 [==============================] - 2s 62ms/step - loss: 0.4151 - accuracy: 0.8105 - val_loss: 0.4319 - val_accuracy: 0.8193\n","Epoch 32/120\n","30/30 [==============================] - 1s 49ms/step - loss: 0.4074 - accuracy: 0.8200 - val_loss: 0.4195 - val_accuracy: 0.8109\n","Epoch 33/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.4172 - accuracy: 0.8189 - val_loss: 0.4340 - val_accuracy: 0.8025\n","Epoch 34/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.4372 - accuracy: 0.8126 - val_loss: 0.4379 - val_accuracy: 0.8067\n","Epoch 35/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.4486 - accuracy: 0.8116 - val_loss: 0.4677 - val_accuracy: 0.7815\n","Epoch 36/120\n","30/30 [==============================] - 1s 41ms/step - loss: 0.4963 - accuracy: 0.7653 - val_loss: 0.6158 - val_accuracy: 0.7143\n","Epoch 37/120\n","30/30 [==============================] - 1s 42ms/step - loss: 0.5050 - accuracy: 0.7589 - val_loss: 0.4681 - val_accuracy: 0.7815\n","Epoch 38/120\n","30/30 [==============================] - 2s 57ms/step - loss: 0.4088 - accuracy: 0.8284 - val_loss: 0.4381 - val_accuracy: 0.8067\n","Epoch 39/120\n","30/30 [==============================] - 2s 60ms/step - loss: 0.4148 - accuracy: 0.8211 - val_loss: 0.4236 - val_accuracy: 0.8193\n","Epoch 40/120\n","30/30 [==============================] - 2s 63ms/step - loss: 0.3882 - accuracy: 0.8337 - val_loss: 0.4381 - val_accuracy: 0.8025\n","Epoch 41/120\n","30/30 [==============================] - 2s 53ms/step - loss: 0.3872 - accuracy: 0.8411 - val_loss: 0.4883 - val_accuracy: 0.7605\n","Epoch 42/120\n","30/30 [==============================] - 1s 41ms/step - loss: 0.4091 - accuracy: 0.8253 - val_loss: 0.4605 - val_accuracy: 0.8067\n","Epoch 43/120\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3855 - accuracy: 0.8389 - val_loss: 0.4118 - val_accuracy: 0.8109\n","Epoch 44/120\n","30/30 [==============================] - 1s 39ms/step - loss: 0.4133 - accuracy: 0.8284 - val_loss: 0.4362 - val_accuracy: 0.8025\n","Epoch 45/120\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3620 - accuracy: 0.8558 - val_loss: 0.4319 - val_accuracy: 0.8151\n","Epoch 46/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3720 - accuracy: 0.8453 - val_loss: 0.5436 - val_accuracy: 0.7899\n","Epoch 47/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.4016 - accuracy: 0.8221 - val_loss: 0.4277 - val_accuracy: 0.7941\n","Epoch 48/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3748 - accuracy: 0.8526 - val_loss: 0.4033 - val_accuracy: 0.8361\n","Epoch 49/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3904 - accuracy: 0.8368 - val_loss: 0.4493 - val_accuracy: 0.7899\n","Epoch 50/120\n","30/30 [==============================] - 2s 60ms/step - loss: 0.3800 - accuracy: 0.8463 - val_loss: 0.6037 - val_accuracy: 0.7605\n","Epoch 51/120\n","30/30 [==============================] - 2s 56ms/step - loss: 0.3673 - accuracy: 0.8432 - val_loss: 0.4038 - val_accuracy: 0.8319\n","Epoch 52/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.4033 - accuracy: 0.8421 - val_loss: 0.4567 - val_accuracy: 0.7941\n","Epoch 53/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3969 - accuracy: 0.8242 - val_loss: 0.4488 - val_accuracy: 0.7941\n","Epoch 54/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3670 - accuracy: 0.8484 - val_loss: 0.4010 - val_accuracy: 0.8319\n","Epoch 55/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3408 - accuracy: 0.8579 - val_loss: 0.3988 - val_accuracy: 0.8403\n","Epoch 56/120\n","30/30 [==============================] - 1s 37ms/step - loss: 0.3594 - accuracy: 0.8537 - val_loss: 0.4901 - val_accuracy: 0.8067\n","Epoch 57/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3969 - accuracy: 0.8432 - val_loss: 0.4235 - val_accuracy: 0.8193\n","Epoch 58/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3545 - accuracy: 0.8632 - val_loss: 0.3940 - val_accuracy: 0.8277\n","Epoch 59/120\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3726 - accuracy: 0.8484 - val_loss: 0.3730 - val_accuracy: 0.8445\n","Epoch 60/120\n","30/30 [==============================] - 2s 61ms/step - loss: 0.3626 - accuracy: 0.8484 - val_loss: 0.4259 - val_accuracy: 0.8067\n","Epoch 61/120\n","30/30 [==============================] - 2s 58ms/step - loss: 0.3467 - accuracy: 0.8547 - val_loss: 0.4612 - val_accuracy: 0.7857\n","Epoch 62/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3927 - accuracy: 0.8411 - val_loss: 0.4154 - val_accuracy: 0.8109\n","Epoch 63/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3843 - accuracy: 0.8389 - val_loss: 0.4559 - val_accuracy: 0.8235\n","Epoch 64/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3336 - accuracy: 0.8705 - val_loss: 0.3755 - val_accuracy: 0.8403\n","Epoch 65/120\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3276 - accuracy: 0.8842 - val_loss: 0.3806 - val_accuracy: 0.8403\n","Epoch 66/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3628 - accuracy: 0.8474 - val_loss: 0.4857 - val_accuracy: 0.7647\n","Epoch 67/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3657 - accuracy: 0.8516 - val_loss: 0.3886 - val_accuracy: 0.8403\n","Epoch 68/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3317 - accuracy: 0.8716 - val_loss: 0.3820 - val_accuracy: 0.8613\n","Epoch 69/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3206 - accuracy: 0.8705 - val_loss: 0.3706 - val_accuracy: 0.8319\n","Epoch 70/120\n","30/30 [==============================] - 2s 60ms/step - loss: 0.3191 - accuracy: 0.8705 - val_loss: 0.4136 - val_accuracy: 0.8151\n","Epoch 71/120\n","30/30 [==============================] - 2s 60ms/step - loss: 0.3365 - accuracy: 0.8674 - val_loss: 0.4079 - val_accuracy: 0.8067\n","Epoch 72/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3357 - accuracy: 0.8642 - val_loss: 0.3891 - val_accuracy: 0.7983\n","Epoch 73/120\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3360 - accuracy: 0.8737 - val_loss: 0.4132 - val_accuracy: 0.8067\n","Epoch 74/120\n","30/30 [==============================] - 1s 37ms/step - loss: 0.3500 - accuracy: 0.8600 - val_loss: 0.3773 - val_accuracy: 0.8403\n","Epoch 75/120\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3395 - accuracy: 0.8632 - val_loss: 0.3791 - val_accuracy: 0.8319\n","Epoch 76/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3371 - accuracy: 0.8716 - val_loss: 0.4060 - val_accuracy: 0.7941\n","Epoch 77/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3631 - accuracy: 0.8442 - val_loss: 0.3853 - val_accuracy: 0.8277\n","Epoch 78/120\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3273 - accuracy: 0.8768 - val_loss: 0.4220 - val_accuracy: 0.8151\n","Epoch 79/120\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3432 - accuracy: 0.8653 - val_loss: 0.3890 - val_accuracy: 0.8277\n","Epoch 80/120\n","30/30 [==============================] - 2s 53ms/step - loss: 0.3600 - accuracy: 0.8505 - val_loss: 0.4427 - val_accuracy: 0.8193\n","Epoch 81/120\n","30/30 [==============================] - 2s 63ms/step - loss: 0.3317 - accuracy: 0.8674 - val_loss: 0.3960 - val_accuracy: 0.8319\n","Epoch 82/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3184 - accuracy: 0.8726 - val_loss: 0.4139 - val_accuracy: 0.8109\n","Epoch 83/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3896 - accuracy: 0.8379 - val_loss: 0.4136 - val_accuracy: 0.8277\n","Epoch 84/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3112 - accuracy: 0.8800 - val_loss: 0.3810 - val_accuracy: 0.8361\n","Epoch 85/120\n","30/30 [==============================] - 1s 39ms/step - loss: 0.2980 - accuracy: 0.8789 - val_loss: 0.4732 - val_accuracy: 0.8319\n","Epoch 86/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3424 - accuracy: 0.8611 - val_loss: 0.4193 - val_accuracy: 0.8067\n","Epoch 87/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3288 - accuracy: 0.8684 - val_loss: 0.4419 - val_accuracy: 0.8193\n","Epoch 88/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3082 - accuracy: 0.8832 - val_loss: 0.3549 - val_accuracy: 0.8445\n","Epoch 89/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3191 - accuracy: 0.8621 - val_loss: 0.3937 - val_accuracy: 0.8655\n","Epoch 90/120\n","30/30 [==============================] - 1s 50ms/step - loss: 0.3027 - accuracy: 0.8789 - val_loss: 0.3704 - val_accuracy: 0.8655\n","Epoch 91/120\n","30/30 [==============================] - 2s 64ms/step - loss: 0.3023 - accuracy: 0.8768 - val_loss: 0.4690 - val_accuracy: 0.8109\n","Epoch 92/120\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3332 - accuracy: 0.8747 - val_loss: 0.3480 - val_accuracy: 0.8571\n","Epoch 93/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3294 - accuracy: 0.8684 - val_loss: 0.3580 - val_accuracy: 0.8109\n","Epoch 94/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3470 - accuracy: 0.8611 - val_loss: 0.3828 - val_accuracy: 0.8067\n","Epoch 95/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3252 - accuracy: 0.8726 - val_loss: 0.4024 - val_accuracy: 0.8025\n","Epoch 96/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.2956 - accuracy: 0.8821 - val_loss: 0.3634 - val_accuracy: 0.8277\n","Epoch 97/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3401 - accuracy: 0.8611 - val_loss: 0.4522 - val_accuracy: 0.8193\n","Epoch 98/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3426 - accuracy: 0.8642 - val_loss: 0.3400 - val_accuracy: 0.8529\n","Epoch 99/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.2846 - accuracy: 0.8863 - val_loss: 0.3864 - val_accuracy: 0.8193\n","Epoch 100/120\n","30/30 [==============================] - 1s 50ms/step - loss: 0.2918 - accuracy: 0.8779 - val_loss: 0.3618 - val_accuracy: 0.8571\n","Epoch 101/120\n","30/30 [==============================] - 2s 63ms/step - loss: 0.3291 - accuracy: 0.8600 - val_loss: 0.4666 - val_accuracy: 0.8067\n","Epoch 102/120\n","30/30 [==============================] - 1s 43ms/step - loss: 0.3372 - accuracy: 0.8568 - val_loss: 0.3652 - val_accuracy: 0.8445\n","Epoch 103/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3417 - accuracy: 0.8547 - val_loss: 0.3611 - val_accuracy: 0.8529\n","Epoch 104/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3171 - accuracy: 0.8695 - val_loss: 0.3899 - val_accuracy: 0.8361\n","Epoch 105/120\n","30/30 [==============================] - 1s 39ms/step - loss: 0.2871 - accuracy: 0.8916 - val_loss: 0.3402 - val_accuracy: 0.8487\n","Epoch 106/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.2833 - accuracy: 0.8937 - val_loss: 0.3464 - val_accuracy: 0.8655\n","Epoch 107/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.2877 - accuracy: 0.8821 - val_loss: 0.3689 - val_accuracy: 0.8613\n","Epoch 108/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3109 - accuracy: 0.8663 - val_loss: 0.4392 - val_accuracy: 0.8193\n","Epoch 109/120\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3012 - accuracy: 0.8800 - val_loss: 0.3729 - val_accuracy: 0.8655\n","Epoch 110/120\n","30/30 [==============================] - 1s 47ms/step - loss: 0.2981 - accuracy: 0.8811 - val_loss: 0.3545 - val_accuracy: 0.8613\n","Epoch 111/120\n","30/30 [==============================] - 2s 64ms/step - loss: 0.2998 - accuracy: 0.8758 - val_loss: 0.3255 - val_accuracy: 0.8739\n","Epoch 112/120\n","30/30 [==============================] - 1s 46ms/step - loss: 0.2729 - accuracy: 0.8884 - val_loss: 0.3338 - val_accuracy: 0.8571\n","Epoch 113/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.2942 - accuracy: 0.8768 - val_loss: 0.3605 - val_accuracy: 0.8613\n","Epoch 114/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.2787 - accuracy: 0.8937 - val_loss: 0.4065 - val_accuracy: 0.8529\n","Epoch 115/120\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3111 - accuracy: 0.8684 - val_loss: 0.3654 - val_accuracy: 0.8529\n","Epoch 116/120\n","30/30 [==============================] - 1s 40ms/step - loss: 0.2793 - accuracy: 0.8905 - val_loss: 0.3522 - val_accuracy: 0.8613\n","Epoch 117/120\n","30/30 [==============================] - 1s 42ms/step - loss: 0.2512 - accuracy: 0.9042 - val_loss: 0.3736 - val_accuracy: 0.8319\n","Epoch 118/120\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3321 - accuracy: 0.8674 - val_loss: 0.4125 - val_accuracy: 0.8403\n","Epoch 119/120\n","30/30 [==============================] - 1s 39ms/step - loss: 0.2822 - accuracy: 0.8937 - val_loss: 0.3267 - val_accuracy: 0.8571\n","Epoch 120/120\n","30/30 [==============================] - 1s 46ms/step - loss: 0.2574 - accuracy: 0.8989 - val_loss: 0.4142 - val_accuracy: 0.8403\n"]}]},{"cell_type":"code","source":["test_predictions = model_ex2v4.predict(test_X)\n","test_predictions_binary = (test_predictions > 0.7).astype(int)\n","\n","test_f1 = f1_score(test_y, test_predictions_binary)\n","test_recall = recall_score(test_y, test_predictions_binary)\n","\n","test_loss, test_accuracy = model_ex2v4.evaluate(test_X, test_y)\n","\n","print(\"Test Loss:\", test_loss)\n","print(\"Test Accuracy:\", test_accuracy)\n","print(\"Test F1-Score:\", test_f1)\n","print(\"Test Recall:\", test_recall)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5WHP8jpS6afv","executionInfo":{"status":"ok","timestamp":1693274059613,"user_tz":300,"elapsed":1921,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"0c58dbd9-07a0-4dbe-cec4-e23ad3244336"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["10/10 [==============================] - 1s 12ms/step\n","10/10 [==============================] - 0s 11ms/step - loss: 0.4415 - accuracy: 0.8456\n","Test Loss: 0.4415385127067566\n","Test Accuracy: 0.8456375598907471\n","Test F1-Score: 0.8187919463087248\n","Test Recall: 0.7625\n"]}]},{"cell_type":"code","source":["# Plot training and validation metrics\n","plt.figure(figsize=(10, 4))\n","plt.subplot(1, 2, 1)\n","plt.plot(history_ex2v4.history['loss'], label='Training Loss')\n","plt.plot(history_ex2v4.history['val_loss'], label='Validation Loss')\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.legend()\n","\n","plt.subplot(1, 2, 2)\n","plt.plot(history_ex2v4.history['accuracy'], label='Training Accuracy')\n","plt.plot(history_ex2v4.history['val_accuracy'], label='Validation Accuracy')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":407},"id":"9zBmYXiA7EgN","executionInfo":{"status":"ok","timestamp":1693274066117,"user_tz":300,"elapsed":1488,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"a00ef435-20c6-4468-d643-70d50f9e1af1"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x400 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["model_ex2v4.save('lstm_model_ex2v4.h5')"],"metadata":{"id":"GIol1I3x_9bZ"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Experiment 3"],"metadata":{"id":"FjWPZjEqDkTL"}},{"cell_type":"code","source":["# Define the LSTM model\n","model_ex3 = Sequential()\n","model_ex3.add(LSTM(units=32, input_shape=(sequence_length, num_features), return_sequences=True))\n","model_ex3.add(Dropout(0.7))\n","model_ex3.add(LSTM(units=64))\n","model_ex3.add(Dropout(0.5))\n","model_ex3.add(Dense(units=1, activation='sigmoid'))\n","\n","custom_learning_rate = 0.001\n","optimizer = Adam(learning_rate=custom_learning_rate)\n","\n","# Compile the model\n","model_ex3.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n","\n","# Print the model summary\n","model_ex3.summary()\n","\n","# Train the model\n","batch_size = 32\n","epochs = 100\n","early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n","history_ex3 = model_ex3.fit(train_X, train_y, batch_size=batch_size, epochs=epochs, validation_split=0.2, verbose=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ker7hBNODE8y","executionInfo":{"status":"ok","timestamp":1693274512809,"user_tz":300,"elapsed":73900,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"1fc808a1-2b6b-41c7-f2f7-73c77b36e12e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_11\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," lstm_31 (LSTM)              (None, 10, 32)            10624     \n","                                                                 \n"," dropout_22 (Dropout)        (None, 10, 32)            0         \n","                                                                 \n"," lstm_32 (LSTM)              (None, 64)                24832     \n","                                                                 \n"," dropout_23 (Dropout)        (None, 64)                0         \n","                                                                 \n"," dense_11 (Dense)            (None, 1)                 65        \n","                                                                 \n","=================================================================\n","Total params: 35,521\n","Trainable params: 35,521\n","Non-trainable params: 0\n","_________________________________________________________________\n","Epoch 1/100\n","30/30 [==============================] - 5s 49ms/step - loss: 0.6842 - accuracy: 0.5579 - val_loss: 0.6823 - val_accuracy: 0.5336\n","Epoch 2/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.6544 - accuracy: 0.6284 - val_loss: 0.6179 - val_accuracy: 0.6891\n","Epoch 3/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.5985 - accuracy: 0.7116 - val_loss: 0.5825 - val_accuracy: 0.7059\n","Epoch 4/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.5545 - accuracy: 0.7432 - val_loss: 0.5650 - val_accuracy: 0.7143\n","Epoch 5/100\n","30/30 [==============================] - 1s 23ms/step - loss: 0.5459 - accuracy: 0.7537 - val_loss: 0.5531 - val_accuracy: 0.7227\n","Epoch 6/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.5571 - accuracy: 0.7168 - val_loss: 0.5812 - val_accuracy: 0.6681\n","Epoch 7/100\n","30/30 [==============================] - 1s 17ms/step - loss: 0.5401 - accuracy: 0.7347 - val_loss: 0.5623 - val_accuracy: 0.7059\n","Epoch 8/100\n","30/30 [==============================] - 1s 19ms/step - loss: 0.5256 - accuracy: 0.7400 - val_loss: 0.5327 - val_accuracy: 0.7521\n","Epoch 9/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.5053 - accuracy: 0.7621 - val_loss: 0.5253 - val_accuracy: 0.7521\n","Epoch 10/100\n","30/30 [==============================] - 1s 20ms/step - loss: 0.5130 - accuracy: 0.7568 - val_loss: 0.5175 - val_accuracy: 0.7311\n","Epoch 11/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.4820 - accuracy: 0.7716 - val_loss: 0.5380 - val_accuracy: 0.7563\n","Epoch 12/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.5005 - accuracy: 0.7600 - val_loss: 0.5031 - val_accuracy: 0.7479\n","Epoch 13/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.4799 - accuracy: 0.7621 - val_loss: 0.5106 - val_accuracy: 0.7689\n","Epoch 14/100\n","30/30 [==============================] - 1s 19ms/step - loss: 0.4888 - accuracy: 0.7768 - val_loss: 0.5096 - val_accuracy: 0.7563\n","Epoch 15/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.4968 - accuracy: 0.7600 - val_loss: 0.5135 - val_accuracy: 0.7689\n","Epoch 16/100\n","30/30 [==============================] - 1s 19ms/step - loss: 0.4717 - accuracy: 0.7884 - val_loss: 0.4774 - val_accuracy: 0.7899\n","Epoch 17/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.4642 - accuracy: 0.7768 - val_loss: 0.4769 - val_accuracy: 0.7857\n","Epoch 18/100\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4650 - accuracy: 0.7684 - val_loss: 0.4592 - val_accuracy: 0.7941\n","Epoch 19/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.4843 - accuracy: 0.7716 - val_loss: 0.4661 - val_accuracy: 0.7941\n","Epoch 20/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.4513 - accuracy: 0.8011 - val_loss: 0.4770 - val_accuracy: 0.7815\n","Epoch 21/100\n","30/30 [==============================] - 1s 17ms/step - loss: 0.4803 - accuracy: 0.7863 - val_loss: 0.4973 - val_accuracy: 0.7647\n","Epoch 22/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.4237 - accuracy: 0.8137 - val_loss: 0.5142 - val_accuracy: 0.7521\n","Epoch 23/100\n","30/30 [==============================] - 1s 19ms/step - loss: 0.4539 - accuracy: 0.7863 - val_loss: 0.4582 - val_accuracy: 0.7983\n","Epoch 24/100\n","30/30 [==============================] - 1s 27ms/step - loss: 0.4266 - accuracy: 0.8189 - val_loss: 0.4420 - val_accuracy: 0.7899\n","Epoch 25/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.4409 - accuracy: 0.8032 - val_loss: 0.4192 - val_accuracy: 0.7983\n","Epoch 26/100\n","30/30 [==============================] - 1s 27ms/step - loss: 0.4298 - accuracy: 0.8189 - val_loss: 0.5385 - val_accuracy: 0.7311\n","Epoch 27/100\n","30/30 [==============================] - 1s 23ms/step - loss: 0.4775 - accuracy: 0.7979 - val_loss: 0.4336 - val_accuracy: 0.8109\n","Epoch 28/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3973 - accuracy: 0.8326 - val_loss: 0.4026 - val_accuracy: 0.8151\n","Epoch 29/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.4155 - accuracy: 0.8284 - val_loss: 0.4362 - val_accuracy: 0.8235\n","Epoch 30/100\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3997 - accuracy: 0.8253 - val_loss: 0.4466 - val_accuracy: 0.7983\n","Epoch 31/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3855 - accuracy: 0.8347 - val_loss: 0.4197 - val_accuracy: 0.8235\n","Epoch 32/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.4009 - accuracy: 0.8221 - val_loss: 0.4052 - val_accuracy: 0.8319\n","Epoch 33/100\n","30/30 [==============================] - 1s 19ms/step - loss: 0.4136 - accuracy: 0.8221 - val_loss: 0.4049 - val_accuracy: 0.8235\n","Epoch 34/100\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3922 - accuracy: 0.8347 - val_loss: 0.4021 - val_accuracy: 0.8319\n","Epoch 35/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3710 - accuracy: 0.8474 - val_loss: 0.3929 - val_accuracy: 0.8151\n","Epoch 36/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3766 - accuracy: 0.8505 - val_loss: 0.4574 - val_accuracy: 0.7899\n","Epoch 37/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.4011 - accuracy: 0.8274 - val_loss: 0.4105 - val_accuracy: 0.8403\n","Epoch 38/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3579 - accuracy: 0.8537 - val_loss: 0.4052 - val_accuracy: 0.8277\n","Epoch 39/100\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3532 - accuracy: 0.8642 - val_loss: 0.4597 - val_accuracy: 0.8151\n","Epoch 40/100\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3658 - accuracy: 0.8600 - val_loss: 0.4063 - val_accuracy: 0.8403\n","Epoch 41/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3508 - accuracy: 0.8589 - val_loss: 0.3668 - val_accuracy: 0.8529\n","Epoch 42/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3558 - accuracy: 0.8611 - val_loss: 0.4147 - val_accuracy: 0.8193\n","Epoch 43/100\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3766 - accuracy: 0.8474 - val_loss: 0.3674 - val_accuracy: 0.8319\n","Epoch 44/100\n","30/30 [==============================] - 1s 19ms/step - loss: 0.3656 - accuracy: 0.8558 - val_loss: 0.4293 - val_accuracy: 0.8067\n","Epoch 45/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3523 - accuracy: 0.8589 - val_loss: 0.4176 - val_accuracy: 0.8193\n","Epoch 46/100\n","30/30 [==============================] - 1s 27ms/step - loss: 0.3453 - accuracy: 0.8600 - val_loss: 0.3898 - val_accuracy: 0.8445\n","Epoch 47/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3366 - accuracy: 0.8663 - val_loss: 0.3724 - val_accuracy: 0.8319\n","Epoch 48/100\n","30/30 [==============================] - 1s 25ms/step - loss: 0.3559 - accuracy: 0.8505 - val_loss: 0.3739 - val_accuracy: 0.8361\n","Epoch 49/100\n","30/30 [==============================] - 1s 23ms/step - loss: 0.3473 - accuracy: 0.8632 - val_loss: 0.3768 - val_accuracy: 0.8235\n","Epoch 50/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3670 - accuracy: 0.8484 - val_loss: 0.3957 - val_accuracy: 0.8277\n","Epoch 51/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3582 - accuracy: 0.8579 - val_loss: 0.4577 - val_accuracy: 0.7731\n","Epoch 52/100\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3766 - accuracy: 0.8337 - val_loss: 0.3818 - val_accuracy: 0.8361\n","Epoch 53/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3504 - accuracy: 0.8653 - val_loss: 0.3729 - val_accuracy: 0.8361\n","Epoch 54/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3396 - accuracy: 0.8600 - val_loss: 0.3843 - val_accuracy: 0.8277\n","Epoch 55/100\n","30/30 [==============================] - 1s 19ms/step - loss: 0.3850 - accuracy: 0.8505 - val_loss: 0.4783 - val_accuracy: 0.7941\n","Epoch 56/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3584 - accuracy: 0.8558 - val_loss: 0.3699 - val_accuracy: 0.8445\n","Epoch 57/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3361 - accuracy: 0.8611 - val_loss: 0.3526 - val_accuracy: 0.8487\n","Epoch 58/100\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3413 - accuracy: 0.8579 - val_loss: 0.3678 - val_accuracy: 0.8487\n","Epoch 59/100\n","30/30 [==============================] - 1s 19ms/step - loss: 0.3390 - accuracy: 0.8621 - val_loss: 0.3483 - val_accuracy: 0.8529\n","Epoch 60/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3210 - accuracy: 0.8705 - val_loss: 0.3435 - val_accuracy: 0.8403\n","Epoch 61/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3411 - accuracy: 0.8579 - val_loss: 0.3723 - val_accuracy: 0.8319\n","Epoch 62/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3712 - accuracy: 0.8568 - val_loss: 0.3822 - val_accuracy: 0.8445\n","Epoch 63/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3683 - accuracy: 0.8453 - val_loss: 0.3952 - val_accuracy: 0.8151\n","Epoch 64/100\n","30/30 [==============================] - 1s 21ms/step - loss: 0.3201 - accuracy: 0.8779 - val_loss: 0.3504 - val_accuracy: 0.8403\n","Epoch 65/100\n","30/30 [==============================] - 1s 28ms/step - loss: 0.3226 - accuracy: 0.8663 - val_loss: 0.3605 - val_accuracy: 0.8529\n","Epoch 66/100\n","30/30 [==============================] - 1s 33ms/step - loss: 0.3285 - accuracy: 0.8779 - val_loss: 0.3815 - val_accuracy: 0.8109\n","Epoch 67/100\n","30/30 [==============================] - 1s 42ms/step - loss: 0.3072 - accuracy: 0.8789 - val_loss: 0.3797 - val_accuracy: 0.8361\n","Epoch 68/100\n","30/30 [==============================] - 1s 42ms/step - loss: 0.3399 - accuracy: 0.8737 - val_loss: 0.4002 - val_accuracy: 0.8403\n","Epoch 69/100\n","30/30 [==============================] - 1s 35ms/step - loss: 0.3301 - accuracy: 0.8600 - val_loss: 0.3478 - val_accuracy: 0.8445\n","Epoch 70/100\n","30/30 [==============================] - 1s 26ms/step - loss: 0.3025 - accuracy: 0.8821 - val_loss: 0.3808 - val_accuracy: 0.8487\n","Epoch 71/100\n","30/30 [==============================] - 1s 22ms/step - loss: 0.2986 - accuracy: 0.8842 - val_loss: 0.3664 - val_accuracy: 0.8487\n","Epoch 72/100\n","30/30 [==============================] - 1s 27ms/step - loss: 0.3242 - accuracy: 0.8726 - val_loss: 0.3938 - val_accuracy: 0.8319\n","Epoch 73/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3254 - accuracy: 0.8579 - val_loss: 0.3722 - val_accuracy: 0.8445\n","Epoch 74/100\n","30/30 [==============================] - 1s 29ms/step - loss: 0.3261 - accuracy: 0.8663 - val_loss: 0.4002 - val_accuracy: 0.8277\n","Epoch 75/100\n","30/30 [==============================] - 1s 29ms/step - loss: 0.3289 - accuracy: 0.8600 - val_loss: 0.3855 - val_accuracy: 0.8151\n","Epoch 76/100\n","30/30 [==============================] - 1s 42ms/step - loss: 0.2974 - accuracy: 0.8842 - val_loss: 0.3408 - val_accuracy: 0.8403\n","Epoch 77/100\n","30/30 [==============================] - 1s 50ms/step - loss: 0.3060 - accuracy: 0.8832 - val_loss: 0.4908 - val_accuracy: 0.7983\n","Epoch 78/100\n","30/30 [==============================] - 1s 33ms/step - loss: 0.3381 - accuracy: 0.8653 - val_loss: 0.3506 - val_accuracy: 0.8361\n","Epoch 79/100\n","30/30 [==============================] - 2s 53ms/step - loss: 0.3382 - accuracy: 0.8579 - val_loss: 0.3755 - val_accuracy: 0.8403\n","Epoch 80/100\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3143 - accuracy: 0.8747 - val_loss: 0.3422 - val_accuracy: 0.8445\n","Epoch 81/100\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3205 - accuracy: 0.8726 - val_loss: 0.4319 - val_accuracy: 0.8067\n","Epoch 82/100\n","30/30 [==============================] - 1s 28ms/step - loss: 0.3430 - accuracy: 0.8589 - val_loss: 0.3405 - val_accuracy: 0.8613\n","Epoch 83/100\n","30/30 [==============================] - 1s 30ms/step - loss: 0.3158 - accuracy: 0.8684 - val_loss: 0.3522 - val_accuracy: 0.8529\n","Epoch 84/100\n","30/30 [==============================] - 1s 34ms/step - loss: 0.3193 - accuracy: 0.8684 - val_loss: 0.3418 - val_accuracy: 0.8571\n","Epoch 85/100\n","30/30 [==============================] - 1s 21ms/step - loss: 0.3221 - accuracy: 0.8768 - val_loss: 0.3590 - val_accuracy: 0.8529\n","Epoch 86/100\n","30/30 [==============================] - 1s 19ms/step - loss: 0.3759 - accuracy: 0.8484 - val_loss: 0.4243 - val_accuracy: 0.8319\n","Epoch 87/100\n","30/30 [==============================] - 1s 17ms/step - loss: 0.3409 - accuracy: 0.8632 - val_loss: 0.4122 - val_accuracy: 0.8109\n","Epoch 88/100\n","30/30 [==============================] - 1s 19ms/step - loss: 0.3200 - accuracy: 0.8768 - val_loss: 0.3463 - val_accuracy: 0.8613\n","Epoch 89/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3381 - accuracy: 0.8579 - val_loss: 0.3625 - val_accuracy: 0.8613\n","Epoch 90/100\n","30/30 [==============================] - 1s 19ms/step - loss: 0.2851 - accuracy: 0.8979 - val_loss: 0.4443 - val_accuracy: 0.8277\n","Epoch 91/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3155 - accuracy: 0.8758 - val_loss: 0.3562 - val_accuracy: 0.8319\n","Epoch 92/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3077 - accuracy: 0.8747 - val_loss: 0.3893 - val_accuracy: 0.8277\n","Epoch 93/100\n","30/30 [==============================] - 1s 19ms/step - loss: 0.2830 - accuracy: 0.8863 - val_loss: 0.3841 - val_accuracy: 0.8319\n","Epoch 94/100\n","30/30 [==============================] - 1s 18ms/step - loss: 0.3026 - accuracy: 0.8726 - val_loss: 0.3410 - val_accuracy: 0.8403\n","Epoch 95/100\n","30/30 [==============================] - 1s 19ms/step - loss: 0.3057 - accuracy: 0.8716 - val_loss: 0.3450 - val_accuracy: 0.8487\n","Epoch 96/100\n","30/30 [==============================] - 1s 19ms/step - loss: 0.2797 - accuracy: 0.8853 - val_loss: 0.4338 - val_accuracy: 0.8403\n","Epoch 97/100\n","30/30 [==============================] - 1s 20ms/step - loss: 0.3079 - accuracy: 0.8853 - val_loss: 0.3890 - val_accuracy: 0.8445\n","Epoch 98/100\n","30/30 [==============================] - 1s 24ms/step - loss: 0.3392 - accuracy: 0.8632 - val_loss: 0.3600 - val_accuracy: 0.8445\n","Epoch 99/100\n","30/30 [==============================] - 1s 27ms/step - loss: 0.2939 - accuracy: 0.8811 - val_loss: 0.3659 - val_accuracy: 0.8445\n","Epoch 100/100\n","30/30 [==============================] - 1s 27ms/step - loss: 0.2878 - accuracy: 0.8811 - val_loss: 0.3796 - val_accuracy: 0.8403\n"]}]},{"cell_type":"code","source":["test_predictions = model_ex3.predict(test_X)\n","test_predictions_binary = (test_predictions > 0.7).astype(int)\n","\n","test_f1 = f1_score(test_y, test_predictions_binary)\n","test_recall = recall_score(test_y, test_predictions_binary)\n","\n","test_loss, test_accuracy = model_ex3.evaluate(test_X, test_y)\n","\n","print(\"Test Loss:\", test_loss)\n","print(\"Test Accuracy:\", test_accuracy)\n","print(\"Test F1-Score:\", test_f1)\n","print(\"Test Recall:\", test_recall)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bk3nnJoZG5zS","executionInfo":{"status":"ok","timestamp":1693274522650,"user_tz":300,"elapsed":1513,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"01898af6-6abd-4345-89b0-2ae75e369152"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["10/10 [==============================] - 1s 4ms/step\n","10/10 [==============================] - 0s 5ms/step - loss: 0.3982 - accuracy: 0.8490\n","Test Loss: 0.3982013165950775\n","Test Accuracy: 0.8489933013916016\n","Test F1-Score: 0.8463949843260189\n","Test Recall: 0.84375\n"]}]},{"cell_type":"code","source":["# Plot training and validation metrics\n","plt.figure(figsize=(10, 4))\n","plt.subplot(1, 2, 1)\n","plt.plot(history_ex3.history['loss'], label='Training Loss')\n","plt.plot(history_ex3.history['val_loss'], label='Validation Loss')\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.legend()\n","\n","plt.subplot(1, 2, 2)\n","plt.plot(history_ex3.history['accuracy'], label='Training Accuracy')\n","plt.plot(history_ex3.history['val_accuracy'], label='Validation Accuracy')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":406},"id":"axw5ZNPTHpsU","executionInfo":{"status":"ok","timestamp":1693274531971,"user_tz":300,"elapsed":1490,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"0b7435b6-36b0-4dfd-e150-b930e958c6b4"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x400 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["model_ex3.save('lstm_model_ex3.h5')"],"metadata":{"id":"R2abqkLOBvg_"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Experiment 4"],"metadata":{"id":"hfJm0u4WIFBd"}},{"cell_type":"code","source":["model_ex4 = Sequential()\n","model_ex4.add(LSTM(units=128, input_shape=(sequence_length, num_features), return_sequences=True))\n","model_ex4.add(Dropout(0.3))\n","model_ex4.add(LSTM(units=64, return_sequences=True))\n","model_ex4.add(Dropout(0.5))\n","model_ex4.add(LSTM(units=64))\n","model_ex4.add(Dropout(0.5))\n","model_ex4.add(Dense(units=1, activation='sigmoid'))\n","\n","optimizer = Adam(learning_rate=0.001)\n","model_ex4.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n","\n","batch_size = 32\n","epochs = 100\n","# early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n","history_ex4 = model_ex4.fit(train_X, train_y, batch_size=batch_size, epochs=epochs, validation_split=0.2, verbose=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"E7258lKmHu8M","executionInfo":{"status":"ok","timestamp":1693274763981,"user_tz":300,"elapsed":161019,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"9b68de3a-163c-4732-96dc-9218ae55a151"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","30/30 [==============================] - 10s 107ms/step - loss: 0.6960 - accuracy: 0.5253 - val_loss: 0.6829 - val_accuracy: 0.6218\n","Epoch 2/100\n","30/30 [==============================] - 2s 75ms/step - loss: 0.6662 - accuracy: 0.6074 - val_loss: 0.6321 - val_accuracy: 0.7185\n","Epoch 3/100\n","30/30 [==============================] - 3s 109ms/step - loss: 0.6235 - accuracy: 0.6811 - val_loss: 0.6524 - val_accuracy: 0.5924\n","Epoch 4/100\n","30/30 [==============================] - 3s 96ms/step - loss: 0.5841 - accuracy: 0.6853 - val_loss: 0.5565 - val_accuracy: 0.7269\n","Epoch 5/100\n","30/30 [==============================] - 3s 84ms/step - loss: 0.5525 - accuracy: 0.7474 - val_loss: 0.5954 - val_accuracy: 0.7059\n","Epoch 6/100\n","30/30 [==============================] - 2s 80ms/step - loss: 0.5285 - accuracy: 0.7695 - val_loss: 0.5547 - val_accuracy: 0.7311\n","Epoch 7/100\n","30/30 [==============================] - 2s 84ms/step - loss: 0.5283 - accuracy: 0.7526 - val_loss: 0.5679 - val_accuracy: 0.7101\n","Epoch 8/100\n","30/30 [==============================] - 3s 91ms/step - loss: 0.5329 - accuracy: 0.7526 - val_loss: 0.5408 - val_accuracy: 0.7437\n","Epoch 9/100\n","30/30 [==============================] - 3s 100ms/step - loss: 0.5277 - accuracy: 0.7442 - val_loss: 0.6289 - val_accuracy: 0.6345\n","Epoch 10/100\n","30/30 [==============================] - 3s 88ms/step - loss: 0.5237 - accuracy: 0.7579 - val_loss: 0.5328 - val_accuracy: 0.7227\n","Epoch 11/100\n","30/30 [==============================] - 2s 65ms/step - loss: 0.5169 - accuracy: 0.7600 - val_loss: 0.5802 - val_accuracy: 0.7269\n","Epoch 12/100\n","30/30 [==============================] - 2s 72ms/step - loss: 0.5302 - accuracy: 0.7347 - val_loss: 0.5403 - val_accuracy: 0.7437\n","Epoch 13/100\n","30/30 [==============================] - 2s 68ms/step - loss: 0.4978 - accuracy: 0.7642 - val_loss: 0.5116 - val_accuracy: 0.7479\n","Epoch 14/100\n","30/30 [==============================] - 2s 66ms/step - loss: 0.4896 - accuracy: 0.7684 - val_loss: 0.5162 - val_accuracy: 0.7479\n","Epoch 15/100\n","30/30 [==============================] - 1s 45ms/step - loss: 0.4715 - accuracy: 0.7768 - val_loss: 0.5480 - val_accuracy: 0.7437\n","Epoch 16/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.5163 - accuracy: 0.7621 - val_loss: 0.5219 - val_accuracy: 0.7479\n","Epoch 17/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.4823 - accuracy: 0.7726 - val_loss: 0.5067 - val_accuracy: 0.7479\n","Epoch 18/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.4764 - accuracy: 0.7832 - val_loss: 0.5180 - val_accuracy: 0.7437\n","Epoch 19/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.4690 - accuracy: 0.7811 - val_loss: 0.4979 - val_accuracy: 0.7605\n","Epoch 20/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.4935 - accuracy: 0.7863 - val_loss: 0.5494 - val_accuracy: 0.6933\n","Epoch 21/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.4815 - accuracy: 0.7747 - val_loss: 0.5047 - val_accuracy: 0.7521\n","Epoch 22/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.4488 - accuracy: 0.8021 - val_loss: 0.4596 - val_accuracy: 0.7899\n","Epoch 23/100\n","30/30 [==============================] - 1s 48ms/step - loss: 0.4369 - accuracy: 0.8053 - val_loss: 0.4210 - val_accuracy: 0.7983\n","Epoch 24/100\n","30/30 [==============================] - 2s 66ms/step - loss: 0.4373 - accuracy: 0.8021 - val_loss: 0.4952 - val_accuracy: 0.7479\n","Epoch 25/100\n","30/30 [==============================] - 1s 46ms/step - loss: 0.4458 - accuracy: 0.8032 - val_loss: 0.4736 - val_accuracy: 0.7983\n","Epoch 26/100\n","30/30 [==============================] - 1s 40ms/step - loss: 0.4538 - accuracy: 0.7863 - val_loss: 0.5913 - val_accuracy: 0.7185\n","Epoch 27/100\n","30/30 [==============================] - 1s 40ms/step - loss: 0.4636 - accuracy: 0.7968 - val_loss: 0.5030 - val_accuracy: 0.7773\n","Epoch 28/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.4260 - accuracy: 0.8221 - val_loss: 0.4345 - val_accuracy: 0.8025\n","Epoch 29/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3944 - accuracy: 0.8305 - val_loss: 0.4005 - val_accuracy: 0.8235\n","Epoch 30/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.4208 - accuracy: 0.8200 - val_loss: 0.5372 - val_accuracy: 0.7647\n","Epoch 31/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.4877 - accuracy: 0.7737 - val_loss: 0.4790 - val_accuracy: 0.7731\n","Epoch 32/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.4463 - accuracy: 0.8032 - val_loss: 0.4063 - val_accuracy: 0.8277\n","Epoch 33/100\n","30/30 [==============================] - 2s 57ms/step - loss: 0.3902 - accuracy: 0.8411 - val_loss: 0.3797 - val_accuracy: 0.8403\n","Epoch 34/100\n","30/30 [==============================] - 2s 66ms/step - loss: 0.3595 - accuracy: 0.8579 - val_loss: 0.4643 - val_accuracy: 0.7857\n","Epoch 35/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3915 - accuracy: 0.8316 - val_loss: 0.6953 - val_accuracy: 0.7647\n","Epoch 36/100\n","30/30 [==============================] - 1s 40ms/step - loss: 0.4243 - accuracy: 0.8211 - val_loss: 0.4279 - val_accuracy: 0.8109\n","Epoch 37/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3822 - accuracy: 0.8316 - val_loss: 0.4298 - val_accuracy: 0.8067\n","Epoch 38/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3477 - accuracy: 0.8653 - val_loss: 0.3992 - val_accuracy: 0.8151\n","Epoch 39/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.4398 - accuracy: 0.8253 - val_loss: 0.4439 - val_accuracy: 0.7983\n","Epoch 40/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3972 - accuracy: 0.8242 - val_loss: 0.3920 - val_accuracy: 0.8319\n","Epoch 41/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3660 - accuracy: 0.8558 - val_loss: 0.4835 - val_accuracy: 0.7605\n","Epoch 42/100\n","30/30 [==============================] - 1s 38ms/step - loss: 0.3880 - accuracy: 0.8316 - val_loss: 0.4176 - val_accuracy: 0.8151\n","Epoch 43/100\n","30/30 [==============================] - 2s 60ms/step - loss: 0.3741 - accuracy: 0.8495 - val_loss: 0.4652 - val_accuracy: 0.7857\n","Epoch 44/100\n","30/30 [==============================] - 2s 60ms/step - loss: 0.3510 - accuracy: 0.8642 - val_loss: 0.3937 - val_accuracy: 0.8277\n","Epoch 45/100\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3760 - accuracy: 0.8421 - val_loss: 0.4186 - val_accuracy: 0.8025\n","Epoch 46/100\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3703 - accuracy: 0.8495 - val_loss: 0.4344 - val_accuracy: 0.8067\n","Epoch 47/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3885 - accuracy: 0.8421 - val_loss: 0.4201 - val_accuracy: 0.8319\n","Epoch 48/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3788 - accuracy: 0.8526 - val_loss: 0.4038 - val_accuracy: 0.8151\n","Epoch 49/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3549 - accuracy: 0.8621 - val_loss: 0.4090 - val_accuracy: 0.8277\n","Epoch 50/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3636 - accuracy: 0.8600 - val_loss: 0.4071 - val_accuracy: 0.8361\n","Epoch 51/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3272 - accuracy: 0.8726 - val_loss: 0.3736 - val_accuracy: 0.8571\n","Epoch 52/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3472 - accuracy: 0.8653 - val_loss: 0.3845 - val_accuracy: 0.8529\n","Epoch 53/100\n","30/30 [==============================] - 2s 66ms/step - loss: 0.3430 - accuracy: 0.8611 - val_loss: 0.4332 - val_accuracy: 0.8319\n","Epoch 54/100\n","30/30 [==============================] - 2s 55ms/step - loss: 0.3296 - accuracy: 0.8653 - val_loss: 0.3602 - val_accuracy: 0.8529\n","Epoch 55/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3331 - accuracy: 0.8621 - val_loss: 0.3641 - val_accuracy: 0.8277\n","Epoch 56/100\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3386 - accuracy: 0.8568 - val_loss: 0.4153 - val_accuracy: 0.8487\n","Epoch 57/100\n","30/30 [==============================] - 1s 48ms/step - loss: 0.3350 - accuracy: 0.8663 - val_loss: 0.3342 - val_accuracy: 0.8529\n","Epoch 58/100\n","30/30 [==============================] - 2s 67ms/step - loss: 0.3263 - accuracy: 0.8800 - val_loss: 0.3436 - val_accuracy: 0.8613\n","Epoch 59/100\n","30/30 [==============================] - 1s 47ms/step - loss: 0.3788 - accuracy: 0.8421 - val_loss: 0.4019 - val_accuracy: 0.8319\n","Epoch 60/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3405 - accuracy: 0.8642 - val_loss: 0.3828 - val_accuracy: 0.8445\n","Epoch 61/100\n","30/30 [==============================] - 1s 44ms/step - loss: 0.3403 - accuracy: 0.8547 - val_loss: 0.4041 - val_accuracy: 0.8445\n","Epoch 62/100\n","30/30 [==============================] - 2s 66ms/step - loss: 0.3310 - accuracy: 0.8737 - val_loss: 0.4050 - val_accuracy: 0.8319\n","Epoch 63/100\n","30/30 [==============================] - 2s 52ms/step - loss: 0.3432 - accuracy: 0.8653 - val_loss: 0.3356 - val_accuracy: 0.8529\n","Epoch 64/100\n","30/30 [==============================] - 1s 42ms/step - loss: 0.3350 - accuracy: 0.8726 - val_loss: 0.3831 - val_accuracy: 0.8193\n","Epoch 65/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3337 - accuracy: 0.8674 - val_loss: 0.3890 - val_accuracy: 0.8319\n","Epoch 66/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3393 - accuracy: 0.8632 - val_loss: 0.3474 - val_accuracy: 0.8613\n","Epoch 67/100\n","30/30 [==============================] - 1s 42ms/step - loss: 0.3651 - accuracy: 0.8516 - val_loss: 0.4153 - val_accuracy: 0.8193\n","Epoch 68/100\n","30/30 [==============================] - 1s 42ms/step - loss: 0.3995 - accuracy: 0.8368 - val_loss: 0.3790 - val_accuracy: 0.8487\n","Epoch 69/100\n","30/30 [==============================] - 1s 42ms/step - loss: 0.3459 - accuracy: 0.8621 - val_loss: 0.3630 - val_accuracy: 0.8613\n","Epoch 70/100\n","30/30 [==============================] - 1s 43ms/step - loss: 0.3148 - accuracy: 0.8789 - val_loss: 0.3663 - val_accuracy: 0.8445\n","Epoch 71/100\n","30/30 [==============================] - 2s 56ms/step - loss: 0.3282 - accuracy: 0.8663 - val_loss: 0.3658 - val_accuracy: 0.8529\n","Epoch 72/100\n","30/30 [==============================] - 2s 67ms/step - loss: 0.3119 - accuracy: 0.8684 - val_loss: 0.4203 - val_accuracy: 0.8361\n","Epoch 73/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3114 - accuracy: 0.8737 - val_loss: 0.4485 - val_accuracy: 0.7731\n","Epoch 74/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3226 - accuracy: 0.8726 - val_loss: 0.3816 - val_accuracy: 0.8571\n","Epoch 75/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3054 - accuracy: 0.8863 - val_loss: 0.3673 - val_accuracy: 0.8655\n","Epoch 76/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3061 - accuracy: 0.8695 - val_loss: 0.3949 - val_accuracy: 0.8571\n","Epoch 77/100\n","30/30 [==============================] - 2s 66ms/step - loss: 0.3049 - accuracy: 0.8737 - val_loss: 0.3566 - val_accuracy: 0.8361\n","Epoch 78/100\n","30/30 [==============================] - 1s 42ms/step - loss: 0.3359 - accuracy: 0.8600 - val_loss: 0.3897 - val_accuracy: 0.8571\n","Epoch 79/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3120 - accuracy: 0.8779 - val_loss: 0.3402 - val_accuracy: 0.8571\n","Epoch 80/100\n","30/30 [==============================] - 2s 51ms/step - loss: 0.3059 - accuracy: 0.8832 - val_loss: 0.4049 - val_accuracy: 0.8487\n","Epoch 81/100\n","30/30 [==============================] - 2s 67ms/step - loss: 0.3070 - accuracy: 0.8768 - val_loss: 0.4827 - val_accuracy: 0.8445\n","Epoch 82/100\n","30/30 [==============================] - 1s 42ms/step - loss: 0.3546 - accuracy: 0.8505 - val_loss: 0.4071 - val_accuracy: 0.8571\n","Epoch 83/100\n","30/30 [==============================] - 1s 42ms/step - loss: 0.3157 - accuracy: 0.8768 - val_loss: 0.3789 - val_accuracy: 0.8613\n","Epoch 84/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.2953 - accuracy: 0.8811 - val_loss: 0.3501 - val_accuracy: 0.8571\n","Epoch 85/100\n","30/30 [==============================] - 1s 40ms/step - loss: 0.2925 - accuracy: 0.8895 - val_loss: 0.3572 - val_accuracy: 0.8571\n","Epoch 86/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.2799 - accuracy: 0.8937 - val_loss: 0.3430 - val_accuracy: 0.8782\n","Epoch 87/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.2872 - accuracy: 0.8874 - val_loss: 0.4083 - val_accuracy: 0.8613\n","Epoch 88/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.3243 - accuracy: 0.8695 - val_loss: 0.3430 - val_accuracy: 0.8529\n","Epoch 89/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.2839 - accuracy: 0.8811 - val_loss: 0.3679 - val_accuracy: 0.8697\n","Epoch 90/100\n","30/30 [==============================] - 2s 58ms/step - loss: 0.3120 - accuracy: 0.8811 - val_loss: 0.4094 - val_accuracy: 0.8445\n","Epoch 91/100\n","30/30 [==============================] - 2s 64ms/step - loss: 0.2808 - accuracy: 0.8926 - val_loss: 0.3602 - val_accuracy: 0.8697\n","Epoch 92/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.3092 - accuracy: 0.8800 - val_loss: 0.3697 - val_accuracy: 0.8403\n","Epoch 93/100\n","30/30 [==============================] - 1s 40ms/step - loss: 0.2864 - accuracy: 0.8821 - val_loss: 0.3621 - val_accuracy: 0.8571\n","Epoch 94/100\n","30/30 [==============================] - 1s 40ms/step - loss: 0.3344 - accuracy: 0.8579 - val_loss: 0.4590 - val_accuracy: 0.8361\n","Epoch 95/100\n","30/30 [==============================] - 1s 42ms/step - loss: 0.2866 - accuracy: 0.8916 - val_loss: 0.4303 - val_accuracy: 0.8613\n","Epoch 96/100\n","30/30 [==============================] - 1s 41ms/step - loss: 0.2919 - accuracy: 0.8811 - val_loss: 0.3923 - val_accuracy: 0.8655\n","Epoch 97/100\n","30/30 [==============================] - 1s 42ms/step - loss: 0.2861 - accuracy: 0.8779 - val_loss: 0.4090 - val_accuracy: 0.8487\n","Epoch 98/100\n","30/30 [==============================] - 1s 39ms/step - loss: 0.2728 - accuracy: 0.8895 - val_loss: 0.3354 - val_accuracy: 0.8655\n","Epoch 99/100\n","30/30 [==============================] - 1s 43ms/step - loss: 0.3115 - accuracy: 0.8716 - val_loss: 0.4070 - val_accuracy: 0.8151\n","Epoch 100/100\n","30/30 [==============================] - 2s 67ms/step - loss: 0.3199 - accuracy: 0.8737 - val_loss: 0.3309 - val_accuracy: 0.8739\n"]}]},{"cell_type":"code","source":["test_predictions = model_ex4.predict(test_X)\n","test_predictions_binary = (test_predictions > 0.7).astype(int)\n","\n","test_f1 = f1_score(test_y, test_predictions_binary)\n","test_recall = recall_score(test_y, test_predictions_binary)\n","\n","test_loss, test_accuracy = model_ex4.evaluate(test_X, test_y)\n","\n","print(\"Test Loss:\", test_loss)\n","print(\"Test Accuracy:\", test_accuracy)\n","print(\"Test F1-Score:\", test_f1)\n","print(\"Test Recall:\", test_recall)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f1aY5SlnIO-M","executionInfo":{"status":"ok","timestamp":1693274794501,"user_tz":300,"elapsed":2004,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"395438a6-c2aa-40fe-91b0-64c2af1b87c5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["10/10 [==============================] - 1s 11ms/step\n","10/10 [==============================] - 0s 12ms/step - loss: 0.3917 - accuracy: 0.8691\n","Test Loss: 0.3917073905467987\n","Test Accuracy: 0.8691275119781494\n","Test F1-Score: 0.8481012658227848\n","Test Recall: 0.8375\n"]}]},{"cell_type":"code","source":["# Plot training and validation metrics\n","plt.figure(figsize=(10, 4))\n","plt.subplot(1, 2, 1)\n","plt.plot(history_ex4.history['loss'], label='Training Loss')\n","plt.plot(history_ex4.history['val_loss'], label='Validation Loss')\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.legend()\n","\n","plt.subplot(1, 2, 2)\n","plt.plot(history_ex4.history['accuracy'], label='Training Accuracy')\n","plt.plot(history_ex4.history['val_accuracy'], label='Validation Accuracy')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":406},"id":"9IfNDHztJg79","executionInfo":{"status":"ok","timestamp":1693274802416,"user_tz":300,"elapsed":1486,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"a6425ed2-eac8-4d56-d202-b7882e14d5aa"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x400 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["model_ex4.save('lstm_model_ex4.h5')"],"metadata":{"id":"X_Zk2HB7CF8I"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Experiment 5"],"metadata":{"id":"CxRuq5J5Kqih"}},{"cell_type":"code","source":["from keras.layers import BatchNormalization\n","\n","model_ex5 = Sequential()\n","model_ex5.add(LSTM(units=128, input_shape=(sequence_length, num_features), return_sequences=True))\n","model_ex5.add(Dropout(0.3))\n","model_ex5.add(LSTM(units=64, return_sequences=True))\n","model_ex5.add(Dropout(0.5))\n","model_ex5.add(LSTM(units=64))\n","model_ex5.add(Dropout(0.5))\n","model_ex5.add(Dense(units=1, activation='sigmoid'))\n","\n","# Add BatchNormalization layers to improve convergence and generalization\n","model_ex5.add(BatchNormalization())\n","\n","optimizer = Adam(learning_rate=0.001)\n","model_ex5.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n","\n","batch_size = 32\n","epochs = 100\n","\n","# Implement EarlyStopping to prevent overfitting\n","early_stopping = EarlyStopping(monitor='val_loss', patience=15, restore_best_weights=True)\n","\n","history_ex5 = model_ex5.fit(train_X, train_y, batch_size=batch_size, epochs=epochs, validation_split=0.2, verbose=1, callbacks=[early_stopping])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":530},"id":"5TOs6zuDJ0Pr","executionInfo":{"status":"error","timestamp":1693274850323,"user_tz":300,"elapsed":16398,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"76a0587f-c52b-4ab4-9054-96b0460301f8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","30/30 [==============================] - 11s 146ms/step - loss: 5.2788 - accuracy: 0.5000 - val_loss: 0.6885 - val_accuracy: 0.4832\n","Epoch 2/100\n","30/30 [==============================] - 2s 52ms/step - loss: 5.3407 - accuracy: 0.4926 - val_loss: 0.7296 - val_accuracy: 0.4664\n","Epoch 3/100\n","30/30 [==============================] - 1s 44ms/step - loss: 5.5098 - accuracy: 0.4705 - val_loss: 0.6941 - val_accuracy: 0.4664\n","Epoch 4/100\n","30/30 [==============================] - 1s 44ms/step - loss: 5.5009 - accuracy: 0.4726 - val_loss: 0.7195 - val_accuracy: 0.4664\n","Epoch 5/100\n"," 9/30 [========>.....................] - ETA: 0s - loss: 5.6671 - accuracy: 0.4514"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-70-7bad353731ab>\u001b[0m in \u001b[0;36m<cell line: 24>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0mearly_stopping\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEarlyStopping\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmonitor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'val_loss'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatience\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrestore_best_weights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mhistory_ex5\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_ex5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_X\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mearly_stopping\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1683\u001b[0m                         ):\n\u001b[1;32m   1684\u001b[0m                             \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1685\u001b[0;31m                             \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1686\u001b[0m                             \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1687\u001b[0m                                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 894\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    895\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    896\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    924\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    925\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 926\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_no_variable_creation_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    927\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable_creation_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    928\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    141\u001b[0m       (concrete_function,\n\u001b[1;32m    142\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m--> 143\u001b[0;31m     return concrete_function._call_flat(\n\u001b[0m\u001b[1;32m    144\u001b[0m         filtered_flat_args, captured_inputs=concrete_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[1;32m    145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1755\u001b[0m         and executing_eagerly):\n\u001b[1;32m   1756\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1757\u001b[0;31m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m   1758\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m   1759\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    379\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 381\u001b[0;31m           outputs = execute.execute(\n\u001b[0m\u001b[1;32m    382\u001b[0m               \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     50\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     53\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     54\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","source":["plt.figure(figsize=(10, 4))\n","plt.subplot(1, 2, 1)\n","plt.plot(history_ex5.history['loss'], label='Training Loss')\n","plt.plot(history_ex5.history['val_loss'], label='Validation Loss')\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.legend()\n","\n","plt.subplot(1, 2, 2)\n","plt.plot(history_ex5.history['accuracy'], label='Training Accuracy')\n","plt.plot(history_ex5.history['val_accuracy'], label='Validation Accuracy')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":407},"id":"lkFT53R4K11B","executionInfo":{"status":"ok","timestamp":1692673095813,"user_tz":300,"elapsed":33604,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"bfbf01f0-a5a7-4534-ca7a-f6810d83e8eb"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x400 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"l7gvTGTgLTDE"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Showing val accuracy and val loss"],"metadata":{"id":"34Zj4fbCMGJD"}},{"cell_type":"code","source":["# Plot training and validation metrics\n","plt.figure(figsize=(10, 4))\n","plt.subplot(1, 2, 1)\n","plt.plot(history_ex0.history['val_loss'], label='loss: Exp 0')\n","plt.plot(history_ex1.history['val_loss'], label='loss: Exp 1')\n","# plt.plot(history_ex2.history['val_loss'], label='loss: Exp 2')\n","# plt.plot(history_ex3.history['val_loss'], label='loss: Exp 3')\n","plt.plot(history_ex4.history['val_loss'], label='loss: Exp 4')\n","plt.plot(history_ex6.history['val_loss'], label='loss: Exp 6')\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.legend()\n","\n","plt.subplot(1, 2, 2)\n","plt.plot(history_ex0.history['val_accuracy'], label='acc: Exp 0')\n","plt.plot(history_ex1.history['val_accuracy'], label='acc: Exp 1')\n","# plt.plot(history_ex2.history['val_accuracy'], label='acc: Exp 2')\n","# plt.plot(history_ex3.history['val_accuracy'], label='acc: Exp 3')\n","plt.plot(history_ex4.history['val_accuracy'], label='acc: Exp 4')\n","plt.plot(history_ex6.history['val_accuracy'], label='acc: Exp 6')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":406},"id":"PJG3e_oRMJId","executionInfo":{"status":"ok","timestamp":1692674960796,"user_tz":300,"elapsed":2015,"user":{"displayName":"Francesco Bassino","userId":"12214195385968794219"}},"outputId":"e6d62113-c6e4-4952-b87f-4df784c2801b"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x400 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"MP5CQKJ6M6FD"},"execution_count":null,"outputs":[]}]}
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