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Complete Handwritten Image Recognition using TensorFlow and Keras. #1

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235 changes: 235 additions & 0 deletions Tensorflow_Handwritten_Digit_Reader.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 60000 samples\n",
"Epoch 1/5\n",
"60000/60000 [==============================] - 8s 131us/sample - loss: 0.2623 - accuracy: 0.9238\n",
"Epoch 2/5\n",
"60000/60000 [==============================] - 7s 120us/sample - loss: 0.1086 - accuracy: 0.9663\n",
"Epoch 3/5\n",
"60000/60000 [==============================] - 7s 122us/sample - loss: 0.0722 - accuracy: 0.9775\n",
"Epoch 4/5\n",
"60000/60000 [==============================] - 7s 121us/sample - loss: 0.0527 - accuracy: 0.9833\n",
"Epoch 5/5\n",
"60000/60000 [==============================] - 7s 120us/sample - loss: 0.0402 - accuracy: 0.9871\n"
]
},
{
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x1ad88ab50c8>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import tensorflow as tf\n",
"\n",
"mnist = tf.keras.datasets.mnist #28x28 images of hand written images 0-9\n",
"\n",
"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
"\n",
"x_train = tf.keras.utils.normalize(x_train, axis=1)\n",
"x_test = tf.keras.utils.normalize(x_test, axis=1)\n",
"\n",
"model = tf.keras.models.Sequential()\n",
"model.add(tf.keras.layers.Flatten())\n",
"model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
"model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
"model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))\n",
"\n",
"model.compile(optimizer='adam',\n",
" loss='sparse_categorical_crossentropy',\n",
" metrics=['accuracy']\n",
" )\n",
"model.fit(x_train, y_train, epochs = 5)\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10000/10000 [==============================] - 1s 80us/sample - loss: 0.0868 - accuracy: 0.9755\n",
"0.0868174561039661 0.9755\n"
]
}
],
"source": [
"val_loss, val_acc = model.evaluate(x_test, y_test)\n",
"print(val_loss, val_acc)\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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fbXndOFwWCIIj6IAgCDsQBGEHgiDsQBCEHQiCsANBEHYgiL8CObYutWTbTN8AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.imshow(x_train[0], cmap = plt.cm.binary)\n",
"plt.show()\n",
"\n",
"#print(x_train[0])"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From C:\\Users\\M QASIM\\Anaconda3\\envs\\tensorenv\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"If using Keras pass *_constraint arguments to layers.\n",
"INFO:tensorflow:Assets written to: epicNumReader.model\\assets\n"
]
}
],
"source": [
"model.save('epicNumReader.model')"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"new_model = tf.keras.models.load_model('epicNumReader.model')\n"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"predictions = new_model.predict(x_test)\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[2.9749966e-11 1.5573759e-09 1.4861869e-06 ... 9.9999845e-01\n",
" 2.8309510e-09 1.4818610e-09]\n",
" [4.1910638e-14 4.5076781e-06 9.9999547e-01 ... 1.4482120e-12\n",
" 4.4190056e-09 9.9420687e-15]\n",
" [1.7135477e-08 9.9995577e-01 2.2569866e-05 ... 3.5316289e-06\n",
" 1.1573670e-05 2.6440327e-07]\n",
" ...\n",
" [3.8316742e-11 3.0548819e-08 3.8839926e-10 ... 1.8519009e-06\n",
" 3.3297312e-07 1.3001729e-05]\n",
" [9.1106722e-10 1.0552093e-08 1.7957068e-09 ... 1.6109321e-10\n",
" 2.6345744e-05 9.1843123e-11]\n",
" [1.9051569e-09 2.3680266e-10 4.8858440e-10 ... 9.4841717e-14\n",
" 1.9254867e-09 2.9086459e-11]]\n"
]
}
],
"source": [
"print(predictions)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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B2IEkCDuQxP8BguwyeA+T5x8AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(x_test[1])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n"
]
}
],
"source": [
"import numpy as np\n",
"print(np.argmax(predictions[1]))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}