Python 检查输入时出错：预期输入_19有4个维度，但得到了具有形状的数组（1190200200）

我是CNN的新手，我无法确定如何解决这个问题。 在这段代码中，我正在训练一组图像，以从卷积网络中获得蒙版。这些图像是带有形状的灰度（200200）。我无法确定我在哪里犯了错误。而且每次我运行代码时，在不同的输入端都会出现错误。如果有任何帮助，我将不胜感激

以下是生成的日志：

Creating training images...
Saving to .npy files done.
Creating test images...
Saving to .npy files done.
------------------------------
Loading and preprocessing train data...
------------------------------
------------------------------
Creating and compiling model...
------------------------------
C:/Users/Asus/Desktop/training.py:101: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(25, (3, 3), activation="relu", padding="same", data_format="channels_last")`
  conv2 = Conv2D(25, (3, 3), activation='relu', padding='same',dim_ordering="th")(inputs)
C:/Users/Asus/Desktop/training.py:102: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(25, (3, 3), activation="relu", padding="same", data_format="channels_first")`
  conv2 = Conv2D(25, (3, 3), activation='relu', padding='same',dim_ordering="th")(conv2)
C:/Users/Asus/Desktop/training.py:103: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(pool_size=(2, 2), data_format="channels_last")`
  pool2 = MaxPooling2D(pool_size=(2, 2), dim_ordering="tf")(conv2)
C:/Users/Asus/Desktop/training.py:105: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(50, (3, 3), activation="relu", padding="same", data_format="channels_first")`
  conv3 = Conv2D(50, (3, 3), activation='relu', padding='same',dim_ordering="th")(pool2)
C:/Users/Asus/Desktop/training.py:106: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(50, (3, 3), activation="relu", padding="same", data_format="channels_first")`
  conv3 = Conv2D(50, (3, 3), activation='relu', padding='same',dim_ordering="th")(conv3)
C:/Users/Asus/Desktop/training.py:107: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(pool_size=(2, 2), data_format="channels_last")`
  pool3 = MaxPooling2D(pool_size=(2, 2),dim_ordering="tf")(conv3)
C:/Users/Asus/Desktop/training.py:109: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(100, (3, 3), activation="relu", padding="same", data_format="channels_first")`
  conv4 = Conv2D(100, (3, 3), activation='relu', padding='same',dim_ordering="th")(pool3)
C:/Users/Asus/Desktop/training.py:110: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(100, (3, 3), activation="relu", padding="same", data_format="channels_first")`
  conv4 = Conv2D(100, (3, 3), activation='relu', padding='same',dim_ordering="th")(conv4)
C:/Users/Asus/Desktop/training.py:111: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(pool_size=(2, 2), data_format="channels_last")`
  pool4 = MaxPooling2D(pool_size=(2, 2), dim_ordering="tf")(conv4)
C:/Users/Asus/Desktop/training.py:113: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(200, (3, 3), activation="relu", padding="same", data_format="channels_first")`
  conv5 = Conv2D(200, (3, 3), activation='relu', padding='same',dim_ordering="th")(pool4)
C:/Users/Asus/Desktop/training.py:114: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(200, (3, 3), activation="relu", padding="same", data_format="channels_first")`
  conv5 = Conv2D(200, (3, 3), activation='relu', padding='same',dim_ordering="th")(conv5)
C:/Users/Asus/Desktop/training.py:116: UserWarning: Update your `Conv2DTranspose` call to the Keras 2 API: `Conv2DTranspose(200, (2, 2), strides=(2, 2), padding="same", data_format="channels_first")`
  up6 = concatenate([Conv2DTranspose(200, (2, 2), strides=(2, 2), padding='same',dim_ordering="th")(conv5), conv4], axis=3)
Traceback (most recent call last):

  File "<ipython-input-25-4b34507d9da0>", line 1, in <module>
    runfile('C:/Users/Asus/Desktop/training.py', wdir='C:/Users/Asus/Desktop')

  File "C:\Users\Asus\AppData\Local\Continuum\anaconda3\envs\tensorflow\lib\site-packages\spyder\utils\site\sitecustomize.py", line 705, in runfile
    execfile(filename, namespace)

  File "C:\Users\Asus\AppData\Local\Continuum\anaconda3\envs\tensorflow\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "C:/Users/Asus/Desktop/training.py", line 205, in <module>
    train_and_predict()

  File "C:/Users/Asus/Desktop/training.py", line 163, in train_and_predict
    model = get_unet()

  File "C:/Users/Asus/Desktop/training.py", line 116, in get_unet
    up6 = concatenate([Conv2DTranspose(200, (2, 2), strides=(2, 2), padding='same',dim_ordering="th")(conv5), conv4], axis=3)

  File "C:\Users\Asus\AppData\Local\Continuum\anaconda3\envs\tensorflow\lib\site-packages\keras\layers\merge.py", line 641, in concatenate
    return Concatenate(axis=axis, **kwargs)(inputs)

  File "C:\Users\Asus\AppData\Local\Continuum\anaconda3\envs\tensorflow\lib\site-packages\keras\engine\topology.py", line 594, in __call__
    self.build(input_shapes)

  File "C:\Users\Asus\AppData\Local\Continuum\anaconda3\envs\tensorflow\lib\site-packages\keras\layers\merge.py", line 354, in build
    'Got inputs shapes: %s' % (input_shape))

ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 200, 50, 50), (None, 100, 50, 25)]

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我成功地编译了这个模型。 我无法重新创建日志中提到的连接错误

您应该检查的另一个问题是，您向模型提供的输入应在4维中进行重塑，正如您在问题中提到的（1190200200）的重塑错误一样，您应该将其转换为（11902002000，1）“1”表示带数

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因此，基本上你应该为灰度图像添加一个额外的维度，并将其转换为（img_行、img_列、条带）

我遇到了与灰度图像相同的情况，通过为灰度通道添加额外维度，对图像进行重塑将解决此问题

train_images_reshape = train_images.reshape(no_images_train, h,w,1)
test_images_reshape = test_images.reshape(no_images_test, h,w,1)

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keras需要额外的尺寸来指定通道

格式为（无图像、高度、宽度、n个通道） n_通道=1用于灰度图像

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=3表示RGB

您测试了哪种数据？感谢您的帮助。因此，它使用expand_dim有效。

train_images_reshape = train_images.reshape(no_images_train, h,w,1)
test_images_reshape = test_images.reshape(no_images_test, h,w,1)