Searched refs:dropout (Results 51 - 64 of 64) sorted by relevance

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/external/tensorflow/tensorflow/contrib/keras/api/keras/backend/
H A D__init__.py53 from tensorflow.python.keras._impl.keras.backend import dropout namespace
/external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/
H A Ddnn_linear_combined.py260 net = layers.dropout(
263 # TODO(b/31209633): Consider adding summary before dropout.
/external/tensorflow/tensorflow/python/keras/backend/
H A D__init__.py53 from tensorflow.python.keras._impl.keras.backend import dropout namespace
/external/tensorflow/tensorflow/stream_executor/
H A Dstream_executor_pimpl.h375 dnn::RnnMode rnn_mode, dnn::DataType data_type, float dropout,
H A Ddnn.h1967 // dropout: the dropout threshold between layers. When it is 0., no dropout
1969 // seed: a seed for initializing the dropout layers.
1971 // for dropout layer. The user has to maintain the memory until the model
1978 float dropout, uint64 seed,
1974 createRnnDescriptor(int num_layers, int hidden_size, int input_size, dnn::RnnInputMode input_mode, dnn::RnnDirectionMode direction_mode, dnn::RnnMode rnn_mode, dnn::DataType data_type, float dropout, uint64 seed, ScratchAllocator* state_allocator) argument
/external/tensorflow/tensorflow/contrib/layers/python/layers/
H A Dlayers_test.py1324 output = _layers.dropout(images)
1325 self.assertEqual(output.op.name, 'Dropout/dropout/mul')
1334 output = _layers.dropout(images, is_training=is_training)
1342 output = _layers.dropout(images, is_training=is_training)
1350 output = _layers.dropout(images, is_training=is_training)
1358 output = _layers.dropout(images, outputs_collections='outputs')
1369 output = _layers.dropout(images)
1381 output1 = _layers.dropout(images, seed=1)
1382 output2 = _layers.dropout(images, seed=1)
1391 output = _layers.dropout(image
[all...]
H A Dlayers.py63 'dropout', 'elu', 'flatten', 'fully_connected', 'GDN', 'gdn',
1430 def dropout(inputs, function
1437 """Returns a dropout op applied to the input.
1444 inputs: The tensor to pass to the nn.dropout op.
1450 is in training mode. If so, dropout is applied and values scaled.
/external/tensorflow/tensorflow/stream_executor/cuda/
H A Dcuda_dnn.cc979 float dropout, uint64 seed,
984 CUDNN_RETURN_IF_FAIL(status, "Failed to create dropout descriptor");
986 if (dropout == 0.f) {
995 CUDNN_RETURN_IF_FAIL(status, "Failed to query dropout state sizes");
1002 port::StrCat("Failed to allocate Cudnn dropout state memory of ",
1010 dropout, state_memory.opaque(),
1014 "Failed to set dropout descriptor with state memory size: ",
1022 CUDNN_RETURN_IF_FAIL(status, "Failed to destroy Cudnn dropout handle: ");
1081 float dropout, uint64 seed,
1092 // Create the dropout handl
978 CudnnDropoutDescriptor(CUDAExecutor* parent, cudnnHandle_t cudnn_handle, float dropout, uint64 seed, ScratchAllocator* state_allocator) argument
1076 CudnnRnnDescriptor(CUDAExecutor* parent, cudnnHandle_t cudnn_handle, int num_layers, int hidden_size, int input_size, cudnnRNNInputMode_t input_mode, cudnnDirectionMode_t direction_mode, cudnnRNNMode_t rnn_mode, cudnnDataType_t data_type, float dropout, uint64 seed, ScratchAllocator* state_allocator) argument
1755 createRnnDescriptor(int num_layers, int hidden_size, int input_size, dnn::RnnInputMode input_mode, dnn::RnnDirectionMode direction_mode, dnn::RnnMode rnn_mode, dnn::DataType data_type, float dropout, uint64 seed, ScratchAllocator* state_allocator) argument
[all...]
H A Dcuda_dnn.h53 dnn::RnnMode rnn_mode, dnn::DataType data_type, float dropout,
/external/tensorflow/tensorflow/python/estimator/canned/
H A Ddnn_testing_utils.py512 dropout=None,
693 dropout=None,
/external/tensorflow/tensorflow/contrib/boosted_trees/kernels/
H A Dtraining_ops.cc302 // Determine whether dropout was used when building this tree.
306 dropout_config_ = learner_config_.learning_rate_tuner().dropout();
346 // Read seed that was used for dropout.
393 // If the tree is fully built and dropout was applied, it also adjusts the
471 // It is possible that the tree was built with dropout. If it is the case,
/external/tensorflow/tensorflow/python/keras/_impl/keras/
H A Dbackend.py3446 @tf_export('keras.backend.dropout')
3447 def dropout(x, level, noise_shape=None, seed=None): function
3466 return nn.dropout(x * 1., retain_prob, noise_shape, seed=seed)
/external/tensorflow/tensorflow/python/ops/
H A Dnn_ops.py2242 @tf_export("nn.dropout")
2243 def dropout(x, keep_prob, noise_shape=None, seed=None, name=None): # pylint: disable=invalid-name function
2244 """Computes dropout.
2276 with ops.name_scope(name, "dropout", [x]) as name:
/external/tensorflow/tensorflow/contrib/rnn/python/ops/
H A Drnn_cell.py1335 """LSTM unit with layer normalization and recurrent dropout.
1337 This class adds layer normalization and recurrent dropout to a
1346 Recurrent dropout is base on:
1378 recurrent dropout probability value. If float and 1.0, no dropout will
1431 """LSTM cell with layer normalization and recurrent dropout."""
1446 g = nn_ops.dropout(g, self._keep_prob, seed=self._seed)

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