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| import numpy as np | |
| from keras import backend as K | |
| from keras.layers import * | |
| from keras.preprocessing.image import ImageDataGenerator | |
| from keras.models import Sequential, Model | |
| from keras.optimizers import SGD, Adam | |
| np.random.seed(1337) # for reproducibility | |
| batch_size = 32 | |
| num_epochs = 1000 | |
| width = 300 | |
| height = 300 | |
| # count folders in data_dir | |
| num_classes = 168 | |
| # Input | |
| inputs = Input(name='the_input', shape=(300, 300, 3), dtype='float32') | |
| # input shape = (batch_size, 300, 300, 3) | |
| conv1 = Conv2D(64, (3, 3), activation='relu')(inputs) | |
| maxpool1 = MaxPooling2D(pool_size=(4, 4))(conv1) | |
| conv2 = Conv2D(128, (3, 3), activation='relu')(maxpool1) | |
| maxpool2 = MaxPooling2D(pool_size=(4, 4))(conv2) | |
| conv3 = Conv2D(256, (3, 3), activation='relu')(maxpool2) | |
| maxpool3 = MaxPooling2D(pool_size=(4, 4))(conv3) | |
| conv4 = Conv2D(512, (3, 3), activation='relu')(maxpool3) | |
| maxpool4 = MaxPooling2D(pool_size=(2, 2))(conv4) | |
| # now the shape = (batch_size, 1, 1, 512), so need to squeeze twice. | |
| features = Lambda(lambda x: K.squeeze(x, 1))(maxpool4) | |
| features = Lambda(lambda x: K.squeeze(x, 1))(features) | |
| # now the shape = (batch_size, 512) | |
| y_pred = Dense(num_classes, activation='softmax')(features) | |
| # now y_pred has the shape = (batch_size, num_classess) | |
| model = Model(inputs=[inputs], outputs=[y_pred]) | |
| # compile model | |
| model.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.01), metrics=['accuracy']) | |
| # datasets and dataloader for train mode | |
| train_datagen = ImageDataGenerator() | |
| train_generator = train_datagen.flow_from_directory('/workspace/hongkong_flowers/dataset/sorted/train/', | |
| batch_size=batch_size, | |
| target_size=(height, width), | |
| class_mode='categorical') | |
| valid_datagen = ImageDataGenerator() | |
| valid_generator = valid_datagen.flow_from_directory('/workspace/hongkong_flowers/dataset/sorted/valid/', | |
| batch_size=batch_size, | |
| target_size=(height, width), | |
| class_mode='categorical') | |
| train_step = train_generator.samples // batch_size | |
| valid_step = valid_generator.samples // batch_size | |
| # # Generate dummy data | |
| # x_train = np.random.random((10, 300, 300, 3)) | |
| # y_train = np.random.randint(num_classes, size=(10, 1)) | |
| # # for trying out few images only | |
| # from PIL import Image | |
| # x_train = [] | |
| # y_train = [] | |
| # import os | |
| # train_dir = '/workspace/hongkong_flowers/dataset/sorted/train/' | |
| # dir_list = os.listdir(train_dir) | |
| # dir_list.remove('.DS_Store') | |
| # for i, dir in enumerate(dir_list): | |
| # try: | |
| # x_train.append(np.asarray(Image.open(train_dir + "%s/00.jpg" % dir))) | |
| # except IOError: | |
| # x_train.append(np.asarray(Image.open(train_dir + '%s/01.jpg' % dir))) | |
| # y_train.append(i) | |
| # x_train = np.asarray(x_train) | |
| # y_train = np.asarray(y_train) | |
| # | |
| # model.fit(x_train, y_train, | |
| # epochs=2000, | |
| # batch_size=2) | |
| # fit the model | |
| model.fit_generator(generator=train_generator, | |
| steps_per_epoch=train_step, | |
| epochs=num_epochs, | |
| validation_data=valid_generator, | |
| validation_steps=valid_step) |
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