The calculation formula of convolution network is ：
N=(W-F+2P)/S+1
among
N： Output size
W： Input size
F： Convolution kernel size
P： The size of the filling value
S： Step size
conv2d_1
Input [150,150,3] Output [148,148,32] The convolution kernel size is [3,3,32]
148=[150-3+2P]/S+1 Solution S=1,P=0
Max_pool2d_1 The convolution kernel size is [2,2,32]
Input [148,148,32] Pooling layer output [74,74,32]
74=[148-2+2P]/S+1 Step size of solution S=2, fill P=0
conv2d_2
Input [74,74,32], Output [72,72,64] The convolution kernel size is [3,3,64]
72=[74-3+2p]/s+1 step S=1, fill P=0
Max_pool2d_2 The convolution kernel size is [2,2,64]
Output [72,72,64], Output [36,36,64]
36=[72-2+2p]/s+1 step S=2, fill P=0
conv2d_3 The convolution kernel size is [3,3,128]
Output [36,36,64], Output [34,34,128]
34=[36-3+2p]/s+1 S=1 P=0
Max_pool2d_3 The convolution kernel size is [2,2,128]
Output [34,34,128], Output [17,17,128]
17=[34-2+2p]/s+1 S=2 P=0
conv2d_4 The convolution kernel size is [3,3,128]
Output [17,17,128], Output [15,15,128]
15=[17-3+2p]/s+1 S=1 P=0
Max_pool2d_4 The convolution kernel size is [2,2,128]
Output [15,15,128], Output [7,7,128]
(15+2p-2)/s+1 Integer =7 therefore S=2 P=0
