# mvtec_ad, len = MVTecAd.get_train_dataset("bottle")
# mvtec_ad = mvtec_ad.map(ImageDatasetUtil.resize(IMAGE_SIZE,IMAGE_SIZE))
# (train, len),(validation, validation_len) =ImageDatasetUtil.devide_train_validation(mvtec_ad,len,0.9)
cats_vs_dogs, total_len = CatsVsDogs.get_train_dataset()
cats_vs_dogs = cats_vs_dogs.map(
ImageDatasetUtil.map_max_square_crop_and_resize(IMAGE_SIZE, IMAGE_SIZE))
(train, len), (validation,
validation_len) = ImageDatasetUtil.devide_train_validation(
cats_vs_dogs, total_len, 0.9)
train = train.map(ImageDatasetUtil.image_reguralization(),
num_parallel_calls=tf.data.experimental.AUTOTUNE
) # .map(ImageDatasetUtil.resize(64,64))
validation_r = validation.map(ImageDatasetUtil.image_reguralization(),
num_parallel_calls=tf.data.experimental.AUTOTUNE
) # .map(ImageDatasetUtil.resize(64,64))
model = SSIMAutoEncoderModel.get_model(input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3))
optimizer = OptimizerBuilder.get_optimizer("rmsprop")
callback = CallbackBuilder.get_callbacks()
loss = ssim_color_loss
ImageTrain.train_image_autoencoder(train, len, BATCH_SIZE, validation_r,
validation_len, 100, model, callback,
optimizer, loss, EPOCHS, False)
# model.load_weights(Context.get_model_path())
ImageTrain.show_autoencoder_results(model, validation, 15)
ImageTrain.calucurate_reconstruction_error(model, validation, 10)
IMAGE_SIZE = 28
EPOCHS = 20
SHUFFLE_SIZE = 1000
train, train_len = Mnist.get_train_dataset()
validation, validation_len = Mnist.get_test_dataset()
train = train.map(ImageDatasetUtil.image_reguralization()).map(
ImageDatasetUtil.one_hot(CLASS_NUM))
validation = validation.map(ImageDatasetUtil.image_reguralization()).map(
ImageDatasetUtil.one_hot(CLASS_NUM))
optimizer = OptimizerBuilder.get_optimizer(name="rmsprop")
model = SimpleClassificationModel.get_model(input_shape=(IMAGE_SIZE,
IMAGE_SIZE, 1),
classes=CLASS_NUM)
callbacks = CallbackBuilder.get_callbacks(tensorboard=False,
reduce_lr_on_plateau=True,
reduce_patience=3,
reduce_factor=0.25,
early_stopping_patience=5)
ImageTrain.train_image_classification(train_data=train,
train_size=train_len,
batch_size=BATCH_SIZE,
validation_data=validation,
validation_size=validation_len,
shuffle_size=SHUFFLE_SIZE,
model=model,
callbacks=callbacks,
optimizer=optimizer,
loss="categorical_crossentropy",
max_epoch=EPOCHS)