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HarshCasper/Hello-World.ipynb

Last active November 27, 2019 03:49
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Hello-World.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Untitled19.ipynb",
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/HarshCasper/fac855d392b87963c0d88295593c61b9/untitled19.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "4nWLHvcx9lnk",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 79
},
"outputId": "12297546-467c-4f06-919c-76089d6302be"
},
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import keras"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<p style=\"color: red;\">\n",
"The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
"We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
"or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
"<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "HgHM6l-59pRH",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 205
},
"outputId": "d2f47041-d565-47e4-8bce-e22a5c65d2e8"
},
"source": [
"model = keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])\n",
"model.compile(optimizer='sgd', loss='mean_squared_error')\n",
"\n",
"xs = np.array([1,2,3,4,5,6,7,8,9,10], dtype=float)\n",
"ys = np.array([2,4,6,8,10,12,14,16,18,20], dtype=float)\n",
"\n",
"print(xs, '\\n', ys)"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
"\n",
"[ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.] \n",
" [ 2. 4. 6. 8. 10. 12. 14. 16. 18. 20.]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "nwHHzpCl-BSo",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"outputId": "6b093cf6-bc09-47a2-893c-cd212543d40e"
},
"source": [
"plt.plot(xs, ys)"
],
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f2d52432668>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
},
{
"output_type": "display_data",
"data": {
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yaeVqSi6vvCbMfVZ6m3flMmVWCgtWbufYTk15cOwAurVqEHRZIhKjwgp6d18L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\nehGRGKegFxGJcf8fl365WPbdchAAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "zLj-99H_-FXa",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "283e695d-1c88-4b12-bf58-6a89321864a9"
},
"source": [
"model.fit(xs, ys, epochs=500)"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
"\n",
"Epoch 1/500\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
"\n",
"10/10 [==============================] - 1s 73ms/step - loss: 13.5489\n",
"Epoch 2/500\n",
"10/10 [==============================] - 0s 203us/step - loss: 0.6231\n",
"Epoch 3/500\n",
"10/10 [==============================] - 0s 121us/step - loss: 0.0300\n",
"Epoch 4/500\n",
"10/10 [==============================] - 0s 102us/step - loss: 0.0028\n",
"Epoch 5/500\n",
"10/10 [==============================] - 0s 104us/step - loss: 0.0015\n",
"Epoch 6/500\n",
"10/10 [==============================] - 0s 144us/step - loss: 0.0014\n",
"Epoch 7/500\n",
"10/10 [==============================] - 0s 89us/step - loss: 0.0014\n",
"Epoch 8/500\n",
"10/10 [==============================] - 0s 95us/step - loss: 0.0014\n",
"Epoch 9/500\n",
"10/10 [==============================] - 0s 130us/step - loss: 0.0014\n",
"Epoch 10/500\n",
"10/10 [==============================] - 0s 110us/step - loss: 0.0014\n",
"Epoch 11/500\n",
"10/10 [==============================] - 0s 104us/step - loss: 0.0014\n",
"Epoch 12/500\n",
"10/10 [==============================] - 0s 91us/step - loss: 0.0014\n",
"Epoch 13/500\n",
"10/10 [==============================] - 0s 90us/step - loss: 0.0014\n",
"Epoch 14/500\n",
"10/10 [==============================] - 0s 174us/step - loss: 0.0013\n",
"Epoch 15/500\n",
"10/10 [==============================] - 0s 96us/step - loss: 0.0013\n",
"Epoch 16/500\n",
"10/10 [==============================] - 0s 109us/step - loss: 0.0013\n",
"Epoch 17/500\n",
"10/10 [==============================] - 0s 98us/step - loss: 0.0013\n",
"Epoch 18/500\n",
"10/10 [==============================] - 0s 89us/step - loss: 0.0013\n",
"Epoch 19/500\n",
"10/10 [==============================] - 0s 97us/step - loss: 0.0013\n",
"Epoch 20/500\n",
"10/10 [==============================] - 0s 124us/step - loss: 0.0013\n",
"Epoch 21/500\n",
"10/10 [==============================] - 0s 115us/step - loss: 0.0013\n",
"Epoch 22/500\n",
"10/10 [==============================] - 0s 115us/step - loss: 0.0013\n",
"Epoch 23/500\n",
"10/10 [==============================] - 0s 116us/step - loss: 0.0012\n",
"Epoch 24/500\n",
"10/10 [==============================] - 0s 117us/step - loss: 0.0012\n",
"Epoch 25/500\n",
"10/10 [==============================] - 0s 126us/step - loss: 0.0012\n",
"Epoch 26/500\n",
"10/10 [==============================] - 0s 151us/step - loss: 0.0012\n",
"Epoch 27/500\n",
"10/10 [==============================] - 0s 171us/step - loss: 0.0012\n",
"Epoch 28/500\n",
"10/10 [==============================] - 0s 153us/step - loss: 0.0012\n",
"Epoch 29/500\n",
"10/10 [==============================] - 0s 116us/step - loss: 0.0012\n",
"Epoch 30/500\n",
"10/10 [==============================] - 0s 123us/step - loss: 0.0012\n",
"Epoch 31/500\n",
"10/10 [==============================] - 0s 178us/step - loss: 0.0012\n",
"Epoch 32/500\n",
"10/10 [==============================] - 0s 131us/step - loss: 0.0012\n",
"Epoch 33/500\n",
"10/10 [==============================] - 0s 133us/step - loss: 0.0011\n",
"Epoch 34/500\n",
"10/10 [==============================] - 0s 115us/step - loss: 0.0011\n",
"Epoch 35/500\n",
"10/10 [==============================] - 0s 130us/step - loss: 0.0011\n",
"Epoch 36/500\n",
"10/10 [==============================] - 0s 131us/step - loss: 0.0011\n",
"Epoch 37/500\n",
"10/10 [==============================] - 0s 311us/step - loss: 0.0011\n",
"Epoch 38/500\n",
"10/10 [==============================] - 0s 150us/step - loss: 0.0011\n",
"Epoch 39/500\n",
"10/10 [==============================] - 0s 140us/step - loss: 0.0011\n",
"Epoch 40/500\n",
"10/10 [==============================] - 0s 126us/step - loss: 0.0011\n",
"Epoch 41/500\n",
"10/10 [==============================] - 0s 108us/step - loss: 0.0011\n",
"Epoch 42/500\n",
"10/10 [==============================] - 0s 122us/step - loss: 0.0011\n",
"Epoch 43/500\n",
"10/10 [==============================] - 0s 137us/step - loss: 0.0010\n",
"Epoch 44/500\n",
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"Epoch 146/500\n",
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"Epoch 154/500\n",
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"Epoch 156/500\n",
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"Epoch 157/500\n",
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"Epoch 159/500\n",
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"Epoch 161/500\n",
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"Epoch 162/500\n",
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"Epoch 165/500\n",
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"Epoch 166/500\n",
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"Epoch 170/500\n",
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"Epoch 171/500\n",
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"Epoch 172/500\n",
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"Epoch 173/500\n",
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"Epoch 174/500\n",
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"Epoch 175/500\n",
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"Epoch 176/500\n",
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"Epoch 177/500\n",
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"Epoch 179/500\n",
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"Epoch 180/500\n",
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"Epoch 181/500\n",
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"Epoch 182/500\n",
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"Epoch 183/500\n",
"10/10 [==============================] - 0s 550us/step - loss: 3.2287e-04\n",
"Epoch 184/500\n",
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"Epoch 185/500\n",
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"Epoch 186/500\n",
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"Epoch 187/500\n",
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"Epoch 188/500\n",
"10/10 [==============================] - 0s 142us/step - loss: 3.0957e-04\n",
"Epoch 189/500\n",
"10/10 [==============================] - 0s 172us/step - loss: 3.0697e-04\n",
"Epoch 190/500\n",
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"Epoch 191/500\n",
"10/10 [==============================] - 0s 175us/step - loss: 3.0184e-04\n",
"Epoch 192/500\n",
"10/10 [==============================] - 0s 170us/step - loss: 2.9931e-04\n",
"Epoch 193/500\n",
"10/10 [==============================] - 0s 171us/step - loss: 2.9680e-04\n",
"Epoch 194/500\n",
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"Epoch 195/500\n",
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"Epoch 196/500\n",
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"Epoch 197/500\n",
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"Epoch 198/500\n",
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"Epoch 199/500\n",
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"Epoch 200/500\n",
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"Epoch 201/500\n",
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"Epoch 202/500\n",
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"Epoch 203/500\n",
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"Epoch 204/500\n",
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"Epoch 205/500\n",
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"Epoch 206/500\n",
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"Epoch 207/500\n",
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"Epoch 208/500\n",
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"Epoch 209/500\n",
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"Epoch 210/500\n",
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"Epoch 211/500\n",
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"Epoch 212/500\n",
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"Epoch 213/500\n",
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"Epoch 214/500\n",
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"Epoch 215/500\n",
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"Epoch 216/500\n",
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"Epoch 217/500\n",
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"Epoch 218/500\n",
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"Epoch 219/500\n",
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"Epoch 220/500\n",
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"Epoch 221/500\n",
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"Epoch 222/500\n",
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"Epoch 223/500\n",
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"Epoch 224/500\n",
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"Epoch 225/500\n",
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"Epoch 226/500\n",
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"Epoch 227/500\n",
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"Epoch 228/500\n",
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"Epoch 229/500\n",
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"Epoch 230/500\n",
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"Epoch 231/500\n",
"10/10 [==============================] - 0s 432us/step - loss: 2.1555e-04\n",
"Epoch 232/500\n",
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"Epoch 233/500\n",
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"Epoch 234/500\n",
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"Epoch 235/500\n",
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"Epoch 236/500\n",
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"Epoch 237/500\n",
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"Epoch 238/500\n",
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"Epoch 239/500\n",
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"Epoch 240/500\n",
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"Epoch 241/500\n",
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"Epoch 242/500\n",
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"Epoch 243/500\n",
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"Epoch 244/500\n",
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"Epoch 245/500\n",
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"Epoch 246/500\n",
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"Epoch 247/500\n",
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"Epoch 248/500\n",
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"Epoch 319/500\n",
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"Epoch 320/500\n",
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"10/10 [==============================] - 0s 117us/step - loss: 9.9374e-05\n",
"Epoch 324/500\n",
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"Epoch 325/500\n",
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"Epoch 327/500\n",
"10/10 [==============================] - 0s 168us/step - loss: 9.6086e-05\n",
"Epoch 328/500\n",
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"Epoch 329/500\n",
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"Epoch 330/500\n",
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"Epoch 331/500\n",
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"10/10 [==============================] - 0s 231us/step - loss: 9.2126e-05\n",
"Epoch 333/500\n",
"10/10 [==============================] - 0s 167us/step - loss: 9.1349e-05\n",
"Epoch 334/500\n",
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"Epoch 335/500\n",
"10/10 [==============================] - 0s 171us/step - loss: 8.9825e-05\n",
"Epoch 336/500\n",
"10/10 [==============================] - 0s 178us/step - loss: 8.9073e-05\n",
"Epoch 337/500\n",
"10/10 [==============================] - 0s 155us/step - loss: 8.8329e-05\n",
"Epoch 338/500\n",
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"Epoch 339/500\n",
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"Epoch 340/500\n",
"10/10 [==============================] - 0s 232us/step - loss: 8.6128e-05\n",
"Epoch 341/500\n",
"10/10 [==============================] - 0s 179us/step - loss: 8.5405e-05\n",
"Epoch 342/500\n",
"10/10 [==============================] - 0s 165us/step - loss: 8.4690e-05\n",
"Epoch 343/500\n",
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"Epoch 344/500\n",
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"Epoch 345/500\n",
"10/10 [==============================] - 0s 188us/step - loss: 8.2578e-05\n",
"Epoch 346/500\n",
"10/10 [==============================] - 0s 161us/step - loss: 8.1889e-05\n",
"Epoch 347/500\n",
"10/10 [==============================] - 0s 169us/step - loss: 8.1200e-05\n",
"Epoch 348/500\n",
"10/10 [==============================] - 0s 159us/step - loss: 8.0519e-05\n",
"Epoch 349/500\n",
"10/10 [==============================] - 0s 140us/step - loss: 7.9841e-05\n",
"Epoch 350/500\n",
"10/10 [==============================] - 0s 212us/step - loss: 7.9176e-05\n",
"Epoch 351/500\n",
"10/10 [==============================] - 0s 200us/step - loss: 7.8512e-05\n",
"Epoch 352/500\n",
"10/10 [==============================] - 0s 169us/step - loss: 7.7850e-05\n",
"Epoch 353/500\n",
"10/10 [==============================] - 0s 207us/step - loss: 7.7199e-05\n",
"Epoch 354/500\n",
"10/10 [==============================] - 0s 147us/step - loss: 7.6553e-05\n",
"Epoch 355/500\n",
"10/10 [==============================] - 0s 214us/step - loss: 7.5913e-05\n",
"Epoch 356/500\n",
"10/10 [==============================] - 0s 164us/step - loss: 7.5275e-05\n",
"Epoch 357/500\n",
"10/10 [==============================] - 0s 142us/step - loss: 7.4643e-05\n",
"Epoch 358/500\n",
"10/10 [==============================] - 0s 141us/step - loss: 7.4016e-05\n",
"Epoch 359/500\n",
"10/10 [==============================] - 0s 144us/step - loss: 7.3397e-05\n",
"Epoch 360/500\n",
"10/10 [==============================] - 0s 141us/step - loss: 7.2785e-05\n",
"Epoch 361/500\n",
"10/10 [==============================] - 0s 117us/step - loss: 7.2174e-05\n",
"Epoch 362/500\n",
"10/10 [==============================] - 0s 119us/step - loss: 7.1568e-05\n",
"Epoch 363/500\n",
"10/10 [==============================] - 0s 121us/step - loss: 7.0966e-05\n",
"Epoch 364/500\n",
"10/10 [==============================] - 0s 120us/step - loss: 7.0371e-05\n",
"Epoch 365/500\n",
"10/10 [==============================] - 0s 122us/step - loss: 6.9779e-05\n",
"Epoch 366/500\n",
"10/10 [==============================] - 0s 116us/step - loss: 6.9197e-05\n",
"Epoch 367/500\n",
"10/10 [==============================] - 0s 119us/step - loss: 6.8620e-05\n",
"Epoch 368/500\n",
"10/10 [==============================] - 0s 116us/step - loss: 6.8044e-05\n",
"Epoch 369/500\n",
"10/10 [==============================] - 0s 118us/step - loss: 6.7471e-05\n",
"Epoch 370/500\n",
"10/10 [==============================] - 0s 135us/step - loss: 6.6905e-05\n",
"Epoch 371/500\n",
"10/10 [==============================] - 0s 112us/step - loss: 6.6347e-05\n",
"Epoch 372/500\n",
"10/10 [==============================] - 0s 119us/step - loss: 6.5791e-05\n",
"Epoch 373/500\n",
"10/10 [==============================] - 0s 122us/step - loss: 6.5237e-05\n",
"Epoch 374/500\n",
"10/10 [==============================] - 0s 125us/step - loss: 6.4691e-05\n",
"Epoch 375/500\n",
"10/10 [==============================] - 0s 136us/step - loss: 6.4148e-05\n",
"Epoch 376/500\n",
"10/10 [==============================] - 0s 126us/step - loss: 6.3611e-05\n",
"Epoch 377/500\n",
"10/10 [==============================] - 0s 124us/step - loss: 6.3079e-05\n",
"Epoch 378/500\n",
"10/10 [==============================] - 0s 148us/step - loss: 6.2549e-05\n",
"Epoch 379/500\n",
"10/10 [==============================] - 0s 118us/step - loss: 6.2029e-05\n",
"Epoch 380/500\n",
"10/10 [==============================] - 0s 134us/step - loss: 6.1504e-05\n",
"Epoch 381/500\n",
"10/10 [==============================] - 0s 121us/step - loss: 6.0989e-05\n",
"Epoch 382/500\n",
"10/10 [==============================] - 0s 116us/step - loss: 6.0476e-05\n",
"Epoch 383/500\n",
"10/10 [==============================] - 0s 113us/step - loss: 5.9970e-05\n",
"Epoch 384/500\n",
"10/10 [==============================] - 0s 114us/step - loss: 5.9469e-05\n",
"Epoch 385/500\n",
"10/10 [==============================] - 0s 110us/step - loss: 5.8970e-05\n",
"Epoch 386/500\n",
"10/10 [==============================] - 0s 125us/step - loss: 5.8475e-05\n",
"Epoch 387/500\n",
"10/10 [==============================] - 0s 118us/step - loss: 5.7988e-05\n",
"Epoch 388/500\n",
"10/10 [==============================] - 0s 117us/step - loss: 5.7501e-05\n",
"Epoch 389/500\n",
"10/10 [==============================] - 0s 128us/step - loss: 5.7019e-05\n",
"Epoch 390/500\n",
"10/10 [==============================] - 0s 120us/step - loss: 5.6540e-05\n",
"Epoch 391/500\n",
"10/10 [==============================] - 0s 117us/step - loss: 5.6067e-05\n",
"Epoch 392/500\n",
"10/10 [==============================] - 0s 122us/step - loss: 5.5595e-05\n",
"Epoch 393/500\n",
"10/10 [==============================] - 0s 120us/step - loss: 5.5132e-05\n",
"Epoch 394/500\n",
"10/10 [==============================] - 0s 119us/step - loss: 5.4668e-05\n",
"Epoch 395/500\n",
"10/10 [==============================] - 0s 132us/step - loss: 5.4208e-05\n",
"Epoch 396/500\n",
"10/10 [==============================] - 0s 115us/step - loss: 5.3755e-05\n",
"Epoch 397/500\n",
"10/10 [==============================] - 0s 123us/step - loss: 5.3306e-05\n",
"Epoch 398/500\n",
"10/10 [==============================] - 0s 124us/step - loss: 5.2861e-05\n",
"Epoch 399/500\n",
"10/10 [==============================] - 0s 136us/step - loss: 5.2421e-05\n",
"Epoch 400/500\n",
"10/10 [==============================] - 0s 193us/step - loss: 5.1978e-05\n",
"Epoch 401/500\n",
"10/10 [==============================] - 0s 80us/step - loss: 5.1539e-05\n",
"Epoch 402/500\n",
"10/10 [==============================] - 0s 878us/step - loss: 5.1107e-05\n",
"Epoch 403/500\n",
"10/10 [==============================] - 0s 193us/step - loss: 5.0678e-05\n",
"Epoch 404/500\n",
"10/10 [==============================] - 0s 305us/step - loss: 5.0254e-05\n",
"Epoch 405/500\n",
"10/10 [==============================] - 0s 150us/step - loss: 4.9834e-05\n",
"Epoch 406/500\n",
"10/10 [==============================] - 0s 109us/step - loss: 4.9420e-05\n",
"Epoch 407/500\n",
"10/10 [==============================] - 0s 147us/step - loss: 4.9005e-05\n",
"Epoch 408/500\n",
"10/10 [==============================] - 0s 118us/step - loss: 4.8591e-05\n",
"Epoch 409/500\n",
"10/10 [==============================] - 0s 97us/step - loss: 4.8182e-05\n",
"Epoch 410/500\n",
"10/10 [==============================] - 0s 132us/step - loss: 4.7778e-05\n",
"Epoch 411/500\n",
"10/10 [==============================] - 0s 96us/step - loss: 4.7379e-05\n",
"Epoch 412/500\n",
"10/10 [==============================] - 0s 92us/step - loss: 4.6983e-05\n",
"Epoch 413/500\n",
"10/10 [==============================] - 0s 139us/step - loss: 4.6592e-05\n",
"Epoch 414/500\n",
"10/10 [==============================] - 0s 100us/step - loss: 4.6200e-05\n",
"Epoch 415/500\n",
"10/10 [==============================] - 0s 97us/step - loss: 4.5813e-05\n",
"Epoch 416/500\n",
"10/10 [==============================] - 0s 99us/step - loss: 4.5430e-05\n",
"Epoch 417/500\n",
"10/10 [==============================] - 0s 165us/step - loss: 4.5051e-05\n",
"Epoch 418/500\n",
"10/10 [==============================] - 0s 100us/step - loss: 4.4670e-05\n",
"Epoch 419/500\n",
"10/10 [==============================] - 0s 96us/step - loss: 4.4295e-05\n",
"Epoch 420/500\n",
"10/10 [==============================] - 0s 96us/step - loss: 4.3925e-05\n",
"Epoch 421/500\n",
"10/10 [==============================] - 0s 97us/step - loss: 4.3559e-05\n",
"Epoch 422/500\n",
"10/10 [==============================] - 0s 97us/step - loss: 4.3190e-05\n",
"Epoch 423/500\n",
"10/10 [==============================] - 0s 134us/step - loss: 4.2827e-05\n",
"Epoch 424/500\n",
"10/10 [==============================] - 0s 123us/step - loss: 4.2468e-05\n",
"Epoch 425/500\n",
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"Epoch 426/500\n",
"10/10 [==============================] - 0s 133us/step - loss: 4.1760e-05\n",
"Epoch 427/500\n",
"10/10 [==============================] - 0s 98us/step - loss: 4.1412e-05\n",
"Epoch 428/500\n",
"10/10 [==============================] - 0s 99us/step - loss: 4.1066e-05\n",
"Epoch 429/500\n",
"10/10 [==============================] - 0s 105us/step - loss: 4.0721e-05\n",
"Epoch 430/500\n",
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"Epoch 431/500\n",
"10/10 [==============================] - 0s 103us/step - loss: 4.0042e-05\n",
"Epoch 432/500\n",
"10/10 [==============================] - 0s 98us/step - loss: 3.9708e-05\n",
"Epoch 433/500\n",
"10/10 [==============================] - 0s 96us/step - loss: 3.9375e-05\n",
"Epoch 434/500\n",
"10/10 [==============================] - 0s 97us/step - loss: 3.9043e-05\n",
"Epoch 435/500\n",
"10/10 [==============================] - 0s 98us/step - loss: 3.8714e-05\n",
"Epoch 436/500\n",
"10/10 [==============================] - 0s 92us/step - loss: 3.8390e-05\n",
"Epoch 437/500\n",
"10/10 [==============================] - 0s 137us/step - loss: 3.8068e-05\n",
"Epoch 438/500\n",
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"Epoch 439/500\n",
"10/10 [==============================] - 0s 92us/step - loss: 3.7433e-05\n",
"Epoch 440/500\n",
"10/10 [==============================] - 0s 99us/step - loss: 3.7118e-05\n",
"Epoch 441/500\n",
"10/10 [==============================] - 0s 98us/step - loss: 3.6808e-05\n",
"Epoch 442/500\n",
"10/10 [==============================] - 0s 97us/step - loss: 3.6499e-05\n",
"Epoch 443/500\n",
"10/10 [==============================] - 0s 132us/step - loss: 3.6195e-05\n",
"Epoch 444/500\n",
"10/10 [==============================] - 0s 99us/step - loss: 3.5890e-05\n",
"Epoch 445/500\n",
"10/10 [==============================] - 0s 135us/step - loss: 3.5590e-05\n",
"Epoch 446/500\n",
"10/10 [==============================] - 0s 97us/step - loss: 3.5290e-05\n",
"Epoch 447/500\n",
"10/10 [==============================] - 0s 101us/step - loss: 3.4995e-05\n",
"Epoch 448/500\n",
"10/10 [==============================] - 0s 99us/step - loss: 3.4701e-05\n",
"Epoch 449/500\n",
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"Epoch 450/500\n",
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"Epoch 451/500\n",
"10/10 [==============================] - 0s 132us/step - loss: 3.3837e-05\n",
"Epoch 452/500\n",
"10/10 [==============================] - 0s 95us/step - loss: 3.3554e-05\n",
"Epoch 453/500\n",
"10/10 [==============================] - 0s 97us/step - loss: 3.3271e-05\n",
"Epoch 454/500\n",
"10/10 [==============================] - 0s 97us/step - loss: 3.2993e-05\n",
"Epoch 455/500\n",
"10/10 [==============================] - 0s 96us/step - loss: 3.2717e-05\n",
"Epoch 456/500\n",
"10/10 [==============================] - 0s 101us/step - loss: 3.2441e-05\n",
"Epoch 457/500\n",
"10/10 [==============================] - 0s 104us/step - loss: 3.2169e-05\n",
"Epoch 458/500\n",
"10/10 [==============================] - 0s 103us/step - loss: 3.1899e-05\n",
"Epoch 459/500\n",
"10/10 [==============================] - 0s 127us/step - loss: 3.1633e-05\n",
"Epoch 460/500\n",
"10/10 [==============================] - 0s 102us/step - loss: 3.1367e-05\n",
"Epoch 461/500\n",
"10/10 [==============================] - 0s 137us/step - loss: 3.1107e-05\n",
"Epoch 462/500\n",
"10/10 [==============================] - 0s 97us/step - loss: 3.0845e-05\n",
"Epoch 463/500\n",
"10/10 [==============================] - 0s 171us/step - loss: 3.0588e-05\n",
"Epoch 464/500\n",
"10/10 [==============================] - 0s 99us/step - loss: 3.0332e-05\n",
"Epoch 465/500\n",
"10/10 [==============================] - 0s 96us/step - loss: 3.0075e-05\n",
"Epoch 466/500\n",
"10/10 [==============================] - 0s 97us/step - loss: 2.9825e-05\n",
"Epoch 467/500\n",
"10/10 [==============================] - 0s 99us/step - loss: 2.9574e-05\n",
"Epoch 468/500\n",
"10/10 [==============================] - 0s 117us/step - loss: 2.9326e-05\n",
"Epoch 469/500\n",
"10/10 [==============================] - 0s 210us/step - loss: 2.9080e-05\n",
"Epoch 470/500\n",
"10/10 [==============================] - 0s 133us/step - loss: 2.8835e-05\n",
"Epoch 471/500\n",
"10/10 [==============================] - 0s 195us/step - loss: 2.8595e-05\n",
"Epoch 472/500\n",
"10/10 [==============================] - 0s 119us/step - loss: 2.8353e-05\n",
"Epoch 473/500\n",
"10/10 [==============================] - 0s 111us/step - loss: 2.8118e-05\n",
"Epoch 474/500\n",
"10/10 [==============================] - 0s 114us/step - loss: 2.7881e-05\n",
"Epoch 475/500\n",
"10/10 [==============================] - 0s 101us/step - loss: 2.7648e-05\n",
"Epoch 476/500\n",
"10/10 [==============================] - 0s 97us/step - loss: 2.7415e-05\n",
"Epoch 477/500\n",
"10/10 [==============================] - 0s 100us/step - loss: 2.7185e-05\n",
"Epoch 478/500\n",
"10/10 [==============================] - 0s 127us/step - loss: 2.6960e-05\n",
"Epoch 479/500\n",
"10/10 [==============================] - 0s 96us/step - loss: 2.6730e-05\n",
"Epoch 480/500\n",
"10/10 [==============================] - 0s 184us/step - loss: 2.6510e-05\n",
"Epoch 481/500\n",
"10/10 [==============================] - 0s 87us/step - loss: 2.6287e-05\n",
"Epoch 482/500\n",
"10/10 [==============================] - 0s 107us/step - loss: 2.6067e-05\n",
"Epoch 483/500\n",
"10/10 [==============================] - 0s 99us/step - loss: 2.5848e-05\n",
"Epoch 484/500\n",
"10/10 [==============================] - 0s 126us/step - loss: 2.5630e-05\n",
"Epoch 485/500\n",
"10/10 [==============================] - 0s 113us/step - loss: 2.5413e-05\n",
"Epoch 486/500\n",
"10/10 [==============================] - 0s 96us/step - loss: 2.5202e-05\n",
"Epoch 487/500\n",
"10/10 [==============================] - 0s 164us/step - loss: 2.4990e-05\n",
"Epoch 488/500\n",
"10/10 [==============================] - 0s 102us/step - loss: 2.4784e-05\n",
"Epoch 489/500\n",
"10/10 [==============================] - 0s 96us/step - loss: 2.4576e-05\n",
"Epoch 490/500\n",
"10/10 [==============================] - 0s 100us/step - loss: 2.4368e-05\n",
"Epoch 491/500\n",
"10/10 [==============================] - 0s 98us/step - loss: 2.4165e-05\n",
"Epoch 492/500\n",
"10/10 [==============================] - 0s 100us/step - loss: 2.3961e-05\n",
"Epoch 493/500\n",
"10/10 [==============================] - 0s 176us/step - loss: 2.3760e-05\n",
"Epoch 494/500\n",
"10/10 [==============================] - 0s 102us/step - loss: 2.3564e-05\n",
"Epoch 495/500\n",
"10/10 [==============================] - 0s 95us/step - loss: 2.3366e-05\n",
"Epoch 496/500\n",
"10/10 [==============================] - 0s 95us/step - loss: 2.3170e-05\n",
"Epoch 497/500\n",
"10/10 [==============================] - 0s 118us/step - loss: 2.2977e-05\n",
"Epoch 498/500\n",
"10/10 [==============================] - 0s 139us/step - loss: 2.2781e-05\n",
"Epoch 499/500\n",
"10/10 [==============================] - 0s 98us/step - loss: 2.2591e-05\n",
"Epoch 500/500\n",
"10/10 [==============================] - 0s 102us/step - loss: 2.2403e-05\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f2d91ce7978>"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "JCf-O19I-G6m",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "ab1e1b58-5379-4ab3-da73-764772b8bd97"
},
"source": [
"model.predict([88])"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[175.88148]], dtype=float32)"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "uxEAlXyv-M5M",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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