Python 如何修复ValueError：检查目标Keras时出错

首先，我是CNN和Keras的新手。我正在尝试创建一个基于U-Net架构的神经网络。这是python中使用Keras的模型：

inputs = Input((512, 512, 11))

    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)

    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    pool1 = Dropout(0.1)(pool1)

    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)

    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    pool2 = Dropout(0.1)(pool2)

    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)

    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    pool3 = Dropout(0.1)(pool3)

    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)

    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
    drop4 = Dropout(0.1)(conv4)

    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)

    up6 = Conv2DTranspose(512, 2, strides = (2, 2), activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
    up6 = concatenate([drop4,up6])
    up6 = Dropout(0.1)(up6)

    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(up6)
    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)

    up7 = Conv2DTranspose(256, 2, strides = (2, 2), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
    up7 = concatenate([conv3,up7])
    up7 = Dropout(0.1)(up7)

    conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(up7)
    conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)

    up8 = Conv2DTranspose(128, 2, strides = (2, 2), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
    up8 = concatenate([conv2,up8])
    up8 = Dropout(0.1)(up8)

    conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(up8)
    conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)

    up9 = Conv2DTranspose(64, 2,  strides = (2, 2),activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
    up9 = concatenate([conv1,up9])
    up9 = Dropout(0.1)(up9)

    conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(up9)
    conv9 = Conv2D(64, 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

model.summary（）

为了训练模型，我使用了两个numpy阵列：

x_序列，每个元素都有形状（512、512、11）

y_序列，每个元素都已成形（512、512）

我的问题是conv2d_19的形状输出，即（512，512，1）

我正在寻找正确的方法来丢弃conv2d_19输出中的最后一个维度，以修复错误，而不必更改y_序列中每个元素的维度。我不知道我的解释是否正确

这就是错误：

Traceback (most recent call last):
  File "main.py", line 14, in <module>
    history = model.fit(x_train, y_train, batch_size=32, epochs=1)
  File "/home/luis/.local/lib/python3.6/site-packages/keras/engine/training.py", line 1154, in fit
    batch_size=batch_size)
  File "/home/luis/.local/lib/python3.6/site-packages/keras/engine/training.py", line 621, in _standardize_user_data
    exception_prefix='target')
  File "/home/luis/.local/lib/python3.6/site-packages/keras/engine/training_utils.py", line 135, in standardize_input_data
    'with shape ' + str(data_shape))
ValueError: Error when checking target: expected conv2d_19 to have 4 dimensions, but got array with shape (32, 512, 512)

回溯（最近一次呼叫最后一次）：
文件“main.py”，第14行，在
历史=模型.fit（x\U系列，y\U系列，批量大小=32，历代数=1）
文件“/home/luis/.local/lib/python3.6/site-packages/keras/engine/training.py”，第1154行，适合
批次大小=批次大小）
文件“/home/luis/.local/lib/python3.6/site packages/keras/engine/training.py”，第621行，在用户数据中
异常（前缀='target'）
文件“/home/luis/.local/lib/python3.6/site packages/keras/engine/training\u utils.py”，第135行，标准化输入数据
“带形状”+str（数据形状））
ValueError:检查目标时出错：预期conv2d_19有4个维度，但得到的数组具有形状（32、512、512）

使

y\u列车

具有与模型输出相同的形状。这就是

（512，512，1）

您的

y\u列车当前是
（512512）


还是在模型中

....
conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
conv10 = Reshape((512,512))(conv10)
....

从

然后使用
out
而不是
conv10
编译您的模型，请“无需更改y_序列中每个元素的尺寸”，我正在寻找一种方法在模型中修复它，或者使用Keras提供的许多参数中的一些。
y_train = y_train.reshape((512,512,1))

....
conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
conv10 = Reshape((512,512))(conv10)
....

from keras import backend as K
out = K.reshape(conv10, (512,512))