multimodal_autoencoder.py 文件源码

def weight_variable(shape, name, var_type='normal', const=1):
    """Initializes a tensorflow weight variable.

    Args:
        shape: An array representing shape of the weight variable
        name: A string name given to the variable.
        var_type: can be either 'normal', for weights following a Gaussian
            distribution around 0, or 'xavier', for the Xavier method
        const: Numeric value that controls the range of the weights within
            the Xavier method.
    Returns: Tensor variable for the weights
    """
    if var_type == 'xavier':
        """ Xavier initialization of network weights.
        Taken from: https://gist.github.com/blackecho/3a6e4d512d3aa8aa6cf9
        https://stackoverflow.com/questions/33640581/how-to-do-xavier-initialization-on-tensorflow
        """
        assert len(shape) == 2
        low = -const * np.sqrt(6.0 / (shape[0] + shape[1]))
        high = const * np.sqrt(6.0 / (shape[0] + shape[1]))
        initial = tf.random_uniform((shape[0], shape[1]), minval=low, maxval=high)
    else:
        initial = tf.truncated_normal(shape, stddev=1.0 / math.sqrt(float(shape[0])), dtype=tf.float32)

    return tf.Variable(initial, name=name)