python - Keras(FIT_GENERATOR)- 错误，当检查目标 : expected activation_1 to have 3 dimensions, 但得到形状为 (32, 416, 608, 3) 的数组时

Question

我已经研究了很多天的分割问题，在终于找到如何正确读取数据集之后，我遇到了这个问题:

ValueError: Error when checking target: expected activation_1(Softmax) to have 3 dimensions, but got array with shape

(32, 416, 608, 3)

I used the functional API, since I took the FCNN architecture from [here](https://github.com/divamgupta/image-segmentation-keras/blob/master/Models/FCN32.py).

根据我的任务(IMAGE_ORDERING = "channels_last"(TensorFlow backend)) 稍作修改和调整。谁能帮帮我吗？非常感谢。下面的架构是针对 FCNN 的，我尝试实现它是为了分割的目的。这是架构(在调用 model.summary() 之后):

1.

2。

1. 具体报错是:

2. “导入数据集”功能:

3. “Fit_Generator 方法调用”:

    img_input = Input(shape=(input_height,input_width,3))
   
    #Block 1
    x = Convolution2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1', data_format=IMAGE_ORDERING)(img_input) 
    x = BatchNormalization()(x)
    x = Convolution2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2', data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool', data_format=IMAGE_ORDERING)(x)
    f1 = x
    # Block 2
    x = Convolution2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1', data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = Convolution2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2', data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool', data_format=IMAGE_ORDERING )(x)
    f2 = x
   
    # Block 3
    x = Convolution2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1', data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = Convolution2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2', data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = Convolution2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3', data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool', data_format=IMAGE_ORDERING )(x)
    f3 = x
   
    # Block 4
    x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1', data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2',data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3',data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool', data_format=IMAGE_ORDERING)(x)
    f4 = x
   
    # Block 5
    x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1', data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2',data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3', data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool', data_format=IMAGE_ORDERING)(x)
    f5 = x
   
    x = (Convolution2D(4096,(7,7) , activation='relu' , padding='same', data_format=IMAGE_ORDERING))(x)
    x = Dropout(0.5)(x)
    x = (Convolution2D(4096,(1,1) , activation='relu' , padding='same',data_format=IMAGE_ORDERING))(x)
    x = Dropout(0.5)(x)
   
    #First parameter = number of classes+1 (de la background)
    x = (Convolution2D(20,(1,1) ,kernel_initializer='he_normal' ,data_format=IMAGE_ORDERING))(x)
    x = Convolution2DTranspose(20,kernel_size=(64,64), strides=(32,32),use_bias=False,data_format=IMAGE_ORDERING)(x)
    o_shape = Model(img_input,x).output_shape
   
    outputHeight = o_shape[1]
    print('Output Height is:', outputHeight)
    outputWidth = o_shape[2]
    print('Output Width is:', outputWidth)
    #https://keras.io/layers/core/#reshape
    x = (Reshape((20,outputHeight*outputWidth)))(x)
    #https://keras.io/layers/core/#permute
    x = (Permute((2, 1)))(x)
    print("Output shape before softmax is", o_shape)
    x = (Activation('softmax'))(x)
    print("Output shape after softmax is", o_shape)
    model = Model(inputs = img_input,outputs = x)
    model.outputWidth = outputWidth
    model.outputHeight = outputHeight
    model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics =['accuracy'])
   

Answer 1

FCNN 架构示例中的原始代码使用输入维度 (416, 608) .而在您的代码中，输入维度是 (192, 192) (忽略 channel 维度)。现在，如果您仔细观察，这个特定的层

x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool', data_format=IMAGE_ORDERING)(x)


生成维度 (6, 6) 的输出(您可以在 model.summary() 中验证)。

下一个卷积层

o = (Convolution2D(4096,(7,7) , activation='relu' , padding='same', data_format=IMAGE_ORDERING))(o)


使用大小为 (7, 7) 的卷积过滤器，但您的输入已经减小到小于该大小(即 (6, 6))。先尝试解决这个问题。

此外，如果您查看 model.summary() 输出，您会注意到它不包含在 block5_pool 层之后定义的层。其中有一个 transposed convolution 层(基本上是对输入进行上采样)。您可能想看看并尝试解决这个问题。

注意:在我的所有维度中，我都忽略了 channel 维度。

---

编辑下面的详细答案

首先，这是我的 keras.json 文件。它使用 Tensorflow 后端，image_ordering 设置为 channel_last。

{
    "floatx": "float32",
    "epsilon": 1e-07,
    "backend": "tensorflow",
    "image_data_format": "channels_last"
}

接下来，我复制粘贴我的确切模型代码。请特别注意以下代码中的内联注释。

from keras.models import *
from keras.layers import *

IMAGE_ORDERING = 'channels_last' # In consistency with the json file

def getFCN32(nb_classes = 20, input_height = 416, input_width = 608):

    img_input = Input(shape=(input_height,input_width, 3)) # Expected input will have channel in the last dimension

    #Block 1
    x = Convolution2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1', data_format=IMAGE_ORDERING)(img_input) 
    x = BatchNormalization()(x)
    x = Convolution2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2', data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool', data_format=IMAGE_ORDERING)(x)
    f1 = x
    # Block 2
    x = Convolution2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1', data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = Convolution2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2', data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool', data_format=IMAGE_ORDERING )(x)
    f2 = x

    # Block 3
    x = Convolution2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1', data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = Convolution2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2', data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = Convolution2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3', data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool', data_format=IMAGE_ORDERING )(x)
    f3 = x

    # Block 4
    x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1', data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2',data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3',data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool', data_format=IMAGE_ORDERING)(x)
    f4 = x

    # Block 5
    x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1', data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2',data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3', data_format=IMAGE_ORDERING)(x)
    x = BatchNormalization()(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool', data_format=IMAGE_ORDERING)(x)
    f5 = x

    x = (Convolution2D(4096,(7,7) , activation='relu' , padding='same', data_format=IMAGE_ORDERING))(x)
    x = Dropout(0.5)(x)
    x = (Convolution2D(4096,(1,1) , activation='relu' , padding='same',data_format=IMAGE_ORDERING))(x)
    x = Dropout(0.5)(x)

    x = (Convolution2D(20,(1,1) ,kernel_initializer='he_normal' ,data_format=IMAGE_ORDERING))(x)
    x = Convolution2DTranspose(20,kernel_size=(64,64), strides=(32,32),use_bias=False,data_format=IMAGE_ORDERING)(x)
    o_shape = Model(img_input, x).output_shape

    # NOTE: Since this is channel last dimension ordering, the height and width dimensions are along [1] and [2], not [2] and [3]
    outputHeight = o_shape[1]
    outputWidth = o_shape[2]

    x = (Reshape((outputHeight*outputWidth, 20)))(x) # Channel should be along the last dimenion of reshape
    # No need of permute layer anymore

    print("Output shape before softmax is", o_shape)
    x = (Activation('softmax'))(x)
    print("Output shape after softmax is", o_shape)
    model = Model(inputs = img_input,outputs = x)
    model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics =['accuracy'])

    return model

model = getFCN32(20)
print(model.summary())

接下来，我将提供有关我的 model.summary() 外观的片段。如果你看一下最后几层，它是这样的:

所以这意味着，conv2d_transpose 层产生维度为 (448, 640, 20) 的输出，并在应用 flatten 之前将其输出em>softmax 就可以了。所以输出的维度是(286720, 20)。同样，您的 target_generator(在您的情况下为 mask_generator)也应该生成相似维度的目标。同样，您的 input_generator 也应该生成大小为 [batch size, input_height,input_width, 3] 的输入批处理，如 img_input 中所述你的功能。

希望这能帮助您找到问题的根源并找出合适的解决方案。请查看代码中的细微变化(以及内联注释)以及如何创建您的输入 和目标 批处理。