Question

我决定尝试训练自动编码器重新着色灰度图像。这种方法可能有点幼稚，但我想玩它，看看它有多好（或坏），并检查我如何改进它。

然而，它意外地抛出了以下错误：

  File "colorise0.py", line 63, in <module>
    validation_data=(val_g_generator, val_c_generator)
  File "/usr/local/lib/python2.7/dist-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 2183, in fit_generator
    val_x, val_y, val_sample_weight)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1483, in _standardize_user_data
    exception_prefix='input')
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 76, in _standardize_input_data
    data = [np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data]
AttributeError: 'DirectoryIterator' object has no attribute 'ndim'

我的代码是：

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.layers import Conv2DTranspose as DeConv2D
from keras.models import Model
from keras import backend as K

from keras.preprocessing.image import ImageDataGenerator

img_width, img_height = 150, 150
batch_size=32

train_data_dir = './train/'
validation_data_dir = './validation/'
input_shape = (img_width, img_height,3)

train_datagen = ImageDataGenerator(rescale = 1./255)
test_datagen =  ImageDataGenerator(rescale = 1./255)


train_c_generator=  train_datagen.flow_from_directory( 
    train_data_dir+'colored',
    target_size=(img_width, img_height),
    batch_size=batch_size
)

train_g_generator=  train_datagen.flow_from_directory(
    train_data_dir+'grey',
    target_size=(img_width, img_height),
    batch_size=batch_size
)

val_c_generator=  test_datagen.flow_from_directory(
    validation_data_dir+'colored',
    target_size=(img_width, img_height),
    batch_size=batch_size
)

val_g_generator=  test_datagen.flow_from_directory(
    validation_data_dir+'grey',
    target_size=(img_width, img_height),
    batch_size=batch_size
)

input_img=Input(shape=(img_width,img_height,3))
x=Conv2D(32,(3,3), activation='relu', padding='same')(input_img)
x=Conv2D(32,(3,3), activation='relu', padding='same')(x)
x=Conv2D(32,(3,3), activation='relu', padding='same')(x)
x=Conv2D(32,(3,3), activation='relu', padding='same')(x)

y=DeConv2D(32,(3,3), activation='relu',padding='same')(x)
y=DeConv2D(32,(3,3), activation='relu',padding='same')(y)
y=DeConv2D(32,(3,3), activation='relu',padding='same')(y)
decoded=DeConv2D(3,(3,3), padding='same')(y)

autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')


autoencoder.fit_generator(
                train_g_generator, train_c_generator,
                epochs=50,
                validation_data=(val_g_generator, val_c_generator)
                )

鉴于错误消息，我认为错误可能源于调用两个生成器（一个提供灰色输入图像，第二个提供原始彩色图像，作为目标）。

我该如何解决这个问题？

非常感谢！

Answer 1

fit_generator只需要一个生成器，而不是两个。生成器输出一个元组（X，y），因此输入和目标都由一个生成器处理。

对于您的特定用例，我认为您需要制作自定义生成器。

Keras fit_generator使用输入和输出图像生成器'ndim'错误

1 个答案: