“连接”层需要具有匹配形状的输入，连接轴除外

Question

我尝试从 zhixuhao 训练 Unet 神经网络，我提交到图像 512x512 的输入但我得到这个错误：

    Traceback (most recent call last):
    File "unet.py", line 168, in <module>
    myunet.train()
    File "unet.py", line 159, in train
    model.fit(imgs_train, imgs_mask_train, batch_size=1, nb_epoch=10, verbose=1, shuffle=True, callbacks=[model_checkpoint])
    File "C:\Users\mimozzza\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 952, in fit
    batch_size=batch_size)
    File "C:\Users\mimozzza\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 751, in _standardize_user_data
    exception_prefix='input')
    File "C:\Users\mimozzza\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training_utils.py", line 138, in standardize_input_data
    str(data_shape))
    ValueError: Error when checking input: expected input_1 to have shape (256, 256, 3) but got array with shape (512, 512, 3)

代码如下：

    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
    print ("conv1 shape:"),conv1.shape
    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
    print ("conv1 shape:"),conv1.shape
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    print ("pool1 shape:"),pool1.shape

    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
    print ("conv2 shape:"),conv2.shape
    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
    print ("conv2 shape:"),conv2.shape
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    print ("pool2 shape:"),pool2.shape

    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
    print ("conv3 shape:"),conv3.shape
    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
    print ("conv3 shape:"),conv3.shape
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    print ("pool3 shape:"),pool3.shape

    conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
    conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

    conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
    conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
    drop5 = Dropout(0.5)(conv5)

    up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
    merge6 = concatenate([drop4, up6], axis=3)
    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)

    up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
    merge7 = concatenate([conv3, up7], axis=3)
    conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
    conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)

    up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
    merge8 = concatenate([conv2, up8], axis=3)
    conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
    conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)

    up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
    merge9 = concatenate([conv1, up9], axis=3)
    conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
    conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
    conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
    conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)

    model = Model(input = inputs, output = conv10)

    model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])

    return model

我查看了有关此主题的其他讨论，并意识到错误可能与图像的分辨率有关，但我的文件与最初应该是 zhixuhao 的分辨率相同。 告诉我，有什么问题，我该如何解决？

PS： 我用的是keras 1.0.0版本，也用的是GitHub上最新版本的代码，我用的是2017年10月24日的版本。

Answer 1

问题解决了，在代码中图像被指定为 256x256 的分辨率，虽然我尝试从 512x512 图像中学习。