Keras 中的形状与 CNN 不兼容

Question

我正在实现一个获取 2d 图像并为其输出 3D 二进制体素的网络。 我正在使用带有 LSTM 模块的自动编码器。 目前图像和体素的形状如下：

print(x_train.shape)
print(y_train.shape)
>>> (792, 127, 127, 3)
>>> (792, 32, 32, 32)

792 RGB 图像 127 x 127

792 个对应的 3D 二进制张量体素 (32 x 32 x 32)

运行以下编码器模型：

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, LeakyReLU, MaxPooling2D, Dense, Flatten, Conv3D, MaxPool3D, GRU, Reshape, UpSampling3D
from tensorflow import keras



enc_filter = [96, 128, 256, 256, 256, 256]
fc_filters = [1024]

model = Sequential()
epochs = 5
batch_size = 24
input_shape=(127,127,3)

model.add(Conv2D(enc_filter[0], kernel_size=(7, 7), strides=(1,1),activation='relu',input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(LeakyReLU(alpha=0.1))
model.add(Flatten())
model.add(Dense(1024, activation='relu'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.SGD(lr=0.01),
              metrics=['accuracy'])
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs)

产生以下结果：

ValueError: Shapes (24, 32, 32, 32) and (24, 1024) are incompatible

有人能解释为什么这些形状不兼容吗？我尝试移除层并测试其他层，但都产生了兼容性问题。

Answer 1

您的模型有一个密集层，输出为 1024，但您传递的是 32、32、32 形状的数组。

您需要重塑模型输出，使其具有适当的形状。

这是一个虚拟模型，您需要更改参数以找到合适的架构。

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, LeakyReLU, MaxPooling2D, Dense, Flatten, Conv3D, MaxPool3D, GRU, Reshape, UpSampling3D
from tensorflow import keras

import numpy as np

# dummy data
x_train = np.random.randn(792, 127, 127, 3)
y_train = np.random.randn(792, 32, 32, 32)

enc_filter = [96, 128, 256, 2]
fc_filters = [1024]

model = Sequential()
epochs = 5
batch_size = 24
input_shape=(127,127,3)

model.add(Conv2D(enc_filter[0], kernel_size=(7, 7), strides=(1,1),activation='relu',input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(LeakyReLU(alpha=0.1))

model.add(Conv2D(enc_filter[1], kernel_size=(7, 7), strides=(1,1),activation='relu',input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(LeakyReLU(alpha=0.1))

model.add(Conv2D(enc_filter[2], kernel_size=(7, 7), strides=(1,1),activation='relu',input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(LeakyReLU(alpha=0.1))

model.add(Conv2D(enc_filter[3], kernel_size=(7, 7), strides=(1,1),activation='relu',input_shape=input_shape)) # bottolneck
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(LeakyReLU(alpha=0.1))

model.add(Flatten())
model.add(Dense(32*32*32, activation='relu'))

model.add(Reshape((32,32,32)))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.SGD(lr=0.01),
              metrics=['accuracy'])

model.summary()
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs)

Model: "sequential_10"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_24 (Conv2D)           (None, 121, 121, 96)      14208     
_________________________________________________________________
max_pooling2d_24 (MaxPooling (None, 60, 60, 96)        0         
_________________________________________________________________
leaky_re_lu_24 (LeakyReLU)   (None, 60, 60, 96)        0         
_________________________________________________________________
conv2d_25 (Conv2D)           (None, 54, 54, 128)       602240    
_________________________________________________________________
max_pooling2d_25 (MaxPooling (None, 27, 27, 128)       0         
_________________________________________________________________
leaky_re_lu_25 (LeakyReLU)   (None, 27, 27, 128)       0         
_________________________________________________________________
conv2d_26 (Conv2D)           (None, 21, 21, 256)       1605888   
_________________________________________________________________
max_pooling2d_26 (MaxPooling (None, 10, 10, 256)       0         
_________________________________________________________________
leaky_re_lu_26 (LeakyReLU)   (None, 10, 10, 256)       0         
_________________________________________________________________
conv2d_27 (Conv2D)           (None, 4, 4, 2)           25090     
_________________________________________________________________
max_pooling2d_27 (MaxPooling (None, 2, 2, 2)           0         
_________________________________________________________________
leaky_re_lu_27 (LeakyReLU)   (None, 2, 2, 2)           0         
_________________________________________________________________
flatten_10 (Flatten)         (None, 8)                 0         
_________________________________________________________________
dense_1 (Dense)              (None, 32768)             294912    
_________________________________________________________________
reshape_10 (Reshape)         (None, 32, 32, 32)        0         
=================================================================
Total params: 2,542,338
Trainable params: 2,542,338
Non-trainable params: 0

在总结中，您可以看到我添加了一个具有 32x32x32 神经元的密集层，然后对其进行重塑。