core.py 文件源码

def affine(input_tensor, output_size, bias=True, bias_start=0.0,
           input_size=None, scope="affine", sparse_input=False):
    """Add an affine transformation of `input_tensor` to the current graph.

    Note: This op is loosely based on tensorflow.python.ops.rnn_cell.linear.

    An affine transformation is a linear transformation with a shift,
    `t = tf.matmul(input_tensor, W) + b`.

    Parameters
    ----------
    input_tensor : tensorflow Tensor object, rank 2
        Input tensor to be transformed.
    output_size : int
        The output will be size [a, output_size] where `input_tensor` has
        shape [a, b].
    bias : bool, optional
        If True, apply a bias to the transformation. If False, only a linear
        transformation is applied (i.e., `t = tf.matmul(W, input_tensor)`).
    bias_start : float, optional
        The initial value for the bias `b`.
    input_size : int, optional
        Second dimension of the rank 2 input tensor. Required for sparse input
        tensors.
    sparse_input : bool, optional
        Set to True if `input_tensor` is sparse.

    Returns
    -------
    t : tensorflow tensor object
        The affine transformation of `input_tensor`.
    """

    # The input size is needed for sparse matrices.
    if input_size is None:
        input_size = input_tensor.get_shape().as_list()[1]

    with tf.variable_scope(scope):
        W_0 = tf.get_variable(
            "weights0",
            [input_size, output_size])
        # If the input is sparse, then use a special matmul routine.
        matmul = tf.sparse_tensor_dense_matmul if sparse_input else tf.matmul
        t = matmul(input_tensor, W_0)

        if bias:
            b_0 = tf.get_variable(
                "bias0",
                [output_size],
                initializer=tf.constant_initializer(bias_start))
            t = tf.add(t, b_0)
    return t