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【python实现卷积神经网络】Flatten层实现

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

padding2D实现:https://www.cnblogs.com/xiximayou/p/12720454.html

 

这就相当于是pytorch中的在全连接层之前使用view()函数类似的操作:

class Flatten(Layer):
    """ Turns a multidimensional matrix into two-dimensional """
    def __init__(self, input_shape=None):
        self.prev_shape = None
        self.trainable = True
        self.input_shape = input_shape

    def forward_pass(self, X, training=True):
        self.prev_shape = X.shape
        return X.reshape((X.shape[0], -1))

    def backward_pass(self, accum_grad):
        return accum_grad.reshape(self.prev_shape)

    def output_shape(self):
        return (np.prod(self.input_shape),)

需要注意反向传播时的形状的改变。

还有Reshape层:

class Reshape(Layer):
    """ Reshapes the input tensor into specified shape
    Parameters:
    -----------
    shape: tuple
        The shape which the input shall be reshaped to.
    """
    def __init__(self, shape, input_shape=None):
        self.prev_shape = None
        self.trainable = True
        self.shape = shape
        self.input_shape = input_shape

    def forward_pass(self, X, training=True):
        self.prev_shape = X.shape
        return X.reshape((X.shape[0], ) + self.shape)

    def backward_pass(self, accum_grad):
        return accum_grad.reshape(self.prev_shape)

    def output_shape(self):
        return self.shape

 

posted @ 2020-04-17 15:58  西西嘛呦  阅读(2873)  评论(0编辑  收藏  举报