batch_size=FLAGS.batch_size,
class_mode='categorical')
test_data = image_data_generator.flow_from_directory(
'/home/meizu/WORK/code/YF_baidu_ML/dataset/flowers/flower_photos/test',
target_size=(FLAGS.input_image_size, FLAGS.input_image_size),
batch_size=FLAGS.batch_size,
class_mode='categorical')
# compile the model (should be done *after* setting layers to non-trainable)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
train_data_count = len(train_data.filenames)
test_data_count = len(test_data.filenames)
model.fit_generator(train_data,
steps_per_epoch=(train_data_count // FLAGS.batch_size + 1),
epochs=1,
verbose=1,
validation_data=test_data,
validation_steps=(test_data_count // FLAGS.batch_size + 1))
# a = model_inception_v3.predict_generator(g, len(g.filenames))
# print ''
# a = model_inception_v3.predict_generator(g, steps=len(g.filenames))
#
# # extract features
# for label_name, label_lists in flowers_dataset.items():
# for category in ['training', 'testing', 'validation']:
# category_list = label_lists[category]
# for index, image_path in enumerate(category_list):
# if not gfile.Exists(image_path):
# tf.logging.fatal('File does not exist %s', image_path)
# img = misc.imresize(misc.imread(image_path), [299, 299]).astype(np.float32)
'\n', ' Returns:\n', ' Numpy array(s) of predictions.\n', '\n', ' Raises:\n', ' ValueError: In case of mismatch between the provided\n', " input data and the model's expectations,\n", ' or in case a stateful model receives a number of samples\n', ' that is not a multiple of the batch size.\n', """ num_epochs = 2 for epoch in range(num_epochs): model.fit_generator(generator=val_batches, steps_per_epoch=3, epochs=1) """ ([' def fit_generator(self,\n', ' generator,\n', ' steps_per_epoch,\n', ' epochs=1,\n', ' verbose=1,\n', ' callbacks=None,\n', ' validation_data=None,\n', ' validation_steps=None,\n', ' class_weight=None,\n', ' max_q_size=10,\n', ' workers=1,\n', ' pickle_safe=False,\n', ' initial_epoch=0):\n',
