Spaces:
Runtime error
Runtime error
File size: 9,440 Bytes
e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 4789c9d e82c862 89e16b4 e82c862 89e16b4 e82c862 89e16b4 e82c862 89e16b4 e82c862 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to Machine Learning\n",
"\n",
"This notebook is an example of a CNN for recognizing handwritten characters.\n",
"\n",
"Most of this code is from https://keras.io/examples/vision/mnist_convnet/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepare the data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x_train shape: (60000, 28, 28, 1)\n",
"60000 train samples\n",
"10000 test samples\n"
]
}
],
"source": [
"# Model / data parameters\n",
"num_classes = 10\n",
"input_shape = (28, 28, 1)\n",
"\n",
"# Load the data and split it between train and test sets\n",
"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
"\n",
"# Scale images to the [0, 1] range\n",
"x_train = x_train.astype(\"float32\") / 255\n",
"x_test = x_test.astype(\"float32\") / 255\n",
"\n",
"# Make sure images have shape (28, 28, 1)\n",
"x_train = np.expand_dims(x_train, -1)\n",
"x_test = np.expand_dims(x_test, -1)\n",
"print(\"x_train shape:\", x_train.shape)\n",
"print(x_train.shape[0], \"train samples\")\n",
"print(x_test.shape[0], \"test samples\")\n",
"\n",
"\n",
"# convert class vectors to binary class matrices\n",
"y_train = keras.utils.to_categorical(y_train, num_classes)\n",
"y_test = keras.utils.to_categorical(y_test, num_classes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build the Model"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" conv2d (Conv2D) (None, 26, 26, 32) 320 \n",
" \n",
" max_pooling2d (MaxPooling2D (None, 13, 13, 32) 0 \n",
" ) \n",
" \n",
" conv2d_1 (Conv2D) (None, 11, 11, 64) 18496 \n",
" \n",
" max_pooling2d_1 (MaxPooling (None, 5, 5, 64) 0 \n",
" 2D) \n",
" \n",
" flatten (Flatten) (None, 1600) 0 \n",
" \n",
" dropout (Dropout) (None, 1600) 0 \n",
" \n",
" dense (Dense) (None, 10) 16010 \n",
" \n",
"=================================================================\n",
"Total params: 34,826\n",
"Trainable params: 34,826\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model = keras.Sequential(\n",
" [\n",
" keras.Input(shape=input_shape),\n",
" layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
" layers.MaxPooling2D(pool_size=(2, 2)),\n",
" layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
" layers.MaxPooling2D(pool_size=(2, 2)),\n",
" layers.Flatten(),\n",
" layers.Dropout(0.5),\n",
" layers.Dense(num_classes, activation=\"softmax\"),\n",
" ]\n",
")\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Model"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"batch_size = 128\n",
"epochs = 15\n",
"\n",
"model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/15\n",
"422/422 [==============================] - 4s 3ms/step - loss: 0.3724 - accuracy: 0.8837 - val_loss: 0.0810 - val_accuracy: 0.9785\n",
"Epoch 2/15\n",
"422/422 [==============================] - 1s 3ms/step - loss: 0.1118 - accuracy: 0.9671 - val_loss: 0.0562 - val_accuracy: 0.9852\n",
"Epoch 3/15\n",
"422/422 [==============================] - 1s 2ms/step - loss: 0.0835 - accuracy: 0.9739 - val_loss: 0.0481 - val_accuracy: 0.9872\n",
"Epoch 4/15\n",
"422/422 [==============================] - 1s 2ms/step - loss: 0.0700 - accuracy: 0.9787 - val_loss: 0.0404 - val_accuracy: 0.9898\n",
"Epoch 5/15\n",
"422/422 [==============================] - 1s 3ms/step - loss: 0.0604 - accuracy: 0.9815 - val_loss: 0.0381 - val_accuracy: 0.9910\n",
"Epoch 6/15\n",
"422/422 [==============================] - 1s 2ms/step - loss: 0.0559 - accuracy: 0.9826 - val_loss: 0.0354 - val_accuracy: 0.9903\n",
"Epoch 7/15\n",
"422/422 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.9829 - val_loss: 0.0338 - val_accuracy: 0.9903\n",
"Epoch 8/15\n",
"422/422 [==============================] - 1s 2ms/step - loss: 0.0484 - accuracy: 0.9847 - val_loss: 0.0296 - val_accuracy: 0.9923\n",
"Epoch 9/15\n",
"422/422 [==============================] - 1s 2ms/step - loss: 0.0443 - accuracy: 0.9860 - val_loss: 0.0333 - val_accuracy: 0.9912\n",
"Epoch 10/15\n",
"422/422 [==============================] - 1s 2ms/step - loss: 0.0422 - accuracy: 0.9869 - val_loss: 0.0308 - val_accuracy: 0.9927\n",
"Epoch 11/15\n",
"422/422 [==============================] - 1s 2ms/step - loss: 0.0397 - accuracy: 0.9874 - val_loss: 0.0321 - val_accuracy: 0.9922\n",
"Epoch 12/15\n",
"422/422 [==============================] - 1s 2ms/step - loss: 0.0375 - accuracy: 0.9875 - val_loss: 0.0269 - val_accuracy: 0.9928\n",
"Epoch 13/15\n",
"422/422 [==============================] - 1s 2ms/step - loss: 0.0355 - accuracy: 0.9886 - val_loss: 0.0296 - val_accuracy: 0.9925\n",
"Epoch 14/15\n",
"422/422 [==============================] - 1s 2ms/step - loss: 0.0340 - accuracy: 0.9888 - val_loss: 0.0291 - val_accuracy: 0.9928\n",
"Epoch 15/15\n",
"422/422 [==============================] - 1s 3ms/step - loss: 0.0321 - accuracy: 0.9894 - val_loss: 0.0277 - val_accuracy: 0.9927\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x14a08dfc3d0>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This line can be run multiple times, but keep in mind that the model will probably be over fitting\n",
"\n",
"model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluate the Trained Model"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test loss: 0.02324853651225567\n",
"Test accuracy: 0.9922000169754028\n"
]
}
],
"source": [
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"print(\"Test loss:\", score[0])\n",
"print(\"Test accuracy:\", score[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Save Model (h5 format)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"model.save(\"mnist.h5\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.7 ('base')",
"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": "3.9.7"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|