keras-tensorflow CAE尺寸失配

Question

我基本上遵循这指南，用tensorflow后端构建卷积自动编码器。指南的主要区别是，我的数据是257x257灰度图像。以下代码：

TRAIN_FOLDER = 'data/OIRDS_gray/'
EPOCHS = 10
SHAPE = (257,257,1)

FILELIST = os.listdir(TRAIN_FOLDER)

def loadTrainData():
    train_data = []
    for fn in FILELIST:
        img = misc.imread(TRAIN_FOLDER + fn)
        img = np.reshape(img,(len(img[0,:]), len(img[:,0]), SHAPE[2]))
        if img.shape != SHAPE:
            print "image shape mismatch!"
            print "Expected: " 
            print SHAPE 
            print "but got:"
            print img.shape
            sys.exit()
        train_data.append (img)
    train_data = np.array(train_data)
    train_data = train_data.astype('float32')/ 255

    return np.array(train_data)

def createModel():
    input_img = Input(shape=SHAPE)
    x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
    x = MaxPooling2D((2, 2), padding='same')(x)
    x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
    x = MaxPooling2D((2, 2), padding='same')(x)
    x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
    encoded = MaxPooling2D((2, 2), padding='same')(x)

    x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
    x = UpSampling2D((2, 2))(x)  
    x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
    x = UpSampling2D((2, 2))(x)
    x = Conv2D(16, (3, 3), activation='relu',padding='same')(x)
    x = UpSampling2D((2, 2))(x)
    decoded = Conv2D(1, (3, 3), activation='sigmoid',padding='same')(x)
    return Model(input_img, decoded)


x_train = loadTrainData()
autoencoder = createModel()
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')

print x_train.shape
autoencoder.summary()

# Run the network
autoencoder.fit(x_train, x_train,
                epochs=EPOCHS,
                batch_size=128,
                shuffle=True)

给我一个错误：ValueError: Error when checking target: expected conv2d_7 to have shape (None, 260, 260, 1) but got array with shape (859, 257, 257, 1)

如您所见，这不是theano/tensorflow后端模糊排序的标准问题，而是其他问题。我检查了我的数据是否与print x_train.shape有关

(859, 257, 257, 1)

我还运行autoencoder.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
input_1 (InputLayer)         (None, 257, 257, 1)       0
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 257, 257, 16)      160
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 129, 129, 16)      0
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 129, 129, 8)       1160
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 65, 65, 8)         0
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 65, 65, 8)         584
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 33, 33, 8)         0
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 33, 33, 8)         584
_________________________________________________________________
up_sampling2d_1 (UpSampling2 (None, 66, 66, 8)         0
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 66, 66, 8)         584
_________________________________________________________________
up_sampling2d_2 (UpSampling2 (None, 132, 132, 8)       0
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 132, 132, 16)      1168
_________________________________________________________________
up_sampling2d_3 (UpSampling2 (None, 264, 264, 16)      0
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 264, 264, 1)       145
=================================================================
Total params: 4,385
Trainable params: 4,385
Non-trainable params: 0
_________________________________________________________________

现在我不太清楚问题出在哪里，但是看起来conv2d_6 (Param #过高)出了问题。我知道CAE在原则上是如何工作的，但我还不太熟悉具体的技术细节，我主要是通过处理反褶积填充(而不是同样的，使用有效的)来解决这个问题。我找到的匹配结果是(None, 258, 258, 1)。我在反褶积方面盲目地尝试不同的填充组合，这并不是一个解决问题的聪明方法。

此刻我不知所措，任何帮助都将不胜感激

Answer 1

因为您的输入和输出数据是相同的，所以您的最终输出形状应该与输入形状相同。

最后一层卷积层应具有形状(None, 257,257,1)。

这个问题正在发生，因为你有一个奇数作为图像的大小(257)。

当您应用MaxPooling时，它应该将这个数字除以2，所以它选择向上或向下舍入(它正在上升，参见129，从257/2 = 128.5)

稍后，当您执行UpSampling时，模型不知道当前的维度是四舍五入的，它只是将值加倍。这是按顺序发生的，在最终结果中增加了7个像素。

您可以尝试剪切结果或填充输入。

我通常处理尺寸一致的图像。如果你有3个MaxPooling层，那么你的尺寸应该是2个立方的倍数。答案是264。

直接填充输入数据：

x_train = numpy.lib.pad(x_train,((0,0),(3,4),(3,4),(0,0)),mode='constant')

这将需要SHAPE=(264,264,1)

模型内部的填充：

import keras.backend as K

input_img = Input(shape=SHAPE)
x = Lambda(lambda x: K.spatial_2d_padding(x, padding=((3, 4), (3, 4))), output_shape=(264,264,1))(input_img)

种植结果：

在不直接更改实际数据(numpy数组)的任何情况下，都需要这样做。

decoded = Lambda(lambda x: x[:,3:-4,3:-4,:], output_shape=SHAPE)(x)