diff options
Diffstat (limited to 'tensorflow/python/ops/rnn.py')
| -rw-r--r-- | tensorflow/python/ops/rnn.py | 32 |
1 files changed, 17 insertions, 15 deletions
diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py index f67f4f35e8..b1270a1937 100644 --- a/tensorflow/python/ops/rnn.py +++ b/tensorflow/python/ops/rnn.py @@ -113,7 +113,7 @@ def rnn(cell, inputs, initial_state=None, dtype=None, dtype. sequence_length: Specifies the length of each sequence in inputs. An int32 or int64 vector (tensor) size `[batch_size]`, values in `[0, T)`. - scope: VariableScope for the created subgraph; defaults to "RNN". + scope: VariableScope for the created subgraph; defaults to "rnn". Returns: A pair (outputs, state) where: @@ -139,7 +139,7 @@ def rnn(cell, inputs, initial_state=None, dtype=None, # Create a new scope in which the caching device is either # determined by the parent scope, or is set to place the cached # Variable using the same placement as for the rest of the RNN. - with vs.variable_scope(scope or "RNN") as varscope: + with vs.variable_scope(scope or "rnn") as varscope: if varscope.caching_device is None: varscope.set_caching_device(lambda op: op.device) @@ -246,7 +246,7 @@ def state_saving_rnn(cell, inputs, state_saver, state_name, be a single string. sequence_length: (optional) An int32/int64 vector size [batch_size]. See the documentation for rnn() for more details about sequence_length. - scope: VariableScope for the created subgraph; defaults to "RNN". + scope: VariableScope for the created subgraph; defaults to "rnn". Returns: A pair (outputs, state) where: @@ -508,7 +508,8 @@ def bidirectional_rnn(cell_fw, cell_bw, inputs, either of the initial states are not provided. sequence_length: (optional) An int32/int64 vector, size `[batch_size]`, containing the actual lengths for each of the sequences. - scope: VariableScope for the created subgraph; defaults to "BiRNN" + scope: VariableScope for the created subgraph; defaults to + "bidirectional_rnn" Returns: A tuple (outputs, output_state_fw, output_state_bw) where: @@ -531,14 +532,14 @@ def bidirectional_rnn(cell_fw, cell_bw, inputs, if not inputs: raise ValueError("inputs must not be empty") - with vs.variable_scope(scope or "BiRNN"): + with vs.variable_scope(scope or "bidirectional_rnn"): # Forward direction - with vs.variable_scope("FW") as fw_scope: + with vs.variable_scope("fw") as fw_scope: output_fw, output_state_fw = rnn(cell_fw, inputs, initial_state_fw, dtype, sequence_length, scope=fw_scope) # Backward direction - with vs.variable_scope("BW") as bw_scope: + with vs.variable_scope("bw") as bw_scope: reversed_inputs = _reverse_seq(inputs, sequence_length) tmp, output_state_bw = rnn(cell_bw, reversed_inputs, initial_state_bw, dtype, sequence_length, scope=bw_scope) @@ -610,7 +611,8 @@ def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, accepts input and emits output in batch-major form. dtype: (optional) The data type for the initial state. Required if either of the initial states are not provided. - scope: VariableScope for the created subgraph; defaults to "BiRNN" + scope: VariableScope for the created subgraph; defaults to + "bidirectional_rnn" Returns: A tuple (outputs, output_states) where: @@ -642,9 +644,9 @@ def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, if not isinstance(cell_bw, rnn_cell.RNNCell): raise TypeError("cell_bw must be an instance of RNNCell") - with vs.variable_scope(scope or "BiRNN"): + with vs.variable_scope(scope or "bidirectional_rnn"): # Forward direction - with vs.variable_scope("FW") as fw_scope: + with vs.variable_scope("fw") as fw_scope: output_fw, output_state_fw = dynamic_rnn( cell=cell_fw, inputs=inputs, sequence_length=sequence_length, initial_state=initial_state_fw, dtype=dtype, @@ -659,7 +661,7 @@ def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, time_dim = 0 batch_dim = 1 - with vs.variable_scope("BW") as bw_scope: + with vs.variable_scope("bw") as bw_scope: inputs_reverse = array_ops.reverse_sequence( input=inputs, seq_lengths=sequence_length, seq_dim=time_dim, batch_dim=batch_dim) @@ -746,7 +748,7 @@ def dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, transposes at the beginning and end of the RNN calculation. However, most TensorFlow data is batch-major, so by default this function accepts input and emits output in batch-major form. - scope: VariableScope for the created subgraph; defaults to "RNN". + scope: VariableScope for the created subgraph; defaults to "rnn". Returns: A pair (outputs, state) where: @@ -801,7 +803,7 @@ def dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, # Create a new scope in which the caching device is either # determined by the parent scope, or is set to place the cached # Variable using the same placement as for the rest of the RNN. - with vs.variable_scope(scope or "RNN") as varscope: + with vs.variable_scope(scope or "rnn") as varscope: if varscope.caching_device is None: varscope.set_caching_device(lambda op: op.device) input_shape = tuple(array_ops.shape(input_) for input_ in flat_input) @@ -1161,7 +1163,7 @@ def raw_rnn(cell, loop_fn, but needed for back prop from GPU to CPU. This allows training RNNs which would typically not fit on a single GPU, with very minimal (or no) performance penalty. - scope: VariableScope for the created subgraph; defaults to "RNN". + scope: VariableScope for the created subgraph; defaults to "rnn". Returns: A tuple `(emit_ta, final_state, final_loop_state)` where: @@ -1201,7 +1203,7 @@ def raw_rnn(cell, loop_fn, # Create a new scope in which the caching device is either # determined by the parent scope, or is set to place the cached # Variable using the same placement as for the rest of the RNN. - with vs.variable_scope(scope or "RNN") as varscope: + with vs.variable_scope(scope or "rnn") as varscope: if varscope.caching_device is None: varscope.set_caching_device(lambda op: op.device) |