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{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"SG-QAFFN_cross_entropy.ipynb","version":"0.3.2","provenance":[{"file_id":"1XIn68D4-toNoNqJ_Zk7GJIrJ2AMXL-sX","timestamp":1556127036366}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"TPU"},"cells":[{"metadata":{"id":"fBYR7rLZBYNS","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":121},"outputId":"7c69f280-3f6b-4d5b-d106-04a072c9f400","executionInfo":{"status":"ok","timestamp":1556127190171,"user_tz":420,"elapsed":22441,"user":{"displayName":"Santosh Gupta","photoUrl":"","userId":"14163791406641115409"}}},"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/gdrive\n"],"name":"stdout"}]},{"metadata":{"id":"ueDiJQmTB17f","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":541},"outputId":"f8b9ea9e-23be-47d1-c03a-933556beab89","executionInfo":{"status":"ok","timestamp":1556127269975,"user_tz":420,"elapsed":102243,"user":{"displayName":"Santosh Gupta","photoUrl":"","userId":"14163791406641115409"}}},"cell_type":"code","source":["# install tf 2.0\n","from __future__ import absolute_import, division, print_function, unicode_literals\n","\n","!pip install tensorflow-gpu==2.0.0-alpha0\n","import tensorflow as tf\n","\n","print(tf.__version__)"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Collecting tensorflow-gpu==2.0.0-alpha0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/1a/66/32cffad095253219d53f6b6c2a436637bbe45ac4e7be0244557210dc3918/tensorflow_gpu-2.0.0a0-cp36-cp36m-manylinux1_x86_64.whl (332.1MB)\n","\u001b[K 100% |████████████████████████████████| 332.1MB 67kB/s \n","\u001b[?25hRequirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.2.2)\n","Collecting tb-nightly<1.14.0a20190302,>=1.14.0a20190301 (from tensorflow-gpu==2.0.0-alpha0)\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/a9/51/aa1d756644bf4624c03844115e4ac4058eff77acd786b26315f051a4b195/tb_nightly-1.14.0a20190301-py3-none-any.whl (3.0MB)\n","\u001b[K 100% |████████████████████████████████| 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tensorflow-gpu==2.0.0-alpha0)\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/13/82/f16063b4eed210dc2ab057930ac1da4fbe1e91b7b051a6c8370b401e6ae7/tf_estimator_nightly-1.14.0.dev2019030115-py2.py3-none-any.whl (411kB)\n","\u001b[K 100% |████████████████████████████████| 419kB 12.4MB/s \n","\u001b[?25hRequirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.1.0)\n","Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.0.7)\n","Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.7.1)\n","Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (3.7.1)\n","Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from 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tb-nightly-1.14.0a20190301 tensorflow-gpu-2.0.0a0 tf-estimator-nightly-1.14.0.dev2019030115\n","2.0.0-alpha0\n"],"name":"stdout"}]},{"metadata":{"id":"uDqguCPeB39D","colab_type":"code","colab":{}},"cell_type":"code","source":["import os\n","from glob import glob\n","\n","import numpy as np\n","import pandas as pd\n","import tensorflow as tf\n","from sklearn.model_selection import train_test_split\n","\n","SEED = 42\n","\n","\n","def _float_list_feature(value):\n"," \"\"\"Returns a float_list from a float / double.\"\"\"\n"," return tf.train.Feature(float_list=tf.train.FloatList(value=value))\n","\n","\n","def _int64_list_feature(value):\n"," \"\"\"Returns an int64_list from a bool / enum / int / uint.\"\"\"\n"," return tf.train.Feature(int64_list=tf.train.Int64List(value=value))\n","\n","\n","def _int64_feature(value):\n"," \"\"\"Returns an int64_list from a bool / enum / int / uint.\"\"\"\n"," return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\n","\n","\n","def create_generator_for_ffn(\n"," file_list,\n"," mode='train'):\n","\n"," # file_list = glob(os.path.join(data_dir, '*.csv'))\n","\n"," for full_file_path in file_list:\n"," # full_file_path = os.path.join(data_dir, file_name)\n"," if not os.path.exists(full_file_path):\n"," raise FileNotFoundError(\"File %s not found\" % full_file_path)\n"," df = pd.read_csv(full_file_path, encoding='utf8')\n","\n"," # so train test split\n"," if mode == 'train':\n"," df, _ = train_test_split(df, test_size=0.2, random_state=SEED)\n"," else:\n"," _, df = train_test_split(df, test_size=0.2, random_state=SEED)\n","\n"," for _, row in df.iterrows():\n"," q_vectors = np.fromstring(row.question_bert.replace(\n"," '[[', '').replace(']]', ''), sep=' ')\n"," a_vectors = np.fromstring(row.answer_bert.replace(\n"," '[[', '').replace(']]', ''), sep=' ')\n"," vectors = np.stack([q_vectors, a_vectors], axis=0)\n"," if mode in ['train', 'eval']:\n"," yield vectors, 1\n"," else:\n"," yield vectors\n","\n","\n","def ffn_serialize_fn(features):\n"," features_tuple = {'features': _float_list_feature(\n"," features[0].flatten()), 'labels': _int64_feature(features[1])}\n"," example_proto = tf.train.Example(\n"," features=tf.train.Features(feature=features_tuple))\n"," return example_proto.SerializeToString()\n","\n","\n","def make_tfrecord(data_dir, generator_fn, serialize_fn, suffix='', **kwargs):\n"," \"\"\"Function to make TF Records from csv files\n"," This function will take all csv files in data_dir, convert them\n"," to tf example and write to *_{suffix}_train/eval.tfrecord to data_dir.\n","\n"," Arguments:\n"," data_dir {str} -- dir that has csv files and store tf record\n"," generator_fn {fn} -- A function that takes a list of filepath and yield the\n"," parsed recored from file.\n"," serialize_fn {fn} -- A function that takes output of generator fn and convert to tf example\n","\n"," Keyword Arguments:\n"," suffix {str} -- suffix to add to tf record files (default: {''})\n"," \"\"\"\n"," file_list = glob(os.path.join(data_dir, '*.csv'))\n"," train_tf_record_file_list = [\n"," f.replace('.csv', '_{0}_train.tfrecord'.format(suffix)) for f in file_list]\n"," test_tf_record_file_list = [\n"," f.replace('.csv', '_{0}_eval.tfrecord'.format(suffix)) for f in file_list]\n"," for full_file_path, train_tf_record_file_path, test_tf_record_file_path in zip(file_list, train_tf_record_file_list, test_tf_record_file_list):\n"," print('Converting file {0} to TF Record'.format(full_file_path))\n"," with tf.io.TFRecordWriter(train_tf_record_file_path) as writer:\n"," for features in generator_fn([full_file_path], mode='train', **kwargs):\n"," example = serialize_fn(features)\n"," writer.write(example)\n"," with tf.io.TFRecordWriter(test_tf_record_file_path) as writer:\n"," for features in generator_fn([full_file_path], mode='eval', **kwargs):\n"," example = serialize_fn(features)\n"," writer.write(example)\n","\n","\n","def create_dataset_for_ffn(\n"," data_dir,\n"," mode='train',\n"," hidden_size=768,\n"," shuffle_buffer=10000,\n"," prefetch=10000,\n"," batch_size=32):\n","\n"," tfrecord_file_list = glob(os.path.join(\n"," data_dir, '*_FFN_{0}.tfrecord'.format((mode))))\n"," if not tfrecord_file_list:\n"," print('TF Record not found')\n"," make_tfrecord(\n"," data_dir, create_generator_for_ffn,\n"," ffn_serialize_fn, 'FFN')\n","\n"," dataset = tf.data.TFRecordDataset(tfrecord_file_list)\n","\n"," def _parse_ffn_example(example_proto):\n"," feature_description = {\n"," 'features': tf.io.FixedLenFeature([2*768], tf.float32),\n"," 'labels': tf.io.FixedLenFeature([], tf.int64, default_value=0),\n"," }\n"," feature_dict = tf.io.parse_single_example(\n"," example_proto, feature_description)\n"," return tf.reshape(feature_dict['features'], (2, 768)), feature_dict['labels']\n"," dataset = dataset.map(_parse_ffn_example)\n","\n"," dataset = dataset.shuffle(shuffle_buffer)\n","\n"," dataset = dataset.prefetch(prefetch)\n","\n"," dataset = dataset.batch(batch_size)\n"," return dataset"],"execution_count":0,"outputs":[]},{"metadata":{"id":"fFByODECB4lV","colab_type":"code","colab":{}},"cell_type":"code","source":["from __future__ import absolute_import, division, print_function, unicode_literals\n","\n","import os\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","import numpy as np\n","\n","import tensorflow as tf\n","import tensorflow.keras.backend as K\n","\n","\n","class FFN(tf.keras.layers.Layer):\n"," def __init__(\n"," self,\n"," hidden_size=768, #SG edit from 768 4-24-19\n"," dropout=0.2,\n"," residual=True,\n"," name='FFN',\n"," **kwargs):\n"," \"\"\"Simple Dense wrapped with various layers\n"," \"\"\"\n","\n"," super(FFN, self).__init__(name=name, **kwargs)\n"," self.hidden_size = hidden_size\n"," self.dropout = dropout\n"," self.residual = residual\n"," self.ffn_layer = tf.keras.layers.Dense(\n"," units=hidden_size,\n"," use_bias=True\n"," )\n","\n"," def call(self, inputs):\n"," ffn_embedding = self.ffn_layer(inputs)\n"," ffn_embedding = tf.keras.layers.ReLU()(ffn_embedding)\n"," if self.dropout > 0:\n"," ffn_embedding = tf.keras.layers.Dropout(\n"," self.dropout)(ffn_embedding)\n","# ffn_embedding = self.ffn_layer(inputs) #SG edit from 768 4-24-19\n","# ffn_embedding = tf.keras.layers.ReLU()(ffn_embedding) #SG edit from 768 4-24-19\n","# if self.dropout > 0: #SG edit from 768 4-24-19\n","# ffn_embedding = tf.keras.layers.Dropout( #SG edit from 768 4-24-19\n","# self.dropout)(ffn_embedding) #SG edit from 768 4-24-19\n","\n","\n"," if self.residual:\n"," ffn_embedding += inputs\n"," return ffn_embedding\n","\n","\n","class MedicalQAModel(tf.keras.Model):\n"," def __init__(self, name=''):\n"," super(MedicalQAModel, self).__init__(name=name)\n"," self.q_ffn = FFN(name='QFFN', input_shape=(768,))\n"," self.a_ffn = FFN(name='AFFN', input_shape=(768,))\n","\n"," def call(self, inputs):\n"," q_bert_embedding, a_bert_embedding = tf.unstack(inputs, axis=1)\n"," q_embedding, a_embedding = self.q_ffn(\n"," q_bert_embedding), self.a_ffn(a_bert_embedding)\n"," return tf.stack([q_embedding, a_embedding], axis=1)\n","\n","\n","class BioBert(tf.keras.Model):\n"," def __init__(self, name=''):\n"," super(BioBert, self).__init__(name=name)\n","\n"," def call(self, inputs):\n","\n"," # inputs is dict with input features\n"," input_ids, input_masks, segment_ids = inputs\n"," # pass to bert\n"," # with shape of (batch_size/2*batch_size, max_seq_len, hidden_size)\n"," # TODO(Alex): Add true bert model\n"," # Input: input_ids, input_masks, segment_ids all with shape (None, max_seq_len)\n"," # Output: a tensor with shape (None, max_seq_len, hidden_size)\n"," fake_bert_output = tf.expand_dims(tf.ones_like(\n"," input_ids, dtype=tf.float32), axis=-1)*tf.ones([1, 1, 768], dtype=tf.float32)\n"," max_seq_length = tf.shape(fake_bert_output)[-2]\n"," hidden_size = tf.shape(fake_bert_output)[-1]\n","\n"," bert_output = tf.reshape(\n"," fake_bert_output, (-1, 2, max_seq_length, hidden_size))\n"," return bert_output\n","\n","\n","class MedicalQAModelwithBert(tf.keras.Model):\n"," def __init__(\n"," self,\n"," hidden_size=768,\n"," dropout=0.2,\n"," residual=True,\n"," activation=tf.keras.layers.ReLU(),\n"," name=''):\n"," super(MedicalQAModelwithBert, self).__init__(name=name)\n"," self.biobert = BioBert()\n"," self.q_ffn_layer = FFN(\n"," hidden_size=hidden_size,\n"," dropout=dropout,\n"," residual=residual,\n"," activation=activation)\n"," self.a_ffn_layer = FFN(\n"," hidden_size=hidden_size,\n"," dropout=dropout,\n"," residual=residual,\n"," activation=activation)\n","\n"," def _avg_across_token(self, tensor):\n"," if tensor is not None:\n"," tensor = tf.reduce_mean(tensor, axis=1)\n"," return tensor\n","\n"," def call(self, inputs):\n","\n"," q_bert_embedding, a_bert_embedding = self.biobert(inputs)\n","\n"," # according to USE, the DAN network average embedding across tokens\n"," q_bert_embedding = self._avg_across_token(q_bert_embedding)\n"," a_bert_embedding = self._avg_across_token(a_bert_embedding)\n","\n"," q_embedding = self.q_ffn_layer(q_bert_embedding)\n"," a_embedding = self.a_ffn_layer(a_bert_embedding)\n","\n"," return tf.stack([q_embedding, a_embedding], axis=1)\n","\n"," \n"," \n","# def qa_pair_cross_entropy_loss(y_true, y_pred):\n","# y_true = tf.eye(tf.shape(y_pred)[0])\n","# q_embedding, a_embedding = tf.unstack(y_pred, axis=1)\n","# similarity_matrix = tf.matmul(\n","# q_embedding, a_embedding, transpose_b=True)\n","# similarity_matrix_logits = tf.math.sigmoid(similarity_matrix)\n","# return tf.keras.losses.categorical_crossentropy(y_true, similarity_matrix_logits, from_logits=True)\n","\n","def qa_pair_cross_entropy_loss(y_true, y_pred):\n"," y_true = tf.eye(tf.shape(y_pred)[0])\n"," q_embedding, a_embedding = tf.unstack(y_pred, axis=1)\n"," similarity_matrix = tf.matmul(\n"," a = q_embedding, b = a_embedding, transpose_b=True)\n"," similarity_matrix_softmaxed = tf.nn.softmax(similarity_matrix)\n"," K.print_tensor(similarity_matrix_softmaxed, message=\"similarity_matrix_softmaxed is: \")\n"," return tf.keras.losses.categorical_crossentropy(y_true, similarity_matrix_softmaxed, from_logits=False)\n","\n","# y_true = tf.reshape(tf.eye(tf.shape(y_pred)[0])*2-1, (-1,))\n","# q_embedding, a_embedding = tf.unstack(y_pred, axis=1)\n","# similarity_matrix = tf.nn.softmax(tf.matmul(\n","# q_embedding, a_embedding, transpose_b=True))\n","# similarity_vector = tf.reshape(similarity_matrix, (-1, 1))\n","# return tf.nn.softmax_cross_entropy_with_logits(similarity_vector, y_true)\n","\n","#to try, with and without softmax\n","# catagorical cross entropy vs binary cross entropy\n","#with and without sigmoid pre transformation\n","#1 layer vs 2 layer \n","\n","#prioritize what he said. so softmax, then catagorical vs binary \n"],"execution_count":0,"outputs":[]},{"metadata":{"id":"Op9nhWzEB-2V","colab_type":"code","colab":{}},"cell_type":"code","source":["# training config\n","batch_size = 64\n","num_epochs=35\n","learning_rate=0.0001\n","validation_split=0.2\n","shuffle_buffer=50000\n","prefetch=50000\n","data_path='/content/gdrive/My Drive/mqa_tf_record'\n","model_path = '/content/gdrive/My Drive/mqa_models/ffn_model_cross_entropy'"],"execution_count":0,"outputs":[]},{"metadata":{"id":"b7QrPxwVB_U2","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1193},"outputId":"b8f4933e-bcf5-4512-df7a-f2cf5a25577e","executionInfo":{"status":"ok","timestamp":1556155849363,"user_tz":420,"elapsed":3419840,"user":{"displayName":"Santosh Gupta","photoUrl":"","userId":"14163791406641115409"}}},"cell_type":"code","source":[" d = create_dataset_for_ffn(\n"," data_path, batch_size=batch_size, shuffle_buffer=shuffle_buffer, prefetch=prefetch)\n"," eval_d = create_dataset_for_ffn(\n"," data_path, batch_size=batch_size, mode='eval')\n"," medical_qa_model = MedicalQAModel()\n"," optimizer = tf.keras.optimizers.Adam(lr=learning_rate)\n"," medical_qa_model.compile(\n"," optimizer=optimizer, loss=qa_pair_cross_entropy_loss)\n","\n"," epochs = num_epochs\n"," loss_metric = tf.keras.metrics.Mean()\n","\n"," history = medical_qa_model.fit(d, epochs=epochs, validation_data=eval_d)\n"],"execution_count":58,"outputs":[{"output_type":"stream","text":["Epoch 1/35\n","3349/3349 [==============================] - 112s 34ms/step - loss: 8.0786 - val_loss: 0.0000e+00\n","Epoch 2/35\n","3349/3349 [==============================] - 98s 29ms/step - loss: 5.1430 - val_loss: 4.0929\n","Epoch 3/35\n","3349/3349 [==============================] - 100s 30ms/step - loss: 4.5094 - val_loss: 4.0164\n","Epoch 4/35\n","3349/3349 [==============================] - 98s 29ms/step - loss: 4.1240 - val_loss: 3.7529\n","Epoch 5/35\n","3349/3349 [==============================] - 97s 29ms/step - loss: 3.8768 - val_loss: 3.8079\n","Epoch 6/35\n","3349/3349 [==============================] - 98s 29ms/step - loss: 3.6785 - val_loss: 3.5953\n","Epoch 7/35\n","3349/3349 [==============================] - 97s 29ms/step - loss: 3.5199 - val_loss: 3.4747\n","Epoch 8/35\n","3349/3349 [==============================] - 98s 29ms/step - loss: 3.3815 - val_loss: 3.4318\n","Epoch 9/35\n","3349/3349 [==============================] - 98s 29ms/step - loss: 3.2739 - val_loss: 3.3712\n","Epoch 10/35\n","3349/3349 [==============================] - 97s 29ms/step - loss: 3.1726 - val_loss: 3.2709\n","Epoch 11/35\n","3349/3349 [==============================] - 97s 29ms/step - loss: 3.0931 - val_loss: 3.3439\n","Epoch 12/35\n","3349/3349 [==============================] - 98s 29ms/step - loss: 3.0028 - val_loss: 3.2604\n","Epoch 13/35\n","3349/3349 [==============================] - 96s 29ms/step - loss: 2.9343 - val_loss: 3.1457\n","Epoch 14/35\n","3349/3349 [==============================] - 97s 29ms/step - loss: 2.8695 - val_loss: 3.1974\n","Epoch 15/35\n","3349/3349 [==============================] - 97s 29ms/step - loss: 2.8025 - val_loss: 3.1653\n","Epoch 16/35\n","3349/3349 [==============================] - 98s 29ms/step - loss: 2.7489 - val_loss: 3.1592\n","Epoch 17/35\n","3349/3349 [==============================] - 97s 29ms/step - loss: 2.7124 - val_loss: 3.0790\n","Epoch 18/35\n","3349/3349 [==============================] - 97s 29ms/step - loss: 2.6590 - val_loss: 3.0410\n","Epoch 19/35\n","3349/3349 [==============================] - 97s 29ms/step - loss: 2.6165 - val_loss: 2.9715\n","Epoch 20/35\n","3349/3349 [==============================] - 98s 29ms/step - loss: 2.5695 - val_loss: 2.9798\n","Epoch 21/35\n","3349/3349 [==============================] - 98s 29ms/step - loss: 2.5289 - val_loss: 2.9825\n","Epoch 22/35\n","3349/3349 [==============================] - 97s 29ms/step - loss: 2.4915 - val_loss: 2.9491\n","Epoch 23/35\n","3349/3349 [==============================] - 97s 29ms/step - loss: 2.4618 - val_loss: 2.9039\n","Epoch 24/35\n","3349/3349 [==============================] - 97s 29ms/step - loss: 2.4194 - val_loss: 2.8728\n","Epoch 25/35\n","3349/3349 [==============================] - 97s 29ms/step - loss: 2.3888 - val_loss: 2.8882\n","Epoch 26/35\n","3349/3349 [==============================] - 97s 29ms/step - loss: 2.3528 - val_loss: 2.9230\n","Epoch 27/35\n","3349/3349 [==============================] - 97s 29ms/step - loss: 2.3325 - val_loss: 2.9123\n","Epoch 28/35\n","3349/3349 [==============================] - 97s 29ms/step - loss: 2.2968 - val_loss: 2.9179\n","Epoch 29/35\n","3349/3349 [==============================] - 97s 29ms/step - loss: 2.2785 - val_loss: 2.8322\n","Epoch 30/35\n","3349/3349 [==============================] - 95s 28ms/step - loss: 2.2459 - val_loss: 2.8944\n","Epoch 31/35\n","3349/3349 [==============================] - 95s 28ms/step - loss: 2.2196 - val_loss: 2.9340\n","Epoch 32/35\n","3349/3349 [==============================] - 96s 29ms/step - loss: 2.1883 - val_loss: 2.8144\n","Epoch 33/35\n","3349/3349 [==============================] - 95s 28ms/step - loss: 2.1685 - val_loss: 2.8969\n","Epoch 34/35\n","3349/3349 [==============================] - 95s 28ms/step - loss: 2.1532 - val_loss: 2.7532\n","Epoch 35/35\n","3349/3349 [==============================] - 95s 28ms/step - loss: 2.1271 - val_loss: 2.8530\n"],"name":"stdout"}]},{"metadata":{"id":"1tTRDVo2wBQd","colab_type":"code","colab":{}},"cell_type":"code","source":["30, 31, 26, 12"],"execution_count":0,"outputs":[]},{"metadata":{"id":"7jK3IN68Vwh1","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"8347608d-d0bc-40bd-b9d4-ceb1becf5990","executionInfo":{"status":"ok","timestamp":1556152159445,"user_tz":420,"elapsed":3734,"user":{"displayName":"Santosh Gupta","photoUrl":"","userId":"14163791406641115409"}}},"cell_type":"code","source":["print(history.history.keys())"],"execution_count":50,"outputs":[{"output_type":"stream","text":["dict_keys(['loss', 'val_loss'])\n"],"name":"stdout"}]},{"metadata":{"id":"_5UiYSV-VtKm","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":574},"outputId":"4bc2017a-6a1f-4cd7-f18e-3362c891d212","executionInfo":{"status":"ok","timestamp":1556155870312,"user_tz":420,"elapsed":5342,"user":{"displayName":"Santosh Gupta","photoUrl":"","userId":"14163791406641115409"}}},"cell_type":"code","source":["import matplotlib.pyplot as plt\n","# summarize history for accuracy\n","# plt.plot(history.history['acc'])\n","# plt.plot(history.history['val_acc'])\n","plt.title('model accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'test'], loc='upper left')\n","plt.show()\n","# summarize history for loss\n","plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'test'], loc='upper left')\n","plt.show()"],"execution_count":61,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqvom8ISt7HMgsK6qrququ2hu1DtimnZ/CbyD5iY+SdKY6RsUk+19FOcCF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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"4ouPl_VSHABc","colab_type":"code","colab":{}},"cell_type":"code","source":["medical_qa_model.save_weights(model_path)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"Nl6zuquoJQJ1","colab_type":"code","colab":{}},"cell_type":"code","source":["model_path2 = '/content/gdrive/My Drive/mqa_models/ffn_model_cross_entropy.ckpt'\n","\n","checkpoint = tf.keras.callbacks.ModelCheckpoint(model_path2, monitor='loss', verbose=1, save_best_only=True, mode='min')"],"execution_count":0,"outputs":[]},{"metadata":{"id":"moMsMhcBJnMj","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":454},"outputId":"4c6e61a7-50a5-463c-9770-18b30c4987d7"},"cell_type":"code","source":["history = medical_qa_model.fit(d, epochs=20, validation_data=eval_d, callbacks=[checkpoint])"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Epoch 1/20\n"," 3349/Unknown - 88s 26ms/step - loss: 2.0000\n","Epoch 00001: loss improved from inf to 1.99994, saving model to /content/gdrive/My Drive/mqa_models/ffn_model_cross_entropy.ckpt\n","3349/3349 [==============================] - 97s 29ms/step - loss: 2.0000 - val_loss: 0.0000e+00\n","Epoch 2/20\n","3346/3349 [============================>.] - ETA: 0s - loss: 1.9841\n","Epoch 00002: loss improved from 1.99994 to 1.98461, saving model to /content/gdrive/My Drive/mqa_models/ffn_model_cross_entropy.ckpt\n","3349/3349 [==============================] - 85s 25ms/step - loss: 1.9846 - val_loss: 2.7002\n","Epoch 3/20\n","3347/3349 [============================>.] - ETA: 0s - loss: 1.9696\n","Epoch 00003: loss improved from 1.98461 to 1.96986, saving model to /content/gdrive/My Drive/mqa_models/ffn_model_cross_entropy.ckpt\n","3349/3349 [==============================] - 86s 26ms/step - loss: 1.9698 - val_loss: 2.6948\n","Epoch 4/20\n","3347/3349 [============================>.] - ETA: 0s - loss: 1.9521\n","Epoch 00004: loss improved from 1.96986 to 1.95240, saving model to /content/gdrive/My Drive/mqa_models/ffn_model_cross_entropy.ckpt\n","3349/3349 [==============================] - 85s 25ms/step - loss: 1.9524 - val_loss: 2.6883\n","Epoch 5/20\n","3347/3349 [============================>.] - ETA: 0s - loss: 1.9342\n","Epoch 00005: loss improved from 1.95240 to 1.93438, saving model to /content/gdrive/My Drive/mqa_models/ffn_model_cross_entropy.ckpt\n","3349/3349 [==============================] - 86s 26ms/step - loss: 1.9344 - val_loss: 2.6770\n","Epoch 6/20\n","3347/3349 [============================>.] - ETA: 0s - loss: 1.9191\n","Epoch 00006: loss improved from 1.93438 to 1.91929, saving model to /content/gdrive/My Drive/mqa_models/ffn_model_cross_entropy.ckpt\n","3349/3349 [==============================] - 86s 26ms/step - loss: 1.9193 - val_loss: 2.7460\n","Epoch 7/20\n"," 344/3349 [==>...........................] - ETA: 1:40 - loss: 1.9665"],"name":"stdout"}]},{"metadata":{"id":"5USYuFtRF2Uo","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":474},"outputId":"a2e798af-c2c0-45ea-b644-7f56ea378f4f","executionInfo":{"status":"error","timestamp":1556156245150,"user_tz":420,"elapsed":2929,"user":{"displayName":"Santosh Gupta","photoUrl":"","userId":"14163791406641115409"}}},"cell_type":"code","source":["medical_qa_model2 = tf.keras.models.load_model(model_path)\n","checkpoint = ModelCheckpoint(model_path, monitor='loss', verbose=1, save_best_only=True, mode='min')\n","callbacks_list = [checkpoint]\n","history = medical_qa_model2.fit(d, epochs=epochs, validation_data=eval_d, callbacks=callbacks_list)\n","\n"],"execution_count":65,"outputs":[{"output_type":"error","ename":"OSError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)","\u001b[0;32m<ipython-input-65-e82f54e294c3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmedical_qa_model2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\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 2\u001b[0m \u001b[0mcheckpoint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mModelCheckpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmonitor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'loss'\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[0msave_best_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'min'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mcallbacks_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmedical_qa_model2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\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_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0meval_d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m 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0)"]}]},{"metadata":{"id":"LVFOo7PJGZNh","colab_type":"code","colab":{}},"cell_type":"code","source":[""],"execution_count":0,"outputs":[]}]}