jsmacifar.py 文件源码

def conv_2d(filters, kernel_shape, strides, padding):
    """
        Defines the right convolutional layer according to the
        version of Keras that is installed.
        :param filters: (required integer) the dimensionality of the output
        space (i.e. the number output of filters in the
        convolution)
        :param kernel_shape: (required tuple or list of 2 integers) specifies
        the strides of the convolution along the width and
        height.
        :param padding: (required string) can be either 'valid' (no padding around
        input or feature map) or 'same' (pad to ensure that the
        output feature map size is identical to the layer input)
        :return: the Keras layer
        """
    if LooseVersion(keras.__version__) >= LooseVersion('2.0.0'):
        return Conv2D(filters=filters, kernel_size=kernel_shape,
                      strides=strides, padding=padding)
    else:
        return Convolution2D(filters, kernel_shape[0], kernel_shape[1],
                             subsample=strides, border_mode=padding)


# the cnn_model used