diff options
Diffstat (limited to 'tensorflow/contrib/layers/python/layers/layers.py')
| -rw-r--r-- | tensorflow/contrib/layers/python/layers/layers.py | 69 |
1 files changed, 66 insertions, 3 deletions
diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py index 1c3af19a6c..dcee775337 100644 --- a/tensorflow/contrib/layers/python/layers/layers.py +++ b/tensorflow/contrib/layers/python/layers/layers.py @@ -60,9 +60,10 @@ __all__ = [ 'conv2d_in_plane', 'conv2d_transpose', 'conv3d_transpose', 'convolution', 'convolution2d', 'convolution2d_in_plane', 'convolution2d_transpose', 'convolution3d', 'convolution3d_transpose', 'dense_to_sparse', 'dropout', - 'elu', 'flatten', 'fully_connected', 'GDN', 'gdn', 'layer_norm', 'linear', - 'pool', 'max_pool2d', 'max_pool3d', 'one_hot_encoding', 'relu', 'relu6', - 'repeat', 'scale_gradient', 'separable_conv2d', 'separable_convolution2d', + 'elu', 'flatten', 'fully_connected', 'GDN', 'gdn', 'images_to_sequence', + 'layer_norm', 'linear', 'pool', 'max_pool2d', 'max_pool3d', + 'one_hot_encoding', 'relu', 'relu6', 'repeat', 'scale_gradient', + 'separable_conv2d', 'separable_convolution2d', 'sequence_to_images', 'softmax', 'spatial_softmax', 'stack', 'unit_norm', 'legacy_fully_connected', 'legacy_linear', 'legacy_relu', 'maxout' ] @@ -2186,6 +2187,36 @@ def layer_norm(inputs, @add_arg_scope +def images_to_sequence(inputs, + data_format=DATA_FORMAT_NHWC, + outputs_collections=None, + scope=None): + """Convert a batch of images into a batch of sequences. + Args: + inputs: a (num_images, height, width, depth) tensor + data_format: A string. `NHWC` (default) and `NCHW` are supported. + outputs_collections: The collections to which the outputs are added. + scope: Optional scope for name_scope. + Returns: + (width, num_images*height, depth) sequence tensor + """ + if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): + raise ValueError('data_format has to be either NCHW or NHWC.') + with ops.name_scope(scope, 'ImagesToSequence', [inputs]) as sc: + inputs = ops.convert_to_tensor(inputs) + df = ('channels_first' + if data_format and data_format.startswith('NC') else 'channels_last') + if df == 'channels_first': + inputs = array_ops.transpose(inputs, [0, 2, 3, 1]) + _, _, width, depth = inputs.get_shape().as_list() + s = array_ops.shape(inputs) + batch_size, height = s[0], s[1] + transposed = array_ops.transpose(inputs, [2, 0, 1, 3]) + outputs = array_ops.reshape(transposed, [width, batch_size * height, depth]) + return utils.collect_named_outputs(outputs_collections, sc, outputs) + + +@add_arg_scope def max_pool2d(inputs, kernel_size, stride=2, @@ -2665,6 +2696,38 @@ def separable_convolution2d( @add_arg_scope +def sequence_to_images(inputs, + height, + output_data_format='channels_last', + outputs_collections=None, + scope=None): + """Convert a batch of sequences into a batch of images. + Args: + inputs: (num_steps, num_batches, depth) sequence tensor + height: the height of the images + output_data_format: Format of output tensor. + Currently supports `'channels_first'` and `'channels_last'`. + outputs_collections: The collections to which the outputs are added. + scope: Optional scope for name_scope. + Returns: + A tensor representing the output of the operation. + """ + with ops.name_scope(scope, 'SequenceToImages', [inputs]) as sc: + inputs = ops.convert_to_tensor(inputs) + width, num_batches, depth = inputs.get_shape().as_list() + if num_batches is None: + num_batches = -1 + else: + num_batches = num_batches // height + reshaped = array_ops.reshape(inputs, [width, num_batches, height, depth]) + if output_data_format == 'channels_first': + outputs = array_ops.transpose(reshaped, [1, 3, 2, 0]) + else: + outputs = array_ops.transpose(reshaped, [1, 2, 0, 3]) + return utils.collect_named_outputs(outputs_collections, sc, outputs) + + +@add_arg_scope def softmax(logits, scope=None): """Performs softmax on Nth dimension of N-dimensional logit tensor. |