def __init__(self):
goods_dataset = GoodsDataset("dataset-181018.list",
"dataset-181018.labels",
settings.IMAGE_SIZE, settings.train_batch,
settings.valid_batch, settings.multiply,
settings.valid_percentage)
train_set = goods_dataset.get_train_dataset()
valid_set = goods_dataset.get_valid_dataset()
input_tensor = keras.layers.Input(shape=(IMAGE_SIZE[0], IMAGE_SIZE[1],
3))
base_model = InceptionV3(weights='imagenet',
include_top=False,
pooling='avg',
input_tensor=input_tensor)
output_layer_number = 248
intermediate_layer_model = keras.Model(
inputs=base_model.input,
outputs=base_model.layers[output_layer_number].output)
def _intermediate_processing(images, labels):
images = intermediate_layer_model.predict(images, steps=77)
return images, labels
self.train_set = train_set.map(
_intermediate_processing) #, num_parallel_calls=8)
self.valid_set = valid_set.map(
_intermediate_processing) #, num_parallel_calls=8)
keras.callbacks.ModelCheckpoint(
"./checkpoints/FINE_TUNE_MODEL_4_DIRECT_inceptionv3-181018-{epoch:02d}-{acc:.3f}-{val_acc:.3f}[{val_top_6:.3f}].hdf5",
save_best_only=True,
monitor='val_top_6',
mode='max'),
keras.callbacks.TensorBoard(
log_dir='./tensorboard-incv4',
write_images=True,
)
]
goods_dataset = GoodsDataset("dataset-181018.list", "dataset-181018.labels",
settings.IMAGE_SIZE, settings.train_batch,
settings.valid_batch, settings.multiply,
settings.valid_percentage)
train_dataset = goods_dataset.get_train_dataset()
valid_dataset = goods_dataset.get_valid_dataset()
results = model.evaluate(
goods_dataset.get_images_for_label(94).batch(16).repeat(), steps=6)
print(results)
model.fit(
train_dataset.prefetch(2).repeat(),
callbacks=callbacks,
epochs=30,
steps_per_epoch=1157,
validation_data=valid_dataset.repeat(),
validation_steps=77,
)
"""