diff options
Diffstat (limited to 'tensorflow/python/ops/nn_ops.py')
| -rw-r--r-- | tensorflow/python/ops/nn_ops.py | 125 |
1 files changed, 10 insertions, 115 deletions
diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index 61fa462988..bdaac65904 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -23,7 +23,6 @@ import numbers import numpy as np from tensorflow.python.eager import context -from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import graph_util from tensorflow.python.framework import ops @@ -38,8 +37,6 @@ from tensorflow.python.ops import random_ops # pylint: disable=wildcard-import from tensorflow.python.ops.gen_nn_ops import * # pylint: enable=wildcard-import -from tensorflow.python.util.deprecation import deprecated_args -from tensorflow.python.util.deprecation import deprecated_argument_lookup from tensorflow.python.util import deprecation @@ -1648,18 +1645,17 @@ def _softmax(logits, compute_op, dim=-1, name=None): return output -@deprecated_args(None, "dim is deprecated, use axis instead", "dim") -def softmax(logits, axis=None, name=None, dim=None): +def softmax(logits, dim=-1, name=None): """Computes softmax activations. This function performs the equivalent of - softmax = tf.exp(logits) / tf.reduce_sum(tf.exp(logits), axis) + softmax = tf.exp(logits) / tf.reduce_sum(tf.exp(logits), dim) Args: logits: A non-empty `Tensor`. Must be one of the following types: `half`, `float32`, `float64`. - axis: The dimension softmax would be performed on. The default is -1 which + dim: The dimension softmax would be performed on. The default is -1 which indicates the last dimension. name: A name for the operation (optional). @@ -1667,27 +1663,23 @@ def softmax(logits, axis=None, name=None, dim=None): A `Tensor`. Has the same type and shape as `logits`. Raises: - InvalidArgumentError: if `logits` is empty or `axis` is beyond the last + InvalidArgumentError: if `logits` is empty or `dim` is beyond the last dimension of `logits`. """ - axis = deprecated_argument_lookup("axis", axis, "dim", dim) - if axis is None: - axis = -1 - return _softmax(logits, gen_nn_ops._softmax, axis, name) + return _softmax(logits, gen_nn_ops._softmax, dim, name) -@deprecated_args(None, "dim is deprecated, use axis instead", "dim") -def log_softmax(logits, axis=None, name=None, dim=None): +def log_softmax(logits, dim=-1, name=None): """Computes log softmax activations. For each batch `i` and class `j` we have - logsoftmax = logits - log(reduce_sum(exp(logits), axis)) + logsoftmax = logits - log(reduce_sum(exp(logits), dim)) Args: logits: A non-empty `Tensor`. Must be one of the following types: `half`, `float32`, `float64`. - axis: The dimension softmax would be performed on. The default is -1 which + dim: The dimension softmax would be performed on. The default is -1 which indicates the last dimension. name: A name for the operation (optional). @@ -1695,13 +1687,10 @@ def log_softmax(logits, axis=None, name=None, dim=None): A `Tensor`. Has the same type as `logits`. Same shape as `logits`. Raises: - InvalidArgumentError: if `logits` is empty or `axis` is beyond the last + InvalidArgumentError: if `logits` is empty or `dim` is beyond the last dimension of `logits`. """ - axis = deprecated_argument_lookup("axis", axis, "dim", dim) - if axis is None: - axis = -1 - return _softmax(logits, gen_nn_ops._log_softmax, axis, name) + return _softmax(logits, gen_nn_ops._log_softmax, dim, name) def _ensure_xent_args(name, sentinel, labels, logits): @@ -2316,100 +2305,6 @@ def conv1d(value, filters, stride, padding, return array_ops.squeeze(result, [spatial_start_dim]) -def conv1d_transpose(value, - filter, - output_shape, - stride, - padding="SAME", - data_format="NWC", - name=None): - """The transpose of `conv1d`. - - This operation is sometimes called "deconvolution" after [Deconvolutional - Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), but is - actually the transpose (gradient) of `conv1d` rather than an actual - deconvolution. - - Args: - value: A 3-D `Tensor` of type `float` and shape - `[batch, in_width, in_channels]` for `NWC` data format or - `[batch, in_channels, in_width]` for `NCW` data format. - filter: A 3-D `Tensor` with the same type as `value` and shape - `[filter_width, output_channels, in_channels]`. `filter`'s - `in_channels` dimension must match that of `value`. - output_shape: A 1-D `Tensor` representing the output shape of the - deconvolution op. - stride: An `integer`. The number of entries by which - the filter is moved right at each step. - padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. - See the @{tf.nn.convolution$comment here} - data_format: A string. 'NHWC' and 'NCHW' are supported. - name: Optional name for the returned tensor. - - Returns: - A `Tensor` with the same type as `value`. - - Raises: - ValueError: If input/output depth does not match `filter`'s shape, or if - padding is other than `'VALID'` or `'SAME'`. - """ - with ops.name_scope(name, "conv1d_transpose", - [value, filter, output_shape]) as name: - output_shape_ = ops.convert_to_tensor(output_shape, name="output_shape") - if not output_shape_.get_shape().is_compatible_with(tensor_shape.vector(3)): - raise ValueError("output_shape must have shape (3,), got {}" - .format(output_shape_.get_shape())) - - # The format could be either NWC or NCW, map to NHWC or NCHW - if data_format is None or data_format == "NWC": - data_format_2d = "NHWC" - axis = 2 - elif data_format == "NCW": - data_format_2d = "NCHW" - axis = 1 - else: - raise ValueError("data_format must be \"NWC\" or \"NCW\".") - - if not value.get_shape()[axis].is_compatible_with(filter.get_shape()[2]): - raise ValueError("input channels does not match filter's input channels, " - "{} != {}".format(value.get_shape()[axis], - filter.get_shape()[2])) - - if isinstance(output_shape, (list, np.ndarray)): - # output_shape's shape should be == [3] if reached this point. - if not filter.get_shape()[1].is_compatible_with(output_shape[axis]): - raise ValueError( - "output_shape does not match filter's output channels, " - "{} != {}".format(output_shape[axis], filter.get_shape()[1])) - - if padding != "VALID" and padding != "SAME": - raise ValueError("padding must be either VALID or SAME:" - " {}".format(padding)) - - # Reshape the input tensor to [batch, 1, in_width, in_channels] - if data_format_2d == "NHWC": - output_shape_ = array_ops.concat([output_shape_[:1], [1], - output_shape_[1:]], axis=0) - spatial_start_dim = 1 - strides = [1, 1, stride, 1] - else: - output_shape_ = array_ops.concat([output_shape_[:2], [1], - output_shape_[2:]], axis=0) - spatial_start_dim = 2 - strides = [1, 1, 1, stride] - value = array_ops.expand_dims(value, spatial_start_dim) - filter = array_ops.expand_dims(filter, 0) - - result = gen_nn_ops.conv2d_backprop_input(input_sizes=output_shape_, - filter=filter, - out_backprop=value, - strides=strides, - padding=padding, - data_format=data_format_2d, - name=name) - return array_ops.squeeze(result, [spatial_start_dim]) - - @ops.RegisterStatistics("Dilation2D", "flops") def _calc_dilation2d_flops(graph, node): """Calculates the compute resources needed for Dilation2D.""" |