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vhsu/mnist-example.ipynb - Fully Mnist example code generated by Colab

Last active October 11, 2024 00:36
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mnist-example.ipynb
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mnist-example.ipynb - Fully Mnist example code generated by Colab
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyMr5e9IsCxg3q+sApsIAZ0/",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/vhsu/9cf41e82281b8d6dae2197369a7f5851/untitled0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "oREhOigP8lDt",
"outputId": "b8030cb6-47db-4942-b79b-cb5526fdd9d6"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.8440 - loss: 0.5319 - val_accuracy: 0.9548 - val_loss: 0.1608\n",
"Epoch 2/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 5ms/step - accuracy: 0.9499 - loss: 0.1665 - val_accuracy: 0.9645 - val_loss: 0.1194\n",
"Epoch 3/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9654 - loss: 0.1208 - val_accuracy: 0.9700 - val_loss: 0.1002\n",
"Epoch 4/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 5ms/step - accuracy: 0.9716 - loss: 0.0921 - val_accuracy: 0.9723 - val_loss: 0.0894\n",
"Epoch 5/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.9746 - loss: 0.0803 - val_accuracy: 0.9730 - val_loss: 0.0882\n",
"Epoch 6/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.9780 - loss: 0.0684 - val_accuracy: 0.9760 - val_loss: 0.0821\n",
"Epoch 7/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.9825 - loss: 0.0584 - val_accuracy: 0.9764 - val_loss: 0.0777\n",
"Epoch 8/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 5ms/step - accuracy: 0.9815 - loss: 0.0552 - val_accuracy: 0.9775 - val_loss: 0.0805\n",
"Epoch 9/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.9836 - loss: 0.0494 - val_accuracy: 0.9771 - val_loss: 0.0802\n",
"Epoch 10/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 5ms/step - accuracy: 0.9861 - loss: 0.0420 - val_accuracy: 0.9771 - val_loss: 0.0823\n",
"Epoch 11/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.9874 - loss: 0.0377 - val_accuracy: 0.9772 - val_loss: 0.0842\n",
"Epoch 12/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9882 - loss: 0.0336 - val_accuracy: 0.9788 - val_loss: 0.0797\n",
"Epoch 13/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.9887 - loss: 0.0325 - val_accuracy: 0.9792 - val_loss: 0.0827\n",
"Epoch 14/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9886 - loss: 0.0344 - val_accuracy: 0.9772 - val_loss: 0.0859\n",
"Epoch 15/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 5ms/step - accuracy: 0.9895 - loss: 0.0305 - val_accuracy: 0.9782 - val_loss: 0.0878\n",
"Epoch 16/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 5ms/step - accuracy: 0.9899 - loss: 0.0293 - val_accuracy: 0.9778 - val_loss: 0.0865\n",
"Epoch 17/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.9922 - loss: 0.0253 - val_accuracy: 0.9795 - val_loss: 0.0851\n",
"Epoch 18/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 5ms/step - accuracy: 0.9909 - loss: 0.0244 - val_accuracy: 0.9781 - val_loss: 0.0863\n",
"Epoch 19/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9915 - loss: 0.0295 - val_accuracy: 0.9773 - val_loss: 0.0940\n",
"Epoch 20/20\n",
"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.9906 - loss: 0.0258 - val_accuracy: 0.9771 - val_loss: 0.0914\n",
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9768 - loss: 0.0942\n",
"Test accuracy: 0.9797999858856201\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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nTtVdd93lfcQ5KipKu3fvbnIbt9xyi1555RUtXbpUn3zyia666ipVVlbqww8/1B133KEbbrhBAQEB+slPfqI333xTQ4cOVXBwsEaMGKERI0ackxpPtWLFinpfwO7SpYsefPBBXXvttXr44Yc1f/58XXHFFfriiy+0bt0675na6YKDgzVu3DjNnz9fJSUlWrlypWJjY3X77bd71/vf//3fSkpK0sUXX6z58+crMjJS3333nbKyshQUFKT33nuv0VrP9DFs6cSx+tZbb2nmzJlaunSpXC6Xnn/+edXU1Oi3v/3tGe+fTsXOw3fnt8Yew27sEeja2lpz//33m5CQENOzZ08zZcoUk5eXV+8xbGOMqaioMCkpKSY2NtZ0797dhISEmCuuuMI89dRT5vjx403WdfJR8IbapEmTjDHGHDhwwEyfPt306dPHuFwu8/Of/9wcPHiw3iO8Jx+p/frrr82sWbNMYGCg6du3r1m8eLH54Ycf6m37nXfeMePGjTO9evUyvXr1MsOHDzfJyclmz5493j5n8xj2SVu2bDGXX3656dGjhwkODja/+tWvTFFRkU+fL774wkgyDzzwQLPri4qKMtdcc02TfU4+hv3kk082OD8/P9/ccsstxu12m27dupnIyEhz7bXXmrffftun3+7du018fLzp0aOHiYyMNI888oh56aWXmn0M25gTjxX/x3/8h4mOjjbdunUzbrfbzJo1y+Tn53v7fPzxx2b06NGme/fu9X6e/q6xISePmYZa165djTEnHsO+5557THh4uAkICDBXXnmlyc3NrfeeTz6G/frrr5uUlBQTGhpqAgICzDXXXGO+/fbbetveuXOnmTFjhunXr59xOp0mKirKzJ4922RmZnr7nM1j2Kfux+nTp5ugoCATEBBgJk6caD755JMzWrYzchhz2vkscJ577rnntGzZMuXn57f4y8AAztz5d9ERaEZWVpb+/d//nfAB2hhnQAAAKzgDAgBYQQABAKwggAAAVhBAAAAr2t0XUevq6nTw4EEFBgY2O1IvAKD9McaooqJCERERTY7w0O4C6ODBgxowYIDtMgAAZ6mwsFAXXnhho/Pb3SW483FAPgDojJr7fd5mAbRq1SoNGjRIPXr0UFxcnM8AhU3hshsAdA7N/T5vkwB68803tXTpUqWmpuqzzz7TqFGjNGXKFL/8QTQAQCfRFgPMjRkzxiQnJ3tf19bWmoiICJORkdHssh6Pp9EBCWk0Go3WcZrH42ny973fz4COHz+uHTt2+Pxxpy5duigxMVG5ubn1+ldXV6u8vNynAQA6P78H0JEjR1RbW1tvIMewsDAVFxfX65+RkSGXy+VtPAEHAOcH60/BpaSkyOPxeFthYaHtkgAA54DfvwcUEhKirl27qqSkxGd6SUmJ3G53vf5Op1NOp9PfZQAA2jm/nwF1795do0ePVmZmpndaXV2dMjMzNXbsWH9vDgDQQbXJSAhLly7V3Llz9dOf/lRjxozRypUrVVlZqfnz57fF5gAAHVCbBNCNN96ow4cP66GHHlJxcbEuvfRSbd68mb8wCQDwand/EbW8vFwul8t2GQCAs+TxeBQUFNTofOtPwQEAzk8EEADACgIIAGAFAQQAsIIAAgBYQQABAKwggAAAVhBAAAArCCAAgBUEEADACgIIAGAFAQQAsKJNRsMG0LSEhIQWL5OVldXiZdLT01u8jCSlpaW1ajmgJTgDAgBYQQABAKwggAAAVhBAAAArCCAAgBUEEADACgIIAGAFAQQAsIIAAgBYQQABAKwggAAAVhBAAAArCCAAgBWMhg1YkJqaek62Ex8f36rlWjNad3Z2dqu2hfMXZ0AAACsIIACAFQQQAMAKAggAYAUBBACwggACAFhBAAEArCCAAABWEEAAACsIIACAFQQQAMAKAggAYAWDkQJnKS0trcXLtGawz3OJgUVxLnAGBACwggACAFhBAAEArCCAAABWEEAAACsIIACAFQQQAMAKAggAYAUBBACwggACAFhBAAEArCCAAABWOIwxxnYRpyovL5fL5bJdBnDG2tl/IR8TJkxo1XIMRgp/8Hg8CgoKanQ+Z0AAACsIIACAFX4PoLS0NDkcDp82fPhwf28GANDBtckfpLv44ov14Ycf/msjF/B37wAAvtokGS644AK53e62WDUAoJNok3tAe/fuVUREhAYPHqybb75Z+/fvb7RvdXW1ysvLfRoAoPPzewDFxcVp7dq12rx5s1avXq2CggJdddVVqqioaLB/RkaGXC6Xtw0YMMDfJQEA2qE2/x5QWVmZoqKitGLFCt1222315ldXV6u6utr7ury8nBBCh8L3gICGNfc9oDZ/OqBPnz4aOnSo8vLyGpzvdDrldDrbugwAQDvT5t8DOnr0qPLz8xUeHt7WmwIAdCB+D6B7771XOTk52rdvnz7++GNNnz5dXbt21S9+8Qt/bwoA0IH5/RLcgQMH9Itf/EKlpaXq37+/xo0bp23btql///7+3hQAoANjMFLgFAkJCS1eJisry/+F+InD4bBdAs5jDEYKAGiXCCAAgBUEEADACgIIAGAFAQQAsIIAAgBYQQABAKwggAAAVhBAAAArCCAAgBUEEADACgIIAGBFm/9BOgD+0Zq/UtqawVVbuy2gpTgDAgBYQQABAKwggAAAVhBAAAArCCAAgBUEEADACgIIAGAFAQQAsIIAAgBYQQABAKwggAAAVhBAAAArCCAAgBWMho1OqbWjQKempvq3ED/Kyclp8TKMao32jDMgAIAVBBAAwAoCCABgBQEEALCCAAIAWEEAAQCsIIAAAFYQQAAAKwggAIAVBBAAwAoCCABgBQEEALCCwUjRKbV2MNLWLtdS6enpLV4mLS3N/4UAFnEGBACwggACAFhBAAEArCCAAABWEEAAACsIIACAFQQQAMAKAggAYAUBBACwggACAFhBAAEArCCAAABWOIwxxnYRpyovL5fL5bJdBjq4dnZY1zNhwoQWL5Odne3/QoA25PF4FBQU1Oh8zoAAAFYQQAAAK1ocQFu3btV1112niIgIORwObdy40We+MUYPPfSQwsPDFRAQoMTERO3du9df9QIAOokWB1BlZaVGjRqlVatWNTj/iSee0DPPPKPnn39e27dvV69evTRlyhRVVVWddbEAgM6jxX8RNSkpSUlJSQ3OM8Zo5cqV+s///E/dcMMNkqRXXnlFYWFh2rhxo+bMmXN21QIAOg2/3gMqKChQcXGxEhMTvdNcLpfi4uKUm5vb4DLV1dUqLy/3aQCAzs+vAVRcXCxJCgsL85keFhbmnXe6jIwMuVwubxswYIA/SwIAtFPWn4JLSUmRx+PxtsLCQtslAQDOAb8GkNvtliSVlJT4TC8pKfHOO53T6VRQUJBPAwB0fn4NoOjoaLndbmVmZnqnlZeXa/v27Ro7dqw/NwUA6OBa/BTc0aNHlZeX531dUFCgXbt2KTg4WAMHDtSSJUv06KOPasiQIYqOjtby5csVERGhadOm+bNuAEAH1+IA+vTTT33GsVq6dKkkae7cuVq7dq2WLVumyspKLViwQGVlZRo3bpw2b96sHj16+K9qAECHx2CkaPfS0tJavExqaqr/C2lEawYJbc1gpO1da35O50p7rq0zYzBSAEC7RAABAKwggAAAVhBAAAArCCAAgBUEEADACgIIAGAFAQQAsIIAAgBYQQABAKwggAAAVhBAAAArCCAAgBWMho12r50dovW0ZmTr1oyg3RoJCQmtWi4rK8u/hVjW2v2dnp5+zrbVGTEaNgCgXSKAAABWEEAAACsIIACAFQQQAMAKAggAYAUBBACwggACAFhBAAEArCCAAABWEEAAACsIIACAFRfYLgDnl9YOjonW7bvU1FT/F9IBtfa4y8nJafEyDEZ65jgDAgBYQQABAKwggAAAVhBAAAArCCAAgBUEEADACgIIAGAFAQQAsIIAAgBYQQABAKwggAAAVhBAAAArGIwUrdaaAR6zsrL8X4gfpaenn5PtpKWltXgZBhY99xhYtG1xBgQAsIIAAgBYQQABAKwggAAAVhBAAAArCCAAgBUEEADACgIIAGAFAQQAsIIAAgBYQQABAKwggAAAVjAYKVqtPQ8sei4HkWzP+wFnh8FI2xZnQAAAKwggAIAVLQ6grVu36rrrrlNERIQcDoc2btzoM3/evHlyOBw+berUqf6qFwDQSbQ4gCorKzVq1CitWrWq0T5Tp05VUVGRt73++utnVSQAoPNp8UMISUlJSkpKarKP0+mU2+1udVEAgM6vTe4BZWdnKzQ0VMOGDdOiRYtUWlraaN/q6mqVl5f7NABA5+f3AJo6dapeeeUVZWZm6vHHH1dOTo6SkpJUW1vbYP+MjAy5XC5vGzBggL9LAgC0Q37/HtCcOXO8/x45cqQuueQSxcTEKDs7W5MmTarXPyUlRUuXLvW+Li8vJ4QA4DzQ5o9hDx48WCEhIcrLy2twvtPpVFBQkE8DAHR+bR5ABw4cUGlpqcLDw9t6UwCADqTFl+COHj3qczZTUFCgXbt2KTg4WMHBwUpPT9fMmTPldruVn5+vZcuWKTY2VlOmTPFr4QCAjq3FAfTpp59qwoQJ3tcn79/MnTtXq1ev1u7du/Xyyy+rrKxMERERmjx5sh555BE5nU7/VQ0A6PAcxhhju4hTlZeXy+Vy2S7jvNLawTQTEhL8WwjQRtLT01u1XFpamn8LOc94PJ4m7+szFhwAwAoCCABgBQEEALCCAAIAWEEAAQCsIIAAAFYQQAAAKwggAIAVBBAAwAoCCABgBQEEALCCAAIAWEEAAQCsYDRsqJ0dAkCTWjOyNaNa28Fo2ACAdokAAgBYQQABAKwggAAAVhBAAAArCCAAgBUEEADACgIIAGAFAQQAsIIAAgBYQQABAKwggAAAVlxguwDYl52d3arlEhIS/FoHgPMLZ0AAACsIIACAFQQQAMAKAggAYAUBBACwggACAFhBAAEArCCAAABWEEAAACsIIACAFQQQAMAKAggAYAWDkQLwi9YMajthwgT/F4IOgzMgAIAVBBAAwAoCCABgBQEEALCCAAIAWEEAAQCsIIAAAFYQQAAAKwggAIAVBBAAwAoCCABgBQEEALCCwUihnJycVi2XkJDg30Lgd60ZIFSS0tPTz9m2cP7iDAgAYAUBBACwokUBlJGRocsuu0yBgYEKDQ3VtGnTtGfPHp8+VVVVSk5OVr9+/dS7d2/NnDlTJSUlfi0aANDxtSiAcnJylJycrG3btmnLli2qqanR5MmTVVlZ6e1z991367333tP69euVk5OjgwcPasaMGX4vHADQsbXoIYTNmzf7vF67dq1CQ0O1Y8cOjR8/Xh6PRy+99JJee+01TZw4UZK0Zs0aXXTRRdq2bZsuv/xy/1UOAOjQzuoekMfjkSQFBwdLknbs2KGamholJiZ6+wwfPlwDBw5Ubm5ug+uorq5WeXm5TwMAdH6tDqC6ujotWbJEV155pUaMGCFJKi4uVvfu3dWnTx+fvmFhYSouLm5wPRkZGXK5XN42YMCA1pYEAOhAWh1AycnJ+vLLL/XGG2+cVQEpKSnyeDzeVlhYeFbrAwB0DK36IurixYv1/vvva+vWrbrwwgu9091ut44fP66ysjKfs6CSkhK53e4G1+V0OuV0OltTBgCgA2vRGZAxRosXL9aGDRv00UcfKTo62mf+6NGj1a1bN2VmZnqn7dmzR/v379fYsWP9UzEAoFNo0RlQcnKyXnvtNW3atEmBgYHe+zoul0sBAQFyuVy67bbbtHTpUgUHBysoKEh33nmnxo4dyxNwAAAfLQqg1atXS6o/BtiaNWs0b948SdLTTz+tLl26aObMmaqurtaUKVP03HPP+aVYAEDn4TDGGNtFnKq8vFwul8t2GTgDrRmMNCsry/+FdECtGewzLS3N/4UAbcjj8SgoKKjR+YwFBwCwggACAFhBAAEArCCAAABWEEAAACsIIACAFQQQAMAKAggAYAUBBACwggACAFhBAAEArCCAAABWEEAAACsYDRsA0CYYDRsA0C4RQAAAKwggAIAVBBAAwAoCCABgBQEEALCCAAIAWEEAAQCsIIAAAFYQQAAAKwggAIAVBBAAwAoCCABgBQEEALCCAAIAWEEAAQCsIIAAAFYQQAAAKwggAIAVBBAAwAoCCABgBQEEALCCAAIAWEEAAQCsIIAAAFYQQAAAKwggAIAVBBAAwAoCCABgBQEEALCCAAIAWEEAAQCsIIAAAFYQQAAAKwggAIAVBBAAwAoCCABgBQEEALCCAAIAWEEAAQCsIIAAAFa0KIAyMjJ02WWXKTAwUKGhoZo2bZr27Nnj0ychIUEOh8OnLVy40K9FAwA6vhYFUE5OjpKTk7Vt2zZt2bJFNTU1mjx5siorK3363X777SoqKvK2J554wq9FAwA6vgta0nnz5s0+r9euXavQ0FDt2LFD48eP907v2bOn3G63fyoEAHRKZ3UPyOPxSJKCg4N9pq9bt04hISEaMWKEUlJSdOzYsUbXUV1drfLycp8GADgPmFaqra0111xzjbnyyit9pr/wwgtm8+bNZvfu3ebVV181kZGRZvr06Y2uJzU11Uii0Wg0WidrHo+nyRxpdQAtXLjQREVFmcLCwib7ZWZmGkkmLy+vwflVVVXG4/F4W2FhofWdRqPRaLSzb80FUIvuAZ20ePFivf/++9q6dasuvPDCJvvGxcVJkvLy8hQTE1NvvtPplNPpbE0ZAIAOrEUBZIzRnXfeqQ0bNig7O1vR0dHNLrNr1y5JUnh4eKsKBAB0Ti0KoOTkZL322mvatGmTAgMDVVxcLElyuVwKCAhQfn6+XnvtNf3sZz9Tv379tHv3bt19990aP368LrnkkjZ5AwCADqol933UyHW+NWvWGGOM2b9/vxk/frwJDg42TqfTxMbGmvvuu6/Z64Cn8ng81q9b0mg0Gu3sW3O/+x3/P1jajfLycrlcLttlAADOksfjUVBQUKPzGQsOAGAFAQQAsIIAAgBYQQABAKwggAAAVhBAAAArCCAAgBUEEADACgIIAGAFAQQAsIIAAgBYQQABAKwggAAAVhBAAAArCCAAgBUEEADACgIIAGAFAQQAsIIAAgBYQQABAKwggAAAVhBAAAArCCAAgBUEEADACgIIAGBFuwsgY4ztEgAAftDc7/N2F0AVFRW2SwAA+EFzv88dpp2dctTV1engwYMKDAyUw+HwmVdeXq4BAwaosLBQQUFBliq0j/1wAvvhBPbDCeyHE9rDfjDGqKKiQhEREerSpfHznAvOYU1npEuXLrrwwgub7BMUFHReH2AnsR9OYD+cwH44gf1wgu394HK5mu3T7i7BAQDODwQQAMCKDhVATqdTqampcjqdtkuxiv1wAvvhBPbDCeyHEzrSfmh3DyEAAM4PHeoMCADQeRBAAAArCCAAgBUEEADACgIIAGBFhwmgVatWadCgQerRo4fi4uL0ySef2C7pnEtLS5PD4fBpw4cPt11Wm9u6dauuu+46RUREyOFwaOPGjT7zjTF66KGHFB4eroCAACUmJmrv3r12im1Dze2HefPm1Ts+pk6daqfYNpKRkaHLLrtMgYGBCg0N1bRp07Rnzx6fPlVVVUpOTla/fv3Uu3dvzZw5UyUlJZYqbhtnsh8SEhLqHQ8LFy60VHHDOkQAvfnmm1q6dKlSU1P12WefadSoUZoyZYoOHTpku7Rz7uKLL1ZRUZG3/e1vf7NdUpurrKzUqFGjtGrVqgbnP/HEE3rmmWf0/PPPa/v27erVq5emTJmiqqqqc1xp22puP0jS1KlTfY6P119//RxW2PZycnKUnJysbdu2acuWLaqpqdHkyZNVWVnp7XP33Xfrvffe0/r165WTk6ODBw9qxowZFqv2vzPZD5J0++23+xwPTzzxhKWKG2E6gDFjxpjk5GTv69raWhMREWEyMjIsVnXupaammlGjRtkuwypJZsOGDd7XdXV1xu12myeffNI7rayszDidTvP6669bqPDcOH0/GGPM3LlzzQ033GClHlsOHTpkJJmcnBxjzImffbdu3cz69eu9ff73f//XSDK5ubm2ymxzp+8HY4yJj483d911l72izkC7PwM6fvy4duzYocTERO+0Ll26KDExUbm5uRYrs2Pv3r2KiIjQ4MGDdfPNN2v//v22S7KqoKBAxcXFPseHy+VSXFzceXl8ZGdnKzQ0VMOGDdOiRYtUWlpqu6Q25fF4JEnBwcGSpB07dqimpsbneBg+fLgGDhzYqY+H0/fDSevWrVNISIhGjBihlJQUHTt2zEZ5jWp3o2Gf7siRI6qtrVVYWJjP9LCwMH3zzTeWqrIjLi5Oa9eu1bBhw1RUVKT09HRdddVV+vLLLxUYGGi7PCuKi4slqcHj4+S888XUqVM1Y8YMRUdHKz8/Xw8++KCSkpKUm5urrl272i7P7+rq6rRkyRJdeeWVGjFihKQTx0P37t3Vp08fn76d+XhoaD9I0k033aSoqChFRERo9+7duv/++7Vnzx69++67Fqv11e4DCP+SlJTk/fcll1yiuLg4RUVF6a233tJtt91msTK0B3PmzPH+e+TIkbrkkksUExOj7OxsTZo0yWJlbSM5OVlffvnleXEftCmN7YcFCxZ4/z1y5EiFh4dr0qRJys/PV0xMzLkus0Ht/hJcSEiIunbtWu8plpKSErndbktVtQ99+vTR0KFDlZeXZ7sUa04eAxwf9Q0ePFghISGd8vhYvHix3n//fWVlZfn8/TC3263jx4+rrKzMp39nPR4a2w8NiYuLk6R2dTy0+wDq3r27Ro8erczMTO+0uro6ZWZmauzYsRYrs+/o0aPKz89XeHi47VKsiY6Oltvt9jk+ysvLtX379vP++Dhw4IBKS0s71fFhjNHixYu1YcMGffTRR4qOjvaZP3r0aHXr1s3neNizZ4/279/fqY6H5vZDQ3bt2iVJ7et4sP0UxJl44403jNPpNGvXrjVff/21WbBggenTp48pLi62Xdo5dc8995js7GxTUFBg/v73v5vExEQTEhJiDh06ZLu0NlVRUWF27txpdu7caSSZFStWmJ07d5pvv/3WGGPM7373O9OnTx+zadMms3v3bnPDDTeY6Oho88MPP1iu3L+a2g8VFRXm3nvvNbm5uaagoMB8+OGH5t/+7d/MkCFDTFVVle3S/WbRokXG5XKZ7OxsU1RU5G3Hjh3z9lm4cKEZOHCg+eijj8ynn35qxo4da8aOHWuxav9rbj/k5eWZhx9+2Hz66aemoKDAbNq0yQwePNiMHz/ecuW+OkQAGWPMs88+awYOHGi6d+9uxowZY7Zt22a7pHPuxhtvNOHh4aZ79+4mMjLS3HjjjSYvL892WW0uKyvLSKrX5s6da4w58Sj28uXLTVhYmHE6nWbSpElmz549dotuA03th2PHjpnJkyeb/v37m27dupmoqChz++23d7oPaQ29f0lmzZo13j4//PCDueOOO0zfvn1Nz549zfTp001RUZG9ottAc/th//79Zvz48SY4ONg4nU4TGxtr7rvvPuPxeOwWfhr+HhAAwIp2fw8IANA5EUAAACsIIACAFQQQAMAKAggAYAUBBACwggACAFhBAAEArCCAAABWEEAAACsIIACAFf8PXVapDkcOyqUAAAAASUVORK5CYII=\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"# prompt: build a neural network using keras for the mnist dataset also add a test where you show 10 random elemnts with the corresponding infered result. Try using the best approach to maximaixe accuracy\n",
"\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Load the MNIST dataset\n",
"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
"\n",
"# Preprocess the data\n",
"x_train = x_train.astype('float32') / 255.0\n",
"x_test = x_test.astype('float32') / 255.0\n",
"y_train = keras.utils.to_categorical(y_train, num_classes=10)\n",
"y_test = keras.utils.to_categorical(y_test, num_classes=10)\n",
"\n",
"# Build the model\n",
"model = keras.Sequential([\n",
" keras.layers.Flatten(input_shape=(28, 28)),\n",
" keras.layers.Dense(128, activation='relu'),\n",
" keras.layers.Dropout(0.2),\n",
" keras.layers.Dense(10, activation='softmax')\n",
"])\n",
"\n",
"# Compile the model\n",
"model.compile(optimizer='adam',\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
"# Train the model\n",
"model.fit(x_train, y_train, epochs=20, batch_size=32, validation_split=0.2)\n",
"\n",
"# Evaluate the model\n",
"test_loss, test_acc = model.evaluate(x_test, y_test)\n",
"print('Test accuracy:', test_acc)\n",
"\n",
"# Show 10 random elements with the corresponding inferred result\n",
"random_indices = np.random.choice(x_test.shape[0], size=10, replace=False)\n",
"for index in random_indices:\n",
" image = x_test[index]\n",
" label = np.argmax(y_test[index])\n",
" prediction = np.argmax(model.predict(np.expand_dims(image, axis=0)))\n",
"\n",
" plt.imshow(image, cmap='gray')\n",
" plt.title(f\"True Label: {label}, Predicted Label: {prediction}\")\n",
" plt.show()\n"
]
}
]
}
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