diff options
| -rw-r--r-- | tensorflow/examples/tutorials/word2vec/word2vec_basic.py | 8 | ||||
| -rw-r--r-- | tensorflow/python/ops/nn_impl.py | 36 | ||||
| -rw-r--r-- | tensorflow/python/ops/nn_test.py | 6 |
3 files changed, 27 insertions, 23 deletions
diff --git a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py index 1131360ab5..bc502edd8b 100644 --- a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py +++ b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py @@ -160,8 +160,12 @@ with graph.as_default(): # tf.nce_loss automatically draws a new sample of the negative labels each # time we evaluate the loss. loss = tf.reduce_mean( - tf.nn.nce_loss(nce_weights, nce_biases, embed, train_labels, - num_sampled, vocabulary_size)) + tf.nn.nce_loss(weights=nce_weights, + biases=nce_biases, + labels=train_labels, + inputs=embed, + num_sampled=num_sampled, + num_classes=vocabulary_size)) # Construct the SGD optimizer using a learning rate of 1.0. optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss) diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py index be46bf305a..6e7c6efb4c 100644 --- a/tensorflow/python/ops/nn_impl.py +++ b/tensorflow/python/ops/nn_impl.py @@ -809,8 +809,8 @@ def _sum_rows(x): def _compute_sampled_logits(weights, biases, - inputs, labels, + inputs, num_sampled, num_classes, num_true=1, @@ -834,11 +834,11 @@ def _compute_sampled_logits(weights, objects whose concatenation along dimension 0 has shape `[num_classes, dim]`. The (possibly-partitioned) class embeddings. biases: A `Tensor` of shape `[num_classes]`. The class biases. - inputs: A `Tensor` of shape `[batch_size, dim]`. The forward - activations of the input network. labels: A `Tensor` of type `int64` and shape `[batch_size, num_true]`. The target classes. Note that this format differs from the `labels` argument of `nn.softmax_cross_entropy_with_logits`. + inputs: A `Tensor` of shape `[batch_size, dim]`. The forward + activations of the input network. num_sampled: An `int`. The number of classes to randomly sample per batch. num_classes: An `int`. The number of possible classes. num_true: An `int`. The number of target classes per training example. @@ -975,8 +975,8 @@ def _compute_sampled_logits(weights, def nce_loss(weights, biases, - inputs, labels, + inputs, num_sampled, num_classes, num_true=1, @@ -1012,10 +1012,10 @@ def nce_loss(weights, objects whose concatenation along dimension 0 has shape [num_classes, dim]. The (possibly-partitioned) class embeddings. biases: A `Tensor` of shape `[num_classes]`. The class biases. - inputs: A `Tensor` of shape `[batch_size, dim]`. The forward - activations of the input network. labels: A `Tensor` of type `int64` and shape `[batch_size, num_true]`. The target classes. + inputs: A `Tensor` of shape `[batch_size, dim]`. The forward + activations of the input network. num_sampled: An `int`. The number of classes to randomly sample per batch. num_classes: An `int`. The number of possible classes. num_true: An `int`. The number of target classes per training example. @@ -1038,12 +1038,12 @@ def nce_loss(weights, A `batch_size` 1-D tensor of per-example NCE losses. """ logits, labels = _compute_sampled_logits( - weights, - biases, - inputs, - labels, - num_sampled, - num_classes, + weights=weights, + biases=biases, + labels=labels, + inputs=inputs, + num_sampled=num_sampled, + num_classes=num_classes, num_true=num_true, sampled_values=sampled_values, subtract_log_q=True, @@ -1114,12 +1114,12 @@ def sampled_softmax_loss(weights, """ logits, labels = _compute_sampled_logits( - weights, - biases, - inputs, - labels, - num_sampled, - num_classes, + weights=weights, + biases=biases, + labels=labels, + inputs=inputs, + num_sampled=num_sampled, + num_classes=num_classes, num_true=num_true, sampled_values=sampled_values, subtract_log_q=True, diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py index a4504cc9f8..7970f79ea0 100644 --- a/tensorflow/python/ops/nn_test.py +++ b/tensorflow/python/ops/nn_test.py @@ -452,8 +452,8 @@ class ComputeSampledLogitsTest(tf.test.TestCase): pred_logits_tf, pred_labels_tf = _compute_sampled_logits( weights_tf, biases_tf, - hidden_acts_tf, labels_tf, + hidden_acts_tf, num_sampled, num_classes, num_true, @@ -672,8 +672,8 @@ class ComputeSampledLogitsTest(tf.test.TestCase): nce_loss_tf = tf.nn.nce_loss( weights_tf, biases_tf, - inputs_tf, labels_tf, + inputs_tf, num_sampled=1, num_classes=self._num_classes, num_true=1, @@ -685,8 +685,8 @@ class ComputeSampledLogitsTest(tf.test.TestCase): nce_loss_tf = tf.nn.nce_loss( [tf.constant(shard) for shard in sharded_weights], biases_tf, - inputs_tf, labels_tf, + inputs_tf, num_sampled=1, num_classes=self._num_classes, num_true=1, |