ee8ff00a2a8a307c952fb8e7bef241188c7fb12b,keras/backend/tensorflow_backend.py,,pool2d,#Any#Any#Any#Any#Any#Any#,1048

Before Change

            // TF input shape: (samples, rows, cols, input_depth)
            // TH kernel shape: (depth, input_depth, rows, cols)
            // TF kernel shape: (rows, cols, input_depth, depth)
            x = tf.transpose(x, (0, 2, 3, 1))
        if pool_mode == "max":
            x = tf.nn.max_pool(x, pool_size, strides, padding=padding)
        elif pool_mode == "avg":
            x = tf.nn.avg_pool(x, pool_size, strides, padding=padding)
        else:
            raise Exception("Invalid pooling mode: " + str(pool_mode))
        if dim_ordering == "th":
            x = tf.transpose(x, (0, 3, 1, 2))
    else:
        raise Exception("Unknown dim_ordering: " + str(dim_ordering))

    if _FLOATX == "float64":
        x = tf.cast(x, "float64")
    return x


// RANDOMNESS

After Change

        dim_ordering: one of "th", "tf".
        pool_mode: one of "max", "avg".
    """
    if dim_ordering not in {"th", "tf"}:
        raise Exception("Unknown dim_ordering " + str(dim_ordering))

    padding = _preprocess_border_mode(border_mode)
    strides = (1,) + strides + (1,)
    pool_size = (1,) + pool_size + (1,)

    x = _preprocess_conv2d_input(x, dim_ordering)

    if pool_mode == "max":
        x = tf.nn.max_pool(x, pool_size, strides, padding=padding)
    elif pool_mode == "avg":
        x = tf.nn.avg_pool(x, pool_size, strides, padding=padding)
    else:
        raise Exception("Invalid pooling mode: " + str(pool_mode))

    return _postprocess_conv2d_output(x, dim_ordering)


def pool3d(x, pool_size, strides=(1, 1, 1), border_mode="valid",
           dim_ordering=_IMAGE_DIM_ORDERING, pool_mode="max"):

Instances