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Tensor Network vs FC Controllability /
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500epochs_3xTNLayers.ipynb
2540 lines (2540 with data), 170.0 kB
{
"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": 122,
"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": 123,
"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": "899edf2f-a8b0-48fe-be8d-09d2267976b0",
"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": 124,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential_20\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" dense_50 (Dense) (None, 1024) 3072 \n",
" \n",
" dense_51 (Dense) (None, 1024) 1049600 \n",
" \n",
" dense_52 (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": "9eebb82f-ff0a-4c43-fa92-7ca3f9fe5878",
"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": 125,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential_21\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" dense_53 (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_54 (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": 126,
"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": "821ddb0a-5c15-4d70-a2af-7b56d70d0046"
},
"execution_count": 127,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1710205343.713163\n",
"Tue Mar 12 01:02:23 2024\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "crc0q1vbIyTj",
"outputId": "aa43fc28-bf8a-4c53-e25d-f2635b494fcf",
"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=500, verbose=2)"
],
"execution_count": 128,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/500\n",
"3/3 - 2s - loss: 1.0018 - 2s/epoch - 697ms/step\n",
"Epoch 2/500\n",
"3/3 - 0s - loss: 1.0018 - 20ms/epoch - 7ms/step\n",
"Epoch 3/500\n",
"3/3 - 0s - loss: 1.0007 - 18ms/epoch - 6ms/step\n",
"Epoch 4/500\n",
"3/3 - 0s - loss: 1.0001 - 18ms/epoch - 6ms/step\n",
"Epoch 5/500\n",
"3/3 - 0s - loss: 1.0006 - 18ms/epoch - 6ms/step\n",
"Epoch 6/500\n",
"3/3 - 0s - loss: 0.9997 - 20ms/epoch - 7ms/step\n",
"Epoch 7/500\n",
"3/3 - 0s - loss: 0.9991 - 19ms/epoch - 6ms/step\n",
"Epoch 8/500\n",
"3/3 - 0s - loss: 0.9978 - 21ms/epoch - 7ms/step\n",
"Epoch 9/500\n",
"3/3 - 0s - loss: 0.9946 - 17ms/epoch - 6ms/step\n",
"Epoch 10/500\n",
"3/3 - 0s - loss: 0.9873 - 18ms/epoch - 6ms/step\n",
"Epoch 11/500\n",
"3/3 - 0s - loss: 0.9702 - 19ms/epoch - 6ms/step\n",
"Epoch 12/500\n",
"3/3 - 0s - loss: 0.9332 - 19ms/epoch - 6ms/step\n",
"Epoch 13/500\n",
"3/3 - 0s - loss: 0.8618 - 18ms/epoch - 6ms/step\n",
"Epoch 14/500\n",
"3/3 - 0s - loss: 0.7187 - 18ms/epoch - 6ms/step\n",
"Epoch 15/500\n",
"3/3 - 0s - loss: 0.4604 - 20ms/epoch - 7ms/step\n",
"Epoch 16/500\n",
"3/3 - 0s - loss: 0.1368 - 18ms/epoch - 6ms/step\n",
"Epoch 17/500\n",
"3/3 - 0s - loss: 0.1065 - 19ms/epoch - 6ms/step\n",
"Epoch 18/500\n",
"3/3 - 0s - loss: 0.0901 - 19ms/epoch - 6ms/step\n",
"Epoch 19/500\n",
"3/3 - 0s - loss: 0.0193 - 19ms/epoch - 6ms/step\n",
"Epoch 20/500\n",
"3/3 - 0s - loss: 0.0450 - 18ms/epoch - 6ms/step\n",
"Epoch 21/500\n",
"3/3 - 0s - loss: 0.0507 - 19ms/epoch - 6ms/step\n",
"Epoch 22/500\n",
"3/3 - 0s - loss: 0.0290 - 18ms/epoch - 6ms/step\n",
"Epoch 23/500\n",
"3/3 - 0s - loss: 0.0136 - 19ms/epoch - 6ms/step\n",
"Epoch 24/500\n",
"3/3 - 0s - loss: 0.0176 - 18ms/epoch - 6ms/step\n",
"Epoch 25/500\n",
"3/3 - 0s - loss: 0.0193 - 18ms/epoch - 6ms/step\n",
"Epoch 26/500\n",
"3/3 - 0s - loss: 0.0116 - 18ms/epoch - 6ms/step\n",
"Epoch 27/500\n",
"3/3 - 0s - loss: 0.0091 - 17ms/epoch - 6ms/step\n",
"Epoch 28/500\n",
"3/3 - 0s - loss: 0.0110 - 20ms/epoch - 7ms/step\n",
"Epoch 29/500\n",
"3/3 - 0s - loss: 0.0104 - 19ms/epoch - 6ms/step\n",
"Epoch 30/500\n",
"3/3 - 0s - loss: 0.0078 - 19ms/epoch - 6ms/step\n",
"Epoch 31/500\n",
"3/3 - 0s - loss: 0.0073 - 18ms/epoch - 6ms/step\n",
"Epoch 32/500\n",
"3/3 - 0s - loss: 0.0076 - 19ms/epoch - 6ms/step\n",
"Epoch 33/500\n",
"3/3 - 0s - loss: 0.0071 - 18ms/epoch - 6ms/step\n",
"Epoch 34/500\n",
"3/3 - 0s - loss: 0.0062 - 17ms/epoch - 6ms/step\n",
"Epoch 35/500\n",
"3/3 - 0s - loss: 0.0061 - 18ms/epoch - 6ms/step\n",
"Epoch 36/500\n",
"3/3 - 0s - loss: 0.0061 - 17ms/epoch - 6ms/step\n",
"Epoch 37/500\n",
"3/3 - 0s - loss: 0.0057 - 19ms/epoch - 6ms/step\n",
"Epoch 38/500\n",
"3/3 - 0s - loss: 0.0053 - 18ms/epoch - 6ms/step\n",
"Epoch 39/500\n",
"3/3 - 0s - loss: 0.0052 - 18ms/epoch - 6ms/step\n",
"Epoch 40/500\n",
"3/3 - 0s - loss: 0.0051 - 18ms/epoch - 6ms/step\n",
"Epoch 41/500\n",
"3/3 - 0s - loss: 0.0048 - 16ms/epoch - 5ms/step\n",
"Epoch 42/500\n",
"3/3 - 0s - loss: 0.0046 - 19ms/epoch - 6ms/step\n",
"Epoch 43/500\n",
"3/3 - 0s - loss: 0.0046 - 18ms/epoch - 6ms/step\n",
"Epoch 44/500\n",
"3/3 - 0s - loss: 0.0044 - 17ms/epoch - 6ms/step\n",
"Epoch 45/500\n",
"3/3 - 0s - loss: 0.0042 - 18ms/epoch - 6ms/step\n",
"Epoch 46/500\n",
"3/3 - 0s - loss: 0.0040 - 18ms/epoch - 6ms/step\n",
"Epoch 47/500\n",
"3/3 - 0s - loss: 0.0040 - 17ms/epoch - 6ms/step\n",
"Epoch 48/500\n",
"3/3 - 0s - loss: 0.0039 - 20ms/epoch - 7ms/step\n",
"Epoch 49/500\n",
"3/3 - 0s - loss: 0.0037 - 18ms/epoch - 6ms/step\n",
"Epoch 50/500\n",
"3/3 - 0s - loss: 0.0036 - 17ms/epoch - 6ms/step\n",
"Epoch 51/500\n",
"3/3 - 0s - loss: 0.0035 - 16ms/epoch - 5ms/step\n",
"Epoch 52/500\n",
"3/3 - 0s - loss: 0.0034 - 17ms/epoch - 6ms/step\n",
"Epoch 53/500\n",
"3/3 - 0s - loss: 0.0033 - 17ms/epoch - 6ms/step\n",
"Epoch 54/500\n",
"3/3 - 0s - loss: 0.0032 - 17ms/epoch - 6ms/step\n",
"Epoch 55/500\n",
"3/3 - 0s - loss: 0.0031 - 18ms/epoch - 6ms/step\n",
"Epoch 56/500\n",
"3/3 - 0s - loss: 0.0031 - 18ms/epoch - 6ms/step\n",
"Epoch 57/500\n",
"3/3 - 0s - loss: 0.0030 - 18ms/epoch - 6ms/step\n",
"Epoch 58/500\n",
"3/3 - 0s - loss: 0.0029 - 17ms/epoch - 6ms/step\n",
"Epoch 59/500\n",
"3/3 - 0s - loss: 0.0028 - 18ms/epoch - 6ms/step\n",
"Epoch 60/500\n",
"3/3 - 0s - loss: 0.0027 - 17ms/epoch - 6ms/step\n",
"Epoch 61/500\n",
"3/3 - 0s - loss: 0.0026 - 16ms/epoch - 5ms/step\n",
"Epoch 62/500\n",
"3/3 - 0s - loss: 0.0025 - 18ms/epoch - 6ms/step\n",
"Epoch 63/500\n",
"3/3 - 0s - loss: 0.0025 - 16ms/epoch - 5ms/step\n",
"Epoch 64/500\n",
"3/3 - 0s - loss: 0.0024 - 17ms/epoch - 6ms/step\n",
"Epoch 65/500\n",
"3/3 - 0s - loss: 0.0024 - 18ms/epoch - 6ms/step\n",
"Epoch 66/500\n",
"3/3 - 0s - loss: 0.0023 - 19ms/epoch - 6ms/step\n",
"Epoch 67/500\n",
"3/3 - 0s - loss: 0.0022 - 17ms/epoch - 6ms/step\n",
"Epoch 68/500\n",
"3/3 - 0s - loss: 0.0022 - 17ms/epoch - 6ms/step\n",
"Epoch 69/500\n",
"3/3 - 0s - loss: 0.0021 - 18ms/epoch - 6ms/step\n",
"Epoch 70/500\n",
"3/3 - 0s - loss: 0.0020 - 18ms/epoch - 6ms/step\n",
"Epoch 71/500\n",
"3/3 - 0s - loss: 0.0020 - 19ms/epoch - 6ms/step\n",
"Epoch 72/500\n",
"3/3 - 0s - loss: 0.0019 - 18ms/epoch - 6ms/step\n",
"Epoch 73/500\n",
"3/3 - 0s - loss: 0.0019 - 18ms/epoch - 6ms/step\n",
"Epoch 74/500\n",
"3/3 - 0s - loss: 0.0018 - 18ms/epoch - 6ms/step\n",
"Epoch 75/500\n",
"3/3 - 0s - loss: 0.0018 - 17ms/epoch - 6ms/step\n",
"Epoch 76/500\n",
"3/3 - 0s - loss: 0.0017 - 17ms/epoch - 6ms/step\n",
"Epoch 77/500\n",
"3/3 - 0s - loss: 0.0017 - 18ms/epoch - 6ms/step\n",
"Epoch 78/500\n",
"3/3 - 0s - loss: 0.0016 - 17ms/epoch - 6ms/step\n",
"Epoch 79/500\n",
"3/3 - 0s - loss: 0.0016 - 16ms/epoch - 5ms/step\n",
"Epoch 80/500\n",
"3/3 - 0s - loss: 0.0016 - 17ms/epoch - 6ms/step\n",
"Epoch 81/500\n",
"3/3 - 0s - loss: 0.0015 - 17ms/epoch - 6ms/step\n",
"Epoch 82/500\n",
"3/3 - 0s - loss: 0.0015 - 18ms/epoch - 6ms/step\n",
"Epoch 83/500\n",
"3/3 - 0s - loss: 0.0014 - 18ms/epoch - 6ms/step\n",
"Epoch 84/500\n",
"3/3 - 0s - loss: 0.0014 - 17ms/epoch - 6ms/step\n",
"Epoch 85/500\n",
"3/3 - 0s - loss: 0.0013 - 17ms/epoch - 6ms/step\n",
"Epoch 86/500\n",
"3/3 - 0s - loss: 0.0013 - 17ms/epoch - 6ms/step\n",
"Epoch 87/500\n",
"3/3 - 0s - loss: 0.0013 - 17ms/epoch - 6ms/step\n",
"Epoch 88/500\n",
"3/3 - 0s - loss: 0.0012 - 18ms/epoch - 6ms/step\n",
"Epoch 89/500\n",
"3/3 - 0s - loss: 0.0012 - 18ms/epoch - 6ms/step\n",
"Epoch 90/500\n",
"3/3 - 0s - loss: 0.0012 - 18ms/epoch - 6ms/step\n",
"Epoch 91/500\n",
"3/3 - 0s - loss: 0.0011 - 19ms/epoch - 6ms/step\n",
"Epoch 92/500\n",
"3/3 - 0s - loss: 0.0011 - 17ms/epoch - 6ms/step\n",
"Epoch 93/500\n",
"3/3 - 0s - loss: 0.0010 - 17ms/epoch - 6ms/step\n",
"Epoch 94/500\n",
"3/3 - 0s - loss: 0.0010 - 18ms/epoch - 6ms/step\n",
"Epoch 95/500\n",
"3/3 - 0s - loss: 9.9175e-04 - 19ms/epoch - 6ms/step\n",
"Epoch 96/500\n",
"3/3 - 0s - loss: 9.7160e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 97/500\n",
"3/3 - 0s - loss: 9.1934e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 98/500\n",
"3/3 - 0s - loss: 9.1939e-04 - 20ms/epoch - 7ms/step\n",
"Epoch 99/500\n",
"3/3 - 0s - loss: 8.9917e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 100/500\n",
"3/3 - 0s - loss: 8.5670e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 101/500\n",
"3/3 - 0s - loss: 8.2558e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 102/500\n",
"3/3 - 0s - loss: 8.0836e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 103/500\n",
"3/3 - 0s - loss: 7.7820e-04 - 17ms/epoch - 6ms/step\n",
"Epoch 104/500\n",
"3/3 - 0s - loss: 7.4222e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 105/500\n",
"3/3 - 0s - loss: 7.2910e-04 - 19ms/epoch - 6ms/step\n",
"Epoch 106/500\n",
"3/3 - 0s - loss: 7.1327e-04 - 19ms/epoch - 6ms/step\n",
"Epoch 107/500\n",
"3/3 - 0s - loss: 6.7226e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 108/500\n",
"3/3 - 0s - loss: 6.5387e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 109/500\n",
"3/3 - 0s - loss: 6.3478e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 110/500\n",
"3/3 - 0s - loss: 6.0961e-04 - 17ms/epoch - 6ms/step\n",
"Epoch 111/500\n",
"3/3 - 0s - loss: 5.8166e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 112/500\n",
"3/3 - 0s - loss: 5.8480e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 113/500\n",
"3/3 - 0s - loss: 5.4503e-04 - 17ms/epoch - 6ms/step\n",
"Epoch 114/500\n",
"3/3 - 0s - loss: 5.3062e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 115/500\n",
"3/3 - 0s - loss: 5.2946e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 116/500\n",
"3/3 - 0s - loss: 4.7874e-04 - 17ms/epoch - 6ms/step\n",
"Epoch 117/500\n",
"3/3 - 0s - loss: 4.7114e-04 - 17ms/epoch - 6ms/step\n",
"Epoch 118/500\n",
"3/3 - 0s - loss: 4.5269e-04 - 17ms/epoch - 6ms/step\n",
"Epoch 119/500\n",
"3/3 - 0s - loss: 4.5380e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 120/500\n",
"3/3 - 0s - loss: 4.2536e-04 - 17ms/epoch - 6ms/step\n",
"Epoch 121/500\n",
"3/3 - 0s - loss: 3.9441e-04 - 20ms/epoch - 7ms/step\n",
"Epoch 122/500\n",
"3/3 - 0s - loss: 3.8316e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 123/500\n",
"3/3 - 0s - loss: 3.6812e-04 - 16ms/epoch - 5ms/step\n",
"Epoch 124/500\n",
"3/3 - 0s - loss: 3.6512e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 125/500\n",
"3/3 - 0s - loss: 3.3016e-04 - 17ms/epoch - 6ms/step\n",
"Epoch 126/500\n",
"3/3 - 0s - loss: 3.1494e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 127/500\n",
"3/3 - 0s - loss: 3.1160e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 128/500\n",
"3/3 - 0s - loss: 2.8627e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 129/500\n",
"3/3 - 0s - loss: 2.6777e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 130/500\n",
"3/3 - 0s - loss: 2.6258e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 131/500\n",
"3/3 - 0s - loss: 2.4426e-04 - 17ms/epoch - 6ms/step\n",
"Epoch 132/500\n",
"3/3 - 0s - loss: 2.4068e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 133/500\n",
"3/3 - 0s - loss: 2.2655e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 134/500\n",
"3/3 - 0s - loss: 2.1242e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 135/500\n",
"3/3 - 0s - loss: 2.0196e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 136/500\n",
"3/3 - 0s - loss: 1.9252e-04 - 20ms/epoch - 7ms/step\n",
"Epoch 137/500\n",
"3/3 - 0s - loss: 1.8062e-04 - 19ms/epoch - 6ms/step\n",
"Epoch 138/500\n",
"3/3 - 0s - loss: 1.7509e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 139/500\n",
"3/3 - 0s - loss: 1.6781e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 140/500\n",
"3/3 - 0s - loss: 1.5383e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 141/500\n",
"3/3 - 0s - loss: 1.4289e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 142/500\n",
"3/3 - 0s - loss: 1.3294e-04 - 17ms/epoch - 6ms/step\n",
"Epoch 143/500\n",
"3/3 - 0s - loss: 1.2580e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 144/500\n",
"3/3 - 0s - loss: 1.1927e-04 - 18ms/epoch - 6ms/step\n",
"Epoch 145/500\n",
"3/3 - 0s - loss: 1.1094e-04 - 17ms/epoch - 6ms/step\n",
"Epoch 146/500\n",
"3/3 - 0s - loss: 1.1226e-04 - 16ms/epoch - 5ms/step\n",
"Epoch 147/500\n",
"3/3 - 0s - loss: 9.7595e-05 - 19ms/epoch - 6ms/step\n",
"Epoch 148/500\n",
"3/3 - 0s - loss: 9.3520e-05 - 20ms/epoch - 7ms/step\n",
"Epoch 149/500\n",
"3/3 - 0s - loss: 8.7133e-05 - 18ms/epoch - 6ms/step\n",
"Epoch 150/500\n",
"3/3 - 0s - loss: 8.2338e-05 - 17ms/epoch - 6ms/step\n",
"Epoch 151/500\n",
"3/3 - 0s - loss: 8.4051e-05 - 17ms/epoch - 6ms/step\n",
"Epoch 152/500\n",
"3/3 - 0s - loss: 7.1404e-05 - 16ms/epoch - 5ms/step\n",
"Epoch 153/500\n",
"3/3 - 0s - loss: 6.8557e-05 - 16ms/epoch - 5ms/step\n",
"Epoch 154/500\n",
"3/3 - 0s - loss: 6.2511e-05 - 17ms/epoch - 6ms/step\n",
"Epoch 155/500\n",
"3/3 - 0s - loss: 6.0561e-05 - 17ms/epoch - 6ms/step\n",
"Epoch 156/500\n",
"3/3 - 0s - loss: 5.4933e-05 - 18ms/epoch - 6ms/step\n",
"Epoch 157/500\n",
"3/3 - 0s - loss: 5.1755e-05 - 18ms/epoch - 6ms/step\n",
"Epoch 158/500\n",
"3/3 - 0s - loss: 4.7015e-05 - 17ms/epoch - 6ms/step\n",
"Epoch 159/500\n",
"3/3 - 0s - loss: 4.2376e-05 - 18ms/epoch - 6ms/step\n",
"Epoch 160/500\n",
"3/3 - 0s - loss: 4.2349e-05 - 18ms/epoch - 6ms/step\n",
"Epoch 161/500\n",
"3/3 - 0s - loss: 4.1130e-05 - 21ms/epoch - 7ms/step\n",
"Epoch 162/500\n",
"3/3 - 0s - loss: 3.6309e-05 - 18ms/epoch - 6ms/step\n",
"Epoch 163/500\n",
"3/3 - 0s - loss: 3.4193e-05 - 19ms/epoch - 6ms/step\n",
"Epoch 164/500\n",
"3/3 - 0s - loss: 3.4451e-05 - 19ms/epoch - 6ms/step\n",
"Epoch 165/500\n",
"3/3 - 0s - loss: 2.9192e-05 - 18ms/epoch - 6ms/step\n",
"Epoch 166/500\n",
"3/3 - 0s - loss: 2.7077e-05 - 18ms/epoch - 6ms/step\n",
"Epoch 167/500\n",
"3/3 - 0s - loss: 2.3977e-05 - 18ms/epoch - 6ms/step\n",
"Epoch 168/500\n",
"3/3 - 0s - loss: 2.3505e-05 - 18ms/epoch - 6ms/step\n",
"Epoch 169/500\n",
"3/3 - 0s - loss: 2.1494e-05 - 19ms/epoch - 6ms/step\n",
"Epoch 170/500\n",
"3/3 - 0s - loss: 1.9601e-05 - 19ms/epoch - 6ms/step\n",
"Epoch 171/500\n",
"3/3 - 0s - loss: 1.8738e-05 - 17ms/epoch - 6ms/step\n",
"Epoch 172/500\n",
"3/3 - 0s - loss: 1.7222e-05 - 18ms/epoch - 6ms/step\n",
"Epoch 173/500\n",
"3/3 - 0s - loss: 1.6006e-05 - 17ms/epoch - 6ms/step\n",
"Epoch 174/500\n",
"3/3 - 0s - loss: 1.5306e-05 - 18ms/epoch - 6ms/step\n",
"Epoch 175/500\n",
"3/3 - 0s - loss: 1.4319e-05 - 19ms/epoch - 6ms/step\n",
"Epoch 176/500\n",
"3/3 - 0s - loss: 1.3512e-05 - 18ms/epoch - 6ms/step\n",
"Epoch 177/500\n",
"3/3 - 0s - loss: 1.3253e-05 - 18ms/epoch - 6ms/step\n",
"Epoch 178/500\n",
"3/3 - 0s - loss: 1.0599e-05 - 18ms/epoch - 6ms/step\n",
"Epoch 179/500\n",
"3/3 - 0s - loss: 1.1178e-05 - 17ms/epoch - 6ms/step\n",
"Epoch 180/500\n",
"3/3 - 0s - loss: 9.1475e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 181/500\n",
"3/3 - 0s - loss: 9.3396e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 182/500\n",
"3/3 - 0s - loss: 8.2424e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 183/500\n",
"3/3 - 0s - loss: 7.5882e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 184/500\n",
"3/3 - 0s - loss: 7.2334e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 185/500\n",
"3/3 - 0s - loss: 6.5040e-06 - 17ms/epoch - 6ms/step\n",
"Epoch 186/500\n",
"3/3 - 0s - loss: 6.0464e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 187/500\n",
"3/3 - 0s - loss: 5.6838e-06 - 19ms/epoch - 6ms/step\n",
"Epoch 188/500\n",
"3/3 - 0s - loss: 5.6350e-06 - 19ms/epoch - 6ms/step\n",
"Epoch 189/500\n",
"3/3 - 0s - loss: 4.8108e-06 - 17ms/epoch - 6ms/step\n",
"Epoch 190/500\n",
"3/3 - 0s - loss: 4.7074e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 191/500\n",
"3/3 - 0s - loss: 4.3411e-06 - 17ms/epoch - 6ms/step\n",
"Epoch 192/500\n",
"3/3 - 0s - loss: 3.8214e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 193/500\n",
"3/3 - 0s - loss: 3.8367e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 194/500\n",
"3/3 - 0s - loss: 3.3282e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 195/500\n",
"3/3 - 0s - loss: 3.1114e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 196/500\n",
"3/3 - 0s - loss: 3.0487e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 197/500\n",
"3/3 - 0s - loss: 2.6585e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 198/500\n",
"3/3 - 0s - loss: 2.6784e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 199/500\n",
"3/3 - 0s - loss: 2.4610e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 200/500\n",
"3/3 - 0s - loss: 2.2299e-06 - 19ms/epoch - 6ms/step\n",
"Epoch 201/500\n",
"3/3 - 0s - loss: 2.3534e-06 - 20ms/epoch - 7ms/step\n",
"Epoch 202/500\n",
"3/3 - 0s - loss: 2.0683e-06 - 19ms/epoch - 6ms/step\n",
"Epoch 203/500\n",
"3/3 - 0s - loss: 1.8921e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 204/500\n",
"3/3 - 0s - loss: 1.9061e-06 - 19ms/epoch - 6ms/step\n",
"Epoch 205/500\n",
"3/3 - 0s - loss: 1.6377e-06 - 17ms/epoch - 6ms/step\n",
"Epoch 206/500\n",
"3/3 - 0s - loss: 1.5557e-06 - 19ms/epoch - 6ms/step\n",
"Epoch 207/500\n",
"3/3 - 0s - loss: 1.4975e-06 - 19ms/epoch - 6ms/step\n",
"Epoch 208/500\n",
"3/3 - 0s - loss: 1.4472e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 209/500\n",
"3/3 - 0s - loss: 1.3231e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 210/500\n",
"3/3 - 0s - loss: 1.2137e-06 - 17ms/epoch - 6ms/step\n",
"Epoch 211/500\n",
"3/3 - 0s - loss: 1.0814e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 212/500\n",
"3/3 - 0s - loss: 1.1012e-06 - 19ms/epoch - 6ms/step\n",
"Epoch 213/500\n",
"3/3 - 0s - loss: 1.0367e-06 - 18ms/epoch - 6ms/step\n",
"Epoch 214/500\n",
"3/3 - 0s - loss: 1.0579e-06 - 20ms/epoch - 7ms/step\n",
"Epoch 215/500\n",
"3/3 - 0s - loss: 8.6901e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 216/500\n",
"3/3 - 0s - loss: 9.2759e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 217/500\n",
"3/3 - 0s - loss: 7.7820e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 218/500\n",
"3/3 - 0s - loss: 7.5039e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 219/500\n",
"3/3 - 0s - loss: 6.8642e-07 - 16ms/epoch - 5ms/step\n",
"Epoch 220/500\n",
"3/3 - 0s - loss: 6.7417e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 221/500\n",
"3/3 - 0s - loss: 6.2442e-07 - 21ms/epoch - 7ms/step\n",
"Epoch 222/500\n",
"3/3 - 0s - loss: 6.4821e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 223/500\n",
"3/3 - 0s - loss: 6.0551e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 224/500\n",
"3/3 - 0s - loss: 5.7419e-07 - 16ms/epoch - 5ms/step\n",
"Epoch 225/500\n",
"3/3 - 0s - loss: 5.5595e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 226/500\n",
"3/3 - 0s - loss: 5.1922e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 227/500\n",
"3/3 - 0s - loss: 5.5299e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 228/500\n",
"3/3 - 0s - loss: 5.0953e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 229/500\n",
"3/3 - 0s - loss: 5.3831e-07 - 16ms/epoch - 5ms/step\n",
"Epoch 230/500\n",
"3/3 - 0s - loss: 4.5238e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 231/500\n",
"3/3 - 0s - loss: 5.3890e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 232/500\n",
"3/3 - 0s - loss: 4.1443e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 233/500\n",
"3/3 - 0s - loss: 5.0137e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 234/500\n",
"3/3 - 0s - loss: 4.2846e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 235/500\n",
"3/3 - 0s - loss: 4.2069e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 236/500\n",
"3/3 - 0s - loss: 5.2210e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 237/500\n",
"3/3 - 0s - loss: 4.9140e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 238/500\n",
"3/3 - 0s - loss: 4.2449e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 239/500\n",
"3/3 - 0s - loss: 4.8237e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 240/500\n",
"3/3 - 0s - loss: 4.1727e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 241/500\n",
"3/3 - 0s - loss: 3.8011e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 242/500\n",
"3/3 - 0s - loss: 4.0372e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 243/500\n",
"3/3 - 0s - loss: 3.6880e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 244/500\n",
"3/3 - 0s - loss: 3.5867e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 245/500\n",
"3/3 - 0s - loss: 3.5779e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 246/500\n",
"3/3 - 0s - loss: 3.3445e-07 - 20ms/epoch - 7ms/step\n",
"Epoch 247/500\n",
"3/3 - 0s - loss: 3.2059e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 248/500\n",
"3/3 - 0s - loss: 3.1275e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 249/500\n",
"3/3 - 0s - loss: 3.0808e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 250/500\n",
"3/3 - 0s - loss: 3.0408e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 251/500\n",
"3/3 - 0s - loss: 2.9309e-07 - 20ms/epoch - 7ms/step\n",
"Epoch 252/500\n",
"3/3 - 0s - loss: 3.4348e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 253/500\n",
"3/3 - 0s - loss: 3.1357e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 254/500\n",
"3/3 - 0s - loss: 2.8772e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 255/500\n",
"3/3 - 0s - loss: 3.0372e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 256/500\n",
"3/3 - 0s - loss: 2.7702e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 257/500\n",
"3/3 - 0s - loss: 2.8394e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 258/500\n",
"3/3 - 0s - loss: 2.6677e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 259/500\n",
"3/3 - 0s - loss: 2.8928e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 260/500\n",
"3/3 - 0s - loss: 2.7944e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 261/500\n",
"3/3 - 0s - loss: 3.1606e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 262/500\n",
"3/3 - 0s - loss: 2.5433e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 263/500\n",
"3/3 - 0s - loss: 2.6045e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 264/500\n",
"3/3 - 0s - loss: 2.6014e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 265/500\n",
"3/3 - 0s - loss: 2.4268e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 266/500\n",
"3/3 - 0s - loss: 2.4573e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 267/500\n",
"3/3 - 0s - loss: 2.4149e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 268/500\n",
"3/3 - 0s - loss: 2.4068e-07 - 20ms/epoch - 7ms/step\n",
"Epoch 269/500\n",
"3/3 - 0s - loss: 2.3706e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 270/500\n",
"3/3 - 0s - loss: 2.3918e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 271/500\n",
"3/3 - 0s - loss: 2.4026e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 272/500\n",
"3/3 - 0s - loss: 2.2306e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 273/500\n",
"3/3 - 0s - loss: 2.3760e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 274/500\n",
"3/3 - 0s - loss: 2.4834e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 275/500\n",
"3/3 - 0s - loss: 2.4366e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 276/500\n",
"3/3 - 0s - loss: 2.2627e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 277/500\n",
"3/3 - 0s - loss: 2.5376e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 278/500\n",
"3/3 - 0s - loss: 2.3523e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 279/500\n",
"3/3 - 0s - loss: 2.1522e-07 - 21ms/epoch - 7ms/step\n",
"Epoch 280/500\n",
"3/3 - 0s - loss: 2.2154e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 281/500\n",
"3/3 - 0s - loss: 2.0595e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 282/500\n",
"3/3 - 0s - loss: 2.0932e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 283/500\n",
"3/3 - 0s - loss: 2.1318e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 284/500\n",
"3/3 - 0s - loss: 2.0770e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 285/500\n",
"3/3 - 0s - loss: 2.3224e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 286/500\n",
"3/3 - 0s - loss: 2.0743e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 287/500\n",
"3/3 - 0s - loss: 1.9832e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 288/500\n",
"3/3 - 0s - loss: 2.3544e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 289/500\n",
"3/3 - 0s - loss: 2.0329e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 290/500\n",
"3/3 - 0s - loss: 1.8422e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 291/500\n",
"3/3 - 0s - loss: 2.1591e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 292/500\n",
"3/3 - 0s - loss: 1.8602e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 293/500\n",
"3/3 - 0s - loss: 1.8403e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 294/500\n",
"3/3 - 0s - loss: 1.8101e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 295/500\n",
"3/3 - 0s - loss: 1.7758e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 296/500\n",
"3/3 - 0s - loss: 1.8401e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 297/500\n",
"3/3 - 0s - loss: 1.9147e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 298/500\n",
"3/3 - 0s - loss: 1.7183e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 299/500\n",
"3/3 - 0s - loss: 2.1222e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 300/500\n",
"3/3 - 0s - loss: 1.9317e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 301/500\n",
"3/3 - 0s - loss: 1.8375e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 302/500\n",
"3/3 - 0s - loss: 1.9362e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 303/500\n",
"3/3 - 0s - loss: 2.1317e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 304/500\n",
"3/3 - 0s - loss: 2.1440e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 305/500\n",
"3/3 - 0s - loss: 2.1560e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 306/500\n",
"3/3 - 0s - loss: 2.0397e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 307/500\n",
"3/3 - 0s - loss: 1.9984e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 308/500\n",
"3/3 - 0s - loss: 2.2129e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 309/500\n",
"3/3 - 0s - loss: 2.3031e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 310/500\n",
"3/3 - 0s - loss: 1.7201e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 311/500\n",
"3/3 - 0s - loss: 1.9411e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 312/500\n",
"3/3 - 0s - loss: 1.9448e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 313/500\n",
"3/3 - 0s - loss: 1.6600e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 314/500\n",
"3/3 - 0s - loss: 1.5323e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 315/500\n",
"3/3 - 0s - loss: 1.6354e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 316/500\n",
"3/3 - 0s - loss: 1.5027e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 317/500\n",
"3/3 - 0s - loss: 1.5395e-07 - 21ms/epoch - 7ms/step\n",
"Epoch 318/500\n",
"3/3 - 0s - loss: 1.4699e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 319/500\n",
"3/3 - 0s - loss: 1.4190e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 320/500\n",
"3/3 - 0s - loss: 1.4405e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 321/500\n",
"3/3 - 0s - loss: 1.4744e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 322/500\n",
"3/3 - 0s - loss: 1.4585e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 323/500\n",
"3/3 - 0s - loss: 1.5841e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 324/500\n",
"3/3 - 0s - loss: 1.5570e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 325/500\n",
"3/3 - 0s - loss: 1.4753e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 326/500\n",
"3/3 - 0s - loss: 1.4586e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 327/500\n",
"3/3 - 0s - loss: 1.3731e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 328/500\n",
"3/3 - 0s - loss: 1.4161e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 329/500\n",
"3/3 - 0s - loss: 1.3163e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 330/500\n",
"3/3 - 0s - loss: 1.7134e-07 - 22ms/epoch - 7ms/step\n",
"Epoch 331/500\n",
"3/3 - 0s - loss: 1.9550e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 332/500\n",
"3/3 - 0s - loss: 1.4891e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 333/500\n",
"3/3 - 0s - loss: 1.6563e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 334/500\n",
"3/3 - 0s - loss: 1.4941e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 335/500\n",
"3/3 - 0s - loss: 1.6838e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 336/500\n",
"3/3 - 0s - loss: 1.9736e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 337/500\n",
"3/3 - 0s - loss: 1.1688e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 338/500\n",
"3/3 - 0s - loss: 1.5850e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 339/500\n",
"3/3 - 0s - loss: 1.4288e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 340/500\n",
"3/3 - 0s - loss: 1.3207e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 341/500\n",
"3/3 - 0s - loss: 1.4376e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 342/500\n",
"3/3 - 0s - loss: 1.6759e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 343/500\n",
"3/3 - 0s - loss: 1.4996e-07 - 20ms/epoch - 7ms/step\n",
"Epoch 344/500\n",
"3/3 - 0s - loss: 1.5474e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 345/500\n",
"3/3 - 0s - loss: 1.5305e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 346/500\n",
"3/3 - 0s - loss: 1.5095e-07 - 20ms/epoch - 7ms/step\n",
"Epoch 347/500\n",
"3/3 - 0s - loss: 1.4360e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 348/500\n",
"3/3 - 0s - loss: 1.0976e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 349/500\n",
"3/3 - 0s - loss: 1.2650e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 350/500\n",
"3/3 - 0s - loss: 1.3316e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 351/500\n",
"3/3 - 0s - loss: 1.1045e-07 - 20ms/epoch - 7ms/step\n",
"Epoch 352/500\n",
"3/3 - 0s - loss: 1.1529e-07 - 20ms/epoch - 7ms/step\n",
"Epoch 353/500\n",
"3/3 - 0s - loss: 1.1211e-07 - 21ms/epoch - 7ms/step\n",
"Epoch 354/500\n",
"3/3 - 0s - loss: 1.0878e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 355/500\n",
"3/3 - 0s - loss: 1.1071e-07 - 20ms/epoch - 7ms/step\n",
"Epoch 356/500\n",
"3/3 - 0s - loss: 9.9950e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 357/500\n",
"3/3 - 0s - loss: 1.1369e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 358/500\n",
"3/3 - 0s - loss: 1.2189e-07 - 20ms/epoch - 7ms/step\n",
"Epoch 359/500\n",
"3/3 - 0s - loss: 1.0373e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 360/500\n",
"3/3 - 0s - loss: 1.2454e-07 - 21ms/epoch - 7ms/step\n",
"Epoch 361/500\n",
"3/3 - 0s - loss: 1.3100e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 362/500\n",
"3/3 - 0s - loss: 1.1431e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 363/500\n",
"3/3 - 0s - loss: 1.1297e-07 - 20ms/epoch - 7ms/step\n",
"Epoch 364/500\n",
"3/3 - 0s - loss: 1.3847e-07 - 20ms/epoch - 7ms/step\n",
"Epoch 365/500\n",
"3/3 - 0s - loss: 1.1059e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 366/500\n",
"3/3 - 0s - loss: 1.0736e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 367/500\n",
"3/3 - 0s - loss: 1.1491e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 368/500\n",
"3/3 - 0s - loss: 1.1969e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 369/500\n",
"3/3 - 0s - loss: 9.6771e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 370/500\n",
"3/3 - 0s - loss: 1.1021e-07 - 21ms/epoch - 7ms/step\n",
"Epoch 371/500\n",
"3/3 - 0s - loss: 1.0336e-07 - 20ms/epoch - 7ms/step\n",
"Epoch 372/500\n",
"3/3 - 0s - loss: 1.1251e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 373/500\n",
"3/3 - 0s - loss: 1.1028e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 374/500\n",
"3/3 - 0s - loss: 9.5262e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 375/500\n",
"3/3 - 0s - loss: 1.0350e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 376/500\n",
"3/3 - 0s - loss: 9.3561e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 377/500\n",
"3/3 - 0s - loss: 1.0377e-07 - 18ms/epoch - 6ms/step\n",
"Epoch 378/500\n",
"3/3 - 0s - loss: 9.5911e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 379/500\n",
"3/3 - 0s - loss: 1.0765e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 380/500\n",
"3/3 - 0s - loss: 9.6007e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 381/500\n",
"3/3 - 0s - loss: 1.0157e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 382/500\n",
"3/3 - 0s - loss: 9.6630e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 383/500\n",
"3/3 - 0s - loss: 9.2116e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 384/500\n",
"3/3 - 0s - loss: 9.2046e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 385/500\n",
"3/3 - 0s - loss: 9.8998e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 386/500\n",
"3/3 - 0s - loss: 9.5565e-08 - 20ms/epoch - 7ms/step\n",
"Epoch 387/500\n",
"3/3 - 0s - loss: 1.2183e-07 - 21ms/epoch - 7ms/step\n",
"Epoch 388/500\n",
"3/3 - 0s - loss: 1.1153e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 389/500\n",
"3/3 - 0s - loss: 9.0460e-08 - 21ms/epoch - 7ms/step\n",
"Epoch 390/500\n",
"3/3 - 0s - loss: 9.9698e-08 - 20ms/epoch - 7ms/step\n",
"Epoch 391/500\n",
"3/3 - 0s - loss: 8.3681e-08 - 20ms/epoch - 7ms/step\n",
"Epoch 392/500\n",
"3/3 - 0s - loss: 1.1799e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 393/500\n",
"3/3 - 0s - loss: 8.8952e-08 - 20ms/epoch - 7ms/step\n",
"Epoch 394/500\n",
"3/3 - 0s - loss: 1.0968e-07 - 17ms/epoch - 6ms/step\n",
"Epoch 395/500\n",
"3/3 - 0s - loss: 1.2105e-07 - 19ms/epoch - 6ms/step\n",
"Epoch 396/500\n",
"3/3 - 0s - loss: 7.4285e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 397/500\n",
"3/3 - 0s - loss: 9.1431e-08 - 21ms/epoch - 7ms/step\n",
"Epoch 398/500\n",
"3/3 - 0s - loss: 7.5279e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 399/500\n",
"3/3 - 0s - loss: 8.7076e-08 - 20ms/epoch - 7ms/step\n",
"Epoch 400/500\n",
"3/3 - 0s - loss: 7.7606e-08 - 20ms/epoch - 7ms/step\n",
"Epoch 401/500\n",
"3/3 - 0s - loss: 8.0799e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 402/500\n",
"3/3 - 0s - loss: 7.7469e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 403/500\n",
"3/3 - 0s - loss: 7.4221e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 404/500\n",
"3/3 - 0s - loss: 7.2481e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 405/500\n",
"3/3 - 0s - loss: 7.8779e-08 - 21ms/epoch - 7ms/step\n",
"Epoch 406/500\n",
"3/3 - 0s - loss: 8.1153e-08 - 21ms/epoch - 7ms/step\n",
"Epoch 407/500\n",
"3/3 - 0s - loss: 8.4900e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 408/500\n",
"3/3 - 0s - loss: 9.6151e-08 - 20ms/epoch - 7ms/step\n",
"Epoch 409/500\n",
"3/3 - 0s - loss: 7.7901e-08 - 22ms/epoch - 7ms/step\n",
"Epoch 410/500\n",
"3/3 - 0s - loss: 8.9594e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 411/500\n",
"3/3 - 0s - loss: 7.2830e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 412/500\n",
"3/3 - 0s - loss: 7.7258e-08 - 20ms/epoch - 7ms/step\n",
"Epoch 413/500\n",
"3/3 - 0s - loss: 7.3764e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 414/500\n",
"3/3 - 0s - loss: 6.9179e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 415/500\n",
"3/3 - 0s - loss: 6.9631e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 416/500\n",
"3/3 - 0s - loss: 6.8140e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 417/500\n",
"3/3 - 0s - loss: 7.6016e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 418/500\n",
"3/3 - 0s - loss: 7.2604e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 419/500\n",
"3/3 - 0s - loss: 7.0878e-08 - 16ms/epoch - 5ms/step\n",
"Epoch 420/500\n",
"3/3 - 0s - loss: 7.3099e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 421/500\n",
"3/3 - 0s - loss: 7.0737e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 422/500\n",
"3/3 - 0s - loss: 7.0299e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 423/500\n",
"3/3 - 0s - loss: 7.4157e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 424/500\n",
"3/3 - 0s - loss: 8.0799e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 425/500\n",
"3/3 - 0s - loss: 8.6588e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 426/500\n",
"3/3 - 0s - loss: 7.2217e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 427/500\n",
"3/3 - 0s - loss: 7.0006e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 428/500\n",
"3/3 - 0s - loss: 8.4730e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 429/500\n",
"3/3 - 0s - loss: 8.4061e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 430/500\n",
"3/3 - 0s - loss: 7.0861e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 431/500\n",
"3/3 - 0s - loss: 6.2573e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 432/500\n",
"3/3 - 0s - loss: 7.9075e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 433/500\n",
"3/3 - 0s - loss: 6.6627e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 434/500\n",
"3/3 - 0s - loss: 6.9767e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 435/500\n",
"3/3 - 0s - loss: 8.2181e-08 - 20ms/epoch - 7ms/step\n",
"Epoch 436/500\n",
"3/3 - 0s - loss: 7.3544e-08 - 20ms/epoch - 7ms/step\n",
"Epoch 437/500\n",
"3/3 - 0s - loss: 6.1450e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 438/500\n",
"3/3 - 0s - loss: 6.2898e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 439/500\n",
"3/3 - 0s - loss: 6.3277e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 440/500\n",
"3/3 - 0s - loss: 5.9180e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 441/500\n",
"3/3 - 0s - loss: 6.7579e-08 - 20ms/epoch - 7ms/step\n",
"Epoch 442/500\n",
"3/3 - 0s - loss: 8.3768e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 443/500\n",
"3/3 - 0s - loss: 6.7668e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 444/500\n",
"3/3 - 0s - loss: 6.1503e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 445/500\n",
"3/3 - 0s - loss: 5.7124e-08 - 20ms/epoch - 7ms/step\n",
"Epoch 446/500\n",
"3/3 - 0s - loss: 5.9290e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 447/500\n",
"3/3 - 0s - loss: 6.0694e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 448/500\n",
"3/3 - 0s - loss: 5.8756e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 449/500\n",
"3/3 - 0s - loss: 5.7755e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 450/500\n",
"3/3 - 0s - loss: 5.9693e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 451/500\n",
"3/3 - 0s - loss: 5.2393e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 452/500\n",
"3/3 - 0s - loss: 7.4925e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 453/500\n",
"3/3 - 0s - loss: 5.8268e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 454/500\n",
"3/3 - 0s - loss: 5.9728e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 455/500\n",
"3/3 - 0s - loss: 5.5067e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 456/500\n",
"3/3 - 0s - loss: 5.6427e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 457/500\n",
"3/3 - 0s - loss: 5.0045e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 458/500\n",
"3/3 - 0s - loss: 5.6459e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 459/500\n",
"3/3 - 0s - loss: 6.9810e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 460/500\n",
"3/3 - 0s - loss: 8.4879e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 461/500\n",
"3/3 - 0s - loss: 7.8481e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 462/500\n",
"3/3 - 0s - loss: 5.7484e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 463/500\n",
"3/3 - 0s - loss: 5.6489e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 464/500\n",
"3/3 - 0s - loss: 6.4932e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 465/500\n",
"3/3 - 0s - loss: 6.4632e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 466/500\n",
"3/3 - 0s - loss: 5.4950e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 467/500\n",
"3/3 - 0s - loss: 6.7931e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 468/500\n",
"3/3 - 0s - loss: 6.3551e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 469/500\n",
"3/3 - 0s - loss: 5.1181e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 470/500\n",
"3/3 - 0s - loss: 4.8830e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 471/500\n",
"3/3 - 0s - loss: 5.4158e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 472/500\n",
"3/3 - 0s - loss: 5.3932e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 473/500\n",
"3/3 - 0s - loss: 5.1400e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 474/500\n",
"3/3 - 0s - loss: 5.0049e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 475/500\n",
"3/3 - 0s - loss: 4.7968e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 476/500\n",
"3/3 - 0s - loss: 5.0733e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 477/500\n",
"3/3 - 0s - loss: 4.8479e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 478/500\n",
"3/3 - 0s - loss: 4.5347e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 479/500\n",
"3/3 - 0s - loss: 4.5587e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 480/500\n",
"3/3 - 0s - loss: 4.7967e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 481/500\n",
"3/3 - 0s - loss: 4.5294e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 482/500\n",
"3/3 - 0s - loss: 4.9768e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 483/500\n",
"3/3 - 0s - loss: 6.2928e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 484/500\n",
"3/3 - 0s - loss: 5.7928e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 485/500\n",
"3/3 - 0s - loss: 5.8018e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 486/500\n",
"3/3 - 0s - loss: 7.2048e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 487/500\n",
"3/3 - 0s - loss: 6.8709e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 488/500\n",
"3/3 - 0s - loss: 5.8280e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 489/500\n",
"3/3 - 0s - loss: 7.0236e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 490/500\n",
"3/3 - 0s - loss: 6.5742e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 491/500\n",
"3/3 - 0s - loss: 6.5217e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 492/500\n",
"3/3 - 0s - loss: 7.9026e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 493/500\n",
"3/3 - 0s - loss: 6.6774e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 494/500\n",
"3/3 - 0s - loss: 4.7015e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 495/500\n",
"3/3 - 0s - loss: 5.3433e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 496/500\n",
"3/3 - 0s - loss: 5.1338e-08 - 19ms/epoch - 6ms/step\n",
"Epoch 497/500\n",
"3/3 - 0s - loss: 4.5060e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 498/500\n",
"3/3 - 0s - loss: 4.7938e-08 - 17ms/epoch - 6ms/step\n",
"Epoch 499/500\n",
"3/3 - 0s - loss: 4.4799e-08 - 18ms/epoch - 6ms/step\n",
"Epoch 500/500\n",
"3/3 - 0s - loss: 5.0362e-08 - 19ms/epoch - 6ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.src.callbacks.History at 0x7ec198c46d70>"
]
},
"metadata": {},
"execution_count": 128
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "n-aNP4n3sqG_",
"outputId": "de3a337f-20d1-4c6f-96c1-7e7736695825",
"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": 129,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"14/14 [==============================] - 0s 3ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7ec198c44a00>"
]
},
"metadata": {},
"execution_count": 129
},
{
"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": "f8d7dc00-3065-4a92-eb6a-e54a1dccfe92"
},
"execution_count": 130,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1710205357.0268257\n",
"Tue Mar 12 01:02:37 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": "d93b4520-a718-4f13-8d98-cb003c8917c2"
},
"execution_count": 131,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1710205357.0355582\n",
"Tue Mar 12 01:02:37 2024\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BMxSJo5gtOmQ"
},
"source": [
"# VS Fully Connected"
]
},
{
"cell_type": "code",
"metadata": {
"id": "NKQx7stYswzU",
"outputId": "d284eda8-fc4e-4d70-8ef9-94add20aee6c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 18802
}
},
"source": [
"fc_model.compile(optimizer=\"adam\", loss=\"mean_squared_error\")\n",
"fc_model.fit(X, Y, epochs=500, 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": 132,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/500\n",
"3/3 - 1s - loss: 0.5656 - 635ms/epoch - 212ms/step\n",
"Epoch 2/500\n",
"3/3 - 0s - loss: 0.1959 - 27ms/epoch - 9ms/step\n",
"Epoch 3/500\n",
"3/3 - 0s - loss: 0.1423 - 25ms/epoch - 8ms/step\n",
"Epoch 4/500\n",
"3/3 - 0s - loss: 0.0917 - 26ms/epoch - 9ms/step\n",
"Epoch 5/500\n",
"3/3 - 0s - loss: 0.0828 - 27ms/epoch - 9ms/step\n",
"Epoch 6/500\n",
"3/3 - 0s - loss: 0.0827 - 25ms/epoch - 8ms/step\n",
"Epoch 7/500\n",
"3/3 - 0s - loss: 0.0680 - 26ms/epoch - 9ms/step\n",
"Epoch 8/500\n",
"3/3 - 0s - loss: 0.0680 - 25ms/epoch - 8ms/step\n",
"Epoch 9/500\n",
"3/3 - 0s - loss: 0.0605 - 23ms/epoch - 8ms/step\n",
"Epoch 10/500\n",
"3/3 - 0s - loss: 0.0632 - 22ms/epoch - 7ms/step\n",
"Epoch 11/500\n",
"3/3 - 0s - loss: 0.0537 - 24ms/epoch - 8ms/step\n",
"Epoch 12/500\n",
"3/3 - 0s - loss: 0.0523 - 27ms/epoch - 9ms/step\n",
"Epoch 13/500\n",
"3/3 - 0s - loss: 0.0522 - 23ms/epoch - 8ms/step\n",
"Epoch 14/500\n",
"3/3 - 0s - loss: 0.0483 - 23ms/epoch - 8ms/step\n",
"Epoch 15/500\n",
"3/3 - 0s - loss: 0.0498 - 25ms/epoch - 8ms/step\n",
"Epoch 16/500\n",
"3/3 - 0s - loss: 0.0444 - 29ms/epoch - 10ms/step\n",
"Epoch 17/500\n",
"3/3 - 0s - loss: 0.0487 - 24ms/epoch - 8ms/step\n",
"Epoch 18/500\n",
"3/3 - 0s - loss: 0.0467 - 24ms/epoch - 8ms/step\n",
"Epoch 19/500\n",
"3/3 - 0s - loss: 0.0419 - 21ms/epoch - 7ms/step\n",
"Epoch 20/500\n",
"3/3 - 0s - loss: 0.0439 - 24ms/epoch - 8ms/step\n",
"Epoch 21/500\n",
"3/3 - 0s - loss: 0.0406 - 26ms/epoch - 9ms/step\n",
"Epoch 22/500\n",
"3/3 - 0s - loss: 0.0414 - 25ms/epoch - 8ms/step\n",
"Epoch 23/500\n",
"3/3 - 0s - loss: 0.0421 - 24ms/epoch - 8ms/step\n",
"Epoch 24/500\n",
"3/3 - 0s - loss: 0.0378 - 22ms/epoch - 7ms/step\n",
"Epoch 25/500\n",
"3/3 - 0s - loss: 0.0382 - 25ms/epoch - 8ms/step\n",
"Epoch 26/500\n",
"3/3 - 0s - loss: 0.0425 - 24ms/epoch - 8ms/step\n",
"Epoch 27/500\n",
"3/3 - 0s - loss: 0.0505 - 24ms/epoch - 8ms/step\n",
"Epoch 28/500\n",
"3/3 - 0s - loss: 0.0423 - 27ms/epoch - 9ms/step\n",
"Epoch 29/500\n",
"3/3 - 0s - loss: 0.0513 - 25ms/epoch - 8ms/step\n",
"Epoch 30/500\n",
"3/3 - 0s - loss: 0.0385 - 24ms/epoch - 8ms/step\n",
"Epoch 31/500\n",
"3/3 - 0s - loss: 0.0392 - 28ms/epoch - 9ms/step\n",
"Epoch 32/500\n",
"3/3 - 0s - loss: 0.0417 - 25ms/epoch - 8ms/step\n",
"Epoch 33/500\n",
"3/3 - 0s - loss: 0.0414 - 27ms/epoch - 9ms/step\n",
"Epoch 34/500\n",
"3/3 - 0s - loss: 0.0374 - 24ms/epoch - 8ms/step\n",
"Epoch 35/500\n",
"3/3 - 0s - loss: 0.0348 - 23ms/epoch - 8ms/step\n",
"Epoch 36/500\n",
"3/3 - 0s - loss: 0.0319 - 22ms/epoch - 7ms/step\n",
"Epoch 37/500\n",
"3/3 - 0s - loss: 0.0429 - 24ms/epoch - 8ms/step\n",
"Epoch 38/500\n",
"3/3 - 0s - loss: 0.0382 - 23ms/epoch - 8ms/step\n",
"Epoch 39/500\n",
"3/3 - 0s - loss: 0.0266 - 21ms/epoch - 7ms/step\n",
"Epoch 40/500\n",
"3/3 - 0s - loss: 0.0399 - 21ms/epoch - 7ms/step\n",
"Epoch 41/500\n",
"3/3 - 0s - loss: 0.0336 - 23ms/epoch - 8ms/step\n",
"Epoch 42/500\n",
"3/3 - 0s - loss: 0.0293 - 21ms/epoch - 7ms/step\n",
"Epoch 43/500\n",
"3/3 - 0s - loss: 0.0304 - 22ms/epoch - 7ms/step\n",
"Epoch 44/500\n",
"3/3 - 0s - loss: 0.0370 - 22ms/epoch - 7ms/step\n",
"Epoch 45/500\n",
"3/3 - 0s - loss: 0.0295 - 22ms/epoch - 7ms/step\n",
"Epoch 46/500\n",
"3/3 - 0s - loss: 0.0278 - 23ms/epoch - 8ms/step\n",
"Epoch 47/500\n",
"3/3 - 0s - loss: 0.0298 - 22ms/epoch - 7ms/step\n",
"Epoch 48/500\n",
"3/3 - 0s - loss: 0.0244 - 22ms/epoch - 7ms/step\n",
"Epoch 49/500\n",
"3/3 - 0s - loss: 0.0270 - 22ms/epoch - 7ms/step\n",
"Epoch 50/500\n",
"3/3 - 0s - loss: 0.0191 - 22ms/epoch - 7ms/step\n",
"Epoch 51/500\n",
"3/3 - 0s - loss: 0.0257 - 24ms/epoch - 8ms/step\n",
"Epoch 52/500\n",
"3/3 - 0s - loss: 0.0229 - 24ms/epoch - 8ms/step\n",
"Epoch 53/500\n",
"3/3 - 0s - loss: 0.0226 - 28ms/epoch - 9ms/step\n",
"Epoch 54/500\n",
"3/3 - 0s - loss: 0.0251 - 25ms/epoch - 8ms/step\n",
"Epoch 55/500\n",
"3/3 - 0s - loss: 0.0231 - 23ms/epoch - 8ms/step\n",
"Epoch 56/500\n",
"3/3 - 0s - loss: 0.0268 - 22ms/epoch - 7ms/step\n",
"Epoch 57/500\n",
"3/3 - 0s - loss: 0.0274 - 23ms/epoch - 8ms/step\n",
"Epoch 58/500\n",
"3/3 - 0s - loss: 0.0182 - 23ms/epoch - 8ms/step\n",
"Epoch 59/500\n",
"3/3 - 0s - loss: 0.0233 - 23ms/epoch - 8ms/step\n",
"Epoch 60/500\n",
"3/3 - 0s - loss: 0.0189 - 25ms/epoch - 8ms/step\n",
"Epoch 61/500\n",
"3/3 - 0s - loss: 0.0133 - 22ms/epoch - 7ms/step\n",
"Epoch 62/500\n",
"3/3 - 0s - loss: 0.0144 - 24ms/epoch - 8ms/step\n",
"Epoch 63/500\n",
"3/3 - 0s - loss: 0.0157 - 23ms/epoch - 8ms/step\n",
"Epoch 64/500\n",
"3/3 - 0s - loss: 0.0119 - 24ms/epoch - 8ms/step\n",
"Epoch 65/500\n",
"3/3 - 0s - loss: 0.0188 - 22ms/epoch - 7ms/step\n",
"Epoch 66/500\n",
"3/3 - 0s - loss: 0.0130 - 28ms/epoch - 9ms/step\n",
"Epoch 67/500\n",
"3/3 - 0s - loss: 0.0116 - 23ms/epoch - 8ms/step\n",
"Epoch 68/500\n",
"3/3 - 0s - loss: 0.0110 - 21ms/epoch - 7ms/step\n",
"Epoch 69/500\n",
"3/3 - 0s - loss: 0.0073 - 25ms/epoch - 8ms/step\n",
"Epoch 70/500\n",
"3/3 - 0s - loss: 0.0097 - 24ms/epoch - 8ms/step\n",
"Epoch 71/500\n",
"3/3 - 0s - loss: 0.0088 - 25ms/epoch - 8ms/step\n",
"Epoch 72/500\n",
"3/3 - 0s - loss: 0.0063 - 26ms/epoch - 9ms/step\n",
"Epoch 73/500\n",
"3/3 - 0s - loss: 0.0058 - 22ms/epoch - 7ms/step\n",
"Epoch 74/500\n",
"3/3 - 0s - loss: 0.0060 - 25ms/epoch - 8ms/step\n",
"Epoch 75/500\n",
"3/3 - 0s - loss: 0.0094 - 23ms/epoch - 8ms/step\n",
"Epoch 76/500\n",
"3/3 - 0s - loss: 0.0106 - 23ms/epoch - 8ms/step\n",
"Epoch 77/500\n",
"3/3 - 0s - loss: 0.0083 - 25ms/epoch - 8ms/step\n",
"Epoch 78/500\n",
"3/3 - 0s - loss: 0.0048 - 24ms/epoch - 8ms/step\n",
"Epoch 79/500\n",
"3/3 - 0s - loss: 0.0048 - 24ms/epoch - 8ms/step\n",
"Epoch 80/500\n",
"3/3 - 0s - loss: 0.0046 - 24ms/epoch - 8ms/step\n",
"Epoch 81/500\n",
"3/3 - 0s - loss: 0.0029 - 26ms/epoch - 9ms/step\n",
"Epoch 82/500\n",
"3/3 - 0s - loss: 0.0026 - 26ms/epoch - 9ms/step\n",
"Epoch 83/500\n",
"3/3 - 0s - loss: 0.0030 - 22ms/epoch - 7ms/step\n",
"Epoch 84/500\n",
"3/3 - 0s - loss: 0.0032 - 25ms/epoch - 8ms/step\n",
"Epoch 85/500\n",
"3/3 - 0s - loss: 0.0040 - 23ms/epoch - 8ms/step\n",
"Epoch 86/500\n",
"3/3 - 0s - loss: 0.0045 - 26ms/epoch - 9ms/step\n",
"Epoch 87/500\n",
"3/3 - 0s - loss: 0.0047 - 26ms/epoch - 9ms/step\n",
"Epoch 88/500\n",
"3/3 - 0s - loss: 0.0033 - 25ms/epoch - 8ms/step\n",
"Epoch 89/500\n",
"3/3 - 0s - loss: 0.0034 - 23ms/epoch - 8ms/step\n",
"Epoch 90/500\n",
"3/3 - 0s - loss: 0.0083 - 23ms/epoch - 8ms/step\n",
"Epoch 91/500\n",
"3/3 - 0s - loss: 0.0109 - 30ms/epoch - 10ms/step\n",
"Epoch 92/500\n",
"3/3 - 0s - loss: 0.0065 - 26ms/epoch - 9ms/step\n",
"Epoch 93/500\n",
"3/3 - 0s - loss: 0.0046 - 23ms/epoch - 8ms/step\n",
"Epoch 94/500\n",
"3/3 - 0s - loss: 0.0068 - 24ms/epoch - 8ms/step\n",
"Epoch 95/500\n",
"3/3 - 0s - loss: 0.0096 - 21ms/epoch - 7ms/step\n",
"Epoch 96/500\n",
"3/3 - 0s - loss: 0.0103 - 21ms/epoch - 7ms/step\n",
"Epoch 97/500\n",
"3/3 - 0s - loss: 0.0119 - 22ms/epoch - 7ms/step\n",
"Epoch 98/500\n",
"3/3 - 0s - loss: 0.0069 - 23ms/epoch - 8ms/step\n",
"Epoch 99/500\n",
"3/3 - 0s - loss: 0.0075 - 24ms/epoch - 8ms/step\n",
"Epoch 100/500\n",
"3/3 - 0s - loss: 0.0057 - 24ms/epoch - 8ms/step\n",
"Epoch 101/500\n",
"3/3 - 0s - loss: 0.0032 - 21ms/epoch - 7ms/step\n",
"Epoch 102/500\n",
"3/3 - 0s - loss: 0.0039 - 22ms/epoch - 7ms/step\n",
"Epoch 103/500\n",
"3/3 - 0s - loss: 0.0029 - 24ms/epoch - 8ms/step\n",
"Epoch 104/500\n",
"3/3 - 0s - loss: 0.0031 - 24ms/epoch - 8ms/step\n",
"Epoch 105/500\n",
"3/3 - 0s - loss: 0.0021 - 23ms/epoch - 8ms/step\n",
"Epoch 106/500\n",
"3/3 - 0s - loss: 0.0015 - 26ms/epoch - 9ms/step\n",
"Epoch 107/500\n",
"3/3 - 0s - loss: 0.0014 - 23ms/epoch - 8ms/step\n",
"Epoch 108/500\n",
"3/3 - 0s - loss: 0.0013 - 22ms/epoch - 7ms/step\n",
"Epoch 109/500\n",
"3/3 - 0s - loss: 0.0022 - 22ms/epoch - 7ms/step\n",
"Epoch 110/500\n",
"3/3 - 0s - loss: 0.0019 - 24ms/epoch - 8ms/step\n",
"Epoch 111/500\n",
"3/3 - 0s - loss: 0.0020 - 24ms/epoch - 8ms/step\n",
"Epoch 112/500\n",
"3/3 - 0s - loss: 6.9314e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 113/500\n",
"3/3 - 0s - loss: 9.3566e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 114/500\n",
"3/3 - 0s - loss: 0.0015 - 23ms/epoch - 8ms/step\n",
"Epoch 115/500\n",
"3/3 - 0s - loss: 0.0017 - 25ms/epoch - 8ms/step\n",
"Epoch 116/500\n",
"3/3 - 0s - loss: 0.0020 - 23ms/epoch - 8ms/step\n",
"Epoch 117/500\n",
"3/3 - 0s - loss: 0.0018 - 24ms/epoch - 8ms/step\n",
"Epoch 118/500\n",
"3/3 - 0s - loss: 0.0010 - 24ms/epoch - 8ms/step\n",
"Epoch 119/500\n",
"3/3 - 0s - loss: 8.8028e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 120/500\n",
"3/3 - 0s - loss: 7.2462e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 121/500\n",
"3/3 - 0s - loss: 8.0890e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 122/500\n",
"3/3 - 0s - loss: 9.8991e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 123/500\n",
"3/3 - 0s - loss: 7.1008e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 124/500\n",
"3/3 - 0s - loss: 4.9597e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 125/500\n",
"3/3 - 0s - loss: 4.7966e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 126/500\n",
"3/3 - 0s - loss: 3.0518e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 127/500\n",
"3/3 - 0s - loss: 2.7030e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 128/500\n",
"3/3 - 0s - loss: 3.4302e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 129/500\n",
"3/3 - 0s - loss: 3.2476e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 130/500\n",
"3/3 - 0s - loss: 1.6305e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 131/500\n",
"3/3 - 0s - loss: 1.8642e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 132/500\n",
"3/3 - 0s - loss: 8.2074e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 133/500\n",
"3/3 - 0s - loss: 6.5955e-05 - 27ms/epoch - 9ms/step\n",
"Epoch 134/500\n",
"3/3 - 0s - loss: 6.8692e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 135/500\n",
"3/3 - 0s - loss: 1.1016e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 136/500\n",
"3/3 - 0s - loss: 1.4056e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 137/500\n",
"3/3 - 0s - loss: 1.0764e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 138/500\n",
"3/3 - 0s - loss: 9.8001e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 139/500\n",
"3/3 - 0s - loss: 2.1907e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 140/500\n",
"3/3 - 0s - loss: 2.4921e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 141/500\n",
"3/3 - 0s - loss: 4.0704e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 142/500\n",
"3/3 - 0s - loss: 5.5095e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 143/500\n",
"3/3 - 0s - loss: 8.7078e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 144/500\n",
"3/3 - 0s - loss: 9.0852e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 145/500\n",
"3/3 - 0s - loss: 0.0014 - 25ms/epoch - 8ms/step\n",
"Epoch 146/500\n",
"3/3 - 0s - loss: 0.0021 - 23ms/epoch - 8ms/step\n",
"Epoch 147/500\n",
"3/3 - 0s - loss: 0.0012 - 27ms/epoch - 9ms/step\n",
"Epoch 148/500\n",
"3/3 - 0s - loss: 0.0011 - 24ms/epoch - 8ms/step\n",
"Epoch 149/500\n",
"3/3 - 0s - loss: 0.0014 - 26ms/epoch - 9ms/step\n",
"Epoch 150/500\n",
"3/3 - 0s - loss: 0.0013 - 26ms/epoch - 9ms/step\n",
"Epoch 151/500\n",
"3/3 - 0s - loss: 0.0012 - 24ms/epoch - 8ms/step\n",
"Epoch 152/500\n",
"3/3 - 0s - loss: 0.0011 - 27ms/epoch - 9ms/step\n",
"Epoch 153/500\n",
"3/3 - 0s - loss: 8.8283e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 154/500\n",
"3/3 - 0s - loss: 5.0875e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 155/500\n",
"3/3 - 0s - loss: 4.6452e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 156/500\n",
"3/3 - 0s - loss: 4.4445e-04 - 30ms/epoch - 10ms/step\n",
"Epoch 157/500\n",
"3/3 - 0s - loss: 4.5507e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 158/500\n",
"3/3 - 0s - loss: 5.0221e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 159/500\n",
"3/3 - 0s - loss: 7.1127e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 160/500\n",
"3/3 - 0s - loss: 5.3585e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 161/500\n",
"3/3 - 0s - loss: 3.0625e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 162/500\n",
"3/3 - 0s - loss: 3.6777e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 163/500\n",
"3/3 - 0s - loss: 2.5530e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 164/500\n",
"3/3 - 0s - loss: 1.7076e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 165/500\n",
"3/3 - 0s - loss: 2.1320e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 166/500\n",
"3/3 - 0s - loss: 2.7991e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 167/500\n",
"3/3 - 0s - loss: 3.3069e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 168/500\n",
"3/3 - 0s - loss: 2.9444e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 169/500\n",
"3/3 - 0s - loss: 4.0663e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 170/500\n",
"3/3 - 0s - loss: 3.3016e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 171/500\n",
"3/3 - 0s - loss: 2.0864e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 172/500\n",
"3/3 - 0s - loss: 3.1231e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 173/500\n",
"3/3 - 0s - loss: 2.9278e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 174/500\n",
"3/3 - 0s - loss: 3.0427e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 175/500\n",
"3/3 - 0s - loss: 4.5326e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 176/500\n",
"3/3 - 0s - loss: 3.3629e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 177/500\n",
"3/3 - 0s - loss: 2.4525e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 178/500\n",
"3/3 - 0s - loss: 2.5538e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 179/500\n",
"3/3 - 0s - loss: 3.3784e-04 - 28ms/epoch - 9ms/step\n",
"Epoch 180/500\n",
"3/3 - 0s - loss: 1.9497e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 181/500\n",
"3/3 - 0s - loss: 1.9737e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 182/500\n",
"3/3 - 0s - loss: 2.2758e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 183/500\n",
"3/3 - 0s - loss: 2.9136e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 184/500\n",
"3/3 - 0s - loss: 1.1060e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 185/500\n",
"3/3 - 0s - loss: 5.0481e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 186/500\n",
"3/3 - 0s - loss: 4.0821e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 187/500\n",
"3/3 - 0s - loss: 5.7687e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 188/500\n",
"3/3 - 0s - loss: 5.1819e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 189/500\n",
"3/3 - 0s - loss: 4.4923e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 190/500\n",
"3/3 - 0s - loss: 5.0681e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 191/500\n",
"3/3 - 0s - loss: 3.9062e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 192/500\n",
"3/3 - 0s - loss: 3.1511e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 193/500\n",
"3/3 - 0s - loss: 3.9896e-05 - 28ms/epoch - 9ms/step\n",
"Epoch 194/500\n",
"3/3 - 0s - loss: 3.6009e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 195/500\n",
"3/3 - 0s - loss: 3.8435e-05 - 21ms/epoch - 7ms/step\n",
"Epoch 196/500\n",
"3/3 - 0s - loss: 6.6916e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 197/500\n",
"3/3 - 0s - loss: 1.2784e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 198/500\n",
"3/3 - 0s - loss: 8.5005e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 199/500\n",
"3/3 - 0s - loss: 6.0588e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 200/500\n",
"3/3 - 0s - loss: 6.8180e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 201/500\n",
"3/3 - 0s - loss: 4.7230e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 202/500\n",
"3/3 - 0s - loss: 3.8133e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 203/500\n",
"3/3 - 0s - loss: 7.4671e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 204/500\n",
"3/3 - 0s - loss: 8.1094e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 205/500\n",
"3/3 - 0s - loss: 7.8872e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 206/500\n",
"3/3 - 0s - loss: 8.7357e-05 - 27ms/epoch - 9ms/step\n",
"Epoch 207/500\n",
"3/3 - 0s - loss: 4.8380e-05 - 26ms/epoch - 9ms/step\n",
"Epoch 208/500\n",
"3/3 - 0s - loss: 7.0697e-05 - 27ms/epoch - 9ms/step\n",
"Epoch 209/500\n",
"3/3 - 0s - loss: 5.2098e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 210/500\n",
"3/3 - 0s - loss: 5.4029e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 211/500\n",
"3/3 - 0s - loss: 2.8489e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 212/500\n",
"3/3 - 0s - loss: 3.3961e-05 - 29ms/epoch - 10ms/step\n",
"Epoch 213/500\n",
"3/3 - 0s - loss: 4.1667e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 214/500\n",
"3/3 - 0s - loss: 3.7597e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 215/500\n",
"3/3 - 0s - loss: 2.7004e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 216/500\n",
"3/3 - 0s - loss: 2.9110e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 217/500\n",
"3/3 - 0s - loss: 3.6687e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 218/500\n",
"3/3 - 0s - loss: 7.2615e-05 - 26ms/epoch - 9ms/step\n",
"Epoch 219/500\n",
"3/3 - 0s - loss: 1.0681e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 220/500\n",
"3/3 - 0s - loss: 1.9565e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 221/500\n",
"3/3 - 0s - loss: 1.9595e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 222/500\n",
"3/3 - 0s - loss: 1.7055e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 223/500\n",
"3/3 - 0s - loss: 1.4371e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 224/500\n",
"3/3 - 0s - loss: 1.0054e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 225/500\n",
"3/3 - 0s - loss: 7.8233e-05 - 26ms/epoch - 9ms/step\n",
"Epoch 226/500\n",
"3/3 - 0s - loss: 2.0859e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 227/500\n",
"3/3 - 0s - loss: 2.3248e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 228/500\n",
"3/3 - 0s - loss: 3.5742e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 229/500\n",
"3/3 - 0s - loss: 3.2267e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 230/500\n",
"3/3 - 0s - loss: 2.6533e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 231/500\n",
"3/3 - 0s - loss: 3.3579e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 232/500\n",
"3/3 - 0s - loss: 2.2141e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 233/500\n",
"3/3 - 0s - loss: 1.3816e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 234/500\n",
"3/3 - 0s - loss: 1.2997e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 235/500\n",
"3/3 - 0s - loss: 1.2696e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 236/500\n",
"3/3 - 0s - loss: 7.3166e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 237/500\n",
"3/3 - 0s - loss: 4.9531e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 238/500\n",
"3/3 - 0s - loss: 5.9576e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 239/500\n",
"3/3 - 0s - loss: 6.9014e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 240/500\n",
"3/3 - 0s - loss: 1.2079e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 241/500\n",
"3/3 - 0s - loss: 1.0165e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 242/500\n",
"3/3 - 0s - loss: 1.1189e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 243/500\n",
"3/3 - 0s - loss: 1.2715e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 244/500\n",
"3/3 - 0s - loss: 2.3746e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 245/500\n",
"3/3 - 0s - loss: 7.2393e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 246/500\n",
"3/3 - 0s - loss: 8.1162e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 247/500\n",
"3/3 - 0s - loss: 6.6941e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 248/500\n",
"3/3 - 0s - loss: 6.1267e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 249/500\n",
"3/3 - 0s - loss: 5.4795e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 250/500\n",
"3/3 - 0s - loss: 8.4581e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 251/500\n",
"3/3 - 0s - loss: 4.3189e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 252/500\n",
"3/3 - 0s - loss: 6.3720e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 253/500\n",
"3/3 - 0s - loss: 8.4664e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 254/500\n",
"3/3 - 0s - loss: 0.0025 - 22ms/epoch - 7ms/step\n",
"Epoch 255/500\n",
"3/3 - 0s - loss: 0.0032 - 23ms/epoch - 8ms/step\n",
"Epoch 256/500\n",
"3/3 - 0s - loss: 0.0040 - 23ms/epoch - 8ms/step\n",
"Epoch 257/500\n",
"3/3 - 0s - loss: 0.0021 - 22ms/epoch - 7ms/step\n",
"Epoch 258/500\n",
"3/3 - 0s - loss: 0.0023 - 24ms/epoch - 8ms/step\n",
"Epoch 259/500\n",
"3/3 - 0s - loss: 0.0034 - 26ms/epoch - 9ms/step\n",
"Epoch 260/500\n",
"3/3 - 0s - loss: 0.0045 - 28ms/epoch - 9ms/step\n",
"Epoch 261/500\n",
"3/3 - 0s - loss: 0.0064 - 24ms/epoch - 8ms/step\n",
"Epoch 262/500\n",
"3/3 - 0s - loss: 0.0050 - 25ms/epoch - 8ms/step\n",
"Epoch 263/500\n",
"3/3 - 0s - loss: 0.0068 - 28ms/epoch - 9ms/step\n",
"Epoch 264/500\n",
"3/3 - 0s - loss: 0.0042 - 24ms/epoch - 8ms/step\n",
"Epoch 265/500\n",
"3/3 - 0s - loss: 0.0047 - 24ms/epoch - 8ms/step\n",
"Epoch 266/500\n",
"3/3 - 0s - loss: 0.0045 - 27ms/epoch - 9ms/step\n",
"Epoch 267/500\n",
"3/3 - 0s - loss: 0.0046 - 25ms/epoch - 8ms/step\n",
"Epoch 268/500\n",
"3/3 - 0s - loss: 0.0032 - 28ms/epoch - 9ms/step\n",
"Epoch 269/500\n",
"3/3 - 0s - loss: 0.0031 - 27ms/epoch - 9ms/step\n",
"Epoch 270/500\n",
"3/3 - 0s - loss: 0.0041 - 24ms/epoch - 8ms/step\n",
"Epoch 271/500\n",
"3/3 - 0s - loss: 0.0034 - 29ms/epoch - 10ms/step\n",
"Epoch 272/500\n",
"3/3 - 0s - loss: 0.0043 - 26ms/epoch - 9ms/step\n",
"Epoch 273/500\n",
"3/3 - 0s - loss: 0.0034 - 25ms/epoch - 8ms/step\n",
"Epoch 274/500\n",
"3/3 - 0s - loss: 0.0036 - 26ms/epoch - 9ms/step\n",
"Epoch 275/500\n",
"3/3 - 0s - loss: 0.0030 - 23ms/epoch - 8ms/step\n",
"Epoch 276/500\n",
"3/3 - 0s - loss: 0.0027 - 26ms/epoch - 9ms/step\n",
"Epoch 277/500\n",
"3/3 - 0s - loss: 0.0033 - 27ms/epoch - 9ms/step\n",
"Epoch 278/500\n",
"3/3 - 0s - loss: 0.0024 - 25ms/epoch - 8ms/step\n",
"Epoch 279/500\n",
"3/3 - 0s - loss: 0.0017 - 23ms/epoch - 8ms/step\n",
"Epoch 280/500\n",
"3/3 - 0s - loss: 0.0017 - 27ms/epoch - 9ms/step\n",
"Epoch 281/500\n",
"3/3 - 0s - loss: 0.0015 - 25ms/epoch - 8ms/step\n",
"Epoch 282/500\n",
"3/3 - 0s - loss: 0.0015 - 27ms/epoch - 9ms/step\n",
"Epoch 283/500\n",
"3/3 - 0s - loss: 0.0019 - 26ms/epoch - 9ms/step\n",
"Epoch 284/500\n",
"3/3 - 0s - loss: 0.0042 - 24ms/epoch - 8ms/step\n",
"Epoch 285/500\n",
"3/3 - 0s - loss: 0.0026 - 24ms/epoch - 8ms/step\n",
"Epoch 286/500\n",
"3/3 - 0s - loss: 0.0035 - 27ms/epoch - 9ms/step\n",
"Epoch 287/500\n",
"3/3 - 0s - loss: 0.0033 - 23ms/epoch - 8ms/step\n",
"Epoch 288/500\n",
"3/3 - 0s - loss: 0.0059 - 23ms/epoch - 8ms/step\n",
"Epoch 289/500\n",
"3/3 - 0s - loss: 0.0073 - 30ms/epoch - 10ms/step\n",
"Epoch 290/500\n",
"3/3 - 0s - loss: 0.0060 - 25ms/epoch - 8ms/step\n",
"Epoch 291/500\n",
"3/3 - 0s - loss: 0.0032 - 23ms/epoch - 8ms/step\n",
"Epoch 292/500\n",
"3/3 - 0s - loss: 0.0022 - 24ms/epoch - 8ms/step\n",
"Epoch 293/500\n",
"3/3 - 0s - loss: 0.0021 - 23ms/epoch - 8ms/step\n",
"Epoch 294/500\n",
"3/3 - 0s - loss: 0.0025 - 24ms/epoch - 8ms/step\n",
"Epoch 295/500\n",
"3/3 - 0s - loss: 0.0011 - 25ms/epoch - 8ms/step\n",
"Epoch 296/500\n",
"3/3 - 0s - loss: 6.3007e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 297/500\n",
"3/3 - 0s - loss: 4.8764e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 298/500\n",
"3/3 - 0s - loss: 5.1926e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 299/500\n",
"3/3 - 0s - loss: 7.6698e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 300/500\n",
"3/3 - 0s - loss: 7.6851e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 301/500\n",
"3/3 - 0s - loss: 7.3852e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 302/500\n",
"3/3 - 0s - loss: 5.9093e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 303/500\n",
"3/3 - 0s - loss: 5.2005e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 304/500\n",
"3/3 - 0s - loss: 8.6820e-04 - 28ms/epoch - 9ms/step\n",
"Epoch 305/500\n",
"3/3 - 0s - loss: 0.0014 - 24ms/epoch - 8ms/step\n",
"Epoch 306/500\n",
"3/3 - 0s - loss: 0.0011 - 24ms/epoch - 8ms/step\n",
"Epoch 307/500\n",
"3/3 - 0s - loss: 0.0016 - 25ms/epoch - 8ms/step\n",
"Epoch 308/500\n",
"3/3 - 0s - loss: 0.0018 - 27ms/epoch - 9ms/step\n",
"Epoch 309/500\n",
"3/3 - 0s - loss: 0.0013 - 24ms/epoch - 8ms/step\n",
"Epoch 310/500\n",
"3/3 - 0s - loss: 0.0011 - 28ms/epoch - 9ms/step\n",
"Epoch 311/500\n",
"3/3 - 0s - loss: 0.0011 - 23ms/epoch - 8ms/step\n",
"Epoch 312/500\n",
"3/3 - 0s - loss: 8.0266e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 313/500\n",
"3/3 - 0s - loss: 4.5370e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 314/500\n",
"3/3 - 0s - loss: 3.2661e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 315/500\n",
"3/3 - 0s - loss: 3.7869e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 316/500\n",
"3/3 - 0s - loss: 3.3854e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 317/500\n",
"3/3 - 0s - loss: 2.9576e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 318/500\n",
"3/3 - 0s - loss: 3.2977e-04 - 28ms/epoch - 9ms/step\n",
"Epoch 319/500\n",
"3/3 - 0s - loss: 5.7998e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 320/500\n",
"3/3 - 0s - loss: 5.9026e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 321/500\n",
"3/3 - 0s - loss: 8.3968e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 322/500\n",
"3/3 - 0s - loss: 0.0012 - 25ms/epoch - 8ms/step\n",
"Epoch 323/500\n",
"3/3 - 0s - loss: 0.0010 - 23ms/epoch - 8ms/step\n",
"Epoch 324/500\n",
"3/3 - 0s - loss: 0.0012 - 29ms/epoch - 10ms/step\n",
"Epoch 325/500\n",
"3/3 - 0s - loss: 9.7469e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 326/500\n",
"3/3 - 0s - loss: 6.8606e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 327/500\n",
"3/3 - 0s - loss: 7.5367e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 328/500\n",
"3/3 - 0s - loss: 9.4139e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 329/500\n",
"3/3 - 0s - loss: 9.8434e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 330/500\n",
"3/3 - 0s - loss: 7.6078e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 331/500\n",
"3/3 - 0s - loss: 4.7059e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 332/500\n",
"3/3 - 0s - loss: 4.6208e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 333/500\n",
"3/3 - 0s - loss: 9.0345e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 334/500\n",
"3/3 - 0s - loss: 4.2509e-04 - 29ms/epoch - 10ms/step\n",
"Epoch 335/500\n",
"3/3 - 0s - loss: 4.0639e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 336/500\n",
"3/3 - 0s - loss: 2.0134e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 337/500\n",
"3/3 - 0s - loss: 1.6695e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 338/500\n",
"3/3 - 0s - loss: 1.7412e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 339/500\n",
"3/3 - 0s - loss: 1.8951e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 340/500\n",
"3/3 - 0s - loss: 2.1406e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 341/500\n",
"3/3 - 0s - loss: 4.2900e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 342/500\n",
"3/3 - 0s - loss: 2.8856e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 343/500\n",
"3/3 - 0s - loss: 2.6222e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 344/500\n",
"3/3 - 0s - loss: 2.6918e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 345/500\n",
"3/3 - 0s - loss: 2.1942e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 346/500\n",
"3/3 - 0s - loss: 4.9804e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 347/500\n",
"3/3 - 0s - loss: 2.5419e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 348/500\n",
"3/3 - 0s - loss: 2.1331e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 349/500\n",
"3/3 - 0s - loss: 2.8852e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 350/500\n",
"3/3 - 0s - loss: 1.8820e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 351/500\n",
"3/3 - 0s - loss: 1.9960e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 352/500\n",
"3/3 - 0s - loss: 5.1399e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 353/500\n",
"3/3 - 0s - loss: 6.0275e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 354/500\n",
"3/3 - 0s - loss: 6.9778e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 355/500\n",
"3/3 - 0s - loss: 6.6073e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 356/500\n",
"3/3 - 0s - loss: 3.8012e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 357/500\n",
"3/3 - 0s - loss: 3.9308e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 358/500\n",
"3/3 - 0s - loss: 2.4925e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 359/500\n",
"3/3 - 0s - loss: 2.4841e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 360/500\n",
"3/3 - 0s - loss: 1.9365e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 361/500\n",
"3/3 - 0s - loss: 1.5913e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 362/500\n",
"3/3 - 0s - loss: 8.7261e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 363/500\n",
"3/3 - 0s - loss: 1.3214e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 364/500\n",
"3/3 - 0s - loss: 1.1200e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 365/500\n",
"3/3 - 0s - loss: 8.5997e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 366/500\n",
"3/3 - 0s - loss: 1.0082e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 367/500\n",
"3/3 - 0s - loss: 8.8290e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 368/500\n",
"3/3 - 0s - loss: 6.7684e-05 - 26ms/epoch - 9ms/step\n",
"Epoch 369/500\n",
"3/3 - 0s - loss: 8.7257e-05 - 21ms/epoch - 7ms/step\n",
"Epoch 370/500\n",
"3/3 - 0s - loss: 6.2689e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 371/500\n",
"3/3 - 0s - loss: 4.3639e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 372/500\n",
"3/3 - 0s - loss: 4.0352e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 373/500\n",
"3/3 - 0s - loss: 5.7696e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 374/500\n",
"3/3 - 0s - loss: 1.5787e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 375/500\n",
"3/3 - 0s - loss: 1.8431e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 376/500\n",
"3/3 - 0s - loss: 3.0870e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 377/500\n",
"3/3 - 0s - loss: 4.0863e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 378/500\n",
"3/3 - 0s - loss: 3.3507e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 379/500\n",
"3/3 - 0s - loss: 3.6209e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 380/500\n",
"3/3 - 0s - loss: 2.1990e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 381/500\n",
"3/3 - 0s - loss: 3.2797e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 382/500\n",
"3/3 - 0s - loss: 2.7877e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 383/500\n",
"3/3 - 0s - loss: 2.8406e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 384/500\n",
"3/3 - 0s - loss: 2.3757e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 385/500\n",
"3/3 - 0s - loss: 3.9205e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 386/500\n",
"3/3 - 0s - loss: 1.2536e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 387/500\n",
"3/3 - 0s - loss: 1.0487e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 388/500\n",
"3/3 - 0s - loss: 8.6785e-05 - 29ms/epoch - 10ms/step\n",
"Epoch 389/500\n",
"3/3 - 0s - loss: 7.2290e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 390/500\n",
"3/3 - 0s - loss: 1.0353e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 391/500\n",
"3/3 - 0s - loss: 6.3309e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 392/500\n",
"3/3 - 0s - loss: 5.1257e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 393/500\n",
"3/3 - 0s - loss: 9.1437e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 394/500\n",
"3/3 - 0s - loss: 6.5759e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 395/500\n",
"3/3 - 0s - loss: 4.8281e-05 - 26ms/epoch - 9ms/step\n",
"Epoch 396/500\n",
"3/3 - 0s - loss: 1.0294e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 397/500\n",
"3/3 - 0s - loss: 5.3189e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 398/500\n",
"3/3 - 0s - loss: 3.6427e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 399/500\n",
"3/3 - 0s - loss: 5.4985e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 400/500\n",
"3/3 - 0s - loss: 4.5614e-05 - 27ms/epoch - 9ms/step\n",
"Epoch 401/500\n",
"3/3 - 0s - loss: 3.4821e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 402/500\n",
"3/3 - 0s - loss: 4.4809e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 403/500\n",
"3/3 - 0s - loss: 5.5315e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 404/500\n",
"3/3 - 0s - loss: 3.5791e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 405/500\n",
"3/3 - 0s - loss: 4.4281e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 406/500\n",
"3/3 - 0s - loss: 3.8300e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 407/500\n",
"3/3 - 0s - loss: 5.8476e-05 - 26ms/epoch - 9ms/step\n",
"Epoch 408/500\n",
"3/3 - 0s - loss: 7.9609e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 409/500\n",
"3/3 - 0s - loss: 3.1058e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 410/500\n",
"3/3 - 0s - loss: 2.5080e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 411/500\n",
"3/3 - 0s - loss: 4.1401e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 412/500\n",
"3/3 - 0s - loss: 0.0010 - 27ms/epoch - 9ms/step\n",
"Epoch 413/500\n",
"3/3 - 0s - loss: 4.5596e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 414/500\n",
"3/3 - 0s - loss: 0.0013 - 26ms/epoch - 9ms/step\n",
"Epoch 415/500\n",
"3/3 - 0s - loss: 0.0015 - 31ms/epoch - 10ms/step\n",
"Epoch 416/500\n",
"3/3 - 0s - loss: 0.0010 - 22ms/epoch - 7ms/step\n",
"Epoch 417/500\n",
"3/3 - 0s - loss: 0.0018 - 28ms/epoch - 9ms/step\n",
"Epoch 418/500\n",
"3/3 - 0s - loss: 0.0025 - 25ms/epoch - 8ms/step\n",
"Epoch 419/500\n",
"3/3 - 0s - loss: 0.0033 - 28ms/epoch - 9ms/step\n",
"Epoch 420/500\n",
"3/3 - 0s - loss: 0.0171 - 28ms/epoch - 9ms/step\n",
"Epoch 421/500\n",
"3/3 - 0s - loss: 0.0179 - 26ms/epoch - 9ms/step\n",
"Epoch 422/500\n",
"3/3 - 0s - loss: 0.0362 - 25ms/epoch - 8ms/step\n",
"Epoch 423/500\n",
"3/3 - 0s - loss: 0.0421 - 29ms/epoch - 10ms/step\n",
"Epoch 424/500\n",
"3/3 - 0s - loss: 0.0694 - 28ms/epoch - 9ms/step\n",
"Epoch 425/500\n",
"3/3 - 0s - loss: 0.0641 - 27ms/epoch - 9ms/step\n",
"Epoch 426/500\n",
"3/3 - 0s - loss: 0.0499 - 28ms/epoch - 9ms/step\n",
"Epoch 427/500\n",
"3/3 - 0s - loss: 0.0507 - 31ms/epoch - 10ms/step\n",
"Epoch 428/500\n",
"3/3 - 0s - loss: 0.0464 - 25ms/epoch - 8ms/step\n",
"Epoch 429/500\n",
"3/3 - 0s - loss: 0.0284 - 27ms/epoch - 9ms/step\n",
"Epoch 430/500\n",
"3/3 - 0s - loss: 0.0232 - 31ms/epoch - 10ms/step\n",
"Epoch 431/500\n",
"3/3 - 0s - loss: 0.0324 - 32ms/epoch - 11ms/step\n",
"Epoch 432/500\n",
"3/3 - 0s - loss: 0.0193 - 28ms/epoch - 9ms/step\n",
"Epoch 433/500\n",
"3/3 - 0s - loss: 0.0096 - 27ms/epoch - 9ms/step\n",
"Epoch 434/500\n",
"3/3 - 0s - loss: 0.0138 - 29ms/epoch - 10ms/step\n",
"Epoch 435/500\n",
"3/3 - 0s - loss: 0.0067 - 27ms/epoch - 9ms/step\n",
"Epoch 436/500\n",
"3/3 - 0s - loss: 0.0071 - 28ms/epoch - 9ms/step\n",
"Epoch 437/500\n",
"3/3 - 0s - loss: 0.0046 - 25ms/epoch - 8ms/step\n",
"Epoch 438/500\n",
"3/3 - 0s - loss: 0.0030 - 28ms/epoch - 9ms/step\n",
"Epoch 439/500\n",
"3/3 - 0s - loss: 0.0043 - 25ms/epoch - 8ms/step\n",
"Epoch 440/500\n",
"3/3 - 0s - loss: 0.0032 - 28ms/epoch - 9ms/step\n",
"Epoch 441/500\n",
"3/3 - 0s - loss: 0.0037 - 27ms/epoch - 9ms/step\n",
"Epoch 442/500\n",
"3/3 - 0s - loss: 0.0029 - 31ms/epoch - 10ms/step\n",
"Epoch 443/500\n",
"3/3 - 0s - loss: 0.0019 - 35ms/epoch - 12ms/step\n",
"Epoch 444/500\n",
"3/3 - 0s - loss: 0.0022 - 31ms/epoch - 10ms/step\n",
"Epoch 445/500\n",
"3/3 - 0s - loss: 0.0020 - 26ms/epoch - 9ms/step\n",
"Epoch 446/500\n",
"3/3 - 0s - loss: 0.0014 - 25ms/epoch - 8ms/step\n",
"Epoch 447/500\n",
"3/3 - 0s - loss: 0.0017 - 23ms/epoch - 8ms/step\n",
"Epoch 448/500\n",
"3/3 - 0s - loss: 0.0014 - 27ms/epoch - 9ms/step\n",
"Epoch 449/500\n",
"3/3 - 0s - loss: 0.0013 - 23ms/epoch - 8ms/step\n",
"Epoch 450/500\n",
"3/3 - 0s - loss: 0.0011 - 25ms/epoch - 8ms/step\n",
"Epoch 451/500\n",
"3/3 - 0s - loss: 0.0011 - 26ms/epoch - 9ms/step\n",
"Epoch 452/500\n",
"3/3 - 0s - loss: 0.0011 - 28ms/epoch - 9ms/step\n",
"Epoch 453/500\n",
"3/3 - 0s - loss: 9.8960e-04 - 31ms/epoch - 10ms/step\n",
"Epoch 454/500\n",
"3/3 - 0s - loss: 0.0011 - 27ms/epoch - 9ms/step\n",
"Epoch 455/500\n",
"3/3 - 0s - loss: 0.0010 - 33ms/epoch - 11ms/step\n",
"Epoch 456/500\n",
"3/3 - 0s - loss: 6.7690e-04 - 30ms/epoch - 10ms/step\n",
"Epoch 457/500\n",
"3/3 - 0s - loss: 7.4283e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 458/500\n",
"3/3 - 0s - loss: 4.8506e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 459/500\n",
"3/3 - 0s - loss: 4.3200e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 460/500\n",
"3/3 - 0s - loss: 3.4006e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 461/500\n",
"3/3 - 0s - loss: 2.3637e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 462/500\n",
"3/3 - 0s - loss: 2.6243e-04 - 29ms/epoch - 10ms/step\n",
"Epoch 463/500\n",
"3/3 - 0s - loss: 2.1565e-04 - 31ms/epoch - 10ms/step\n",
"Epoch 464/500\n",
"3/3 - 0s - loss: 2.2652e-04 - 30ms/epoch - 10ms/step\n",
"Epoch 465/500\n",
"3/3 - 0s - loss: 1.4571e-04 - 28ms/epoch - 9ms/step\n",
"Epoch 466/500\n",
"3/3 - 0s - loss: 1.0692e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 467/500\n",
"3/3 - 0s - loss: 1.2521e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 468/500\n",
"3/3 - 0s - loss: 1.1410e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 469/500\n",
"3/3 - 0s - loss: 9.9680e-05 - 30ms/epoch - 10ms/step\n",
"Epoch 470/500\n",
"3/3 - 0s - loss: 8.0550e-05 - 26ms/epoch - 9ms/step\n",
"Epoch 471/500\n",
"3/3 - 0s - loss: 7.7929e-05 - 28ms/epoch - 9ms/step\n",
"Epoch 472/500\n",
"3/3 - 0s - loss: 8.0573e-05 - 29ms/epoch - 10ms/step\n",
"Epoch 473/500\n",
"3/3 - 0s - loss: 9.0820e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 474/500\n",
"3/3 - 0s - loss: 9.9753e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 475/500\n",
"3/3 - 0s - loss: 9.5174e-05 - 26ms/epoch - 9ms/step\n",
"Epoch 476/500\n",
"3/3 - 0s - loss: 7.7605e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 477/500\n",
"3/3 - 0s - loss: 1.0790e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 478/500\n",
"3/3 - 0s - loss: 7.8259e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 479/500\n",
"3/3 - 0s - loss: 9.2448e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 480/500\n",
"3/3 - 0s - loss: 8.8744e-05 - 28ms/epoch - 9ms/step\n",
"Epoch 481/500\n",
"3/3 - 0s - loss: 7.9199e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 482/500\n",
"3/3 - 0s - loss: 5.2364e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 483/500\n",
"3/3 - 0s - loss: 6.0705e-05 - 27ms/epoch - 9ms/step\n",
"Epoch 484/500\n",
"3/3 - 0s - loss: 9.1279e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 485/500\n",
"3/3 - 0s - loss: 1.7429e-04 - 28ms/epoch - 9ms/step\n",
"Epoch 486/500\n",
"3/3 - 0s - loss: 2.3553e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 487/500\n",
"3/3 - 0s - loss: 1.7582e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 488/500\n",
"3/3 - 0s - loss: 1.3630e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 489/500\n",
"3/3 - 0s - loss: 1.2006e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 490/500\n",
"3/3 - 0s - loss: 1.3694e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 491/500\n",
"3/3 - 0s - loss: 1.7018e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 492/500\n",
"3/3 - 0s - loss: 1.7374e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 493/500\n",
"3/3 - 0s - loss: 1.0515e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 494/500\n",
"3/3 - 0s - loss: 5.7228e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 495/500\n",
"3/3 - 0s - loss: 8.8101e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 496/500\n",
"3/3 - 0s - loss: 6.1029e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 497/500\n",
"3/3 - 0s - loss: 9.8694e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 498/500\n",
"3/3 - 0s - loss: 8.7701e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 499/500\n",
"3/3 - 0s - loss: 5.5231e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 500/500\n",
"3/3 - 0s - loss: 3.7914e-05 - 22ms/epoch - 7ms/step\n",
"14/14 [==============================] - 0s 3ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7ec18041fee0>"
]
},
"metadata": {},
"execution_count": 132
},
{
"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": "0345c639-815b-4aa6-87cd-60512751d04d"
},
"execution_count": 133,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1710205371.7136042\n",
"Tue Mar 12 01:02:51 2024\n"
]
}
]
}
]
}