theano_utils.py 文件源码

def dot(inp, matrix, bias=None):
    """
    Decide the right type of dot product depending on the input
    arguments
    """
    if 'int' in inp.dtype and inp.ndim == 2:
        return matrix[inp.flatten()]
    elif 'int' in inp.dtype:
        return matrix[inp]
    elif 'float' in inp.dtype and inp.ndim == 3:
        shape0 = inp.shape[0]
        shape1 = inp.shape[1]
        shape2 = inp.shape[2]
        if bias:
            return (T.dot(inp.reshape((shape0 * shape1, shape2)), matrix) + bias).reshape((shape0, shape1, matrix.shape[1]))
        else:
            return T.dot(inp.reshape((shape0 * shape1, shape2)), matrix).reshape((shape0, shape1, matrix.shape[1]))
    else:
        if bias:
            return T.dot(inp, matrix) + bias
        else:
            return T.dot(inp, matrix)


# Numerically stable log(sum(exp(A))). Can also be used in softmax function.