Keras - CNN模型综述

Question

我正在使用Keras库来构建这个深度学习模型: INPUT(depth=1，height=15，width=27) -> CONVdepth=8 ->池(height=2，width=1) -> (回归)输出。

我期望convolution2d_1的输出形状(None，8，12，1)和pooling2d_1的输出形状(None，8，6，1)，而我分别得到(None，8，15，27)和(None，8，7，27)。

我在做什么或者解释错了什么？

P.S.：而且，这个设置给出了一个基线错误: 99.23%！

print "SHAPE OF INPUT IS:", num_train_3D, depth, height, width
inp = Input(shape=(depth, height, width)) 
conv_1 = Convolution2D(8, 4, 27, border_mode='same', activation='relu')(inp)
pool_1 = MaxPooling2D(pool_size=(2, 1))(conv_1)
''' Now flatten to 1D, apply FC -> ReLU (with dropout) -> softmax '''
flat = Flatten()(pool_1)
out = Dense(1)(flat)  #regression

model = Model(input=inp, output=out) # To define a model, just specify its input and output layers

print "Model Summary:"
print model.summary()

=====================================

SHAPE OF INPUT IS: 53745 1 15 27
Model Summary:
____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
input_1 (InputLayer)             (None, 1, 15, 27)     0                                            
____________________________________________________________________________________________________
convolution2d_1 (Convolution2D)  (None, 8, 15, 27)     872         input_1[0][0]                    
____________________________________________________________________________________________________
maxpooling2d_1 (MaxPooling2D)    (None, 8, 7, 27)      0           convolution2d_1[0][0]            
____________________________________________________________________________________________________
flatten_1 (Flatten)              (None, 1512)          0           maxpooling2d_1[0][0]             
____________________________________________________________________________________________________
dense_1 (Dense)                  (None, 1)             1513        flatten_1[0][0]                  
====================================================================================================
Total params: 2,385
Trainable params: 2,385
Non-trainable params: 0

Answer 1

将border_mode='same'更改为border_mode='valid'。边界模式same在输入中添加零填充，以确保卷积层的输出与其输入具有相同的形状。在边界模式下，valid卷积只在输入和滤波器完全重叠的情况下进行。