Keras-Conv2D期望（1,1,1）并得到（258，540，3）

Question

我正在尝试在Cleaning Up Dirty Scanned Documents with Deep Learning中重新创建结果，使用ImageDataGenerator读取图像并使用model.fit_generator训练模型，但是当我尝试拟合时，出现以下错误和堆栈跟踪：

---------------------------------------------------------------------------

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)

这是我的剧本：

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)

model.summary()的输出可以在下面找到：

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

我对Keras不太熟悉，似乎错误在于最后一层的输入形状，但我不知道如何更改预期的输入大小。我尝试将参数input_shape=input_shape传递到最后一层，但没有任何改变。

即使对与回答我的问题没有直接关系，也要对我编写的代码提出任何其他批评。谢谢！

EDIT：这是我要重新创建的网络的图像：