maxpooling 结果未显示在 model.summary() 输出中

Question

我是 Keras 的初学者。我正在尝试构建一个我正在使用顺序模型的模型。当我试图通过使用 maxpooling 函数将输入大小从 28 减少到 14 或更少时，maxpooling 函数结果不会在调用 model.summary() 函数时显示。我试图在训练后达到 0.99 或更高的准确度，即调用 model.score() 时，准确度结果应为 0.99 或更高。 Model build my me so far can be seen here

from keras.layers import Activation, MaxPooling2D
model = Sequential()
model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=(28,28,1)))
model.add(Convolution2D(32, 1, activation='relu'))
MaxPooling2D(pool_size=(2, 2))
model.add(Convolution2D(32, 26))
model.add(Convolution2D(10, 1))
model.add(Flatten())
model.add(Activation('softmax'))

model.summary()

输出 -

Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_29 (Conv2D)           (None, 26, 26, 32)        320       
_________________________________________________________________
conv2d_30 (Conv2D)           (None, 26, 26, 32)        1056      
_________________________________________________________________
conv2d_31 (Conv2D)           (None, 1, 1, 32)          692256    
_________________________________________________________________
conv2d_32 (Conv2D)           (None, 1, 1, 10)          330       
_________________________________________________________________
flatten_7 (Flatten)          (None, 10)                0         
_________________________________________________________________
activation_7 (Activation)    (None, 10)                0         
=================================================================
Total params: 693,962
Trainable params: 693,962
Non-trainable params: 0
____________________________

我使用的批量大小是 32，epoch 数是 10。

model.compile(loss='categorical_crossentropy',
         optimizer='adam',
         metrics=['accuracy'])
model.fit(X_train, Y_train, batch_size=32, nb_epoch=10, verbose=1)

score = model.evaluate(X_test, Y_test, verbose=0)
print(score)

训练后的输出 -

[0.09016687796734459, 0.9814]

Answer 1

您没有将 Maxpooling2D 层添加到您的模型中...

model.add(MaxPooling2D(pool_size=(2, 2)))

此外，您的 maxpooling 的输出将具有形状 (None, 13, 13, 32)，下一层中的卷积核（在您的情况下为 26）不能大于您当前的尺寸 (13)。你的代码应该是这样的：

from keras.layers import Activation, MaxPooling2D, Dense

model = Sequential()
model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=(28,28,1)))
model.add(Convolution2D(32, 1, activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(32, 8))
model.add(Convolution2D(10, 6))
model.add(Flatten())
model.add(Activation('softmax'))
print(model.summary())

输出

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 26, 26, 32)        1056      
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 6, 6, 32)          65568     
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 1, 1, 10)          11530     
_________________________________________________________________
flatten_1 (Flatten)          (None, 10)                0         
_________________________________________________________________
activation_1 (Activation)    (None, 10)                0         
=================================================================
Total params: 78,474
Trainable params: 78,474
Non-trainable params: 0
___________________________________

P.S.：我会考虑在输出端使用更小的内核大小和 FC 层，因为在大多数情况下，它比尝试匹配卷积输出形状更实用