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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import math\n",
"import matplotlib.pyplot as plt\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout, Flatten\n",
"from keras.layers import Conv2D, MaxPooling2D\n",
"from keras.optimizers import Adam, SGD\n",
"from keras.callbacks import ModelCheckpoint\n",
"from keras.preprocessing.image import load_img, img_to_array, ImageDataGenerator\n",
"from PIL import Image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"DATA_DIR = '../data/preprocessed/'\n",
"BATCH_SIZE = 32\n",
"BATCH_SIZE_TEST = 16\n",
"GRAYSCALE = True\n",
"INPUT_DIM = (64, 64, 1 if GRAYSCALE else 3)\n",
"AUGMENTATION_FACTOR = 3\n",
"EPOCHS = 100"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_datagen = ImageDataGenerator(\n",
" rotation_range=10,\n",
" rescale=1./255,\n",
" shear_range=0.2,\n",
" zoom_range=0.2,\n",
" horizontal_flip=True)\n",
"\n",
"test_datagen = ImageDataGenerator(rescale=1./255)\n",
"\n",
"train_generator = train_datagen.flow_from_directory(\n",
" DATA_DIR + 'train',\n",
" target_size=INPUT_DIM[:2],\n",
" batch_size=BATCH_SIZE,\n",
" class_mode='categorical',\n",
" color_mode='grayscale' if GRAYSCALE else 'rgb')\n",
"\n",
"validation_generator = test_datagen.flow_from_directory(\n",
" DATA_DIR + 'validation',\n",
" target_size=INPUT_DIM[:2],\n",
" batch_size=BATCH_SIZE_TEST,\n",
" class_mode='categorical',\n",
" color_mode='grayscale' if GRAYSCALE else 'rgb')\n",
"\n",
"test_generator = test_datagen.flow_from_directory(\n",
" DATA_DIR + 'test',\n",
" target_size=INPUT_DIM[:2],\n",
" batch_size=BATCH_SIZE_TEST,\n",
" class_mode='categorical',\n",
" color_mode='grayscale' if GRAYSCALE else 'rgb')\n",
"\n",
"n_train = train_generator.n\n",
"n_validation = validation_generator.n\n",
"n_test = test_generator.n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_model():\n",
" model = Sequential()\n",
" model.add(Conv2D(32, (3, 3), activation='relu', input_shape=INPUT_DIM))\n",
" model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"\n",
" model.add(Conv2D(32, (3, 3), activation='relu'))\n",
" model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"\n",
" model.add(Conv2D(32, (3, 3), activation='relu'))\n",
" model.add(MaxPooling2D(pool_size=(2, 2)))\n",
" \n",
" model.add(Flatten())\n",
" model.add(Dense(64, activation='relu'))\n",
" model.add(Dropout(0.5))\n",
" model.add(Dense(4, activation='softmax'))\n",
" \n",
" model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = get_model()\n",
"name_prefix = 'gray' if GRAYSCALE else 'rgb'\n",
"callbacks = [ModelCheckpoint(name_prefix + '-{epoch:02d}-{val_acc:.2f}.hdf5', monitor='val_acc', verbose=1, save_best_only=False, mode='max')]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"history = model.fit_generator(\n",
" train_generator,\n",
" steps_per_epoch= (n_train // BATCH_SIZE) * AUGMENTATION_FACTOR,\n",
" epochs=EPOCHS,\n",
" validation_data=validation_generator,\n",
" validation_steps=n_validation // BATCH_SIZE_TEST,\n",
" callbacks=callbacks)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.plot(history.history['acc'])\n",
"plt.plot(history.history['val_acc'])\n",
"plt.title('Model accuracy')\n",
"plt.ylabel('Accuracy')\n",
"plt.xlabel('Epoch')\n",
"plt.legend(['Train', 'Test'], loc='upper left')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"modelfiles = [f for f in os.listdir('.') if f.endswith('.hdf5') and f.startswith('rgb' if not GRAYSCALE else 'gray')]\n",
"for f in modelfiles:\n",
" model.load_weights(f)\n",
" result = model.evaluate_generator(\n",
" test_generator,\n",
" steps=n_test // BATCH_SIZE\n",
" )\n",
" print(f'{f}: {result[1]}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(model.predict(img_to_array(examples_cs[0].convert('L')).reshape(1, *INPUT_DIM), batch_size=1))\n",
"print(model.predict(img_to_array(examples_econ[0].convert('L')).reshape(1, *INPUT_DIM), batch_size=1))\n",
"print(model.predict(img_to_array(examples_german[0].convert('L')).reshape(1, *INPUT_DIM), batch_size=1))\n",
"print(model.predict(img_to_array(examples_mechanical[0].convert('L')).reshape(1, *INPUT_DIM), batch_size=1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"test_generator.class_indices"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Some results\n",
"* 64x64x3, 100 epochs, 3x augmentation, simple net: **0.571**\n",
"* 64x64x1, 100 epochs, 3x augmentation, simple net: **0.500**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.6.7"
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