【python实现卷积神经网络】padding2D层实现

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代码来源：https://github.com/eriklindernoren/ML-From-Scratch

卷积神经网络中卷积层Conv2D（带stride、padding）的具体实现：https://www.cnblogs.com/xiximayou/p/12706576.html

激活函数的实现（sigmoid、softmax、tanh、relu、leakyrelu、elu、selu、softplus）：https://www.cnblogs.com/xiximayou/p/12713081.html

损失函数定义（均方误差、交叉熵损失）：https://www.cnblogs.com/xiximayou/p/12713198.html

优化器的实现（SGD、Nesterov、Adagrad、Adadelta、RMSprop、Adam）：https://www.cnblogs.com/xiximayou/p/12713594.html

卷积层反向传播过程：https://www.cnblogs.com/xiximayou/p/12713930.html

全连接层实现：https://www.cnblogs.com/xiximayou/p/12720017.html

批量归一化层实现：https://www.cnblogs.com/xiximayou/p/12720211.html

池化层实现：https://www.cnblogs.com/xiximayou/p/12720324.html

class ConstantPadding2D(Layer):
"""Adds rows and columns of constant values to the input.
Expects the input to be of shape (batch_size, channels, height, width)
Parameters:
-----------
padding: tuple
The amount of padding along the height and width dimension of the input.
If (pad_h, pad_w) the same symmetric padding is applied along height and width dimension.
If ((pad_h0, pad_h1), (pad_w0, pad_w1)) the specified padding is added to beginning and end of
the height and width dimension.
padding_value: int or tuple
The value the is added as padding.
"""
def __init__(self, padding, padding_value=0):
self.padding = padding
self.trainable = True
if not isinstance(padding[0], tuple):
self.padding = ((padding[0], padding[0]), padding[1])
if not isinstance(padding[1], tuple):
self.padding = (self.padding[0], (padding[1], padding[1]))
self.padding_value = padding_value def forward_pass(self, X, training=True):
output = np.pad(X,
pad_width=((0,0), (0,0), self.padding[0], self.padding[1]),
mode="constant",
constant_values=self.padding_value)
return output def backward_pass(self, accum_grad):
pad_top, pad_left = self.padding[0][0], self.padding[1][0]
height, width = self.input_shape[1], self.input_shape[2]
accum_grad = accum_grad[:, :, pad_top:pad_top+height, pad_left:pad_left+width]
return accum_grad def output_shape(self):
new_height = self.input_shape[1] + np.sum(self.padding[0])
new_width = self.input_shape[2] + np.sum(self.padding[1])
return (self.input_shape[0], new_height, new_width)class ZeroPadding2D(ConstantPadding2D):
"""Adds rows and columns of zero values to the input.
Expects the input to be of shape (batch_size, channels, height, width)
Parameters:
-----------
padding: tuple
The amount of padding along the height and width dimension of the input.
If (pad_h, pad_w) the same symmetric padding is applied along height and width dimension.
If ((pad_h0, pad_h1), (pad_w0, pad_w1)) the specified padding is added to beginning and end of
the height and width dimension.
"""
def __init__(self, padding):
self.padding = padding
if isinstance(padding[0], int):
self.padding = ((padding[0], padding[0]), padding[1])
if isinstance(padding[1], int):
self.padding = (self.padding[0], (padding[1], padding[1]))
self.padding_value = 0