第1关：实现卷积层的前向传播
import numpy as np
from utils import im2col
class Convolution:
def __init__(self, W, b, stride=1, pad=0):
r'''
卷积层的初始化
Parameter:
- W: numpy.array, (C_out, C_in, K_h, K_w)
- b: numpy.array, (C_out)
- stride: int
- pad: int
'''
self.W = W
self.b = b
self.stride = stride
self.pad = pad
def forward(self, x):
r'''
卷积层的前向传播
Parameter:
- x: numpy.array, (B, C, H, W)
Return:
- y: numpy.array, (B, C', H', W')
H' = (H - Kh + 2P) / S + 1
W' = (W - Kw + 2P) / S + 1
'''
########## Begin ##########
FN, C, FH, FW = self.W.shape
N, C, H, W = x.shape
out_h = int(1 + (H + 2 * self.pad - FH) / self.stride)
out_w = int(1 + (W + 2 * self.pad - FW) / self.stride)
col = im2col(x, FH, FW, self.stride, self.pad)
col_w = self.W.reshape(FN, -1).T
out = np.dot(col, col_w) + self.b
out = out.reshape(N, out_h, out_w, -1).transpose(0, 3, 1, 2)
return out
########## End ##########
第2关：实现池化层的前向传播
import numpy as np
from utils import im2col
class MaxPool:
def __init__(self, pool_h, pool_w, stride=1, pad=0):
r'''
池化层的初始化
Parameter:
- pool_h: int
- pool_h: int
- stride: int
- pad: int
'''
self.pool_h = pool_h
self.pool_w = pool_w
self.stride = stride
self.pad = pad
def forward(self, x):
r'''
池化层的前向传播
Parameter:
- x: numpy.array, (B, C, H, W)
Return:
- y: numpy.array, (B, C, H', W')
H' = (H - Kh + 2P) / S + 1
W' = (W - Kw + 2P) / S + 1
'''
########## Begin ##########
N, C, H, W = x.shape
out_h = int(1 + (H - self.pool_h) / self.stride)
out_w = int(1 + (W - self.pool_w) / self.stride)
col = im2col(x, self.pool_h, self.pool_w, self.stride, self.pad)
col = col.reshape(-1, self.pool_h * self.pool_w)
out = np.max(col, axis=1)
out = out.reshape(N, out_h, out_w, C).transpose(0, 3, 1, 2)
return out
########## End ##########
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