Numpy实现UpSampling2D(上采样)
【摘要】
class UpSampling2D(Layer):
""" Nearest neighbor up sampling of the input. Repeats the rows and
...
class UpSampling2D(Layer):
""" Nearest neighbor up sampling of the input. Repeats the rows and
columns of the data by size[0] and size[1] respectively.
Parameters:
-----------
size: tuple
(size_y, size_x) - The number of times each axis will be repeated.
"""
def __init__(self, size=(2,2), input_shape=None):
self.prev_shape = None
self.trainable = True
self.size = size
self.input_shape = input_shape
def forward_pass(self, X, training=True):
self.prev_shape = X.shape
# Repeat each axis as specified by size
X_new = X.repeat(self.size[0], axis=2).repeat(self.size[1], axis=3)
return X_new
def backward_pass(self, accum_grad):
# Down sample input to previous shape
accum_grad = accum_grad[:, :, ::self.size[0], ::self.size[1]]
return accum_grad
def output_shape(self):
channels, height, width = self.input_shape
return channels, self.size[0] * height, self.size[1] * width
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文章来源: wanghao.blog.csdn.net,作者:AI浩,版权归原作者所有,如需转载,请联系作者。
原文链接:wanghao.blog.csdn.net/article/details/120322323
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