| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | recurrent_v2.py | 152 recurrent_activation='sigmoid', argument
182 recurrent_activation=recurrent_activation,
207 activation == 'tanh' and recurrent_activation == 'sigmoid' and
294 recurrent_activation=self.recurrent_activation,
311 recurrent_activation=self.recurrent_activation,
322 recurrent_activation, time_major): argument
374 z = recurrent_activation(x_z + recurrent_z)
375 r = recurrent_activation(x_r + recurrent_r)
521 recurrent_activation='sigmoid', argument
551 recurrent_activation=recurrent_activation,
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| D | recurrent.py | 1572 recurrent_activation='hard_sigmoid', argument
1591 self.recurrent_activation = activations.get(recurrent_activation)
1696 z = self.recurrent_activation(x_z + recurrent_z)
1697 r = self.recurrent_activation(x_r + recurrent_r)
1739 z = self.recurrent_activation(x_z + recurrent_z)
1740 r = self.recurrent_activation(x_r + recurrent_r)
1758 activations.serialize(self.recurrent_activation),
1873 recurrent_activation='hard_sigmoid', argument
1902 recurrent_activation=recurrent_activation,
1943 def recurrent_activation(self): member in GRU
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| D | convolutional_recurrent.py | 557 recurrent_activation='hard_sigmoid', argument
581 self.recurrent_activation = activations.get(recurrent_activation)
701 i = self.recurrent_activation(x_i + h_i)
702 f = self.recurrent_activation(x_f + h_f)
704 o = self.recurrent_activation(x_o + h_o)
733 self.recurrent_activation),
885 recurrent_activation='hard_sigmoid', argument
911 recurrent_activation=recurrent_activation,
969 def recurrent_activation(self): member in ConvLSTM2D
970 return self.cell.recurrent_activation
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| D | gru_v2_test.py | 69 def test_could_use_defun_backend(self, activation, recurrent_activation, argument
74 recurrent_activation=recurrent_activation,
158 recurrent_activation='sigmoid',
170 recurrent_activation='sigmoid',
250 recurrent_activation='sigmoid',
279 recurrent_activation='sigmoid',
517 recurrent_activation='sigmoid',
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| D | lstm_v2_test.py | 69 def test_could_use_defun_backend(self, activation, recurrent_activation, argument
74 recurrent_activation=recurrent_activation,
326 recurrent_activation='sigmoid')
422 recurrent_activation='sigmoid',
539 layer = rnn_v1.LSTM(rnn_state_size, recurrent_activation='sigmoid')
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| D | recurrent_test.py | 1197 recurrent_activation='sigmoid',
1202 recurrent_activation='sigmoid',
1207 recurrent_activation='sigmoid',
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| /external/libopus/scripts/ |
| D | rnn_train.py | 30 x = GRU(12, dropout=0.1, recurrent_dropout=0.1, activation='tanh', recurrent_activation='sigmoid', …
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| /external/tensorflow/tensorflow/tools/api/golden/v2/ |
| D | tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt | 122 name: "recurrent_activation"
179 …'padding\', \'data_format\', \'dilation_rate\', \'activation\', \'recurrent_activation\', \'use_bi…
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| D | tensorflow.keras.layers.-g-r-u.pbtxt | 107 name: "recurrent_activation"
164 …argspec: "args=[\'self\', \'units\', \'activation\', \'recurrent_activation\', \'use_bias\', \'ker…
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| D | tensorflow.keras.layers.-l-s-t-m.pbtxt | 107 name: "recurrent_activation"
164 …argspec: "args=[\'self\', \'units\', \'activation\', \'recurrent_activation\', \'use_bias\', \'ker…
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| D | tensorflow.keras.layers.-g-r-u-cell.pbtxt | 90 …argspec: "args=[\'self\', \'units\', \'activation\', \'recurrent_activation\', \'use_bias\', \'ker…
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| D | tensorflow.keras.layers.-l-s-t-m-cell.pbtxt | 90 …argspec: "args=[\'self\', \'units\', \'activation\', \'recurrent_activation\', \'use_bias\', \'ker…
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| D | tensorflow.keras.experimental.-peephole-l-s-t-m-cell.pbtxt | 91 …argspec: "args=[\'self\', \'units\', \'activation\', \'recurrent_activation\', \'use_bias\', \'ker…
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| /external/tensorflow/tensorflow/tools/api/golden/v1/ |
| D | tensorflow.keras.layers.-g-r-u.pbtxt | 105 name: "recurrent_activation"
162 …argspec: "args=[\'self\', \'units\', \'activation\', \'recurrent_activation\', \'use_bias\', \'ker…
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| D | tensorflow.keras.layers.-l-s-t-m.pbtxt | 105 name: "recurrent_activation"
162 …argspec: "args=[\'self\', \'units\', \'activation\', \'recurrent_activation\', \'use_bias\', \'ker…
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| D | tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt | 122 name: "recurrent_activation"
179 …'padding\', \'data_format\', \'dilation_rate\', \'activation\', \'recurrent_activation\', \'use_bi…
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| D | tensorflow.keras.layers.-g-r-u-cell.pbtxt | 90 …argspec: "args=[\'self\', \'units\', \'activation\', \'recurrent_activation\', \'use_bias\', \'ker…
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| D | tensorflow.keras.layers.-l-s-t-m-cell.pbtxt | 90 …argspec: "args=[\'self\', \'units\', \'activation\', \'recurrent_activation\', \'use_bias\', \'ker…
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| D | tensorflow.keras.experimental.-peephole-l-s-t-m-cell.pbtxt | 91 …argspec: "args=[\'self\', \'units\', \'activation\', \'recurrent_activation\', \'use_bias\', \'ker…
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| /external/tensorflow/tensorflow/contrib/seq2seq/python/kernel_tests/ |
| D | attention_wrapper_v2_test.py | 308 recurrent_activation="sigmoid",
408 cell = keras.layers.LSTMCell(self.units, recurrent_activation="sigmoid")
435 cell = keras.layers.LSTMCell(self.units, recurrent_activation="sigmoid")
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| /external/tensorflow/tensorflow/contrib/eager/python/examples/nmt_with_attention/ |
| D | nmt_with_attention.ipynb | 401 " recurrent_activation='sigmoid', \n",
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| /external/tensorflow/ |
| D | RELEASE.md | 55 … 1.x pre-trained checkpoint, please construct the layer with LSTM(recurrent_activation='hard_sigmo…
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