pool.py 文件源码

def __init__(self, input_shape, output_shape,
                 kernel_shape=(2, 2), pool_mode='max', stride=(2, 2), padding = (0,0)):
        """
        This function initializes the class.
        Input is 4D tensor, output is 4D tensor.

        Parameters
        ----------
        input_shape: tuple
            a tuple of three values, i.e., (input channel, input width, input height).
        output_shape: tuple
            a tuple of three values, i.e., (output channel, output width, output height).
            output width and height should be match to real convolution output.
        kernel_shape: tuple, default: (2, 2)
            a tuple of two values, i.e., (kernel width, kernel height).
        pool_mode: string {'max', 'sum', 'average_inc_pad', 'average_exc_pad'}, default: 'max'
            a string to determine which mode theano pooling will use.
            'max': max pooling
            'sum': sum pooling
            'average_inc_pad': average pooling contains padding
            'average_exc_pad': average pooling does not contain padding
            'half': pad input with (kernel width //2, kernel height //2) symmetrically and do 'valid'.
                if kernel width and height is odd number, output = input
            int: pad input with (int, int) symmetrically.
            (int1, int2): pad input with (int1, int2) symmetrically.
        stride: tuple, default: (1,1)
            a tuple of two value, i.e., (stride width, stride height).
            also known as subsample.
        padding: tuple, default: (0, 0)
            a tuple of two value, i.e., (padding updown, padding leftright).
            a symmetric padding. padding first, pooling second.
        """
        super(Pooling3DLayer, self).__init__()
        # check asserts
        assert isinstance(input_shape, tuple) and len(input_shape) == 3, '"input_shape" should be a tuple with three values.'
        assert isinstance(output_shape, tuple) and len(output_shape) == 3, '"output_shape" should be a tuple with three values.'
        assert isinstance(kernel_shape, tuple) and len(kernel_shape) == 2, '"kernel_shape" should be a tuple with two values.'
        assert isinstance(stride, tuple) and len(stride) == 2, '"stride" should be a tuple with two values.'
        assert isinstance(padding, tuple) and len(padding) == 2, '"padding" should be a tuple with two values.'
        assert pool_mode in ['max', 'sum', 'average_inc_pad', 'average_exc_pad'], '"poolmode should be a string mode. see theano.tensor.signal.pool.pool_2d for details.'

        # set members
        self.input_shape = input_shape
        self.output_shape = output_shape
        self.kernel_shape = kernel_shape
        self.pool_mode = pool_mode
        self.stride = stride
        self.padding = padding