/external/tensorflow/tensorflow/python/layers/ |
H A D | core.py | 265 rate: The dropout rate, between 0 and 1. E.g. `rate=0.1` would drop out 268 binary dropout mask that will be multiplied with the input. 270 `(batch_size, timesteps, features)`, and you want the dropout mask 300 return nn.dropout(inputs, 1 - self.rate, 311 @tf_export('layers.dropout') 312 def dropout(inputs, function 327 rate: The dropout rate, between 0 and 1. E.g. "rate=0.1" would drop out 330 binary dropout mask that will be multiplied with the input. 332 `(batch_size, timesteps, features)`, and you want the dropout mask 340 (apply dropout) o [all...] |
H A D | layers.py | 42 @@dropout 79 from tensorflow.python.layers.core import dropout namespace
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/external/tensorflow/tensorflow/python/keras/_impl/keras/layers/ |
H A D | recurrent.py | 833 dropout: Float between 0 and 1. 854 dropout=0., 874 self.dropout = min(1., max(0., dropout)) 907 if 0 < self.dropout < 1 and self._dropout_mask is None: 911 self.dropout, 937 if 0 < self.dropout + self.recurrent_dropout: 968 'dropout': 969 self.dropout, 1006 dropout 1137 def dropout(self): member in class:SimpleRNN 1622 def dropout(self): member in class:GRU 2157 def dropout(self): member in class:LSTM [all...] |
/external/tensorflow/tensorflow/python/ops/ |
H A D | nn_ops.py | 2242 @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:
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H A D | rnn_cell_impl.py | 837 # Do not perform dropout on the memory state. 846 """Operator adding dropout to inputs and outputs of the given cell.""" 852 """Create a cell with added input, state, and/or output dropout. 855 then the same dropout mask is applied at every step, as described in: 860 Otherwise a different dropout mask is applied at every time step. 870 probability; if it is constant and 1, no input dropout will be added. 872 probability; if it is constant and 1, no output dropout will be added. 874 probability; if it is constant and 1, no output dropout will be added. 875 State dropout is performed on the outgoing states of the cell. 876 **Note** the state components to which dropout i 1028 def dropout(i, do_dropout, v): function in function:DropoutWrapper._dropout 1038 def dropout(i, do_dropout, v, n): function in function:DropoutWrapper._dropout [all...] |
/external/tensorflow/tensorflow/contrib/layers/python/layers/ |
H A D | layers.py | 63 '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.
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/external/tensorflow/tensorflow/contrib/cudnn_rnn/kernels/ |
H A D | cudnn_rnn_ops.cc | 489 OP_REQUIRES_OK(context, context->GetAttr("dropout", &dropout_)); 514 float dropout() const { return dropout_; } function in class:tensorflow::TFRNNInputMode::CudnnRNNKernelCommon 549 rnn_mode(), ToDataType<T>::value, dropout(), seed(), 897 rnn_mode(), data_type, dropout(), seed(), dropout_state_allocator); 1101 rnn_mode(), data_type, dropout(), seed(), dropout_state_allocator);
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/external/tensorflow/tensorflow/contrib/keras/api/keras/backend/ |
H A D | __init__.py | 53 from tensorflow.python.keras._impl.keras.backend import dropout namespace
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/external/tensorflow/tensorflow/python/keras/backend/ |
H A D | __init__.py | 53 from tensorflow.python.keras._impl.keras.backend import dropout namespace
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/external/tensorflow/tensorflow/stream_executor/ |
H A D | stream_executor_pimpl.cc | 347 dnn::RnnMode rnn_mode, dnn::DataType data_type, float dropout, uint64 seed, 356 data_type, dropout, seed, state_allocator); 344 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
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H A D | dnn.h | 1967 // 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
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/external/tensorflow/tensorflow/python/keras/_impl/keras/ |
H A D | backend.py | 3446 @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)
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/external/tensorflow/tensorflow/stream_executor/cuda/ |
H A D | cuda_dnn.cc | 979 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...] |