6b106ab4ec9a1c0eb3e24ae590ce63f84022ad40,keras/backend/tensorflow_backend.py,,pool2d,#Any#Any#Any#Any#Any#Any#,3466

Before Change

    padding = _preprocess_padding(padding)
    strides = (1,) + strides + (1,)
    pool_size = (1,) + pool_size + (1,)

    x = _preprocess_conv2d_input(x, data_format)

    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 ValueError("Invalid pooling mode:", pool_mode)

    return _postprocess_conv2d_output(x, data_format)


def pool3d(x, pool_size, strides=(1, 1, 1), padding="valid",
           data_format=None, pool_mode="max"):

After Change

    if data_format not in {"channels_first", "channels_last"}:
        raise ValueError("Unknown data_format " + str(data_format))

    x, tf_data_format = _preprocess_conv2d_input(x, data_format)
    padding = _preprocess_padding(padding)
    if tf_data_format == "NHWC":
        strides = (1,) + strides + (1,)
        pool_size = (1,) + pool_size + (1,)
    else:
        strides = (1, 1) + strides
        pool_size = (1, 1) + pool_size

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

    if data_format == "channels_first" and tf_data_format == "NHWC":
        x = tf.transpose(x, (0, 3, 1, 2))  // NHWC -> NCHW
    return x


def pool3d(x, pool_size, strides=(1, 1, 1), padding="valid",
           data_format=None, pool_mode="max"):

Instances