【python實現卷積神經網絡】上采樣層upSampling2D實現

代碼來源：https://github.com/eriklindernoren/ML-From-Scratch

卷積神經網絡中卷積層Conv2D（帶stride、padding）的具體實現：https://www.cnblogs.com/xiximayou/p/12706576.html

激活函數的實現（sigmoid、softmax、tanh、relu、leakyrelu、elu、selu、softplus）：https://www.cnblogs.com/xiximayou/p/12713081.html

損失函數定義（均方誤差、交叉熵損失）：https://www.cnblogs.com/xiximayou/p/12713198.html

優化器的實現（SGD、Nesterov、Adagrad、Adadelta、RMSprop、Adam）：https://www.cnblogs.com/xiximayou/p/12713594.html

卷積層反向傳播過程：https://www.cnblogs.com/xiximayou/p/12713930.html

全連接層實現：https://www.cnblogs.com/xiximayou/p/12720017.html

批量歸一化層實現：https://www.cnblogs.com/xiximayou/p/12720211.html

池化層實現：https://www.cnblogs.com/xiximayou/p/12720324.html

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

Flatten層實現：https://www.cnblogs.com/xiximayou/p/12720518.html

 

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

核心就是numpy.repeat()函數。