def initialize(self):
self.pool_method = self.pool_dict["method"]
self.pool_size = self.pool_dict["size"] #卷积核的大小，为一个正方形
self.stride = self.pool_dict["stride"]
self.padding = self.pool_dict["padding"]
h, w = util.getPadedOutShape(self.preLayer.A1_shape, self.pool_size,
self.stride, self.padding)
self.A1_shape = (h, w, self.preLayer.A1_shape[2])
self.A1_size = h * w * self.preLayer.A1_shape[2]
def initialize(self):
channels = self.preLayer.A1_shape[2]
size = self.filter_dict["size"] #卷积核的大小，为一个正方形
filter_count = self.filter_dict["count"] #卷积后的通道数
#就是过滤器filter的矩阵，实质就是权重参数
self.W = np.random.randn(size, size, channels, filter_count) * 0.01
#确定B的shape，这其实也是本层输出的shape
stride = self.filter_dict["stride"]
padding = self.filter_dict["padding"]
h, w = util.getPadedOutShape(self.preLayer.A1_shape, size, stride,
padding)
self.A1_shape = (h, w, filter_count)
self.A1_size = h * w * filter_count
# self.B = np.random.randn(h,w,filter_count) #初始化B
self.B = np.zeros((1, 1, filter_count), dtype=float) #初始化B