Ошибка при проверке цели: ожидаемая форма для conv2d_transpose

Я хочу реализовать автоматический кодировщик для набора данных Faces с помощью Keras. Я использовал train_on_batch, потому что набор данных слишком велик, но я столкнулся с этой проблемой:

for i in range(10):
    batch_index = 0
    while batch_index <= train_data.batch_index:
        data = train_data.next()
        result = train_result.next()
        model.train_on_batch(data[0],result[0])
        batch_index = batch_index + 1
          ---------------------------------------------------------------------------
    ValueError                                Traceback (most recent     call last)
      <ipython-input-54-d7d64e954a89> in <module>
  4         data = train_data.next()
  5         result = train_result.next()
----> 6         model.train_on_batch(data[0],result[0])
  7         batch_index = batch_index + 1

~/.local/lib/python3.5/site-packages/keras/engine/training.py in     train_on_batch(self, x, y, sample_weight, class_weight)
   1209             x, y,
   1210             sample_weight=sample_weight,
-> 1211             class_weight=class_weight)
   1212         if self._uses_dynamic_learning_phase():
   1213             ins = x + y + sample_weights + [1.]

     ~/.local/lib/python3.5/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
787                 feed_output_shapes,
788                 check_batch_axis=False,  # Don't enforce the batch size.
--> 789                 exception_prefix='target')
    790 
    791             # Generate sample-wise weight values given the     `sample_weight` and

~/.local/lib/python3.5/site-packages/keras/engine/training_utils.py in              standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
136                             ': expected ' + names[i] + ' to have shape ' +
137                             str(shape) + ' but got array with shape ' +
--> 138                             str(data_shape))
    139     return data
    140 

ValueError: Error when checking target: expected conv2d_transpose_21 to      have shape (250, 250, 1) but got array with shape (250, 250, 3)

Моя модель такая же, как у собрата:

Input_Layer = keras.Input((250,250,3))
x = keras.layers.Conv2D(20,5,activation='relu')(Input_Layer)
x = keras.layers.MaxPooling2D(2)(x)
x = keras.layers.Conv2D(20,2,activation = 'relu')(x)
x = keras.layers.MaxPooling2D(2)(x)
encoded = x
x = keras.layers.UpSampling2D(2)(x)
x = keras.layers.Conv2DTranspose(20,2,activation='relu')(x)
x = keras.layers.UpSampling2D(2)(x)
x = keras.layers.Conv2DTranspose(20,5,activation= 'relu')(x)
model = keras.Model(input = Input_Layer ,output = x)

Я загружаю изображения с помощью keras ImageDataGenerator, которые загружаются:

train_data = trainGenerator.flow_from_directory('lfw',batch_size=67,target_size=(250, 250))
Found 13199 images belonging to 1 classes.

Вот весь код

from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
import matplotlib.pyplot as plt
import numpy as np
import keras

def cutHalf(img):
    for j in range(125):
        for i in range(250):
            img[i][j][0]=1
            img[i][j][1]=1
            img[i][j][2]=1
    return img

img_width = 250
img_height = 250
train_datagen = ImageDataGenerator(rescale=1./255)
train_datagen2 = ImageDataGenerator(rescale=1./255,preprocessing_function=cutHalf)

train_generator = train_datagen.flow_from_directory(
        'lfw',target_size=(img_width, img_height),
        class_mode=None)
train_generator2 = train_datagen2.flow_from_directory(
        'lfw',target_size=(img_width, img_height),
        class_mode=None)

def fixed_generator(generator,generator2):
    batch_index = 0
    while batch_index <= generator.batch_index:
        yield (generator.next(), generator2.next())

Input_Layer = keras.Input(shape=(img_width, img_height,3))
x = keras.layers.Conv2D(20,5,activation='relu')(Input_Layer)
x = keras.layers.MaxPooling2D(2)(x)
x = keras.layers.Conv2D(20,2,activation = 'relu')(x)
x = keras.layers.MaxPooling2D(2)(x)
encoded = x
x = keras.layers.UpSampling2D(2)(x)
x = keras.layers.Conv2DTranspose(20,2,activation='relu')(x)
x = keras.layers.UpSampling2D(2)(x)
x = keras.layers.Conv2DTranspose(20,5,activation= 'relu')(x)
model = keras.Model(input = Input_Layer ,output = x)

model.compile(optimizer='adam', 
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit_generator(
        fixed_generator(train_generator,train_generator2),
        nb_epoch=20,
        steps_per_epoch=50
        )