| /external/tensorflow/tensorflow/contrib/training/python/training/ |
| sequence_queueing_state_saver_test.py | 60 num_unroll = 2
79 num_unroll=num_unroll,
164 num_unroll = 17
179 num_unroll=num_unroll,
198 num_unroll = 2
214 num_unroll=num_unroll,
226 sequences["seq1"]: np.random.rand(num_unroll, 5)
[all...] |
| batch_sequences_with_states_test.py | 104 def _testBasics(self, num_unroll, length, pad,
118 num_unroll=num_unroll,
188 self.assertAllEqual(length_value, [num_unroll, num_unroll])
231 num_unroll = 2 # Divisor of value_length - so no padding necessary.
233 self.sequences["seq1"][np.newaxis, 0:num_unroll, :],
236 self.sequences["seq2"][np.newaxis, 0:num_unroll, :, :],
239 self.sequences["seq1"][np.newaxis, num_unroll:self.value_length, :],
242 self.sequences["seq2"][np.newaxis, num_unroll:self.value_length, :, :]
[all...] |
| sequence_queueing_state_saver.py | 381 For initial iterations, for which `sequence * num_unroll < length`,
382 this number is `num_unroll`. For the remainder,
383 this number is between `0` and `num_unroll`.
478 `padded_length / num_unroll`. This is the sequence_count.
521 sequences["name"].get_shape() == [batch_size, num_unroll, d1, d2, ...].
631 `num_unroll`. All other dimensions must be fixed and accessible via
638 `num_unroll`. Across examples minibatches of size `batch_size` are formed.
674 num_unroll = 20
686 batch_size, num_unroll,
695 inputs_by_time = tf.split(value=inputs, num_or_size_splits=num_unroll, axis=1
900 def num_unroll(self): member in class:SequenceQueueingStateSaver
[all...] |
| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| state_saving_rnn_estimator.py | 239 num_unroll,
256 num_unroll: Python integer, how many time steps to unroll at a time.
257 The input sequences of length `k` are then split into `k / num_unroll`
291 num_unroll=num_unroll,
293 pad=True, # pad to a multiple of num_unroll
329 sequence_feature_columns, num_unroll):
334 `SparseTensor`, with `Tensor`s of shape `[batch_size, num_unroll, ...]`
335 or `SparseTensors` of dense shape `[batch_size, num_unroll, d]`.
341 num_unroll: Python integer, how many time steps to unroll at a time
[all...] |
| state_saving_rnn_estimator_test.py | 49 sequence_feature_columns, num_unroll,
54 num_unroll)
63 num_unroll = 3
87 sequence_feature_columns, num_unroll,
92 num_unroll = 3
120 sequence_feature_columns, num_unroll,
124 num_unroll = 3
148 sequence_feature_columns, num_unroll,
152 num_unroll = 2
189 sequence_feature_columns, num_unroll,
[all...] |
| /external/tensorflow/tensorflow/python/framework/ |
| function_test.py | [all...] |
