在Keras的Conv2D和Dense期间,数据如何变化？

Question

正如标题所说.此代码仅适用于:

x = Flatten()(x)

在卷积层和密集层之间.

import numpy as np
import keras
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Flatten, Input
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import SGD

# Generate dummy data
x_train = np.random.random((100, 100, 100, 3))
y_train = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)

#Build Model
input_layer = Input(shape=(100, 100, 3))
x = Conv2D(32, (3, 3), activation='relu')(input_layer)
x = Dense(256, activation='relu')(x)
x = Dense(10, activation='softmax')(x)
model = Model(inputs=[input_layer],outputs=[x])

#compile network
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd)

#train network
model.fit(x_train, y_train, batch_size=32, epochs=10)

否则,我收到此错误:

Traceback (most recent call last):

File "/home/michael/practice_example.py", line 44, in <module>
    model.fit(x_train, y_train, batch_size=32, epochs=10)

File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1435, in fit
    batch_size=batch_size)

File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1315, in _standardize_user_data
    exception_prefix='target')

File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 127, in _standardize_input_data
    str(array.shape))

ValueError: Error when checking target: expected dense_2 to have 4 dimensions, but got array with shape (100, 10)

为什么输出在没有flatten()图层的情况下有4个尺寸？

Answer 1

根据keras doc,

Conv2D输出形状

4D张量与形状:(样本,过滤器,new_rows,new_cols)如果data_format ='channels_first'或4D张量与形状:(samples,new_rows,new_cols,filters)如果data_format ='channels_last'.由于填充,行和列值可能已更改.

由于您使用channels_last,图层输出的形状将是:

# shape=(100, 100, 100, 3)

x = Conv2D(32, (3, 3), activation='relu')(input_layer)
# shape=(100, row, col, 32)

x = Flatten()(x)
# shape=(100, row*col*32)    

x = Dense(256, activation='relu')(x)
# shape=(100, 256)

x = Dense(10, activation='softmax')(x)
# shape=(100, 10)

错误说明(编辑,感谢@Marcin)

使用Dense图层将4D张量(shape =(100,row,col,32))与2D图像(shape =(100,256))相关联仍将形成4D张量(shape =(100,row,col,256) )这不是你想要的.

# shape=(100, 100, 100, 3)

x = Conv2D(32, (3, 3), activation='relu')(input_layer)
# shape=(100, row, col, 32)

x = Dense(256, activation='relu')(x)
# shape=(100, row, col, 256)

x = Dense(10, activation='softmax')(x)
# shape=(100, row, col, 10)

并且当输出4D张量和目标2D张量之间的不匹配发生时将发生错误.

这就是为什么你需要一个Flatten层来将它从4D平移到2D.

参考

Conv2D Dense