ops.py 文件源码

def T_one_hot(inp_tensor, n_classes):
    """
    :todo:
        - Implement other methods from here: 
        - Compare them speed-wise for different sizes
        - Implement N_one_hot for Numpy version, with speed tests.

    Theano one-hot (1-of-k) from an input tensor of indecies.
    If the indecies are of the shape (a0, a1, ..., an) the output
    shape would be (a0, a1, ..., a2, n_classes).

    :params:
        - inp_tensor: any theano tensor with dtype int* as indecies and all of
                      them between [0, n_classes-1].
        - n_classes: number of classes which determines the output size.

    :usage:
        >>> idx = T.itensor3()
        >>> idx_val = numpy.array([[[0,1,2,3],[4,5,6,7]]], dtype='int32')
        >>> one_hot = T_one_hot(t, 8)
        >>> one_hot.eval({idx:idx_val})
        >>> print out
        array([[[[ 1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
         [ 0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.],
         [ 0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.],
         [ 0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.]],
        [[ 0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.],
         [ 0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.],
         [ 0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.],
         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.]]]])
        >>> print idx_val.shape, out.shape
        (1, 2, 4) (1, 2, 4, 8)
    """
    flattened = inp_tensor.flatten()
    z = T.zeros((flattened.shape[0], n_classes), dtype=theano.config.floatX)
    one_hot = T.set_subtensor(z[T.arange(flattened.shape[0]), flattened], 1)
    out_shape = [inp_tensor.shape[i] for i in xrange(inp_tensor.ndim)] + [n_classes]
    one_hot = one_hot.reshape(out_shape)
    return one_hot