Datasets
Models
Downloads: 1
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"machine_shape": "hm"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "8XnVMPBXmtRa"
},
"source": [
"# TensorNetworks in Neural Networks.\n",
"\n",
"Here, we have a small toy example of how to use a TN inside of a fully connected neural network.\n",
"\n",
"First off, let's install tensornetwork"
]
},
{
"cell_type": "code",
"metadata": {
"id": "7HGRsYNAFxME"
},
"source": [
"# !pip install tensornetwork\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import tensorflow as tf\n",
"# Import tensornetwork\n",
"import tensornetwork as tn\n",
"import random\n",
"import time\n",
"# Set the backend to tesorflow\n",
"# (default is numpy)\n",
"tn.set_default_backend(\"tensorflow\")\n",
"np.random.seed(42)\n",
"random.seed(42)\n",
"tf.random.set_seed(42)"
],
"execution_count": 271,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "g1OMCo5XmrYu"
},
"source": [
"# TensorNetwork layer definition\n",
"\n",
"Here, we define the TensorNetwork layer we wish to use to replace the fully connected layer. Here, we simply use a 2 node Matrix Product Operator network to replace the normal dense weight matrix.\n",
"\n",
"We TensorNetwork's NCon API to keep the code short."
]
},
{
"cell_type": "code",
"metadata": {
"id": "wvSMKtPufnLp"
},
"source": [
"class TNLayer(tf.keras.layers.Layer):\n",
"\n",
" def __init__(self):\n",
" super(TNLayer, self).__init__()\n",
" # Create the variables for the layer.\n",
" self.a_var = tf.Variable(tf.random.normal(shape=(32, 32, 2),\n",
" stddev=1.0/32.0),\n",
" name=\"a\", trainable=True)\n",
" self.b_var = tf.Variable(tf.random.normal(shape=(32, 32, 2),\n",
" stddev=1.0/32.0),\n",
" name=\"b\", trainable=True)\n",
" self.bias = tf.Variable(tf.zeros(shape=(32, 32)),\n",
" name=\"bias\", trainable=True)\n",
"\n",
" def call(self, inputs):\n",
" # Define the contraction.\n",
" # We break it out so we can parallelize a batch using\n",
" # tf.vectorized_map (see below).\n",
" def f(input_vec, a_var, b_var, bias_var):\n",
" # Reshape to a matrix instead of a vector.\n",
" input_vec = tf.reshape(input_vec, (32, 32))\n",
"\n",
" # Now we create the network.\n",
" a = tn.Node(a_var)\n",
" b = tn.Node(b_var)\n",
" x_node = tn.Node(input_vec)\n",
" a[1] ^ x_node[0]\n",
" b[1] ^ x_node[1]\n",
" a[2] ^ b[2]\n",
"\n",
" # The TN should now look like this\n",
" # | |\n",
" # a --- b\n",
" # \\ /\n",
" # x\n",
"\n",
" # Now we begin the contraction.\n",
" c = a @ x_node\n",
" result = (c @ b).tensor\n",
"\n",
" # To make the code shorter, we also could've used Ncon.\n",
" # The above few lines of code is the same as this:\n",
" # result = tn.ncon([x, a_var, b_var], [[1, 2], [-1, 1, 3], [-2, 2, 3]])\n",
"\n",
" # Finally, add bias.\n",
" return result + bias_var\n",
"\n",
" # To deal with a batch of items, we can use the tf.vectorized_map\n",
" # function.\n",
" # https://www.tensorflow.org/api_docs/python/tf/vectorized_map\n",
" result = tf.vectorized_map(\n",
" lambda vec: f(vec, self.a_var, self.b_var, self.bias), inputs)\n",
" return tf.nn.relu(tf.reshape(result, (-1, 1024)))"
],
"execution_count": 272,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "V-CVqIhPnhY_"
},
"source": [
"# Smaller model\n",
"These two models are effectively the same, but notice how the TN layer has nearly 10x fewer parameters."
]
},
{
"cell_type": "code",
"metadata": {
"id": "XPBvnB95jg4b",
"outputId": "35788eb3-8b8a-49af-c983-bc1d3a942bbc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"Dense = tf.keras.layers.Dense\n",
"fc_model = tf.keras.Sequential(\n",
" [\n",
" tf.keras.Input(shape=(2,)),\n",
" Dense(1024, activation=tf.nn.relu),\n",
" Dense(1024, activation=tf.nn.relu),\n",
" Dense(1, activation=None)])\n",
"fc_model.summary()"
],
"execution_count": 273,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential_46\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" dense_115 (Dense) (None, 1024) 3072 \n",
" \n",
" dense_116 (Dense) (None, 1024) 1049600 \n",
" \n",
" dense_117 (Dense) (None, 1) 1025 \n",
" \n",
"=================================================================\n",
"Total params: 1053697 (4.02 MB)\n",
"Trainable params: 1053697 (4.02 MB)\n",
"Non-trainable params: 0 (0.00 Byte)\n",
"_________________________________________________________________\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "bbKsmK8wIFTp",
"outputId": "f3c6092d-b29d-4d22-ddc1-b7ba785be2bd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"tn_model = tf.keras.Sequential(\n",
" [\n",
" tf.keras.Input(shape=(2,)),\n",
" Dense(1024, activation=tf.nn.relu),\n",
" # Here, we replace the dense layer with our MPS.\n",
" TNLayer(),\n",
" TNLayer(),\n",
" TNLayer(),\n",
" Dense(1, activation=None)])\n",
"tn_model.summary()"
],
"execution_count": 274,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential_47\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" dense_118 (Dense) (None, 1024) 3072 \n",
" \n",
" tn_layer_30 (TNLayer) (None, 1024) 5120 \n",
" \n",
" tn_layer_31 (TNLayer) (None, 1024) 5120 \n",
" \n",
" tn_layer_32 (TNLayer) (None, 1024) 5120 \n",
" \n",
" dense_119 (Dense) (None, 1) 1025 \n",
" \n",
"=================================================================\n",
"Total params: 19457 (76.00 KB)\n",
"Trainable params: 19457 (76.00 KB)\n",
"Non-trainable params: 0 (0.00 Byte)\n",
"_________________________________________________________________\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GWwoYp0WnsLA"
},
"source": [
"# Training a model\n",
"\n",
"You can train the TN model just as you would a normal neural network model! Here, we give an example of how to do it in Keras."
]
},
{
"cell_type": "code",
"metadata": {
"id": "qDFzOC7sDBJ-"
},
"source": [
"X = np.concatenate([np.random.randn(20, 2) + np.array([3, 3]),\n",
" np.random.randn(20, 2) + np.array([-3, -3]),\n",
" np.random.randn(20, 2) + np.array([-3, 3]),\n",
" np.random.randn(20, 2) + np.array([3, -3])])\n",
"\n",
"Y = np.concatenate([np.ones((40)), -np.ones((40))])"
],
"execution_count": 275,
"outputs": []
},
{
"cell_type": "code",
"source": [
"seconds = time.time()\n",
"print(\"Time in seconds since beginning of run:\", seconds)\n",
"local_time = time.ctime(seconds)\n",
"print(local_time)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "19TWP-1eKURB",
"outputId": "275dcdbe-61a2-487d-8a92-4db9d29a3963"
},
"execution_count": 276,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1709522300.7213488\n",
"Mon Mar 4 03:18:20 2024\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "crc0q1vbIyTj",
"outputId": "304adfe3-0595-48a9-95c7-e38460167e26",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"tn_model.compile(optimizer=\"adam\", loss=\"mean_squared_error\")\n",
"tn_model.fit(X, Y, epochs=300, verbose=2)"
],
"execution_count": 277,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/300\n",
"3/3 - 2s - loss: 1.0018 - 2s/epoch - 777ms/step\n",
"Epoch 2/300\n",
"3/3 - 0s - loss: 1.0018 - 22ms/epoch - 7ms/step\n",
"Epoch 3/300\n",
"3/3 - 0s - loss: 1.0007 - 24ms/epoch - 8ms/step\n",
"Epoch 4/300\n",
"3/3 - 0s - loss: 1.0001 - 26ms/epoch - 9ms/step\n",
"Epoch 5/300\n",
"3/3 - 0s - loss: 1.0006 - 22ms/epoch - 7ms/step\n",
"Epoch 6/300\n",
"3/3 - 0s - loss: 0.9997 - 23ms/epoch - 8ms/step\n",
"Epoch 7/300\n",
"3/3 - 0s - loss: 0.9991 - 27ms/epoch - 9ms/step\n",
"Epoch 8/300\n",
"3/3 - 0s - loss: 0.9978 - 26ms/epoch - 9ms/step\n",
"Epoch 9/300\n",
"3/3 - 0s - loss: 0.9946 - 25ms/epoch - 8ms/step\n",
"Epoch 10/300\n",
"3/3 - 0s - loss: 0.9873 - 22ms/epoch - 7ms/step\n",
"Epoch 11/300\n",
"3/3 - 0s - loss: 0.9702 - 25ms/epoch - 8ms/step\n",
"Epoch 12/300\n",
"3/3 - 0s - loss: 0.9332 - 26ms/epoch - 9ms/step\n",
"Epoch 13/300\n",
"3/3 - 0s - loss: 0.8618 - 25ms/epoch - 8ms/step\n",
"Epoch 14/300\n",
"3/3 - 0s - loss: 0.7187 - 24ms/epoch - 8ms/step\n",
"Epoch 15/300\n",
"3/3 - 0s - loss: 0.4604 - 22ms/epoch - 7ms/step\n",
"Epoch 16/300\n",
"3/3 - 0s - loss: 0.1368 - 23ms/epoch - 8ms/step\n",
"Epoch 17/300\n",
"3/3 - 0s - loss: 0.1065 - 29ms/epoch - 10ms/step\n",
"Epoch 18/300\n",
"3/3 - 0s - loss: 0.0901 - 23ms/epoch - 8ms/step\n",
"Epoch 19/300\n",
"3/3 - 0s - loss: 0.0193 - 26ms/epoch - 9ms/step\n",
"Epoch 20/300\n",
"3/3 - 0s - loss: 0.0450 - 23ms/epoch - 8ms/step\n",
"Epoch 21/300\n",
"3/3 - 0s - loss: 0.0507 - 22ms/epoch - 7ms/step\n",
"Epoch 22/300\n",
"3/3 - 0s - loss: 0.0290 - 26ms/epoch - 9ms/step\n",
"Epoch 23/300\n",
"3/3 - 0s - loss: 0.0136 - 27ms/epoch - 9ms/step\n",
"Epoch 24/300\n",
"3/3 - 0s - loss: 0.0176 - 26ms/epoch - 9ms/step\n",
"Epoch 25/300\n",
"3/3 - 0s - loss: 0.0193 - 23ms/epoch - 8ms/step\n",
"Epoch 26/300\n",
"3/3 - 0s - loss: 0.0116 - 25ms/epoch - 8ms/step\n",
"Epoch 27/300\n",
"3/3 - 0s - loss: 0.0091 - 25ms/epoch - 8ms/step\n",
"Epoch 28/300\n",
"3/3 - 0s - loss: 0.0110 - 23ms/epoch - 8ms/step\n",
"Epoch 29/300\n",
"3/3 - 0s - loss: 0.0104 - 26ms/epoch - 9ms/step\n",
"Epoch 30/300\n",
"3/3 - 0s - loss: 0.0078 - 23ms/epoch - 8ms/step\n",
"Epoch 31/300\n",
"3/3 - 0s - loss: 0.0073 - 24ms/epoch - 8ms/step\n",
"Epoch 32/300\n",
"3/3 - 0s - loss: 0.0076 - 29ms/epoch - 10ms/step\n",
"Epoch 33/300\n",
"3/3 - 0s - loss: 0.0071 - 27ms/epoch - 9ms/step\n",
"Epoch 34/300\n",
"3/3 - 0s - loss: 0.0062 - 24ms/epoch - 8ms/step\n",
"Epoch 35/300\n",
"3/3 - 0s - loss: 0.0061 - 22ms/epoch - 7ms/step\n",
"Epoch 36/300\n",
"3/3 - 0s - loss: 0.0061 - 21ms/epoch - 7ms/step\n",
"Epoch 37/300\n",
"3/3 - 0s - loss: 0.0057 - 20ms/epoch - 7ms/step\n",
"Epoch 38/300\n",
"3/3 - 0s - loss: 0.0053 - 23ms/epoch - 8ms/step\n",
"Epoch 39/300\n",
"3/3 - 0s - loss: 0.0052 - 24ms/epoch - 8ms/step\n",
"Epoch 40/300\n",
"3/3 - 0s - loss: 0.0051 - 24ms/epoch - 8ms/step\n",
"Epoch 41/300\n",
"3/3 - 0s - loss: 0.0048 - 23ms/epoch - 8ms/step\n",
"Epoch 42/300\n",
"3/3 - 0s - loss: 0.0046 - 23ms/epoch - 8ms/step\n",
"Epoch 43/300\n",
"3/3 - 0s - loss: 0.0046 - 23ms/epoch - 8ms/step\n",
"Epoch 44/300\n",
"3/3 - 0s - loss: 0.0044 - 21ms/epoch - 7ms/step\n",
"Epoch 45/300\n",
"3/3 - 0s - loss: 0.0042 - 23ms/epoch - 8ms/step\n",
"Epoch 46/300\n",
"3/3 - 0s - loss: 0.0040 - 21ms/epoch - 7ms/step\n",
"Epoch 47/300\n",
"3/3 - 0s - loss: 0.0040 - 20ms/epoch - 7ms/step\n",
"Epoch 48/300\n",
"3/3 - 0s - loss: 0.0039 - 26ms/epoch - 9ms/step\n",
"Epoch 49/300\n",
"3/3 - 0s - loss: 0.0037 - 23ms/epoch - 8ms/step\n",
"Epoch 50/300\n",
"3/3 - 0s - loss: 0.0036 - 24ms/epoch - 8ms/step\n",
"Epoch 51/300\n",
"3/3 - 0s - loss: 0.0035 - 20ms/epoch - 7ms/step\n",
"Epoch 52/300\n",
"3/3 - 0s - loss: 0.0034 - 27ms/epoch - 9ms/step\n",
"Epoch 53/300\n",
"3/3 - 0s - loss: 0.0033 - 23ms/epoch - 8ms/step\n",
"Epoch 54/300\n",
"3/3 - 0s - loss: 0.0032 - 26ms/epoch - 9ms/step\n",
"Epoch 55/300\n",
"3/3 - 0s - loss: 0.0031 - 21ms/epoch - 7ms/step\n",
"Epoch 56/300\n",
"3/3 - 0s - loss: 0.0031 - 24ms/epoch - 8ms/step\n",
"Epoch 57/300\n",
"3/3 - 0s - loss: 0.0030 - 22ms/epoch - 7ms/step\n",
"Epoch 58/300\n",
"3/3 - 0s - loss: 0.0029 - 21ms/epoch - 7ms/step\n",
"Epoch 59/300\n",
"3/3 - 0s - loss: 0.0028 - 19ms/epoch - 6ms/step\n",
"Epoch 60/300\n",
"3/3 - 0s - loss: 0.0027 - 25ms/epoch - 8ms/step\n",
"Epoch 61/300\n",
"3/3 - 0s - loss: 0.0026 - 24ms/epoch - 8ms/step\n",
"Epoch 62/300\n",
"3/3 - 0s - loss: 0.0025 - 26ms/epoch - 9ms/step\n",
"Epoch 63/300\n",
"3/3 - 0s - loss: 0.0025 - 23ms/epoch - 8ms/step\n",
"Epoch 64/300\n",
"3/3 - 0s - loss: 0.0024 - 21ms/epoch - 7ms/step\n",
"Epoch 65/300\n",
"3/3 - 0s - loss: 0.0024 - 26ms/epoch - 9ms/step\n",
"Epoch 66/300\n",
"3/3 - 0s - loss: 0.0023 - 20ms/epoch - 7ms/step\n",
"Epoch 67/300\n",
"3/3 - 0s - loss: 0.0022 - 23ms/epoch - 8ms/step\n",
"Epoch 68/300\n",
"3/3 - 0s - loss: 0.0022 - 23ms/epoch - 8ms/step\n",
"Epoch 69/300\n",
"3/3 - 0s - loss: 0.0021 - 25ms/epoch - 8ms/step\n",
"Epoch 70/300\n",
"3/3 - 0s - loss: 0.0020 - 23ms/epoch - 8ms/step\n",
"Epoch 71/300\n",
"3/3 - 0s - loss: 0.0020 - 27ms/epoch - 9ms/step\n",
"Epoch 72/300\n",
"3/3 - 0s - loss: 0.0019 - 23ms/epoch - 8ms/step\n",
"Epoch 73/300\n",
"3/3 - 0s - loss: 0.0019 - 21ms/epoch - 7ms/step\n",
"Epoch 74/300\n",
"3/3 - 0s - loss: 0.0018 - 22ms/epoch - 7ms/step\n",
"Epoch 75/300\n",
"3/3 - 0s - loss: 0.0018 - 25ms/epoch - 8ms/step\n",
"Epoch 76/300\n",
"3/3 - 0s - loss: 0.0017 - 25ms/epoch - 8ms/step\n",
"Epoch 77/300\n",
"3/3 - 0s - loss: 0.0017 - 26ms/epoch - 9ms/step\n",
"Epoch 78/300\n",
"3/3 - 0s - loss: 0.0016 - 26ms/epoch - 9ms/step\n",
"Epoch 79/300\n",
"3/3 - 0s - loss: 0.0016 - 19ms/epoch - 6ms/step\n",
"Epoch 80/300\n",
"3/3 - 0s - loss: 0.0016 - 21ms/epoch - 7ms/step\n",
"Epoch 81/300\n",
"3/3 - 0s - loss: 0.0015 - 22ms/epoch - 7ms/step\n",
"Epoch 82/300\n",
"3/3 - 0s - loss: 0.0015 - 23ms/epoch - 8ms/step\n",
"Epoch 83/300\n",
"3/3 - 0s - loss: 0.0014 - 24ms/epoch - 8ms/step\n",
"Epoch 84/300\n",
"3/3 - 0s - loss: 0.0014 - 26ms/epoch - 9ms/step\n",
"Epoch 85/300\n",
"3/3 - 0s - loss: 0.0013 - 24ms/epoch - 8ms/step\n",
"Epoch 86/300\n",
"3/3 - 0s - loss: 0.0013 - 24ms/epoch - 8ms/step\n",
"Epoch 87/300\n",
"3/3 - 0s - loss: 0.0013 - 21ms/epoch - 7ms/step\n",
"Epoch 88/300\n",
"3/3 - 0s - loss: 0.0012 - 24ms/epoch - 8ms/step\n",
"Epoch 89/300\n",
"3/3 - 0s - loss: 0.0012 - 23ms/epoch - 8ms/step\n",
"Epoch 90/300\n",
"3/3 - 0s - loss: 0.0012 - 20ms/epoch - 7ms/step\n",
"Epoch 91/300\n",
"3/3 - 0s - loss: 0.0011 - 25ms/epoch - 8ms/step\n",
"Epoch 92/300\n",
"3/3 - 0s - loss: 0.0011 - 26ms/epoch - 9ms/step\n",
"Epoch 93/300\n",
"3/3 - 0s - loss: 0.0010 - 23ms/epoch - 8ms/step\n",
"Epoch 94/300\n",
"3/3 - 0s - loss: 0.0010 - 21ms/epoch - 7ms/step\n",
"Epoch 95/300\n",
"3/3 - 0s - loss: 9.9175e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 96/300\n",
"3/3 - 0s - loss: 9.7160e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 97/300\n",
"3/3 - 0s - loss: 9.1934e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 98/300\n",
"3/3 - 0s - loss: 9.1939e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 99/300\n",
"3/3 - 0s - loss: 8.9917e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 100/300\n",
"3/3 - 0s - loss: 8.5670e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 101/300\n",
"3/3 - 0s - loss: 8.2558e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 102/300\n",
"3/3 - 0s - loss: 8.0836e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 103/300\n",
"3/3 - 0s - loss: 7.7820e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 104/300\n",
"3/3 - 0s - loss: 7.4222e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 105/300\n",
"3/3 - 0s - loss: 7.2910e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 106/300\n",
"3/3 - 0s - loss: 7.1327e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 107/300\n",
"3/3 - 0s - loss: 6.7226e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 108/300\n",
"3/3 - 0s - loss: 6.5387e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 109/300\n",
"3/3 - 0s - loss: 6.3478e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 110/300\n",
"3/3 - 0s - loss: 6.0961e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 111/300\n",
"3/3 - 0s - loss: 5.8166e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 112/300\n",
"3/3 - 0s - loss: 5.8480e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 113/300\n",
"3/3 - 0s - loss: 5.4503e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 114/300\n",
"3/3 - 0s - loss: 5.3062e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 115/300\n",
"3/3 - 0s - loss: 5.2946e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 116/300\n",
"3/3 - 0s - loss: 4.7874e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 117/300\n",
"3/3 - 0s - loss: 4.7114e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 118/300\n",
"3/3 - 0s - loss: 4.5269e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 119/300\n",
"3/3 - 0s - loss: 4.5380e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 120/300\n",
"3/3 - 0s - loss: 4.2536e-04 - 19ms/epoch - 6ms/step\n",
"Epoch 121/300\n",
"3/3 - 0s - loss: 3.9441e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 122/300\n",
"3/3 - 0s - loss: 3.8316e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 123/300\n",
"3/3 - 0s - loss: 3.6812e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 124/300\n",
"3/3 - 0s - loss: 3.6512e-04 - 19ms/epoch - 6ms/step\n",
"Epoch 125/300\n",
"3/3 - 0s - loss: 3.3016e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 126/300\n",
"3/3 - 0s - loss: 3.1494e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 127/300\n",
"3/3 - 0s - loss: 3.1160e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 128/300\n",
"3/3 - 0s - loss: 2.8627e-04 - 20ms/epoch - 7ms/step\n",
"Epoch 129/300\n",
"3/3 - 0s - loss: 2.6777e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 130/300\n",
"3/3 - 0s - loss: 2.6258e-04 - 20ms/epoch - 7ms/step\n",
"Epoch 131/300\n",
"3/3 - 0s - loss: 2.4426e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 132/300\n",
"3/3 - 0s - loss: 2.4068e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 133/300\n",
"3/3 - 0s - loss: 2.2655e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 134/300\n",
"3/3 - 0s - loss: 2.1242e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 135/300\n",
"3/3 - 0s - loss: 2.0196e-04 - 20ms/epoch - 7ms/step\n",
"Epoch 136/300\n",
"3/3 - 0s - loss: 1.9252e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 137/300\n",
"3/3 - 0s - loss: 1.8062e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 138/300\n",
"3/3 - 0s - loss: 1.7509e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 139/300\n",
"3/3 - 0s - loss: 1.6781e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 140/300\n",
"3/3 - 0s - loss: 1.5383e-04 - 19ms/epoch - 6ms/step\n",
"Epoch 141/300\n",
"3/3 - 0s - loss: 1.4289e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 142/300\n",
"3/3 - 0s - loss: 1.3294e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 143/300\n",
"3/3 - 0s - loss: 1.2580e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 144/300\n",
"3/3 - 0s - loss: 1.1927e-04 - 20ms/epoch - 7ms/step\n",
"Epoch 145/300\n",
"3/3 - 0s - loss: 1.1094e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 146/300\n",
"3/3 - 0s - loss: 1.1226e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 147/300\n",
"3/3 - 0s - loss: 9.7595e-05 - 27ms/epoch - 9ms/step\n",
"Epoch 148/300\n",
"3/3 - 0s - loss: 9.3520e-05 - 26ms/epoch - 9ms/step\n",
"Epoch 149/300\n",
"3/3 - 0s - loss: 8.7133e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 150/300\n",
"3/3 - 0s - loss: 8.2338e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 151/300\n",
"3/3 - 0s - loss: 8.4051e-05 - 29ms/epoch - 10ms/step\n",
"Epoch 152/300\n",
"3/3 - 0s - loss: 7.1404e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 153/300\n",
"3/3 - 0s - loss: 6.8557e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 154/300\n",
"3/3 - 0s - loss: 6.2511e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 155/300\n",
"3/3 - 0s - loss: 6.0561e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 156/300\n",
"3/3 - 0s - loss: 5.4933e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 157/300\n",
"3/3 - 0s - loss: 5.1755e-05 - 21ms/epoch - 7ms/step\n",
"Epoch 158/300\n",
"3/3 - 0s - loss: 4.7015e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 159/300\n",
"3/3 - 0s - loss: 4.2376e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 160/300\n",
"3/3 - 0s - loss: 4.2349e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 161/300\n",
"3/3 - 0s - loss: 4.1130e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 162/300\n",
"3/3 - 0s - loss: 3.6309e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 163/300\n",
"3/3 - 0s - loss: 3.4193e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 164/300\n",
"3/3 - 0s - loss: 3.4451e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 165/300\n",
"3/3 - 0s - loss: 2.9192e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 166/300\n",
"3/3 - 0s - loss: 2.7077e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 167/300\n",
"3/3 - 0s - loss: 2.3977e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 168/300\n",
"3/3 - 0s - loss: 2.3505e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 169/300\n",
"3/3 - 0s - loss: 2.1494e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 170/300\n",
"3/3 - 0s - loss: 1.9601e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 171/300\n",
"3/3 - 0s - loss: 1.8738e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 172/300\n",
"3/3 - 0s - loss: 1.7222e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 173/300\n",
"3/3 - 0s - loss: 1.6006e-05 - 19ms/epoch - 6ms/step\n",
"Epoch 174/300\n",
"3/3 - 0s - loss: 1.5306e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 175/300\n",
"3/3 - 0s - loss: 1.4319e-05 - 20ms/epoch - 7ms/step\n",
"Epoch 176/300\n",
"3/3 - 0s - loss: 1.3512e-05 - 20ms/epoch - 7ms/step\n",
"Epoch 177/300\n",
"3/3 - 0s - loss: 1.3253e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 178/300\n",
"3/3 - 0s - loss: 1.0599e-05 - 21ms/epoch - 7ms/step\n",
"Epoch 179/300\n",
"3/3 - 0s - loss: 1.1178e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 180/300\n",
"3/3 - 0s - loss: 9.1475e-06 - 21ms/epoch - 7ms/step\n",
"Epoch 181/300\n",
"3/3 - 0s - loss: 9.3396e-06 - 21ms/epoch - 7ms/step\n",
"Epoch 182/300\n",
"3/3 - 0s - loss: 8.2424e-06 - 23ms/epoch - 8ms/step\n",
"Epoch 183/300\n",
"3/3 - 0s - loss: 7.5882e-06 - 23ms/epoch - 8ms/step\n",
"Epoch 184/300\n",
"3/3 - 0s - loss: 7.2334e-06 - 24ms/epoch - 8ms/step\n",
"Epoch 185/300\n",
"3/3 - 0s - loss: 6.5040e-06 - 20ms/epoch - 7ms/step\n",
"Epoch 186/300\n",
"3/3 - 0s - loss: 6.0464e-06 - 23ms/epoch - 8ms/step\n",
"Epoch 187/300\n",
"3/3 - 0s - loss: 5.6838e-06 - 26ms/epoch - 9ms/step\n",
"Epoch 188/300\n",
"3/3 - 0s - loss: 5.6350e-06 - 23ms/epoch - 8ms/step\n",
"Epoch 189/300\n",
"3/3 - 0s - loss: 4.8108e-06 - 25ms/epoch - 8ms/step\n",
"Epoch 190/300\n",
"3/3 - 0s - loss: 4.7074e-06 - 22ms/epoch - 7ms/step\n",
"Epoch 191/300\n",
"3/3 - 0s - loss: 4.3411e-06 - 28ms/epoch - 9ms/step\n",
"Epoch 192/300\n",
"3/3 - 0s - loss: 3.8214e-06 - 28ms/epoch - 9ms/step\n",
"Epoch 193/300\n",
"3/3 - 0s - loss: 3.8367e-06 - 30ms/epoch - 10ms/step\n",
"Epoch 194/300\n",
"3/3 - 0s - loss: 3.3282e-06 - 24ms/epoch - 8ms/step\n",
"Epoch 195/300\n",
"3/3 - 0s - loss: 3.1114e-06 - 28ms/epoch - 9ms/step\n",
"Epoch 196/300\n",
"3/3 - 0s - loss: 3.0487e-06 - 25ms/epoch - 8ms/step\n",
"Epoch 197/300\n",
"3/3 - 0s - loss: 2.6585e-06 - 23ms/epoch - 8ms/step\n",
"Epoch 198/300\n",
"3/3 - 0s - loss: 2.6784e-06 - 26ms/epoch - 9ms/step\n",
"Epoch 199/300\n",
"3/3 - 0s - loss: 2.4610e-06 - 23ms/epoch - 8ms/step\n",
"Epoch 200/300\n",
"3/3 - 0s - loss: 2.2299e-06 - 23ms/epoch - 8ms/step\n",
"Epoch 201/300\n",
"3/3 - 0s - loss: 2.3534e-06 - 20ms/epoch - 7ms/step\n",
"Epoch 202/300\n",
"3/3 - 0s - loss: 2.0683e-06 - 24ms/epoch - 8ms/step\n",
"Epoch 203/300\n",
"3/3 - 0s - loss: 1.8921e-06 - 23ms/epoch - 8ms/step\n",
"Epoch 204/300\n",
"3/3 - 0s - loss: 1.9061e-06 - 25ms/epoch - 8ms/step\n",
"Epoch 205/300\n",
"3/3 - 0s - loss: 1.6377e-06 - 24ms/epoch - 8ms/step\n",
"Epoch 206/300\n",
"3/3 - 0s - loss: 1.5557e-06 - 26ms/epoch - 9ms/step\n",
"Epoch 207/300\n",
"3/3 - 0s - loss: 1.4975e-06 - 25ms/epoch - 8ms/step\n",
"Epoch 208/300\n",
"3/3 - 0s - loss: 1.4472e-06 - 25ms/epoch - 8ms/step\n",
"Epoch 209/300\n",
"3/3 - 0s - loss: 1.3231e-06 - 23ms/epoch - 8ms/step\n",
"Epoch 210/300\n",
"3/3 - 0s - loss: 1.2137e-06 - 25ms/epoch - 8ms/step\n",
"Epoch 211/300\n",
"3/3 - 0s - loss: 1.0814e-06 - 25ms/epoch - 8ms/step\n",
"Epoch 212/300\n",
"3/3 - 0s - loss: 1.1012e-06 - 23ms/epoch - 8ms/step\n",
"Epoch 213/300\n",
"3/3 - 0s - loss: 1.0367e-06 - 20ms/epoch - 7ms/step\n",
"Epoch 214/300\n",
"3/3 - 0s - loss: 1.0579e-06 - 22ms/epoch - 7ms/step\n",
"Epoch 215/300\n",
"3/3 - 0s - loss: 8.6901e-07 - 20ms/epoch - 7ms/step\n",
"Epoch 216/300\n",
"3/3 - 0s - loss: 9.2759e-07 - 22ms/epoch - 7ms/step\n",
"Epoch 217/300\n",
"3/3 - 0s - loss: 7.7820e-07 - 27ms/epoch - 9ms/step\n",
"Epoch 218/300\n",
"3/3 - 0s - loss: 7.5039e-07 - 24ms/epoch - 8ms/step\n",
"Epoch 219/300\n",
"3/3 - 0s - loss: 6.8642e-07 - 21ms/epoch - 7ms/step\n",
"Epoch 220/300\n",
"3/3 - 0s - loss: 6.7417e-07 - 29ms/epoch - 10ms/step\n",
"Epoch 221/300\n",
"3/3 - 0s - loss: 6.2442e-07 - 24ms/epoch - 8ms/step\n",
"Epoch 222/300\n",
"3/3 - 0s - loss: 6.4821e-07 - 26ms/epoch - 9ms/step\n",
"Epoch 223/300\n",
"3/3 - 0s - loss: 6.0551e-07 - 25ms/epoch - 8ms/step\n",
"Epoch 224/300\n",
"3/3 - 0s - loss: 5.7419e-07 - 24ms/epoch - 8ms/step\n",
"Epoch 225/300\n",
"3/3 - 0s - loss: 5.5595e-07 - 22ms/epoch - 7ms/step\n",
"Epoch 226/300\n",
"3/3 - 0s - loss: 5.1922e-07 - 26ms/epoch - 9ms/step\n",
"Epoch 227/300\n",
"3/3 - 0s - loss: 5.5299e-07 - 24ms/epoch - 8ms/step\n",
"Epoch 228/300\n",
"3/3 - 0s - loss: 5.0953e-07 - 22ms/epoch - 7ms/step\n",
"Epoch 229/300\n",
"3/3 - 0s - loss: 5.3831e-07 - 26ms/epoch - 9ms/step\n",
"Epoch 230/300\n",
"3/3 - 0s - loss: 4.5238e-07 - 25ms/epoch - 8ms/step\n",
"Epoch 231/300\n",
"3/3 - 0s - loss: 5.3890e-07 - 23ms/epoch - 8ms/step\n",
"Epoch 232/300\n",
"3/3 - 0s - loss: 4.1443e-07 - 23ms/epoch - 8ms/step\n",
"Epoch 233/300\n",
"3/3 - 0s - loss: 5.0137e-07 - 23ms/epoch - 8ms/step\n",
"Epoch 234/300\n",
"3/3 - 0s - loss: 4.2846e-07 - 24ms/epoch - 8ms/step\n",
"Epoch 235/300\n",
"3/3 - 0s - loss: 4.2069e-07 - 23ms/epoch - 8ms/step\n",
"Epoch 236/300\n",
"3/3 - 0s - loss: 5.2210e-07 - 26ms/epoch - 9ms/step\n",
"Epoch 237/300\n",
"3/3 - 0s - loss: 4.9140e-07 - 26ms/epoch - 9ms/step\n",
"Epoch 238/300\n",
"3/3 - 0s - loss: 4.2449e-07 - 27ms/epoch - 9ms/step\n",
"Epoch 239/300\n",
"3/3 - 0s - loss: 4.8237e-07 - 24ms/epoch - 8ms/step\n",
"Epoch 240/300\n",
"3/3 - 0s - loss: 4.1727e-07 - 20ms/epoch - 7ms/step\n",
"Epoch 241/300\n",
"3/3 - 0s - loss: 3.8011e-07 - 21ms/epoch - 7ms/step\n",
"Epoch 242/300\n",
"3/3 - 0s - loss: 4.0372e-07 - 26ms/epoch - 9ms/step\n",
"Epoch 243/300\n",
"3/3 - 0s - loss: 3.6880e-07 - 27ms/epoch - 9ms/step\n",
"Epoch 244/300\n",
"3/3 - 0s - loss: 3.5867e-07 - 21ms/epoch - 7ms/step\n",
"Epoch 245/300\n",
"3/3 - 0s - loss: 3.5779e-07 - 22ms/epoch - 7ms/step\n",
"Epoch 246/300\n",
"3/3 - 0s - loss: 3.3445e-07 - 24ms/epoch - 8ms/step\n",
"Epoch 247/300\n",
"3/3 - 0s - loss: 3.2059e-07 - 25ms/epoch - 8ms/step\n",
"Epoch 248/300\n",
"3/3 - 0s - loss: 3.1275e-07 - 23ms/epoch - 8ms/step\n",
"Epoch 249/300\n",
"3/3 - 0s - loss: 3.0808e-07 - 23ms/epoch - 8ms/step\n",
"Epoch 250/300\n",
"3/3 - 0s - loss: 3.0408e-07 - 20ms/epoch - 7ms/step\n",
"Epoch 251/300\n",
"3/3 - 0s - loss: 2.9309e-07 - 23ms/epoch - 8ms/step\n",
"Epoch 252/300\n",
"3/3 - 0s - loss: 3.4348e-07 - 26ms/epoch - 9ms/step\n",
"Epoch 253/300\n",
"3/3 - 0s - loss: 3.1357e-07 - 28ms/epoch - 9ms/step\n",
"Epoch 254/300\n",
"3/3 - 0s - loss: 2.8772e-07 - 27ms/epoch - 9ms/step\n",
"Epoch 255/300\n",
"3/3 - 0s - loss: 3.0372e-07 - 25ms/epoch - 8ms/step\n",
"Epoch 256/300\n",
"3/3 - 0s - loss: 2.7702e-07 - 29ms/epoch - 10ms/step\n",
"Epoch 257/300\n",
"3/3 - 0s - loss: 2.8394e-07 - 25ms/epoch - 8ms/step\n",
"Epoch 258/300\n",
"3/3 - 0s - loss: 2.6677e-07 - 25ms/epoch - 8ms/step\n",
"Epoch 259/300\n",
"3/3 - 0s - loss: 2.8928e-07 - 26ms/epoch - 9ms/step\n",
"Epoch 260/300\n",
"3/3 - 0s - loss: 2.7944e-07 - 24ms/epoch - 8ms/step\n",
"Epoch 261/300\n",
"3/3 - 0s - loss: 3.1606e-07 - 25ms/epoch - 8ms/step\n",
"Epoch 262/300\n",
"3/3 - 0s - loss: 2.5433e-07 - 24ms/epoch - 8ms/step\n",
"Epoch 263/300\n",
"3/3 - 0s - loss: 2.6045e-07 - 29ms/epoch - 10ms/step\n",
"Epoch 264/300\n",
"3/3 - 0s - loss: 2.6014e-07 - 28ms/epoch - 9ms/step\n",
"Epoch 265/300\n",
"3/3 - 0s - loss: 2.4268e-07 - 24ms/epoch - 8ms/step\n",
"Epoch 266/300\n",
"3/3 - 0s - loss: 2.4573e-07 - 25ms/epoch - 8ms/step\n",
"Epoch 267/300\n",
"3/3 - 0s - loss: 2.4149e-07 - 24ms/epoch - 8ms/step\n",
"Epoch 268/300\n",
"3/3 - 0s - loss: 2.4068e-07 - 23ms/epoch - 8ms/step\n",
"Epoch 269/300\n",
"3/3 - 0s - loss: 2.3706e-07 - 28ms/epoch - 9ms/step\n",
"Epoch 270/300\n",
"3/3 - 0s - loss: 2.3918e-07 - 25ms/epoch - 8ms/step\n",
"Epoch 271/300\n",
"3/3 - 0s - loss: 2.4026e-07 - 25ms/epoch - 8ms/step\n",
"Epoch 272/300\n",
"3/3 - 0s - loss: 2.2306e-07 - 25ms/epoch - 8ms/step\n",
"Epoch 273/300\n",
"3/3 - 0s - loss: 2.3760e-07 - 26ms/epoch - 9ms/step\n",
"Epoch 274/300\n",
"3/3 - 0s - loss: 2.4834e-07 - 24ms/epoch - 8ms/step\n",
"Epoch 275/300\n",
"3/3 - 0s - loss: 2.4366e-07 - 23ms/epoch - 8ms/step\n",
"Epoch 276/300\n",
"3/3 - 0s - loss: 2.2627e-07 - 26ms/epoch - 9ms/step\n",
"Epoch 277/300\n",
"3/3 - 0s - loss: 2.5376e-07 - 27ms/epoch - 9ms/step\n",
"Epoch 278/300\n",
"3/3 - 0s - loss: 2.3523e-07 - 30ms/epoch - 10ms/step\n",
"Epoch 279/300\n",
"3/3 - 0s - loss: 2.1522e-07 - 28ms/epoch - 9ms/step\n",
"Epoch 280/300\n",
"3/3 - 0s - loss: 2.2154e-07 - 28ms/epoch - 9ms/step\n",
"Epoch 281/300\n",
"3/3 - 0s - loss: 2.0595e-07 - 25ms/epoch - 8ms/step\n",
"Epoch 282/300\n",
"3/3 - 0s - loss: 2.0932e-07 - 26ms/epoch - 9ms/step\n",
"Epoch 283/300\n",
"3/3 - 0s - loss: 2.1318e-07 - 24ms/epoch - 8ms/step\n",
"Epoch 284/300\n",
"3/3 - 0s - loss: 2.0770e-07 - 27ms/epoch - 9ms/step\n",
"Epoch 285/300\n",
"3/3 - 0s - loss: 2.3224e-07 - 22ms/epoch - 7ms/step\n",
"Epoch 286/300\n",
"3/3 - 0s - loss: 2.0743e-07 - 21ms/epoch - 7ms/step\n",
"Epoch 287/300\n",
"3/3 - 0s - loss: 1.9832e-07 - 21ms/epoch - 7ms/step\n",
"Epoch 288/300\n",
"3/3 - 0s - loss: 2.3544e-07 - 20ms/epoch - 7ms/step\n",
"Epoch 289/300\n",
"3/3 - 0s - loss: 2.0329e-07 - 24ms/epoch - 8ms/step\n",
"Epoch 290/300\n",
"3/3 - 0s - loss: 1.8422e-07 - 26ms/epoch - 9ms/step\n",
"Epoch 291/300\n",
"3/3 - 0s - loss: 2.1591e-07 - 28ms/epoch - 9ms/step\n",
"Epoch 292/300\n",
"3/3 - 0s - loss: 1.8602e-07 - 29ms/epoch - 10ms/step\n",
"Epoch 293/300\n",
"3/3 - 0s - loss: 1.8403e-07 - 27ms/epoch - 9ms/step\n",
"Epoch 294/300\n",
"3/3 - 0s - loss: 1.8101e-07 - 21ms/epoch - 7ms/step\n",
"Epoch 295/300\n",
"3/3 - 0s - loss: 1.7758e-07 - 21ms/epoch - 7ms/step\n",
"Epoch 296/300\n",
"3/3 - 0s - loss: 1.8401e-07 - 23ms/epoch - 8ms/step\n",
"Epoch 297/300\n",
"3/3 - 0s - loss: 1.9147e-07 - 29ms/epoch - 10ms/step\n",
"Epoch 298/300\n",
"3/3 - 0s - loss: 1.7183e-07 - 22ms/epoch - 7ms/step\n",
"Epoch 299/300\n",
"3/3 - 0s - loss: 2.1222e-07 - 24ms/epoch - 8ms/step\n",
"Epoch 300/300\n",
"3/3 - 0s - loss: 1.9317e-07 - 24ms/epoch - 8ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.src.callbacks.History at 0x7cd652db9c00>"
]
},
"metadata": {},
"execution_count": 277
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "n-aNP4n3sqG_",
"outputId": "f10eb13d-3120-468c-8a6c-98d077d04c75",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 443
}
},
"source": [
"# Plotting code, feel free to ignore.\n",
"h = 1.0\n",
"x_min, x_max = X[:, 0].min() - 5, X[:, 0].max() + 5\n",
"y_min, y_max = X[:, 1].min() - 5, X[:, 1].max() + 5\n",
"xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
" np.arange(y_min, y_max, h))\n",
"\n",
"# here \"model\" is your model's prediction (classification) function\n",
"Z = tn_model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
"\n",
"# Put the result into a color plot\n",
"Z = Z.reshape(xx.shape)\n",
"plt.contourf(xx, yy, Z)\n",
"plt.axis('off')\n",
"\n",
"# Plot also the training points\n",
"plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)"
],
"execution_count": 278,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"14/14 [==============================] - 1s 5ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7cd6505ee0e0>"
]
},
"metadata": {},
"execution_count": 278
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"seconds = time.time()\n",
"print(\"Time in seconds since end of run:\", seconds)\n",
"local_time = time.ctime(seconds)\n",
"print(local_time)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "wfZCzuq9KY9b",
"outputId": "ee02c6a4-27f9-4089-8974-83db45583fad"
},
"execution_count": 279,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1709522312.0136921\n",
"Mon Mar 4 03:18:32 2024\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"seconds = time.time()\n",
"print(\"Time in seconds since beginning of run:\", seconds)\n",
"local_time = time.ctime(seconds)\n",
"print(local_time)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "Ft6S13x6KuEQ",
"outputId": "6902b373-ceb3-41d1-e5ed-9058b7c525c2"
},
"execution_count": 280,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1709522312.0274274\n",
"Mon Mar 4 03:18:32 2024\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BMxSJo5gtOmQ"
},
"source": [
"# VS Fully Connected"
]
},
{
"cell_type": "code",
"metadata": {
"id": "NKQx7stYswzU",
"outputId": "16085537-3a6c-4bc1-eb8a-f3aa570a55db",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 11458
}
},
"source": [
"fc_model.compile(optimizer=\"adam\", loss=\"mean_squared_error\")\n",
"fc_model.fit(X, Y, epochs=300, verbose=2)\n",
"# Plotting code, feel free to ignore.\n",
"h = 1.0\n",
"x_min, x_max = X[:, 0].min() - 5, X[:, 0].max() + 5\n",
"y_min, y_max = X[:, 1].min() - 5, X[:, 1].max() + 5\n",
"xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
" np.arange(y_min, y_max, h))\n",
"\n",
"# here \"model\" is your model's prediction (classification) function\n",
"Z = fc_model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
"\n",
"# Put the result into a color plot\n",
"Z = Z.reshape(xx.shape)\n",
"plt.contourf(xx, yy, Z)\n",
"plt.axis('off')\n",
"\n",
"# Plot also the training points\n",
"plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)"
],
"execution_count": 281,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/300\n",
"3/3 - 1s - loss: 0.5656 - 725ms/epoch - 242ms/step\n",
"Epoch 2/300\n",
"3/3 - 0s - loss: 0.1959 - 26ms/epoch - 9ms/step\n",
"Epoch 3/300\n",
"3/3 - 0s - loss: 0.1423 - 30ms/epoch - 10ms/step\n",
"Epoch 4/300\n",
"3/3 - 0s - loss: 0.0917 - 29ms/epoch - 10ms/step\n",
"Epoch 5/300\n",
"3/3 - 0s - loss: 0.0828 - 34ms/epoch - 11ms/step\n",
"Epoch 6/300\n",
"3/3 - 0s - loss: 0.0827 - 34ms/epoch - 11ms/step\n",
"Epoch 7/300\n",
"3/3 - 0s - loss: 0.0680 - 32ms/epoch - 11ms/step\n",
"Epoch 8/300\n",
"3/3 - 0s - loss: 0.0680 - 28ms/epoch - 9ms/step\n",
"Epoch 9/300\n",
"3/3 - 0s - loss: 0.0605 - 29ms/epoch - 10ms/step\n",
"Epoch 10/300\n",
"3/3 - 0s - loss: 0.0632 - 31ms/epoch - 10ms/step\n",
"Epoch 11/300\n",
"3/3 - 0s - loss: 0.0537 - 30ms/epoch - 10ms/step\n",
"Epoch 12/300\n",
"3/3 - 0s - loss: 0.0523 - 32ms/epoch - 11ms/step\n",
"Epoch 13/300\n",
"3/3 - 0s - loss: 0.0522 - 29ms/epoch - 10ms/step\n",
"Epoch 14/300\n",
"3/3 - 0s - loss: 0.0483 - 29ms/epoch - 10ms/step\n",
"Epoch 15/300\n",
"3/3 - 0s - loss: 0.0498 - 28ms/epoch - 9ms/step\n",
"Epoch 16/300\n",
"3/3 - 0s - loss: 0.0444 - 32ms/epoch - 11ms/step\n",
"Epoch 17/300\n",
"3/3 - 0s - loss: 0.0487 - 29ms/epoch - 10ms/step\n",
"Epoch 18/300\n",
"3/3 - 0s - loss: 0.0467 - 32ms/epoch - 11ms/step\n",
"Epoch 19/300\n",
"3/3 - 0s - loss: 0.0419 - 30ms/epoch - 10ms/step\n",
"Epoch 20/300\n",
"3/3 - 0s - loss: 0.0439 - 28ms/epoch - 9ms/step\n",
"Epoch 21/300\n",
"3/3 - 0s - loss: 0.0406 - 34ms/epoch - 11ms/step\n",
"Epoch 22/300\n",
"3/3 - 0s - loss: 0.0414 - 30ms/epoch - 10ms/step\n",
"Epoch 23/300\n",
"3/3 - 0s - loss: 0.0421 - 28ms/epoch - 9ms/step\n",
"Epoch 24/300\n",
"3/3 - 0s - loss: 0.0378 - 31ms/epoch - 10ms/step\n",
"Epoch 25/300\n",
"3/3 - 0s - loss: 0.0382 - 30ms/epoch - 10ms/step\n",
"Epoch 26/300\n",
"3/3 - 0s - loss: 0.0425 - 29ms/epoch - 10ms/step\n",
"Epoch 27/300\n",
"3/3 - 0s - loss: 0.0505 - 29ms/epoch - 10ms/step\n",
"Epoch 28/300\n",
"3/3 - 0s - loss: 0.0423 - 32ms/epoch - 11ms/step\n",
"Epoch 29/300\n",
"3/3 - 0s - loss: 0.0513 - 32ms/epoch - 11ms/step\n",
"Epoch 30/300\n",
"3/3 - 0s - loss: 0.0385 - 28ms/epoch - 9ms/step\n",
"Epoch 31/300\n",
"3/3 - 0s - loss: 0.0392 - 28ms/epoch - 9ms/step\n",
"Epoch 32/300\n",
"3/3 - 0s - loss: 0.0417 - 27ms/epoch - 9ms/step\n",
"Epoch 33/300\n",
"3/3 - 0s - loss: 0.0414 - 32ms/epoch - 11ms/step\n",
"Epoch 34/300\n",
"3/3 - 0s - loss: 0.0374 - 34ms/epoch - 11ms/step\n",
"Epoch 35/300\n",
"3/3 - 0s - loss: 0.0348 - 28ms/epoch - 9ms/step\n",
"Epoch 36/300\n",
"3/3 - 0s - loss: 0.0319 - 35ms/epoch - 12ms/step\n",
"Epoch 37/300\n",
"3/3 - 0s - loss: 0.0429 - 25ms/epoch - 8ms/step\n",
"Epoch 38/300\n",
"3/3 - 0s - loss: 0.0382 - 28ms/epoch - 9ms/step\n",
"Epoch 39/300\n",
"3/3 - 0s - loss: 0.0266 - 24ms/epoch - 8ms/step\n",
"Epoch 40/300\n",
"3/3 - 0s - loss: 0.0399 - 35ms/epoch - 12ms/step\n",
"Epoch 41/300\n",
"3/3 - 0s - loss: 0.0336 - 27ms/epoch - 9ms/step\n",
"Epoch 42/300\n",
"3/3 - 0s - loss: 0.0293 - 33ms/epoch - 11ms/step\n",
"Epoch 43/300\n",
"3/3 - 0s - loss: 0.0304 - 28ms/epoch - 9ms/step\n",
"Epoch 44/300\n",
"3/3 - 0s - loss: 0.0370 - 29ms/epoch - 10ms/step\n",
"Epoch 45/300\n",
"3/3 - 0s - loss: 0.0295 - 33ms/epoch - 11ms/step\n",
"Epoch 46/300\n",
"3/3 - 0s - loss: 0.0278 - 27ms/epoch - 9ms/step\n",
"Epoch 47/300\n",
"3/3 - 0s - loss: 0.0298 - 31ms/epoch - 10ms/step\n",
"Epoch 48/300\n",
"3/3 - 0s - loss: 0.0244 - 32ms/epoch - 11ms/step\n",
"Epoch 49/300\n",
"3/3 - 0s - loss: 0.0270 - 28ms/epoch - 9ms/step\n",
"Epoch 50/300\n",
"3/3 - 0s - loss: 0.0191 - 35ms/epoch - 12ms/step\n",
"Epoch 51/300\n",
"3/3 - 0s - loss: 0.0257 - 30ms/epoch - 10ms/step\n",
"Epoch 52/300\n",
"3/3 - 0s - loss: 0.0229 - 24ms/epoch - 8ms/step\n",
"Epoch 53/300\n",
"3/3 - 0s - loss: 0.0226 - 31ms/epoch - 10ms/step\n",
"Epoch 54/300\n",
"3/3 - 0s - loss: 0.0251 - 36ms/epoch - 12ms/step\n",
"Epoch 55/300\n",
"3/3 - 0s - loss: 0.0231 - 35ms/epoch - 12ms/step\n",
"Epoch 56/300\n",
"3/3 - 0s - loss: 0.0268 - 36ms/epoch - 12ms/step\n",
"Epoch 57/300\n",
"3/3 - 0s - loss: 0.0274 - 34ms/epoch - 11ms/step\n",
"Epoch 58/300\n",
"3/3 - 0s - loss: 0.0182 - 31ms/epoch - 10ms/step\n",
"Epoch 59/300\n",
"3/3 - 0s - loss: 0.0233 - 30ms/epoch - 10ms/step\n",
"Epoch 60/300\n",
"3/3 - 0s - loss: 0.0189 - 36ms/epoch - 12ms/step\n",
"Epoch 61/300\n",
"3/3 - 0s - loss: 0.0133 - 35ms/epoch - 12ms/step\n",
"Epoch 62/300\n",
"3/3 - 0s - loss: 0.0144 - 34ms/epoch - 11ms/step\n",
"Epoch 63/300\n",
"3/3 - 0s - loss: 0.0157 - 27ms/epoch - 9ms/step\n",
"Epoch 64/300\n",
"3/3 - 0s - loss: 0.0119 - 29ms/epoch - 10ms/step\n",
"Epoch 65/300\n",
"3/3 - 0s - loss: 0.0188 - 31ms/epoch - 10ms/step\n",
"Epoch 66/300\n",
"3/3 - 0s - loss: 0.0130 - 30ms/epoch - 10ms/step\n",
"Epoch 67/300\n",
"3/3 - 0s - loss: 0.0116 - 40ms/epoch - 13ms/step\n",
"Epoch 68/300\n",
"3/3 - 0s - loss: 0.0110 - 36ms/epoch - 12ms/step\n",
"Epoch 69/300\n",
"3/3 - 0s - loss: 0.0073 - 34ms/epoch - 11ms/step\n",
"Epoch 70/300\n",
"3/3 - 0s - loss: 0.0097 - 36ms/epoch - 12ms/step\n",
"Epoch 71/300\n",
"3/3 - 0s - loss: 0.0088 - 37ms/epoch - 12ms/step\n",
"Epoch 72/300\n",
"3/3 - 0s - loss: 0.0063 - 36ms/epoch - 12ms/step\n",
"Epoch 73/300\n",
"3/3 - 0s - loss: 0.0058 - 28ms/epoch - 9ms/step\n",
"Epoch 74/300\n",
"3/3 - 0s - loss: 0.0060 - 32ms/epoch - 11ms/step\n",
"Epoch 75/300\n",
"3/3 - 0s - loss: 0.0094 - 31ms/epoch - 10ms/step\n",
"Epoch 76/300\n",
"3/3 - 0s - loss: 0.0106 - 28ms/epoch - 9ms/step\n",
"Epoch 77/300\n",
"3/3 - 0s - loss: 0.0083 - 32ms/epoch - 11ms/step\n",
"Epoch 78/300\n",
"3/3 - 0s - loss: 0.0048 - 37ms/epoch - 12ms/step\n",
"Epoch 79/300\n",
"3/3 - 0s - loss: 0.0048 - 36ms/epoch - 12ms/step\n",
"Epoch 80/300\n",
"3/3 - 0s - loss: 0.0046 - 30ms/epoch - 10ms/step\n",
"Epoch 81/300\n",
"3/3 - 0s - loss: 0.0029 - 37ms/epoch - 12ms/step\n",
"Epoch 82/300\n",
"3/3 - 0s - loss: 0.0026 - 34ms/epoch - 11ms/step\n",
"Epoch 83/300\n",
"3/3 - 0s - loss: 0.0030 - 30ms/epoch - 10ms/step\n",
"Epoch 84/300\n",
"3/3 - 0s - loss: 0.0032 - 34ms/epoch - 11ms/step\n",
"Epoch 85/300\n",
"3/3 - 0s - loss: 0.0040 - 38ms/epoch - 13ms/step\n",
"Epoch 86/300\n",
"3/3 - 0s - loss: 0.0045 - 29ms/epoch - 10ms/step\n",
"Epoch 87/300\n",
"3/3 - 0s - loss: 0.0047 - 34ms/epoch - 11ms/step\n",
"Epoch 88/300\n",
"3/3 - 0s - loss: 0.0033 - 30ms/epoch - 10ms/step\n",
"Epoch 89/300\n",
"3/3 - 0s - loss: 0.0034 - 35ms/epoch - 12ms/step\n",
"Epoch 90/300\n",
"3/3 - 0s - loss: 0.0083 - 35ms/epoch - 12ms/step\n",
"Epoch 91/300\n",
"3/3 - 0s - loss: 0.0109 - 31ms/epoch - 10ms/step\n",
"Epoch 92/300\n",
"3/3 - 0s - loss: 0.0065 - 38ms/epoch - 13ms/step\n",
"Epoch 93/300\n",
"3/3 - 0s - loss: 0.0046 - 32ms/epoch - 11ms/step\n",
"Epoch 94/300\n",
"3/3 - 0s - loss: 0.0068 - 26ms/epoch - 9ms/step\n",
"Epoch 95/300\n",
"3/3 - 0s - loss: 0.0096 - 30ms/epoch - 10ms/step\n",
"Epoch 96/300\n",
"3/3 - 0s - loss: 0.0103 - 33ms/epoch - 11ms/step\n",
"Epoch 97/300\n",
"3/3 - 0s - loss: 0.0119 - 31ms/epoch - 10ms/step\n",
"Epoch 98/300\n",
"3/3 - 0s - loss: 0.0069 - 28ms/epoch - 9ms/step\n",
"Epoch 99/300\n",
"3/3 - 0s - loss: 0.0075 - 33ms/epoch - 11ms/step\n",
"Epoch 100/300\n",
"3/3 - 0s - loss: 0.0057 - 30ms/epoch - 10ms/step\n",
"Epoch 101/300\n",
"3/3 - 0s - loss: 0.0032 - 27ms/epoch - 9ms/step\n",
"Epoch 102/300\n",
"3/3 - 0s - loss: 0.0039 - 34ms/epoch - 11ms/step\n",
"Epoch 103/300\n",
"3/3 - 0s - loss: 0.0029 - 33ms/epoch - 11ms/step\n",
"Epoch 104/300\n",
"3/3 - 0s - loss: 0.0031 - 34ms/epoch - 11ms/step\n",
"Epoch 105/300\n",
"3/3 - 0s - loss: 0.0021 - 27ms/epoch - 9ms/step\n",
"Epoch 106/300\n",
"3/3 - 0s - loss: 0.0015 - 38ms/epoch - 13ms/step\n",
"Epoch 107/300\n",
"3/3 - 0s - loss: 0.0014 - 29ms/epoch - 10ms/step\n",
"Epoch 108/300\n",
"3/3 - 0s - loss: 0.0013 - 26ms/epoch - 9ms/step\n",
"Epoch 109/300\n",
"3/3 - 0s - loss: 0.0022 - 32ms/epoch - 11ms/step\n",
"Epoch 110/300\n",
"3/3 - 0s - loss: 0.0019 - 32ms/epoch - 11ms/step\n",
"Epoch 111/300\n",
"3/3 - 0s - loss: 0.0020 - 30ms/epoch - 10ms/step\n",
"Epoch 112/300\n",
"3/3 - 0s - loss: 6.9314e-04 - 28ms/epoch - 9ms/step\n",
"Epoch 113/300\n",
"3/3 - 0s - loss: 9.3566e-04 - 36ms/epoch - 12ms/step\n",
"Epoch 114/300\n",
"3/3 - 0s - loss: 0.0015 - 29ms/epoch - 10ms/step\n",
"Epoch 115/300\n",
"3/3 - 0s - loss: 0.0017 - 33ms/epoch - 11ms/step\n",
"Epoch 116/300\n",
"3/3 - 0s - loss: 0.0020 - 32ms/epoch - 11ms/step\n",
"Epoch 117/300\n",
"3/3 - 0s - loss: 0.0018 - 33ms/epoch - 11ms/step\n",
"Epoch 118/300\n",
"3/3 - 0s - loss: 0.0010 - 29ms/epoch - 10ms/step\n",
"Epoch 119/300\n",
"3/3 - 0s - loss: 8.8028e-04 - 29ms/epoch - 10ms/step\n",
"Epoch 120/300\n",
"3/3 - 0s - loss: 7.2462e-04 - 28ms/epoch - 9ms/step\n",
"Epoch 121/300\n",
"3/3 - 0s - loss: 8.0890e-04 - 32ms/epoch - 11ms/step\n",
"Epoch 122/300\n",
"3/3 - 0s - loss: 9.8991e-04 - 35ms/epoch - 12ms/step\n",
"Epoch 123/300\n",
"3/3 - 0s - loss: 7.1008e-04 - 32ms/epoch - 11ms/step\n",
"Epoch 124/300\n",
"3/3 - 0s - loss: 4.9597e-04 - 30ms/epoch - 10ms/step\n",
"Epoch 125/300\n",
"3/3 - 0s - loss: 4.7966e-04 - 37ms/epoch - 12ms/step\n",
"Epoch 126/300\n",
"3/3 - 0s - loss: 3.0518e-04 - 36ms/epoch - 12ms/step\n",
"Epoch 127/300\n",
"3/3 - 0s - loss: 2.7030e-04 - 34ms/epoch - 11ms/step\n",
"Epoch 128/300\n",
"3/3 - 0s - loss: 3.4302e-04 - 37ms/epoch - 12ms/step\n",
"Epoch 129/300\n",
"3/3 - 0s - loss: 3.2476e-04 - 34ms/epoch - 11ms/step\n",
"Epoch 130/300\n",
"3/3 - 0s - loss: 1.6305e-04 - 35ms/epoch - 12ms/step\n",
"Epoch 131/300\n",
"3/3 - 0s - loss: 1.8642e-04 - 37ms/epoch - 12ms/step\n",
"Epoch 132/300\n",
"3/3 - 0s - loss: 8.2074e-05 - 39ms/epoch - 13ms/step\n",
"Epoch 133/300\n",
"3/3 - 0s - loss: 6.5955e-05 - 35ms/epoch - 12ms/step\n",
"Epoch 134/300\n",
"3/3 - 0s - loss: 6.8692e-05 - 37ms/epoch - 12ms/step\n",
"Epoch 135/300\n",
"3/3 - 0s - loss: 1.1016e-04 - 37ms/epoch - 12ms/step\n",
"Epoch 136/300\n",
"3/3 - 0s - loss: 1.4056e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 137/300\n",
"3/3 - 0s - loss: 1.0764e-04 - 36ms/epoch - 12ms/step\n",
"Epoch 138/300\n",
"3/3 - 0s - loss: 9.8001e-05 - 28ms/epoch - 9ms/step\n",
"Epoch 139/300\n",
"3/3 - 0s - loss: 2.1907e-04 - 31ms/epoch - 10ms/step\n",
"Epoch 140/300\n",
"3/3 - 0s - loss: 2.4921e-04 - 32ms/epoch - 11ms/step\n",
"Epoch 141/300\n",
"3/3 - 0s - loss: 4.0704e-04 - 33ms/epoch - 11ms/step\n",
"Epoch 142/300\n",
"3/3 - 0s - loss: 5.5095e-04 - 35ms/epoch - 12ms/step\n",
"Epoch 143/300\n",
"3/3 - 0s - loss: 8.7078e-04 - 30ms/epoch - 10ms/step\n",
"Epoch 144/300\n",
"3/3 - 0s - loss: 9.0852e-04 - 28ms/epoch - 9ms/step\n",
"Epoch 145/300\n",
"3/3 - 0s - loss: 0.0014 - 28ms/epoch - 9ms/step\n",
"Epoch 146/300\n",
"3/3 - 0s - loss: 0.0021 - 31ms/epoch - 10ms/step\n",
"Epoch 147/300\n",
"3/3 - 0s - loss: 0.0012 - 35ms/epoch - 12ms/step\n",
"Epoch 148/300\n",
"3/3 - 0s - loss: 0.0011 - 33ms/epoch - 11ms/step\n",
"Epoch 149/300\n",
"3/3 - 0s - loss: 0.0014 - 30ms/epoch - 10ms/step\n",
"Epoch 150/300\n",
"3/3 - 0s - loss: 0.0013 - 31ms/epoch - 10ms/step\n",
"Epoch 151/300\n",
"3/3 - 0s - loss: 0.0012 - 29ms/epoch - 10ms/step\n",
"Epoch 152/300\n",
"3/3 - 0s - loss: 0.0011 - 29ms/epoch - 10ms/step\n",
"Epoch 153/300\n",
"3/3 - 0s - loss: 8.8283e-04 - 33ms/epoch - 11ms/step\n",
"Epoch 154/300\n",
"3/3 - 0s - loss: 5.0875e-04 - 37ms/epoch - 12ms/step\n",
"Epoch 155/300\n",
"3/3 - 0s - loss: 4.6452e-04 - 37ms/epoch - 12ms/step\n",
"Epoch 156/300\n",
"3/3 - 0s - loss: 4.4445e-04 - 33ms/epoch - 11ms/step\n",
"Epoch 157/300\n",
"3/3 - 0s - loss: 4.5507e-04 - 32ms/epoch - 11ms/step\n",
"Epoch 158/300\n",
"3/3 - 0s - loss: 5.0221e-04 - 28ms/epoch - 9ms/step\n",
"Epoch 159/300\n",
"3/3 - 0s - loss: 7.1127e-04 - 33ms/epoch - 11ms/step\n",
"Epoch 160/300\n",
"3/3 - 0s - loss: 5.3585e-04 - 37ms/epoch - 12ms/step\n",
"Epoch 161/300\n",
"3/3 - 0s - loss: 3.0625e-04 - 30ms/epoch - 10ms/step\n",
"Epoch 162/300\n",
"3/3 - 0s - loss: 3.6777e-04 - 30ms/epoch - 10ms/step\n",
"Epoch 163/300\n",
"3/3 - 0s - loss: 2.5530e-04 - 35ms/epoch - 12ms/step\n",
"Epoch 164/300\n",
"3/3 - 0s - loss: 1.7076e-04 - 33ms/epoch - 11ms/step\n",
"Epoch 165/300\n",
"3/3 - 0s - loss: 2.1320e-04 - 30ms/epoch - 10ms/step\n",
"Epoch 166/300\n",
"3/3 - 0s - loss: 2.7991e-04 - 38ms/epoch - 13ms/step\n",
"Epoch 167/300\n",
"3/3 - 0s - loss: 3.3069e-04 - 39ms/epoch - 13ms/step\n",
"Epoch 168/300\n",
"3/3 - 0s - loss: 2.9444e-04 - 33ms/epoch - 11ms/step\n",
"Epoch 169/300\n",
"3/3 - 0s - loss: 4.0663e-04 - 36ms/epoch - 12ms/step\n",
"Epoch 170/300\n",
"3/3 - 0s - loss: 3.3016e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 171/300\n",
"3/3 - 0s - loss: 2.0864e-04 - 36ms/epoch - 12ms/step\n",
"Epoch 172/300\n",
"3/3 - 0s - loss: 3.1231e-04 - 37ms/epoch - 12ms/step\n",
"Epoch 173/300\n",
"3/3 - 0s - loss: 2.9278e-04 - 28ms/epoch - 9ms/step\n",
"Epoch 174/300\n",
"3/3 - 0s - loss: 3.0427e-04 - 31ms/epoch - 10ms/step\n",
"Epoch 175/300\n",
"3/3 - 0s - loss: 4.5326e-04 - 31ms/epoch - 10ms/step\n",
"Epoch 176/300\n",
"3/3 - 0s - loss: 3.3629e-04 - 34ms/epoch - 11ms/step\n",
"Epoch 177/300\n",
"3/3 - 0s - loss: 2.4525e-04 - 29ms/epoch - 10ms/step\n",
"Epoch 178/300\n",
"3/3 - 0s - loss: 2.5538e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 179/300\n",
"3/3 - 0s - loss: 3.3784e-04 - 35ms/epoch - 12ms/step\n",
"Epoch 180/300\n",
"3/3 - 0s - loss: 1.9497e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 181/300\n",
"3/3 - 0s - loss: 1.9737e-04 - 32ms/epoch - 11ms/step\n",
"Epoch 182/300\n",
"3/3 - 0s - loss: 2.2758e-04 - 35ms/epoch - 12ms/step\n",
"Epoch 183/300\n",
"3/3 - 0s - loss: 2.9136e-04 - 33ms/epoch - 11ms/step\n",
"Epoch 184/300\n",
"3/3 - 0s - loss: 1.1060e-04 - 30ms/epoch - 10ms/step\n",
"Epoch 185/300\n",
"3/3 - 0s - loss: 5.0481e-05 - 27ms/epoch - 9ms/step\n",
"Epoch 186/300\n",
"3/3 - 0s - loss: 4.0821e-05 - 33ms/epoch - 11ms/step\n",
"Epoch 187/300\n",
"3/3 - 0s - loss: 5.7687e-05 - 29ms/epoch - 10ms/step\n",
"Epoch 188/300\n",
"3/3 - 0s - loss: 5.1819e-05 - 31ms/epoch - 10ms/step\n",
"Epoch 189/300\n",
"3/3 - 0s - loss: 4.4923e-05 - 28ms/epoch - 9ms/step\n",
"Epoch 190/300\n",
"3/3 - 0s - loss: 5.0681e-05 - 33ms/epoch - 11ms/step\n",
"Epoch 191/300\n",
"3/3 - 0s - loss: 3.9062e-05 - 28ms/epoch - 9ms/step\n",
"Epoch 192/300\n",
"3/3 - 0s - loss: 3.1511e-05 - 29ms/epoch - 10ms/step\n",
"Epoch 193/300\n",
"3/3 - 0s - loss: 3.9896e-05 - 26ms/epoch - 9ms/step\n",
"Epoch 194/300\n",
"3/3 - 0s - loss: 3.6009e-05 - 26ms/epoch - 9ms/step\n",
"Epoch 195/300\n",
"3/3 - 0s - loss: 3.8435e-05 - 30ms/epoch - 10ms/step\n",
"Epoch 196/300\n",
"3/3 - 0s - loss: 6.6916e-05 - 28ms/epoch - 9ms/step\n",
"Epoch 197/300\n",
"3/3 - 0s - loss: 1.2784e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 198/300\n",
"3/3 - 0s - loss: 8.5005e-05 - 27ms/epoch - 9ms/step\n",
"Epoch 199/300\n",
"3/3 - 0s - loss: 6.0588e-05 - 30ms/epoch - 10ms/step\n",
"Epoch 200/300\n",
"3/3 - 0s - loss: 6.8180e-05 - 33ms/epoch - 11ms/step\n",
"Epoch 201/300\n",
"3/3 - 0s - loss: 4.7230e-05 - 29ms/epoch - 10ms/step\n",
"Epoch 202/300\n",
"3/3 - 0s - loss: 3.8133e-05 - 28ms/epoch - 9ms/step\n",
"Epoch 203/300\n",
"3/3 - 0s - loss: 7.4671e-05 - 30ms/epoch - 10ms/step\n",
"Epoch 204/300\n",
"3/3 - 0s - loss: 8.1094e-05 - 30ms/epoch - 10ms/step\n",
"Epoch 205/300\n",
"3/3 - 0s - loss: 7.8872e-05 - 30ms/epoch - 10ms/step\n",
"Epoch 206/300\n",
"3/3 - 0s - loss: 8.7357e-05 - 29ms/epoch - 10ms/step\n",
"Epoch 207/300\n",
"3/3 - 0s - loss: 4.8380e-05 - 37ms/epoch - 12ms/step\n",
"Epoch 208/300\n",
"3/3 - 0s - loss: 7.0697e-05 - 29ms/epoch - 10ms/step\n",
"Epoch 209/300\n",
"3/3 - 0s - loss: 5.2098e-05 - 31ms/epoch - 10ms/step\n",
"Epoch 210/300\n",
"3/3 - 0s - loss: 5.4029e-05 - 34ms/epoch - 11ms/step\n",
"Epoch 211/300\n",
"3/3 - 0s - loss: 2.8489e-05 - 29ms/epoch - 10ms/step\n",
"Epoch 212/300\n",
"3/3 - 0s - loss: 3.3961e-05 - 31ms/epoch - 10ms/step\n",
"Epoch 213/300\n",
"3/3 - 0s - loss: 4.1667e-05 - 28ms/epoch - 9ms/step\n",
"Epoch 214/300\n",
"3/3 - 0s - loss: 3.7597e-05 - 32ms/epoch - 11ms/step\n",
"Epoch 215/300\n",
"3/3 - 0s - loss: 2.7004e-05 - 32ms/epoch - 11ms/step\n",
"Epoch 216/300\n",
"3/3 - 0s - loss: 2.9110e-05 - 34ms/epoch - 11ms/step\n",
"Epoch 217/300\n",
"3/3 - 0s - loss: 3.6687e-05 - 29ms/epoch - 10ms/step\n",
"Epoch 218/300\n",
"3/3 - 0s - loss: 7.2615e-05 - 27ms/epoch - 9ms/step\n",
"Epoch 219/300\n",
"3/3 - 0s - loss: 1.0681e-04 - 35ms/epoch - 12ms/step\n",
"Epoch 220/300\n",
"3/3 - 0s - loss: 1.9565e-04 - 31ms/epoch - 10ms/step\n",
"Epoch 221/300\n",
"3/3 - 0s - loss: 1.9595e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 222/300\n",
"3/3 - 0s - loss: 1.7055e-04 - 32ms/epoch - 11ms/step\n",
"Epoch 223/300\n",
"3/3 - 0s - loss: 1.4371e-04 - 28ms/epoch - 9ms/step\n",
"Epoch 224/300\n",
"3/3 - 0s - loss: 1.0054e-04 - 29ms/epoch - 10ms/step\n",
"Epoch 225/300\n",
"3/3 - 0s - loss: 7.8233e-05 - 36ms/epoch - 12ms/step\n",
"Epoch 226/300\n",
"3/3 - 0s - loss: 2.0859e-04 - 28ms/epoch - 9ms/step\n",
"Epoch 227/300\n",
"3/3 - 0s - loss: 2.3248e-04 - 30ms/epoch - 10ms/step\n",
"Epoch 228/300\n",
"3/3 - 0s - loss: 3.5742e-04 - 31ms/epoch - 10ms/step\n",
"Epoch 229/300\n",
"3/3 - 0s - loss: 3.2267e-04 - 33ms/epoch - 11ms/step\n",
"Epoch 230/300\n",
"3/3 - 0s - loss: 2.6533e-04 - 30ms/epoch - 10ms/step\n",
"Epoch 231/300\n",
"3/3 - 0s - loss: 3.3579e-04 - 34ms/epoch - 11ms/step\n",
"Epoch 232/300\n",
"3/3 - 0s - loss: 2.2141e-04 - 30ms/epoch - 10ms/step\n",
"Epoch 233/300\n",
"3/3 - 0s - loss: 1.3816e-04 - 28ms/epoch - 9ms/step\n",
"Epoch 234/300\n",
"3/3 - 0s - loss: 1.2997e-04 - 31ms/epoch - 10ms/step\n",
"Epoch 235/300\n",
"3/3 - 0s - loss: 1.2696e-04 - 29ms/epoch - 10ms/step\n",
"Epoch 236/300\n",
"3/3 - 0s - loss: 7.3166e-05 - 34ms/epoch - 11ms/step\n",
"Epoch 237/300\n",
"3/3 - 0s - loss: 4.9531e-05 - 32ms/epoch - 11ms/step\n",
"Epoch 238/300\n",
"3/3 - 0s - loss: 5.9576e-05 - 33ms/epoch - 11ms/step\n",
"Epoch 239/300\n",
"3/3 - 0s - loss: 6.9014e-05 - 29ms/epoch - 10ms/step\n",
"Epoch 240/300\n",
"3/3 - 0s - loss: 1.2079e-04 - 28ms/epoch - 9ms/step\n",
"Epoch 241/300\n",
"3/3 - 0s - loss: 1.0165e-04 - 34ms/epoch - 11ms/step\n",
"Epoch 242/300\n",
"3/3 - 0s - loss: 1.1189e-04 - 39ms/epoch - 13ms/step\n",
"Epoch 243/300\n",
"3/3 - 0s - loss: 1.2715e-04 - 28ms/epoch - 9ms/step\n",
"Epoch 244/300\n",
"3/3 - 0s - loss: 2.3746e-04 - 35ms/epoch - 12ms/step\n",
"Epoch 245/300\n",
"3/3 - 0s - loss: 7.2393e-04 - 31ms/epoch - 10ms/step\n",
"Epoch 246/300\n",
"3/3 - 0s - loss: 8.1162e-04 - 34ms/epoch - 11ms/step\n",
"Epoch 247/300\n",
"3/3 - 0s - loss: 6.6941e-04 - 35ms/epoch - 12ms/step\n",
"Epoch 248/300\n",
"3/3 - 0s - loss: 6.1267e-04 - 31ms/epoch - 10ms/step\n",
"Epoch 249/300\n",
"3/3 - 0s - loss: 5.4795e-04 - 31ms/epoch - 10ms/step\n",
"Epoch 250/300\n",
"3/3 - 0s - loss: 8.4581e-04 - 35ms/epoch - 12ms/step\n",
"Epoch 251/300\n",
"3/3 - 0s - loss: 4.3189e-04 - 36ms/epoch - 12ms/step\n",
"Epoch 252/300\n",
"3/3 - 0s - loss: 6.3720e-04 - 29ms/epoch - 10ms/step\n",
"Epoch 253/300\n",
"3/3 - 0s - loss: 8.4664e-04 - 33ms/epoch - 11ms/step\n",
"Epoch 254/300\n",
"3/3 - 0s - loss: 0.0025 - 33ms/epoch - 11ms/step\n",
"Epoch 255/300\n",
"3/3 - 0s - loss: 0.0032 - 30ms/epoch - 10ms/step\n",
"Epoch 256/300\n",
"3/3 - 0s - loss: 0.0040 - 31ms/epoch - 10ms/step\n",
"Epoch 257/300\n",
"3/3 - 0s - loss: 0.0021 - 29ms/epoch - 10ms/step\n",
"Epoch 258/300\n",
"3/3 - 0s - loss: 0.0023 - 31ms/epoch - 10ms/step\n",
"Epoch 259/300\n",
"3/3 - 0s - loss: 0.0034 - 25ms/epoch - 8ms/step\n",
"Epoch 260/300\n",
"3/3 - 0s - loss: 0.0045 - 28ms/epoch - 9ms/step\n",
"Epoch 261/300\n",
"3/3 - 0s - loss: 0.0064 - 26ms/epoch - 9ms/step\n",
"Epoch 262/300\n",
"3/3 - 0s - loss: 0.0050 - 32ms/epoch - 11ms/step\n",
"Epoch 263/300\n",
"3/3 - 0s - loss: 0.0068 - 33ms/epoch - 11ms/step\n",
"Epoch 264/300\n",
"3/3 - 0s - loss: 0.0042 - 32ms/epoch - 11ms/step\n",
"Epoch 265/300\n",
"3/3 - 0s - loss: 0.0047 - 36ms/epoch - 12ms/step\n",
"Epoch 266/300\n",
"3/3 - 0s - loss: 0.0045 - 31ms/epoch - 10ms/step\n",
"Epoch 267/300\n",
"3/3 - 0s - loss: 0.0046 - 32ms/epoch - 11ms/step\n",
"Epoch 268/300\n",
"3/3 - 0s - loss: 0.0032 - 34ms/epoch - 11ms/step\n",
"Epoch 269/300\n",
"3/3 - 0s - loss: 0.0031 - 30ms/epoch - 10ms/step\n",
"Epoch 270/300\n",
"3/3 - 0s - loss: 0.0041 - 33ms/epoch - 11ms/step\n",
"Epoch 271/300\n",
"3/3 - 0s - loss: 0.0034 - 31ms/epoch - 10ms/step\n",
"Epoch 272/300\n",
"3/3 - 0s - loss: 0.0043 - 34ms/epoch - 11ms/step\n",
"Epoch 273/300\n",
"3/3 - 0s - loss: 0.0034 - 29ms/epoch - 10ms/step\n",
"Epoch 274/300\n",
"3/3 - 0s - loss: 0.0036 - 29ms/epoch - 10ms/step\n",
"Epoch 275/300\n",
"3/3 - 0s - loss: 0.0030 - 28ms/epoch - 9ms/step\n",
"Epoch 276/300\n",
"3/3 - 0s - loss: 0.0027 - 31ms/epoch - 10ms/step\n",
"Epoch 277/300\n",
"3/3 - 0s - loss: 0.0033 - 27ms/epoch - 9ms/step\n",
"Epoch 278/300\n",
"3/3 - 0s - loss: 0.0024 - 31ms/epoch - 10ms/step\n",
"Epoch 279/300\n",
"3/3 - 0s - loss: 0.0017 - 28ms/epoch - 9ms/step\n",
"Epoch 280/300\n",
"3/3 - 0s - loss: 0.0017 - 26ms/epoch - 9ms/step\n",
"Epoch 281/300\n",
"3/3 - 0s - loss: 0.0015 - 24ms/epoch - 8ms/step\n",
"Epoch 282/300\n",
"3/3 - 0s - loss: 0.0015 - 31ms/epoch - 10ms/step\n",
"Epoch 283/300\n",
"3/3 - 0s - loss: 0.0019 - 30ms/epoch - 10ms/step\n",
"Epoch 284/300\n",
"3/3 - 0s - loss: 0.0042 - 31ms/epoch - 10ms/step\n",
"Epoch 285/300\n",
"3/3 - 0s - loss: 0.0026 - 27ms/epoch - 9ms/step\n",
"Epoch 286/300\n",
"3/3 - 0s - loss: 0.0035 - 29ms/epoch - 10ms/step\n",
"Epoch 287/300\n",
"3/3 - 0s - loss: 0.0033 - 31ms/epoch - 10ms/step\n",
"Epoch 288/300\n",
"3/3 - 0s - loss: 0.0059 - 31ms/epoch - 10ms/step\n",
"Epoch 289/300\n",
"3/3 - 0s - loss: 0.0073 - 32ms/epoch - 11ms/step\n",
"Epoch 290/300\n",
"3/3 - 0s - loss: 0.0060 - 30ms/epoch - 10ms/step\n",
"Epoch 291/300\n",
"3/3 - 0s - loss: 0.0032 - 30ms/epoch - 10ms/step\n",
"Epoch 292/300\n",
"3/3 - 0s - loss: 0.0022 - 27ms/epoch - 9ms/step\n",
"Epoch 293/300\n",
"3/3 - 0s - loss: 0.0021 - 30ms/epoch - 10ms/step\n",
"Epoch 294/300\n",
"3/3 - 0s - loss: 0.0025 - 27ms/epoch - 9ms/step\n",
"Epoch 295/300\n",
"3/3 - 0s - loss: 0.0011 - 29ms/epoch - 10ms/step\n",
"Epoch 296/300\n",
"3/3 - 0s - loss: 6.3007e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 297/300\n",
"3/3 - 0s - loss: 4.8764e-04 - 28ms/epoch - 9ms/step\n",
"Epoch 298/300\n",
"3/3 - 0s - loss: 5.1926e-04 - 30ms/epoch - 10ms/step\n",
"Epoch 299/300\n",
"3/3 - 0s - loss: 7.6698e-04 - 30ms/epoch - 10ms/step\n",
"Epoch 300/300\n",
"3/3 - 0s - loss: 7.6851e-04 - 31ms/epoch - 10ms/step\n",
"14/14 [==============================] - 0s 4ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7cd65162a440>"
]
},
"metadata": {},
"execution_count": 281
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"seconds = time.time()\n",
"print(\"Time in seconds since end of run:\", seconds)\n",
"local_time = time.ctime(seconds)\n",
"print(local_time)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "YyOarWssKyjN",
"outputId": "c0f42ef5-035e-4a29-f0d5-4ebd91142d6c"
},
"execution_count": 282,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1709522323.4574454\n",
"Mon Mar 4 03:18:43 2024\n"
]
}
]
}
]
}