Я пытаюсь воссоздать результаты в Очистка грязных отсканированных документов с помощью глубокого обучения , используя ImageDataGenerator для чтения изображений и model.fit_generator для обучения моей модели, но когда я попробуйте подгонять, я получаю следующую ошибку и трассировку стека:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-34-2dedade68c7a> in <module>()
4 epochs=epochs,
5 validation_data=validation_generator,
----> 6 validation_steps=nb_validation_samples // batch_size)
/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name + '` call to the ' +
90 'Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1656 use_multiprocessing=use_multiprocessing,
1657 shuffle=shuffle,
-> 1658 initial_epoch=initial_epoch)
1659
1660 @interfaces.legacy_generator_methods_support
/usr/local/lib/python3.6/dist-packages/keras/engine/training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
213 outs = model.train_on_batch(x, y,
214 sample_weight=sample_weight,
--> 215 class_weight=class_weight)
216
217 outs = to_list(outs)
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in train_on_batch(self, x, y, sample_weight, class_weight)
1441 x, y,
1442 sample_weight=sample_weight,
-> 1443 class_weight=class_weight)
1444 if self._uses_dynamic_learning_phase():
1445 ins = x + y + sample_weights + [1.]
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
793 feed_output_shapes,
794 check_batch_axis=False, # Don't enforce the batch size.
--> 795 exception_prefix='target')
796
797 # Generate sample-wise weight values given the `sample_weight` and
/usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
139 ': expected ' + names[i] + ' to have shape ' +
140 str(shape) + ' but got array with shape ' +
--> 141 str(data_shape))
142 return data
143
ValueError: Error when checking target: expected conv2d_10 to have shape (1, 1, 1) but got array with shape (258, 540, 3)
Вот мой скрипт:
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Conv2D, Activation, BatchNormalization, LeakyReLU, MaxPooling2D, UpSampling2D
import numpy as np
from keras import backend as K
img_width, img_height = 258,540
train_data_dir = 'drive/My Drive/train'
validation_data_dir = 'drive/My Drive/train_cleaned'
nb_train_samples = 144
nb_validation_samples = 144
epochs = 200
batch_size = 20
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
model = Sequential([
Conv2D(input_shape=input_shape, filters=64, kernel_size=(258, 540), padding='same'),
LeakyReLU(),
BatchNormalization(),
Conv2D(filters=64, kernel_size=(258, 540), padding='same'),
LeakyReLU(),
MaxPooling2D((2,2), padding='same'),
Conv2D(filters=64, kernel_size=(129, 270), padding='same'),
LeakyReLU(),
BatchNormalization(),
Conv2D(filters=64, kernel_size=(129, 270), padding='same'),
LeakyReLU(),
UpSampling2D((2, 2)),
Conv2D(filters=1, kernel_size=(258,540), activation='sigmoid')
])
model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
class_mode='input',
batch_size=batch_size)
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
class_mode='input',
batch_size=batch_size)
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)
Вывод model.summary() можно найти ниже:
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_6 (Conv2D) (None, 258, 540, 64) 26749504
_________________________________________________________________
leaky_re_lu_5 (LeakyReLU) (None, 258, 540, 64) 0
_________________________________________________________________
batch_normalization_3 (Batch (None, 258, 540, 64) 256
_________________________________________________________________
conv2d_7 (Conv2D) (None, 258, 540, 64) 570654784
_________________________________________________________________
leaky_re_lu_6 (LeakyReLU) (None, 258, 540, 64) 0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 129, 270, 64) 0
_________________________________________________________________
conv2d_8 (Conv2D) (None, 129, 270, 64) 142663744
_________________________________________________________________
leaky_re_lu_7 (LeakyReLU) (None, 129, 270, 64) 0
_________________________________________________________________
batch_normalization_4 (Batch (None, 129, 270, 64) 256
_________________________________________________________________
conv2d_9 (Conv2D) (None, 129, 270, 64) 142663744
_________________________________________________________________
leaky_re_lu_8 (LeakyReLU) (None, 129, 270, 64) 0
_________________________________________________________________
up_sampling2d_2 (UpSampling2 (None, 258, 540, 64) 0
_________________________________________________________________
conv2d_10 (Conv2D) (None, 1, 1, 1) 8916481
=================================================================
Total params: 891,648,769
Trainable params: 891,648,513
Non-trainable params: 256
_________________________________________________________________
None
Я не очень знаком с Keras, и похоже, что ошибка связана с формой ввода моего последнего слоя, но я не знаю, как изменить ожидаемый размер ввода. Я попытался передать параметр input_shape=input_shape на последний уровень, и это ничего не изменило.
Буду признателен за любую другую критику в отношении написанного мной кода, даже если он не имеет прямого отношения к отвечая на мой вопрос. Спасибо!
РЕДАКТИРОВАТЬ: Вот изображение сети, которую я пытаюсь воссоздать: 