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/external/tensorflow/tensorflow/python/keras/layers/
Dgru_v2_test.py77 layer = rnn.GRU(1,
88 layer = rnn.GRU(1, activation=nn.tanh)
91 layer = rnn.GRU(1, recurrent_activation=nn.sigmoid)
109 layer = rnn.GRU(rnn_state_size)
126 layer = rnn.GRU(units, input_shape=(None, embedding_dim))
139 model.add(rnn.GRU(10, return_sequences=True, unroll=False))
140 model.add(rnn.GRU(5, return_sequences=True, unroll=False))
147 layer_class = rnn.GRU
179 gru_layer = rnn_v1.GRU(rnn_state_size,
191 cudnn_layer = rnn.GRU(rnn_state_size,
[all …]
Dgru_test.py45 keras.layers.GRU,
59 keras.layers.GRU,
71 layer = keras.layers.GRU(units, input_shape=(None, embedding_dim))
88 keras.layers.GRU,
95 layer = keras.layers.GRU(2, recurrent_dropout=0.1, implementation=2)
106 keras.layers.GRU,
125 gru_layer = keras.layers.GRU(units,
139 layer_class = keras.layers.GRU
159 layer_class = keras.layers.GRU
233 layer_class = keras.layers.GRU
[all …]
Drecurrent_v2_test.py41 @parameterized.parameters([rnn_v2.LSTM, rnn_v2.GRU])
68 @parameterized.parameters([rnn_v2.LSTM, rnn_v2.GRU])
95 @parameterized.parameters([rnn_v2.LSTM, rnn_v2.GRU])
109 x = keras.layers.GRU(64, activation='relu', name='GRU', dropout=0.1)(inputs)
117 @parameterized.parameters([rnn_v2.LSTM, rnn_v2.GRU])
Dserialization_test.py154 @parameterized.parameters([rnn_v1.GRU, rnn_v2.GRU])
163 self.assertIsInstance(new_layer, rnn_v2.GRU)
165 self.assertIsInstance(new_layer, rnn_v1.GRU)
166 self.assertNotIsInstance(new_layer, rnn_v2.GRU)
D__init__.py224 from tensorflow.python.keras.layers.recurrent_v2 import GRU
228 from tensorflow.python.keras.layers.recurrent import GRU as GRUV1
232 GRUV2 = GRU
237 from tensorflow.python.keras.layers.recurrent import GRU
241 from tensorflow.python.keras.layers.recurrent_v2 import GRU as GRUV2
245 GRUV1 = GRU
Drecurrent_test.py641 [keras.layers.SimpleRNN, keras.layers.GRU, keras.layers.LSTM])
756 layer=[rnn_v1.SimpleRNN, rnn_v1.GRU, rnn_v1.LSTM,
757 rnn_v2.GRU, rnn_v2.LSTM],
1473 rnn_output = keras.layers.GRU(
1585 rnn_v1.SimpleRNN, rnn_v1.GRU, rnn_v1.LSTM, rnn_v2.GRU, rnn_v2.LSTM
1716 [rnn_v1.SimpleRNN, rnn_v1.GRU, rnn_v1.LSTM, rnn_v2.GRU, rnn_v2.LSTM])
Dcudnn_recurrent_test.py298 rnn_layer_class = keras.layers.GRU
380 rnn_layer_class = keras.layers.GRU
Drecurrent_v2.py221 class GRU(recurrent.DropoutRNNCellMixin, recurrent.GRU): class
378 super(GRU, self).__init__(
/external/tensorflow/tensorflow/core/api_def/base_api/
Dapi_def_GRUBlockCell.pbtxt4 summary: "Computes the GRU cell forward propagation for 1 time step."
7 x: Input to the GRU cell.
8 h_prev: State input from the previous GRU cell.
18 h: Current state of the GRU cell.
Dapi_def_GRUBlockCellGrad.pbtxt4 summary: "Computes the GRU cell back-propagation for 1 time step."
7 x: Input to the GRU cell.
8 h_prev: State input from the previous GRU cell.
/external/rnnoise/training/
Drnn_train.py11 from keras.layers import GRU
65 vad_gru = GRU(24, activation='tanh', recurrent_activation='sigmoid', return_sequences=True, name='v…
68 noise_gru = GRU(48, activation='relu', recurrent_activation='sigmoid', return_sequences=True, name=…
71 denoise_gru = GRU(96, activation='tanh', recurrent_activation='sigmoid', return_sequences=True, nam…
Ddump_rnn.py8 from keras.layers import GRU
/external/rnnoise/src/
Drnn_train.py10 from keras.layers import GRU
24 x = GRU(128, activation='tanh', recurrent_activation='sigmoid', return_sequences=True)(x)
/external/libopus/scripts/
Drnn_train.py10 from keras.layers import GRU
30 x = GRU(12, dropout=0.1, recurrent_dropout=0.1, activation='tanh', recurrent_activation='sigmoid', …
Ddump_rnn.py8 from keras.layers import GRU
/external/libopus/training/
Drnn_dump.py10 from keras.layers import GRU
42 x = GRU(24, activation='tanh', recurrent_activation='sigmoid', return_sequences=True)(x)
Drnn_train.py10 from keras.layers import GRU
61 x = GRU(24, recurrent_activation='sigmoid', activation='tanh', return_sequences=True, kernel_constr…
/external/tensorflow/tensorflow/security/advisory/
Dtfsa-2020-033.md7 Running an LSTM/GRU model where the LSTM/GRU layer receives an input with
/external/tensorflow/tensorflow/python/keras/distribute/
Dkeras_rnn_model_correctness_test.py82 return rnn_v2.GRU
84 return rnn_v1.GRU
/external/tensorflow/tensorflow/tools/api/golden/v2/
Dtensorflow.keras.layers.-g-r-u.pbtxt1 path: "tensorflow.keras.layers.GRU"
3 is_instance: "<class \'tensorflow.python.keras.layers.recurrent_v2.GRU\'>"
5 is_instance: "<class \'tensorflow.python.keras.layers.recurrent.GRU\'>"
Dtensorflow.keras.layers.pbtxt168 name: "GRU"
/external/tensorflow/tensorflow/python/keras/mixed_precision/
Dlayer_correctness_test.py135 ('GRU', lambda: recurrent.GRU(units=4), (4, 4, 4)),
137 ('GRUV2', lambda: recurrent_v2.GRU(units=4), (4, 4, 4)),
/external/tensorflow/tensorflow/python/keras/benchmarks/layer_benchmarks/
Dlayer_benchmarks_test.py121 ("GRU_small_shape", tf.keras.layers.GRU,
284 tf.keras.layers.ConvLSTM2D, tf.keras.layers.GRU, tf.keras.layers.RNN,
/external/tensorflow/tensorflow/tools/api/golden/v1/
Dtensorflow.keras.layers.-g-r-u.pbtxt1 path: "tensorflow.keras.layers.GRU"
3 is_instance: "<class \'tensorflow.python.keras.layers.recurrent.GRU\'>"
/external/tensorflow/tensorflow/python/ops/numpy_ops/g3doc/
DTensorFlow_NumPy_Text_Generation.ipynb428 …nd some low-level TF ops. A `Model` object has three layers: `Embedding`, `GRU` and `Dense`. `Embe…
437 …"Our model (more precisely the `GRU` layer) is stateful, because each call of `__call__` will chan…
486 " \"\"\"Builds a traditional GRU cell with dense internal transformations.\n",
512 " # The dense layer input is the input and half of the GRU state.\n",
528 "class GRU:\n",
607 " self._gru = GRU(rnn_units, forget_bias=forget_bias, stateful=stateful)\n",
654 …"For each character the model looks up the embedding, runs the GRU one timestep with the embedding…

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