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Searched refs:recurrent_kernel (Results 1 – 14 of 14) sorted by relevance

/external/tensorflow/tensorflow/python/keras/layers/
Drecurrent_v2.py495 'recurrent_kernel': _read_variable_value(self.cell.recurrent_kernel),
510 'recurrent_kernel': _read_variable_value(self.cell.recurrent_kernel),
543 def standard_gru(inputs, init_h, kernel, recurrent_kernel, bias, mask, argument
600 matrix_inner = K.dot(h_tm1, recurrent_kernel)
627 def gpu_gru(inputs, init_h, kernel, recurrent_kernel, bias, mask, time_major, argument
640 weights += array_ops.split(recurrent_kernel, 3, axis=1)
713 def gru_with_backend_selection(inputs, init_h, kernel, recurrent_kernel, bias, argument
754 'recurrent_kernel': recurrent_kernel,
763 def gpu_gru_with_fallback(inputs, init_h, kernel, recurrent_kernel, bias, argument
772 recurrent_kernel=recurrent_kernel,
[all …]
Dcudnn_recurrent.py143 return [self.kernel, self.recurrent_kernel, self.bias]
149 return [self.kernel, self.recurrent_kernel, self.bias]
256 self.recurrent_kernel = self.add_weight(
283 self.recurrent_kernel[:, self.units:self.units * 2],
284 self.recurrent_kernel[:, :self.units],
285 self.recurrent_kernel[:, self.units * 2:],
442 self.recurrent_kernel = self.add_weight(
482 self.recurrent_kernel[:, :self.units],
483 self.recurrent_kernel[:, self.units:self.units * 2],
484 self.recurrent_kernel[:, self.units * 2:self.units * 3],
[all …]
Drecurrent.py1355 self.recurrent_kernel = self.add_weight(
1389 output = h + K.dot(prev_output, self.recurrent_kernel)
1803 self.recurrent_kernel = self.add_weight(
1871 recurrent_z = K.dot(h_tm1_z, self.recurrent_kernel[:, :self.units])
1873 self.recurrent_kernel[:, self.units:self.units * 2])
1884 recurrent_h = K.dot(h_tm1_h, self.recurrent_kernel[:, self.units * 2:])
1890 self.recurrent_kernel[:, self.units * 2:])
1907 matrix_inner = K.dot(h_tm1, self.recurrent_kernel)
1912 matrix_inner = K.dot(h_tm1, self.recurrent_kernel[:, :2 * self.units])
1924 self.recurrent_kernel[:, 2 * self.units:])
[all …]
Drecurrent_test.py161 self.recurrent_kernel = self.add_weight(
170 output = h + keras.backend.dot(prev_output, self.recurrent_kernel)
245 self.recurrent_kernel = self.add_weight(
254 output = h + keras.backend.dot(prev_output, self.recurrent_kernel)
686 self.recurrent_kernel = self.add_weight(
695 output = h + keras.backend.dot(prev_output, self.recurrent_kernel)
1764 self.recurrent_kernel = self.add_weight(
1778 h_state = keras.backend.dot(prev_output, self.recurrent_kernel)
Dsimplernn_test.py116 self.assertEqual(layer.cell.recurrent_kernel.constraint, r_constraint)
Dgru_test.py247 self.assertEqual(layer.cell.recurrent_kernel.constraint, r_constraint)
Dconvolutional_recurrent.py562 self.recurrent_kernel = self.add_weight(
627 recurrent_kernel_o) = array_ops.split(self.recurrent_kernel, 4, axis=3)
Dlstm_test.py142 self.assertEqual(layer.cell.recurrent_kernel.constraint, r_constraint)
Dwrappers_test.py63 self.recurrent_kernel = self.add_weight(
77 h_state = keras.backend.dot(prev_output, self.recurrent_kernel)
Dgru_v2_test.py434 self.assertEqual(layer.cell.recurrent_kernel.constraint, r_constraint)
Dlstm_v2_test.py405 self.assertEqual(layer.cell.recurrent_kernel.constraint, r_constraint)
/external/tensorflow/tensorflow/python/keras/saving/
Dhdf5_format.py374 recurrent_kernel = np.concatenate(
377 weights = [kernel, recurrent_kernel, bias]
385 recurrent_kernel = np.concatenate(
389 weights = [kernel, recurrent_kernel, bias]
395 recurrent_kernel = np.concatenate(
403 recurrent_kernel = np.transpose(recurrent_kernel, (2, 3, 1, 0))
404 weights = [kernel, recurrent_kernel, bias]
/external/tensorflow/tensorflow/compiler/mlir/lite/utils/
Dlstm_utils.cc635 Value recurrent_kernel = func_op.getArgument(4); in ConvertKerasLSTMLayer() local
681 recurrent_kernel.getType().cast<RankedTensorType>(); in ConvertKerasLSTMLayer()
685 builder, recurrent_kernel, recurrent_kernel_type, func_op.getLoc()); in ConvertKerasLSTMLayer()
/external/tensorflow/tensorflow/python/keras/integration_test/
Dlegacy_rnn_test.py288 kernel, recurrent_kernel, bias = keras_weights
289 tf_weights = [np.concatenate((kernel, recurrent_kernel)), bias]