得到錯誤，同時運行卷積的自動編碼在keras

Question

我得到的錯誤，而在keras

Traceback (most recent call last): 
    File "my_conv_ae.py", line 74, in <module> 
validation_steps = nb_validation_samples // batch_size) 
    File "C:\Users\Administrator\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\legacy\interfaces.py", line 88, in wrapper 
return func(*args, **kwargs) 
    File "C:\Users\Administrator\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\engine\training.py", line 1890, in fit_generator 
class_weight=class_weight) 
    File "C:\Users\Administrator\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\engine\training.py", line 1627, in train_on_batch 
check_batch_axis=True) 
    File "C:\Users\Administrator\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\engine\training.py", line 1309, in _standardize_user_data 
exception_prefix='target') 
    File "C:\Users\Administrator\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\engine\training.py", line 127, in _standardize_input_data 
str(array.shape)) 
ValueError: Error when checking target: expected conv2d_transpose_8 to have 4 dimensions, but got array with shape (20, 1) 

運行下面的代碼的代碼是：

import keras 
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Conv2DTranspose 
from keras.models import Model 
from keras import backend as K 
from keras.preprocessing.image import ImageDataGenerator 
import numpy as np 

input_img = Input(shape=(512, 512, 1)) 
nb_train_samples = 1700 
nb_validation_samples = 420 
epochs = 10 
batch_size = 20 

x = Conv2D(64, (11, 11), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(input_img) 
x = Conv2D(64, (11, 11), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = MaxPooling2D((2, 2))(x) 
x = Conv2D(128, (7, 7), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = Conv2D(128, (5, 5), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = MaxPooling2D((2, 2))(x) 
x = Conv2D(256, (5, 5), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = Conv2D(256, (3, 3), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = MaxPooling2D((2, 2))(x) 
x = Conv2D(512, (3, 3), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = Conv2D(512, (3, 3), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
encoded = MaxPooling2D((2, 2))(x) 

print (K.int_shape(encoded)) 

在這一點上表現爲（26，26模型，512）

x = UpSampling2D((2, 2))(encoded) 
x = Conv2DTranspose(512, (3, 3), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = Conv2DTranspose(512, (3, 3), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = UpSampling2D((2, 2))(x) 
x = Conv2DTranspose(256, (3, 3), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = Conv2DTranspose(256, (5, 5), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = UpSampling2D((2, 2))(x) 
x = Conv2DTranspose(128, (5, 5), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = Conv2DTranspose(128, (7, 7), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = UpSampling2D((2, 2))(x) 
x = Conv2DTranspose(64, (11, 11), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
decoded = Conv2DTranspose(1, (11, 11), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 

print (K.int_shape(decoded)) 

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

x_train = train_datagen.flow_from_directory(
    'data/train', 
    target_size = (512, 512), color_mode = 'grayscale', 
    batch_size = batch_size, 
    class_mode = 'binary') 

x_test = test_datagen.flow_from_directory(
    'data/validation', 
    target_size = (512, 512), color_mode = 'grayscale', 
    batch_size = batch_size, 
    class_mode = 'binary') 

autoencoder.fit_generator(
x_train, 
steps_per_epoch = nb_train_samples // batch_size, 
epochs = epochs, 
validation_data = x_test, 
validation_steps = nb_validation_samples // batch_size) 

decoded_imgs = autoencoder.predict(x_test) 

總結如下：

Layer (type)     Output Shape    Param # 
================================================================= 
input_1 (InputLayer)   (None, 512, 512, 1)  0 
_________________________________________________________________ 
conv2d_1 (Conv2D)   (None, 502, 502, 64)  7808 
_________________________________________________________________ 
conv2d_2 (Conv2D)   (None, 492, 492, 64)  495680 
_________________________________________________________________ 
max_pooling2d_1 (MaxPooling2 (None, 246, 246, 64)  0 
_________________________________________________________________ 
conv2d_3 (Conv2D)   (None, 240, 240, 128)  401536 
_________________________________________________________________ 
conv2d_4 (Conv2D)   (None, 236, 236, 128)  409728 
_________________________________________________________________ 
max_pooling2d_2 (MaxPooling2 (None, 118, 118, 128)  0 
_________________________________________________________________ 
conv2d_5 (Conv2D)   (None, 114, 114, 256)  819456 
_________________________________________________________________ 
conv2d_6 (Conv2D)   (None, 112, 112, 256)  590080 
_________________________________________________________________ 
max_pooling2d_3 (MaxPooling2 (None, 56, 56, 256)  0 
_________________________________________________________________ 
conv2d_7 (Conv2D)   (None, 54, 54, 512)  1180160 
_________________________________________________________________ 
conv2d_8 (Conv2D)   (None, 52, 52, 512)  2359808 
_________________________________________________________________ 
max_pooling2d_4 (MaxPooling2 (None, 26, 26, 512)  0 
_________________________________________________________________ 
up_sampling2d_1 (UpSampling2 (None, 52, 52, 512)  0 
_________________________________________________________________ 
conv2d_transpose_1 (Conv2DTr (None, 54, 54, 512)  2359808 
_________________________________________________________________ 
conv2d_transpose_2 (Conv2DTr (None, 56, 56, 512)  2359808 
_________________________________________________________________ 
up_sampling2d_2 (UpSampling2 (None, 112, 112, 512)  0 
_________________________________________________________________ 
conv2d_transpose_3 (Conv2DTr (None, 114, 114, 256)  1179904 
_________________________________________________________________ 
conv2d_transpose_4 (Conv2DTr (None, 118, 118, 256)  1638656 
_________________________________________________________________ 
up_sampling2d_3 (UpSampling2 (None, 236, 236, 256)  0 
_________________________________________________________________ 
conv2d_transpose_5 (Conv2DTr (None, 240, 240, 128)  819328 
_________________________________________________________________ 
conv2d_transpose_6 (Conv2DTr (None, 246, 246, 128)  802944 
_________________________________________________________________ 
up_sampling2d_4 (UpSampling2 (None, 492, 492, 128)  0 
_________________________________________________________________ 
conv2d_transpose_7 (Conv2DTr (None, 502, 502, 64)  991296 
_________________________________________________________________ 
conv2d_transpose_8 (Conv2DTr (None, 512, 512, 1)  7745 
================================================================= 
Total params: 16,423,745 
Trainable params: 16,423,745 
Non-trainable params: 0 
_________________________________________________________________ 

請幫幫我。這是因爲我用於解碼的Conv2DTranspose（）嗎？

Answer 1

模型架構本身並不是一個問題（因爲它在我身邊工作）。看起來像你的地面實況數據有問題。它必須與您的輸入圖像具有相同的尺寸，但flow_from_directory不提供這樣的地面實況數據。我想你需要使用你自己的自定義數據生成器。