# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """RNN helpers for TensorFlow models.""" from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import control_flow_util from tensorflow.python.ops import control_flow_util_v2 from tensorflow.python.ops import math_ops from tensorflow.python.ops import rnn_cell_impl from tensorflow.python.ops import tensor_array_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.util import deprecation from tensorflow.python.util import dispatch from tensorflow.python.util import nest from tensorflow.python.util.tf_export import tf_export # pylint: disable=protected-access _concat = rnn_cell_impl._concat # pylint: enable=protected-access def _transpose_batch_time(x): """Transposes the batch and time dimensions of a Tensor. If the input tensor has rank < 2 it returns the original tensor. Retains as much of the static shape information as possible. Args: x: A Tensor. Returns: x transposed along the first two dimensions. """ x_static_shape = x.get_shape() if x_static_shape.rank is not None and x_static_shape.rank < 2: return x x_rank = array_ops.rank(x) x_t = array_ops.transpose( x, array_ops.concat(([1, 0], math_ops.range(2, x_rank)), axis=0)) x_t.set_shape( tensor_shape.TensorShape( [x_static_shape.dims[1].value, x_static_shape.dims[0].value]).concatenate(x_static_shape[2:])) return x_t def _best_effort_input_batch_size(flat_input): """Get static input batch size if available, with fallback to the dynamic one. Args: flat_input: An iterable of time major input Tensors of shape `[max_time, batch_size, ...]`. All inputs should have compatible batch sizes. Returns: The batch size in Python integer if available, or a scalar Tensor otherwise. Raises: ValueError: if there is any input with an invalid shape. """ for input_ in flat_input: shape = input_.shape if shape.rank is None: continue if shape.rank < 2: raise ValueError("Input tensor should have rank >= 2. Received input=" f"{input_} of rank {shape.rank}") batch_size = shape.dims[1].value if batch_size is not None: return batch_size # Fallback to the dynamic batch size of the first input. return array_ops.shape(flat_input[0])[1] def _infer_state_dtype(explicit_dtype, state): """Infer the dtype of an RNN state. Args: explicit_dtype: explicitly declared dtype or None. state: RNN's hidden state. Must be a Tensor or a nested iterable containing Tensors. Returns: dtype: inferred dtype of hidden state. Raises: ValueError: if `state` has heterogeneous dtypes or is empty. """ if explicit_dtype is not None: return explicit_dtype elif nest.is_nested(state): inferred_dtypes = [element.dtype for element in nest.flatten(state)] if not inferred_dtypes: raise ValueError(f"Unable to infer dtype from argument state={state}.") all_same = all(x == inferred_dtypes[0] for x in inferred_dtypes) if not all_same: raise ValueError( f"Argument state={state} has tensors of different inferred dtypes. " "Unable to infer a single representative dtype. Dtypes received: " f"{inferred_dtypes}") return inferred_dtypes[0] else: return state.dtype def _maybe_tensor_shape_from_tensor(shape): if isinstance(shape, ops.Tensor): return tensor_shape.as_shape(tensor_util.constant_value(shape)) else: return shape def _should_cache(): """Returns True if a default caching device should be set, otherwise False.""" if context.executing_eagerly(): return False # Don't set a caching device when running in a loop, since it is possible that # train steps could be wrapped in a tf.while_loop. In that scenario caching # prevents forward computations in loop iterations from re-reading the # updated weights. graph = ops.get_default_graph() ctxt = graph._get_control_flow_context() # pylint: disable=protected-access in_v1_while_loop = ( control_flow_util.GetContainingWhileContext(ctxt) is not None) in_v2_while_loop = control_flow_util_v2.in_while_loop_defun(graph) return not in_v1_while_loop and not in_v2_while_loop # pylint: disable=unused-argument def _rnn_step(time, sequence_length, min_sequence_length, max_sequence_length, zero_output, state, call_cell, state_size, skip_conditionals=False): """Calculate one step of a dynamic RNN minibatch. Returns an (output, state) pair conditioned on `sequence_length`. When skip_conditionals=False, the pseudocode is something like: if t >= max_sequence_length: return (zero_output, state) if t < min_sequence_length: return call_cell() # Selectively output zeros or output, old state or new state depending # on whether we've finished calculating each row. new_output, new_state = call_cell() final_output = np.vstack([ zero_output if time >= sequence_length[r] else new_output_r for r, new_output_r in enumerate(new_output) ]) final_state = np.vstack([ state[r] if time >= sequence_length[r] else new_state_r for r, new_state_r in enumerate(new_state) ]) return (final_output, final_state) Args: time: int32 `Tensor` scalar. sequence_length: int32 `Tensor` vector of size [batch_size]. min_sequence_length: int32 `Tensor` scalar, min of sequence_length. max_sequence_length: int32 `Tensor` scalar, max of sequence_length. zero_output: `Tensor` vector of shape [output_size]. state: Either a single `Tensor` matrix of shape `[batch_size, state_size]`, or a list/tuple of such tensors. call_cell: lambda returning tuple of (new_output, new_state) where new_output is a `Tensor` matrix of shape `[batch_size, output_size]`. new_state is a `Tensor` matrix of shape `[batch_size, state_size]`. state_size: The `cell.state_size` associated with the state. skip_conditionals: Python bool, whether to skip using the conditional calculations. This is useful for `dynamic_rnn`, where the input tensor matches `max_sequence_length`, and using conditionals just slows everything down. Returns: A tuple of (`final_output`, `final_state`) as given by the pseudocode above: final_output is a `Tensor` matrix of shape [batch_size, output_size] final_state is either a single `Tensor` matrix, or a tuple of such matrices (matching length and shapes of input `state`). Raises: ValueError: If the cell returns a state tuple whose length does not match that returned by `state_size`. """ # Convert state to a list for ease of use flat_state = nest.flatten(state) flat_zero_output = nest.flatten(zero_output) # Vector describing which batch entries are finished. copy_cond = time >= sequence_length def _copy_one_through(output, new_output): # TensorArray and scalar get passed through. if isinstance(output, tensor_array_ops.TensorArray): return new_output if output.shape.rank == 0: return new_output # Otherwise propagate the old or the new value. with ops.colocate_with(new_output): return array_ops.where(copy_cond, output, new_output) def _copy_some_through(flat_new_output, flat_new_state): # Use broadcasting select to determine which values should get # the previous state & zero output, and which values should get # a calculated state & output. flat_new_output = [ _copy_one_through(zero_output, new_output) for zero_output, new_output in zip(flat_zero_output, flat_new_output) ] flat_new_state = [ _copy_one_through(state, new_state) for state, new_state in zip(flat_state, flat_new_state) ] return flat_new_output + flat_new_state def _maybe_copy_some_through(): """Run RNN step. Pass through either no or some past state.""" new_output, new_state = call_cell() nest.assert_same_structure(zero_output, new_output) nest.assert_same_structure(state, new_state) flat_new_state = nest.flatten(new_state) flat_new_output = nest.flatten(new_output) return control_flow_ops.cond( # if t < min_seq_len: calculate and return everything time < min_sequence_length, lambda: flat_new_output + flat_new_state, # else copy some of it through lambda: _copy_some_through(flat_new_output, flat_new_state)) # TODO(ebrevdo): skipping these conditionals may cause a slowdown, # but benefits from removing cond() and its gradient. We should # profile with and without this switch here. if skip_conditionals: # Instead of using conditionals, perform the selective copy at all time # steps. This is faster when max_seq_len is equal to the number of unrolls # (which is typical for dynamic_rnn). new_output, new_state = call_cell() nest.assert_same_structure(zero_output, new_output) nest.assert_same_structure(state, new_state) new_state = nest.flatten(new_state) new_output = nest.flatten(new_output) final_output_and_state = _copy_some_through(new_output, new_state) else: empty_update = lambda: flat_zero_output + flat_state final_output_and_state = control_flow_ops.cond( # if t >= max_seq_len: copy all state through, output zeros time >= max_sequence_length, empty_update, # otherwise calculation is required: copy some or all of it through _maybe_copy_some_through) if len(final_output_and_state) != len(flat_zero_output) + len(flat_state): raise ValueError("Internal error: state and output were not concatenated " f"correctly. Received state length: {len(flat_state)}, " f"output length: {len(flat_zero_output)}. Expected " f"contatenated length: {len(final_output_and_state)}.") final_output = final_output_and_state[:len(flat_zero_output)] final_state = final_output_and_state[len(flat_zero_output):] for output, flat_output in zip(final_output, flat_zero_output): output.set_shape(flat_output.get_shape()) for substate, flat_substate in zip(final_state, flat_state): if not isinstance(substate, tensor_array_ops.TensorArray): substate.set_shape(flat_substate.get_shape()) final_output = nest.pack_sequence_as( structure=zero_output, flat_sequence=final_output) final_state = nest.pack_sequence_as( structure=state, flat_sequence=final_state) return final_output, final_state def _reverse_seq(input_seq, lengths): """Reverse a list of Tensors up to specified lengths. Args: input_seq: Sequence of seq_len tensors of dimension (batch_size, n_features) or nested tuples of tensors. lengths: A `Tensor` of dimension batch_size, containing lengths for each sequence in the batch. If "None" is specified, simply reverses the list. Returns: time-reversed sequence """ if lengths is None: return list(reversed(input_seq)) flat_input_seq = tuple(nest.flatten(input_) for input_ in input_seq) flat_results = [[] for _ in range(len(input_seq))] for sequence in zip(*flat_input_seq): input_shape = tensor_shape.unknown_shape(rank=sequence[0].get_shape().rank) for input_ in sequence: input_shape.assert_is_compatible_with(input_.get_shape()) input_.set_shape(input_shape) # Join into (time, batch_size, depth) s_joined = array_ops.stack(sequence) # Reverse along dimension 0 s_reversed = array_ops.reverse_sequence(s_joined, lengths, 0, 1) # Split again into list result = array_ops.unstack(s_reversed) for r, flat_result in zip(result, flat_results): r.set_shape(input_shape) flat_result.append(r) results = [ nest.pack_sequence_as(structure=input_, flat_sequence=flat_result) for input_, flat_result in zip(input_seq, flat_results) ] return results @deprecation.deprecated(None, "Please use `keras.layers.Bidirectional(" "keras.layers.RNN(cell))`, which is equivalent to " "this API") @tf_export(v1=["nn.bidirectional_dynamic_rnn"]) @dispatch.add_dispatch_support def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, initial_state_fw=None, initial_state_bw=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None): """Creates a dynamic version of bidirectional recurrent neural network. Takes input and builds independent forward and backward RNNs. The input_size of forward and backward cell must match. The initial state for both directions is zero by default (but can be set optionally) and no intermediate states are ever returned -- the network is fully unrolled for the given (passed in) length(s) of the sequence(s) or completely unrolled if length(s) is not given. Args: cell_fw: An instance of RNNCell, to be used for forward direction. cell_bw: An instance of RNNCell, to be used for backward direction. inputs: The RNN inputs. If time_major == False (default), this must be a tensor of shape: `[batch_size, max_time, ...]`, or a nested tuple of such elements. If time_major == True, this must be a tensor of shape: `[max_time, batch_size, ...]`, or a nested tuple of such elements. sequence_length: (optional) An int32/int64 vector, size `[batch_size]`, containing the actual lengths for each of the sequences in the batch. If not provided, all batch entries are assumed to be full sequences; and time reversal is applied from time `0` to `max_time` for each sequence. initial_state_fw: (optional) An initial state for the forward RNN. This must be a tensor of appropriate type and shape `[batch_size, cell_fw.state_size]`. If `cell_fw.state_size` is a tuple, this should be a tuple of tensors having shapes `[batch_size, s] for s in cell_fw.state_size`. initial_state_bw: (optional) Same as for `initial_state_fw`, but using the corresponding properties of `cell_bw`. dtype: (optional) The data type for the initial states and expected output. Required if initial_states are not provided or RNN states have a heterogeneous dtype. parallel_iterations: (Default: 32). The number of iterations to run in parallel. Those operations which do not have any temporal dependency and can be run in parallel, will be. This parameter trades off time for space. Values >> 1 use more memory but take less time, while smaller values use less memory but computations take longer. swap_memory: Transparently swap the tensors produced in forward inference 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. time_major: The shape format of the `inputs` and `outputs` Tensors. If true, these `Tensors` must be shaped `[max_time, batch_size, depth]`. If false, these `Tensors` must be shaped `[batch_size, max_time, depth]`. Using `time_major = True` is a bit more efficient because it avoids 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 "bidirectional_rnn" Returns: A tuple (outputs, output_states) where: outputs: A tuple (output_fw, output_bw) containing the forward and the backward rnn output `Tensor`. If time_major == False (default), output_fw will be a `Tensor` shaped: `[batch_size, max_time, cell_fw.output_size]` and output_bw will be a `Tensor` shaped: `[batch_size, max_time, cell_bw.output_size]`. If time_major == True, output_fw will be a `Tensor` shaped: `[max_time, batch_size, cell_fw.output_size]` and output_bw will be a `Tensor` shaped: `[max_time, batch_size, cell_bw.output_size]`. It returns a tuple instead of a single concatenated `Tensor`, unlike in the `bidirectional_rnn`. If the concatenated one is preferred, the forward and backward outputs can be concatenated as `tf.concat(outputs, 2)`. output_states: A tuple (output_state_fw, output_state_bw) containing the forward and the backward final states of bidirectional rnn. Raises: TypeError: If `cell_fw` or `cell_bw` is not an instance of `RNNCell`. """ rnn_cell_impl.assert_like_rnncell("cell_fw", cell_fw) rnn_cell_impl.assert_like_rnncell("cell_bw", cell_bw) with vs.variable_scope(scope or "bidirectional_rnn"): # Forward direction 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, parallel_iterations=parallel_iterations, swap_memory=swap_memory, time_major=time_major, scope=fw_scope) # Backward direction if not time_major: time_axis = 1 batch_axis = 0 else: time_axis = 0 batch_axis = 1 def _reverse(input_, seq_lengths, seq_axis, batch_axis): if seq_lengths is not None: return array_ops.reverse_sequence( input=input_, seq_lengths=seq_lengths, seq_axis=seq_axis, batch_axis=batch_axis) else: return array_ops.reverse(input_, axis=[seq_axis]) with vs.variable_scope("bw") as bw_scope: def _map_reverse(inp): return _reverse( inp, seq_lengths=sequence_length, seq_axis=time_axis, batch_axis=batch_axis) inputs_reverse = nest.map_structure(_map_reverse, inputs) tmp, output_state_bw = dynamic_rnn( cell=cell_bw, inputs=inputs_reverse, sequence_length=sequence_length, initial_state=initial_state_bw, dtype=dtype, parallel_iterations=parallel_iterations, swap_memory=swap_memory, time_major=time_major, scope=bw_scope) output_bw = _reverse( tmp, seq_lengths=sequence_length, seq_axis=time_axis, batch_axis=batch_axis) outputs = (output_fw, output_bw) output_states = (output_state_fw, output_state_bw) return (outputs, output_states) @deprecation.deprecated( None, "Please use `keras.layers.RNN(cell)`, which is equivalent to this API") @tf_export(v1=["nn.dynamic_rnn"]) @dispatch.add_dispatch_support def dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None): """Creates a recurrent neural network specified by RNNCell `cell`. Performs fully dynamic unrolling of `inputs`. Example: ```python # create a BasicRNNCell rnn_cell = tf.compat.v1.nn.rnn_cell.BasicRNNCell(hidden_size) # 'outputs' is a tensor of shape [batch_size, max_time, cell_state_size] # defining initial state initial_state = rnn_cell.zero_state(batch_size, dtype=tf.float32) # 'state' is a tensor of shape [batch_size, cell_state_size] outputs, state = tf.compat.v1.nn.dynamic_rnn(rnn_cell, input_data, initial_state=initial_state, dtype=tf.float32) ``` ```python # create 2 LSTMCells rnn_layers = [tf.compat.v1.nn.rnn_cell.LSTMCell(size) for size in [128, 256]] # create a RNN cell composed sequentially of a number of RNNCells multi_rnn_cell = tf.compat.v1.nn.rnn_cell.MultiRNNCell(rnn_layers) # 'outputs' is a tensor of shape [batch_size, max_time, 256] # 'state' is a N-tuple where N is the number of LSTMCells containing a # tf.nn.rnn_cell.LSTMStateTuple for each cell outputs, state = tf.compat.v1.nn.dynamic_rnn(cell=multi_rnn_cell, inputs=data, dtype=tf.float32) ``` Args: cell: An instance of RNNCell. inputs: The RNN inputs. If `time_major == False` (default), this must be a `Tensor` of shape: `[batch_size, max_time, ...]`, or a nested tuple of such elements. If `time_major == True`, this must be a `Tensor` of shape: `[max_time, batch_size, ...]`, or a nested tuple of such elements. This may also be a (possibly nested) tuple of Tensors satisfying this property. The first two dimensions must match across all the inputs, but otherwise the ranks and other shape components may differ. In this case, input to `cell` at each time-step will replicate the structure of these tuples, except for the time dimension (from which the time is taken). The input to `cell` at each time step will be a `Tensor` or (possibly nested) tuple of Tensors each with dimensions `[batch_size, ...]`. sequence_length: (optional) An int32/int64 vector sized `[batch_size]`. Used to copy-through state and zero-out outputs when past a batch element's sequence length. This parameter enables users to extract the last valid state and properly padded outputs, so it is provided for correctness. initial_state: (optional) An initial state for the RNN. If `cell.state_size` is an integer, this must be a `Tensor` of appropriate type and shape `[batch_size, cell.state_size]`. If `cell.state_size` is a tuple, this should be a tuple of tensors having shapes `[batch_size, s] for s in cell.state_size`. dtype: (optional) The data type for the initial state and expected output. Required if initial_state is not provided or RNN state has a heterogeneous dtype. parallel_iterations: (Default: 32). The number of iterations to run in parallel. Those operations which do not have any temporal dependency and can be run in parallel, will be. This parameter trades off time for space. Values >> 1 use more memory but take less time, while smaller values use less memory but computations take longer. swap_memory: Transparently swap the tensors produced in forward inference 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. time_major: The shape format of the `inputs` and `outputs` Tensors. If true, these `Tensors` must be shaped `[max_time, batch_size, depth]`. If false, these `Tensors` must be shaped `[batch_size, max_time, depth]`. Using `time_major = True` is a bit more efficient because it avoids 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". Returns: A pair (outputs, state) where: outputs: The RNN output `Tensor`. If time_major == False (default), this will be a `Tensor` shaped: `[batch_size, max_time, cell.output_size]`. If time_major == True, this will be a `Tensor` shaped: `[max_time, batch_size, cell.output_size]`. Note, if `cell.output_size` is a (possibly nested) tuple of integers or `TensorShape` objects, then `outputs` will be a tuple having the same structure as `cell.output_size`, containing Tensors having shapes corresponding to the shape data in `cell.output_size`. state: The final state. If `cell.state_size` is an int, this will be shaped `[batch_size, cell.state_size]`. If it is a `TensorShape`, this will be shaped `[batch_size] + cell.state_size`. If it is a (possibly nested) tuple of ints or `TensorShape`, this will be a tuple having the corresponding shapes. If cells are `LSTMCells` `state` will be a tuple containing a `LSTMStateTuple` for each cell. Raises: TypeError: If `cell` is not an instance of RNNCell. ValueError: If inputs is None or an empty list. @compatibility(TF2) `tf.compat.v1.nn.dynamic_rnn` is not compatible with eager execution and `tf.function`. Please use `tf.keras.layers.RNN` instead for TF2 migration. Take LSTM as an example, you can instantiate a `tf.keras.layers.RNN` layer with `tf.keras.layers.LSTMCell`, or directly via `tf.keras.layers.LSTM`. Once the keras layer is created, you can get the output and states by calling the layer with input and states. Please refer to [this guide](https://www.tensorflow.org/guide/keras/rnn) for more details about Keras RNN. You can also find more details about the difference and comparison between Keras RNN and TF compat v1 rnn in [this document](https://github.com/tensorflow/community/blob/master/rfcs/20180920-unify-rnn-interface.md) #### Structural Mapping to Native TF2 Before: ```python # create 2 LSTMCells rnn_layers = [tf.compat.v1.nn.rnn_cell.LSTMCell(size) for size in [128, 256]] # create a RNN cell composed sequentially of a number of RNNCells multi_rnn_cell = tf.compat.v1.nn.rnn_cell.MultiRNNCell(rnn_layers) # 'outputs' is a tensor of shape [batch_size, max_time, 256] # 'state' is a N-tuple where N is the number of LSTMCells containing a # tf.nn.rnn_cell.LSTMStateTuple for each cell outputs, state = tf.compat.v1.nn.dynamic_rnn(cell=multi_rnn_cell, inputs=data, dtype=tf.float32) ``` After: ```python # RNN layer can take a list of cells, which will then stack them together. # By default, keras RNN will only return the last timestep output and will not # return states. If you need whole time sequence output as well as the states, # you can set `return_sequences` and `return_state` to True. rnn_layer = tf.keras.layers.RNN([tf.keras.layers.LSTMCell(128), tf.keras.layers.LSTMCell(256)], return_sequences=True, return_state=True) outputs, output_states = rnn_layer(inputs, states) ``` #### How to Map Arguments | TF1 Arg Name | TF2 Arg Name | Note | | :-------------------- | :-------------- | :------------------------------- | | `cell` | `cell` | In the RNN layer constructor | | `inputs` | `inputs` | In the RNN layer `__call__` | | `sequence_length` | Not used | Adding masking layer before RNN : : : : to achieve the same result. : | `initial_state` | `initial_state` | In the RNN layer `__call__` | | `dtype` | `dtype` | In the RNN layer constructor | | `parallel_iterations` | Not supported | | | `swap_memory` | Not supported | | | `time_major` | `time_major` | In the RNN layer constructor | | `scope` | Not supported | | @end_compatibility """ rnn_cell_impl.assert_like_rnncell("cell", cell) with vs.variable_scope(scope or "rnn") as varscope: # 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. if _should_cache(): if varscope.caching_device is None: varscope.set_caching_device(lambda op: op.device) # By default, time_major==False and inputs are batch-major: shaped # [batch, time, depth] # For internal calculations, we transpose to [time, batch, depth] flat_input = nest.flatten(inputs) if not time_major: # (B,T,D) => (T,B,D) flat_input = [ops.convert_to_tensor(input_) for input_ in flat_input] flat_input = tuple(_transpose_batch_time(input_) for input_ in flat_input) parallel_iterations = parallel_iterations or 32 if sequence_length is not None: sequence_length = math_ops.cast(sequence_length, dtypes.int32) if sequence_length.get_shape().rank not in (None, 1): raise ValueError( f"Argument sequence_length must be a vector of length batch_size." f" Received sequence_length={sequence_length} of shape: " f"{sequence_length.get_shape()}") sequence_length = array_ops.identity( # Just to find it in the graph. sequence_length, name="sequence_length") batch_size = _best_effort_input_batch_size(flat_input) if initial_state is not None: state = initial_state else: if not dtype: raise ValueError("If no initial_state is provided, argument `dtype` " "must be specified") if getattr(cell, "get_initial_state", None) is not None: state = cell.get_initial_state( inputs=None, batch_size=batch_size, dtype=dtype) else: state = cell.zero_state(batch_size, dtype) def _assert_has_shape(x, shape): x_shape = array_ops.shape(x) packed_shape = array_ops.stack(shape) return control_flow_ops.Assert( math_ops.reduce_all(math_ops.equal(x_shape, packed_shape)), [ "Expected shape for Tensor %s is " % x.name, packed_shape, " but saw shape: ", x_shape ]) if not context.executing_eagerly() and sequence_length is not None: # Perform some shape validation with ops.control_dependencies( [_assert_has_shape(sequence_length, [batch_size])]): sequence_length = array_ops.identity( sequence_length, name="CheckSeqLen") inputs = nest.pack_sequence_as(structure=inputs, flat_sequence=flat_input) (outputs, final_state) = _dynamic_rnn_loop( cell, inputs, state, parallel_iterations=parallel_iterations, swap_memory=swap_memory, sequence_length=sequence_length, dtype=dtype) # Outputs of _dynamic_rnn_loop are always shaped [time, batch, depth]. # If we are performing batch-major calculations, transpose output back # to shape [batch, time, depth] if not time_major: # (T,B,D) => (B,T,D) outputs = nest.map_structure(_transpose_batch_time, outputs) return (outputs, final_state) def _dynamic_rnn_loop(cell, inputs, initial_state, parallel_iterations, swap_memory, sequence_length=None, dtype=None): """Internal implementation of Dynamic RNN. Args: cell: An instance of RNNCell. inputs: A `Tensor` of shape [time, batch_size, input_size], or a nested tuple of such elements. initial_state: A `Tensor` of shape `[batch_size, state_size]`, or if `cell.state_size` is a tuple, then this should be a tuple of tensors having shapes `[batch_size, s] for s in cell.state_size`. parallel_iterations: Positive Python int. swap_memory: A Python boolean sequence_length: (optional) An `int32` `Tensor` of shape [batch_size]. dtype: (optional) Expected dtype of output. If not specified, inferred from initial_state. Returns: Tuple `(final_outputs, final_state)`. final_outputs: A `Tensor` of shape `[time, batch_size, cell.output_size]`. If `cell.output_size` is a (possibly nested) tuple of ints or `TensorShape` objects, then this returns a (possibly nested) tuple of Tensors matching the corresponding shapes. final_state: A `Tensor`, or possibly nested tuple of Tensors, matching in length and shapes to `initial_state`. Raises: ValueError: If the input depth cannot be inferred via shape inference from the inputs. ValueError: If time_step is not the same for all the elements in the inputs. ValueError: If batch_size is not the same for all the elements in the inputs. """ state = initial_state assert isinstance(parallel_iterations, int), "parallel_iterations must be int" state_size = cell.state_size flat_input = nest.flatten(inputs) flat_output_size = nest.flatten(cell.output_size) # Construct an initial output input_shape = array_ops.shape(flat_input[0]) time_steps = input_shape[0] batch_size = _best_effort_input_batch_size(flat_input) inputs_got_shape = tuple( input_.get_shape().with_rank_at_least(3) for input_ in flat_input) const_time_steps, const_batch_size = inputs_got_shape[0].as_list()[:2] for i, shape in enumerate(inputs_got_shape): if not shape[2:].is_fully_defined(): raise ValueError( "Input size (depth of inputs) must be accessible via shape inference," f" but saw value None for input={flat_input[i]}.") got_time_steps = shape.dims[0].value got_batch_size = shape.dims[1].value if const_time_steps != got_time_steps: raise ValueError( "Time steps is not the same for all the elements in the input in a " f"batch. Received time steps={got_time_steps} for input=" f"{flat_input[i]}.") if const_batch_size != got_batch_size: raise ValueError( "Batch_size is not the same for all the elements in the input. " f"Received batch size={got_batch_size} for input={flat_input[i]}.") # Prepare dynamic conditional copying of state & output def _create_zero_arrays(size): size = _concat(batch_size, size) return array_ops.zeros( array_ops.stack(size), _infer_state_dtype(dtype, state)) flat_zero_output = tuple( _create_zero_arrays(output) for output in flat_output_size) zero_output = nest.pack_sequence_as( structure=cell.output_size, flat_sequence=flat_zero_output) if sequence_length is not None: min_sequence_length = math_ops.reduce_min(sequence_length) max_sequence_length = math_ops.reduce_max(sequence_length) else: max_sequence_length = time_steps time = array_ops.constant(0, dtype=dtypes.int32, name="time") with ops.name_scope("dynamic_rnn") as scope: base_name = scope def _create_ta(name, element_shape, dtype): return tensor_array_ops.TensorArray( dtype=dtype, size=time_steps, element_shape=element_shape, tensor_array_name=base_name + name) in_graph_mode = not context.executing_eagerly() if in_graph_mode: output_ta = tuple( _create_ta( "output_%d" % i, element_shape=( tensor_shape.TensorShape([const_batch_size]).concatenate( _maybe_tensor_shape_from_tensor(out_size))), dtype=_infer_state_dtype(dtype, state)) for i, out_size in enumerate(flat_output_size)) input_ta = tuple( _create_ta( "input_%d" % i, element_shape=flat_input_i.shape[1:], dtype=flat_input_i.dtype) for i, flat_input_i in enumerate(flat_input)) input_ta = tuple( ta.unstack(input_) for ta, input_ in zip(input_ta, flat_input)) else: output_ta = tuple([0 for _ in range(time_steps.numpy())] for i in range(len(flat_output_size))) input_ta = flat_input def _time_step(time, output_ta_t, state): """Take a time step of the dynamic RNN. Args: time: int32 scalar Tensor. output_ta_t: List of `TensorArray`s that represent the output. state: nested tuple of vector tensors that represent the state. Returns: The tuple (time + 1, output_ta_t with updated flow, new_state). """ if in_graph_mode: input_t = tuple(ta.read(time) for ta in input_ta) # Restore some shape information for input_, shape in zip(input_t, inputs_got_shape): input_.set_shape(shape[1:]) else: input_t = tuple(ta[time.numpy()] for ta in input_ta) input_t = nest.pack_sequence_as(structure=inputs, flat_sequence=input_t) # Keras RNN cells only accept state as list, even if it's a single tensor. call_cell = lambda: cell(input_t, state) if sequence_length is not None: (output, new_state) = _rnn_step( time=time, sequence_length=sequence_length, min_sequence_length=min_sequence_length, max_sequence_length=max_sequence_length, zero_output=zero_output, state=state, call_cell=call_cell, state_size=state_size, skip_conditionals=True) else: (output, new_state) = call_cell() # Pack state if using state tuples output = nest.flatten(output) if in_graph_mode: output_ta_t = tuple( ta.write(time, out) for ta, out in zip(output_ta_t, output)) else: for ta, out in zip(output_ta_t, output): ta[time.numpy()] = out return (time + 1, output_ta_t, new_state) if in_graph_mode: # Make sure that we run at least 1 step, if necessary, to ensure # the TensorArrays pick up the dynamic shape. loop_bound = math_ops.minimum(time_steps, math_ops.maximum(1, max_sequence_length)) else: # Using max_sequence_length isn't currently supported in the Eager branch. loop_bound = time_steps _, output_final_ta, final_state = control_flow_ops.while_loop( cond=lambda time, *_: time < loop_bound, body=_time_step, loop_vars=(time, output_ta, state), parallel_iterations=parallel_iterations, maximum_iterations=time_steps, swap_memory=swap_memory) # Unpack final output if not using output tuples. if in_graph_mode: final_outputs = tuple(ta.stack() for ta in output_final_ta) # Restore some shape information for output, output_size in zip(final_outputs, flat_output_size): shape = _concat([const_time_steps, const_batch_size], output_size, static=True) output.set_shape(shape) else: final_outputs = output_final_ta final_outputs = nest.pack_sequence_as( structure=cell.output_size, flat_sequence=final_outputs) if not in_graph_mode: final_outputs = nest.map_structure_up_to( cell.output_size, lambda x: array_ops.stack(x, axis=0), final_outputs) return (final_outputs, final_state) @tf_export(v1=["nn.raw_rnn"]) @dispatch.add_dispatch_support def raw_rnn(cell, loop_fn, parallel_iterations=None, swap_memory=False, scope=None): """Creates an `RNN` specified by RNNCell `cell` and loop function `loop_fn`. **NOTE: This method is still in testing, and the API may change.** This function is a more primitive version of `dynamic_rnn` that provides more direct access to the inputs each iteration. It also provides more control over when to start and finish reading the sequence, and what to emit for the output. For example, it can be used to implement the dynamic decoder of a seq2seq model. Instead of working with `Tensor` objects, most operations work with `TensorArray` objects directly. The operation of `raw_rnn`, in pseudo-code, is basically the following: ```python time = tf.constant(0, dtype=tf.int32) (finished, next_input, initial_state, emit_structure, loop_state) = loop_fn( time=time, cell_output=None, cell_state=None, loop_state=None) emit_ta = TensorArray(dynamic_size=True, dtype=initial_state.dtype) state = initial_state while not all(finished): (output, cell_state) = cell(next_input, state) (next_finished, next_input, next_state, emit, loop_state) = loop_fn( time=time + 1, cell_output=output, cell_state=cell_state, loop_state=loop_state) # Emit zeros and copy forward state for minibatch entries that are finished. state = tf.where(finished, state, next_state) emit = tf.where(finished, tf.zeros_like(emit_structure), emit) emit_ta = emit_ta.write(time, emit) # If any new minibatch entries are marked as finished, mark these. finished = tf.logical_or(finished, next_finished) time += 1 return (emit_ta, state, loop_state) ``` with the additional properties that output and state may be (possibly nested) tuples, as determined by `cell.output_size` and `cell.state_size`, and as a result the final `state` and `emit_ta` may themselves be tuples. A simple implementation of `dynamic_rnn` via `raw_rnn` looks like this: ```python inputs = tf.compat.v1.placeholder(shape=(max_time, batch_size, input_depth), dtype=tf.float32) sequence_length = tf.compat.v1.placeholder(shape=(batch_size,), dtype=tf.int32) inputs_ta = tf.TensorArray(dtype=tf.float32, size=max_time) inputs_ta = inputs_ta.unstack(inputs) cell = tf.compat.v1.nn.rnn_cell.LSTMCell(num_units) def loop_fn(time, cell_output, cell_state, loop_state): emit_output = cell_output # == None for time == 0 if cell_output is None: # time == 0 next_cell_state = cell.zero_state(batch_size, tf.float32) else: next_cell_state = cell_state elements_finished = (time >= sequence_length) finished = tf.reduce_all(elements_finished) next_input = tf.cond( finished, lambda: tf.zeros([batch_size, input_depth], dtype=tf.float32), lambda: inputs_ta.read(time)) next_loop_state = None return (elements_finished, next_input, next_cell_state, emit_output, next_loop_state) outputs_ta, final_state, _ = raw_rnn(cell, loop_fn) outputs = outputs_ta.stack() ``` Args: cell: An instance of RNNCell. loop_fn: A callable that takes inputs `(time, cell_output, cell_state, loop_state)` and returns the tuple `(finished, next_input, next_cell_state, emit_output, next_loop_state)`. Here `time` is an int32 scalar `Tensor`, `cell_output` is a `Tensor` or (possibly nested) tuple of tensors as determined by `cell.output_size`, and `cell_state` is a `Tensor` or (possibly nested) tuple of tensors, as determined by the `loop_fn` on its first call (and should match `cell.state_size`). The outputs are: `finished`, a boolean `Tensor` of shape `[batch_size]`, `next_input`: the next input to feed to `cell`, `next_cell_state`: the next state to feed to `cell`, and `emit_output`: the output to store for this iteration. Note that `emit_output` should be a `Tensor` or (possibly nested) tuple of tensors which is aggregated in the `emit_ta` inside the `while_loop`. For the first call to `loop_fn`, the `emit_output` corresponds to the `emit_structure` which is then used to determine the size of the `zero_tensor` for the `emit_ta` (defaults to `cell.output_size`). For the subsequent calls to the `loop_fn`, the `emit_output` corresponds to the actual output tensor that is to be aggregated in the `emit_ta`. The parameter `cell_state` and output `next_cell_state` may be either a single or (possibly nested) tuple of tensors. The parameter `loop_state` and output `next_loop_state` may be either a single or (possibly nested) tuple of `Tensor` and `TensorArray` objects. This last parameter may be ignored by `loop_fn` and the return value may be `None`. If it is not `None`, then the `loop_state` will be propagated through the RNN loop, for use purely by `loop_fn` to keep track of its own state. The `next_loop_state` parameter returned may be `None`. The first call to `loop_fn` will be `time = 0`, `cell_output = None`, `cell_state = None`, and `loop_state = None`. For this call: The `next_cell_state` value should be the value with which to initialize the cell's state. It may be a final state from a previous RNN or it may be the output of `cell.zero_state()`. It should be a (possibly nested) tuple structure of tensors. If `cell.state_size` is an integer, this must be a `Tensor` of appropriate type and shape `[batch_size, cell.state_size]`. If `cell.state_size` is a `TensorShape`, this must be a `Tensor` of appropriate type and shape `[batch_size] + cell.state_size`. If `cell.state_size` is a (possibly nested) tuple of ints or `TensorShape`, this will be a tuple having the corresponding shapes. The `emit_output` value may be either `None` or a (possibly nested) tuple structure of tensors, e.g., `(tf.zeros(shape_0, dtype=dtype_0), tf.zeros(shape_1, dtype=dtype_1))`. If this first `emit_output` return value is `None`, then the `emit_ta` result of `raw_rnn` will have the same structure and dtypes as `cell.output_size`. Otherwise `emit_ta` will have the same structure, shapes (prepended with a `batch_size` dimension), and dtypes as `emit_output`. The actual values returned for `emit_output` at this initializing call are ignored. Note, this emit structure must be consistent across all time steps. parallel_iterations: (Default: 32). The number of iterations to run in parallel. Those operations which do not have any temporal dependency and can be run in parallel, will be. This parameter trades off time for space. Values >> 1 use more memory but take less time, while smaller values use less memory but computations take longer. swap_memory: Transparently swap the tensors produced in forward inference 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". Returns: A tuple `(emit_ta, final_state, final_loop_state)` where: `emit_ta`: The RNN output `TensorArray`. If `loop_fn` returns a (possibly nested) set of Tensors for `emit_output` during initialization, (inputs `time = 0`, `cell_output = None`, and `loop_state = None`), then `emit_ta` will have the same structure, dtypes, and shapes as `emit_output` instead. If `loop_fn` returns `emit_output = None` during this call, the structure of `cell.output_size` is used: If `cell.output_size` is a (possibly nested) tuple of integers or `TensorShape` objects, then `emit_ta` will be a tuple having the same structure as `cell.output_size`, containing TensorArrays whose elements' shapes correspond to the shape data in `cell.output_size`. `final_state`: The final cell state. If `cell.state_size` is an int, this will be shaped `[batch_size, cell.state_size]`. If it is a `TensorShape`, this will be shaped `[batch_size] + cell.state_size`. If it is a (possibly nested) tuple of ints or `TensorShape`, this will be a tuple having the corresponding shapes. `final_loop_state`: The final loop state as returned by `loop_fn`. Raises: TypeError: If `cell` is not an instance of RNNCell, or `loop_fn` is not a `callable`. """ rnn_cell_impl.assert_like_rnncell("cell", cell) if not callable(loop_fn): raise TypeError("Argument `loop_fn` must be a callable. Received: " f"{loop_fn}.") parallel_iterations = parallel_iterations or 32 # 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: if _should_cache(): if varscope.caching_device is None: varscope.set_caching_device(lambda op: op.device) time = constant_op.constant(0, dtype=dtypes.int32) (elements_finished, next_input, initial_state, emit_structure, init_loop_state) = loop_fn( time, None, None, None) # time, cell_output, cell_state, loop_state flat_input = nest.flatten(next_input) # Need a surrogate loop state for the while_loop if none is available. loop_state = ( init_loop_state if init_loop_state is not None else constant_op.constant(0, dtype=dtypes.int32)) input_shape = [input_.get_shape() for input_ in flat_input] static_batch_size = tensor_shape.dimension_at_index(input_shape[0], 0) for input_shape_i in input_shape: # Static verification that batch sizes all match static_batch_size.assert_is_compatible_with( tensor_shape.dimension_at_index(input_shape_i, 0)) batch_size = tensor_shape.dimension_value(static_batch_size) const_batch_size = batch_size if batch_size is None: batch_size = array_ops.shape(flat_input[0])[0] nest.assert_same_structure(initial_state, cell.state_size) state = initial_state flat_state = nest.flatten(state) flat_state = [ops.convert_to_tensor(s) for s in flat_state] state = nest.pack_sequence_as(structure=state, flat_sequence=flat_state) if emit_structure is not None: flat_emit_structure = nest.flatten(emit_structure) flat_emit_size = [ emit.shape if emit.shape.is_fully_defined() else array_ops.shape(emit) for emit in flat_emit_structure ] flat_emit_dtypes = [emit.dtype for emit in flat_emit_structure] else: emit_structure = cell.output_size flat_emit_size = nest.flatten(emit_structure) flat_emit_dtypes = [flat_state[0].dtype] * len(flat_emit_size) flat_emit_ta = [ tensor_array_ops.TensorArray( dtype=dtype_i, dynamic_size=True, element_shape=(tensor_shape.TensorShape([ const_batch_size ]).concatenate(_maybe_tensor_shape_from_tensor(size_i))), size=0, name="rnn_output_%d" % i) for i, (dtype_i, size_i) in enumerate(zip(flat_emit_dtypes, flat_emit_size)) ] emit_ta = nest.pack_sequence_as( structure=emit_structure, flat_sequence=flat_emit_ta) flat_zero_emit = [ array_ops.zeros(_concat(batch_size, size_i), dtype_i) for size_i, dtype_i in zip(flat_emit_size, flat_emit_dtypes) ] zero_emit = nest.pack_sequence_as( structure=emit_structure, flat_sequence=flat_zero_emit) def condition(unused_time, elements_finished, *_): return math_ops.logical_not(math_ops.reduce_all(elements_finished)) def body(time, elements_finished, current_input, emit_ta, state, loop_state): """Internal while loop body for raw_rnn. Args: time: time scalar. elements_finished: batch-size vector. current_input: possibly nested tuple of input tensors. emit_ta: possibly nested tuple of output TensorArrays. state: possibly nested tuple of state tensors. loop_state: possibly nested tuple of loop state tensors. Returns: Tuple having the same size as Args but with updated values. """ (next_output, cell_state) = cell(current_input, state) nest.assert_same_structure(state, cell_state) nest.assert_same_structure(cell.output_size, next_output) next_time = time + 1 (next_finished, next_input, next_state, emit_output, next_loop_state) = loop_fn(next_time, next_output, cell_state, loop_state) nest.assert_same_structure(state, next_state) nest.assert_same_structure(current_input, next_input) nest.assert_same_structure(emit_ta, emit_output) # If loop_fn returns None for next_loop_state, just reuse the # previous one. loop_state = loop_state if next_loop_state is None else next_loop_state def _copy_some_through(current, candidate): """Copy some tensors through via array_ops.where.""" def copy_fn(cur_i, cand_i): # TensorArray and scalar get passed through. if isinstance(cur_i, tensor_array_ops.TensorArray): return cand_i if cur_i.shape.rank == 0: return cand_i # Otherwise propagate the old or the new value. with ops.colocate_with(cand_i): return array_ops.where(elements_finished, cur_i, cand_i) return nest.map_structure(copy_fn, current, candidate) emit_output = _copy_some_through(zero_emit, emit_output) next_state = _copy_some_through(state, next_state) emit_ta = nest.map_structure(lambda ta, emit: ta.write(time, emit), emit_ta, emit_output) elements_finished = math_ops.logical_or(elements_finished, next_finished) return (next_time, elements_finished, next_input, emit_ta, next_state, loop_state) returned = control_flow_ops.while_loop( condition, body, loop_vars=[ time, elements_finished, next_input, emit_ta, state, loop_state ], parallel_iterations=parallel_iterations, swap_memory=swap_memory) (emit_ta, final_state, final_loop_state) = returned[-3:] if init_loop_state is None: final_loop_state = None return (emit_ta, final_state, final_loop_state) @deprecation.deprecated(None, "Please use `keras.layers.RNN(cell, unroll=True)`, " "which is equivalent to this API") @tf_export(v1=["nn.static_rnn"]) @dispatch.add_dispatch_support def static_rnn(cell, inputs, initial_state=None, dtype=None, sequence_length=None, scope=None): """Creates a recurrent neural network specified by RNNCell `cell`. The simplest form of RNN network generated is: ```python state = cell.zero_state(...) outputs = [] for input_ in inputs: output, state = cell(input_, state) outputs.append(output) return (outputs, state) ``` However, a few other options are available: An initial state can be provided. If the sequence_length vector is provided, dynamic calculation is performed. This method of calculation does not compute the RNN steps past the maximum sequence length of the minibatch (thus saving computational time), and properly propagates the state at an example's sequence length to the final state output. The dynamic calculation performed is, at time `t` for batch row `b`, ```python (output, state)(b, t) = (t >= sequence_length(b)) ? (zeros(cell.output_size), states(b, sequence_length(b) - 1)) : cell(input(b, t), state(b, t - 1)) ``` Args: cell: An instance of RNNCell. inputs: A length T list of inputs, each a `Tensor` of shape `[batch_size, input_size]`, or a nested tuple of such elements. initial_state: (optional) An initial state for the RNN. If `cell.state_size` is an integer, this must be a `Tensor` of appropriate type and shape `[batch_size, cell.state_size]`. If `cell.state_size` is a tuple, this should be a tuple of tensors having shapes `[batch_size, s] for s in cell.state_size`. dtype: (optional) The data type for the initial state and expected output. Required if initial_state is not provided or RNN state has a heterogeneous 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". Returns: A pair (outputs, state) where: - outputs is a length T list of outputs (one for each input), or a nested tuple of such elements. - state is the final state Raises: TypeError: If `cell` is not an instance of RNNCell. ValueError: If `inputs` is `None` or an empty list, or if the input depth (column size) cannot be inferred from inputs via shape inference. """ rnn_cell_impl.assert_like_rnncell("cell", cell) if not nest.is_nested(inputs): raise TypeError(f"Argument `inputs` must be a sequence. Received: {inputs}") if not inputs: raise ValueError("Argument `inputs` must not be empty.") outputs = [] # 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: if _should_cache(): if varscope.caching_device is None: varscope.set_caching_device(lambda op: op.device) # Obtain the first sequence of the input first_input = inputs while nest.is_nested(first_input): first_input = first_input[0] # Temporarily avoid EmbeddingWrapper and seq2seq badness # TODO(lukaszkaiser): remove EmbeddingWrapper if first_input.get_shape().rank != 1: input_shape = first_input.get_shape().with_rank_at_least(2) fixed_batch_size = input_shape.dims[0] flat_inputs = nest.flatten(inputs) for flat_input in flat_inputs: input_shape = flat_input.get_shape().with_rank_at_least(2) batch_size, input_size = tensor_shape.dimension_at_index( input_shape, 0), input_shape[1:] fixed_batch_size.assert_is_compatible_with(batch_size) for i, size in enumerate(input_size.dims): if tensor_shape.dimension_value(size) is None: raise ValueError( f"Input size (dimension {i} of input {flat_input}) must be " "accessible via shape inference, but saw value None.") else: fixed_batch_size = first_input.get_shape().with_rank_at_least(1)[0] if tensor_shape.dimension_value(fixed_batch_size): batch_size = tensor_shape.dimension_value(fixed_batch_size) else: batch_size = array_ops.shape(first_input)[0] if initial_state is not None: state = initial_state else: if not dtype: raise ValueError("If no initial_state is provided, argument `dtype` " "must be specified") if getattr(cell, "get_initial_state", None) is not None: state = cell.get_initial_state( inputs=None, batch_size=batch_size, dtype=dtype) else: state = cell.zero_state(batch_size, dtype) if sequence_length is not None: # Prepare variables sequence_length = ops.convert_to_tensor( sequence_length, name="sequence_length") if sequence_length.get_shape().rank not in (None, 1): raise ValueError( "Argument `sequence_length` must be a vector of length " f"{batch_size}. Received sequence_length={sequence_length}.") def _create_zero_output(output_size): # convert int to TensorShape if necessary size = _concat(batch_size, output_size) output = array_ops.zeros( array_ops.stack(size), _infer_state_dtype(dtype, state)) shape = _concat( tensor_shape.dimension_value(fixed_batch_size), output_size, static=True) output.set_shape(tensor_shape.TensorShape(shape)) return output output_size = cell.output_size flat_output_size = nest.flatten(output_size) flat_zero_output = tuple( _create_zero_output(size) for size in flat_output_size) zero_output = nest.pack_sequence_as( structure=output_size, flat_sequence=flat_zero_output) sequence_length = math_ops.cast(sequence_length, dtypes.int32) min_sequence_length = math_ops.reduce_min(sequence_length) max_sequence_length = math_ops.reduce_max(sequence_length) for time, input_ in enumerate(inputs): if time > 0: varscope.reuse_variables() # pylint: disable=cell-var-from-loop call_cell = lambda: cell(input_, state) # pylint: enable=cell-var-from-loop if sequence_length is not None: (output, state) = _rnn_step( time=time, sequence_length=sequence_length, min_sequence_length=min_sequence_length, max_sequence_length=max_sequence_length, zero_output=zero_output, state=state, call_cell=call_cell, state_size=cell.state_size) else: (output, state) = call_cell() outputs.append(output) return (outputs, state) @deprecation.deprecated(None, "Please use `keras.layers.RNN(cell, stateful=True)`, " "which is equivalent to this API") @tf_export(v1=["nn.static_state_saving_rnn"]) @dispatch.add_dispatch_support def static_state_saving_rnn(cell, inputs, state_saver, state_name, sequence_length=None, scope=None): """RNN that accepts a state saver for time-truncated RNN calculation. Args: cell: An instance of `RNNCell`. inputs: A length T list of inputs, each a `Tensor` of shape `[batch_size, input_size]`. state_saver: A state saver object with methods `state` and `save_state`. state_name: Python string or tuple of strings. The name to use with the state_saver. If the cell returns tuples of states (i.e., `cell.state_size` is a tuple) then `state_name` should be a tuple of strings having the same length as `cell.state_size`. Otherwise it should 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". Returns: A pair (outputs, state) where: outputs is a length T list of outputs (one for each input) states is the final state Raises: TypeError: If `cell` is not an instance of RNNCell. ValueError: If `inputs` is `None` or an empty list, or if the arity and type of `state_name` does not match that of `cell.state_size`. """ state_size = cell.state_size state_is_tuple = nest.is_nested(state_size) state_name_tuple = nest.is_nested(state_name) if state_is_tuple != state_name_tuple: raise ValueError("Argument `state_name` should be the same type as " f"`cell.state_size`. Received: state_name={state_name!s}, " f"cell.state_size={state_size!s}.") if state_is_tuple: state_name_flat = nest.flatten(state_name) state_size_flat = nest.flatten(state_size) if len(state_name_flat) != len(state_size_flat): raise ValueError("Number of elements in argument `state_name` and " "`cell.state_size` are mismatched. Received " f"state_name={state_name} with {len(state_name_flat)} " f"elements and cell.state_size={cell.state_size} with " f"{len(state_size_flat)} elements.") initial_state = nest.pack_sequence_as( structure=state_size, flat_sequence=[state_saver.state(s) for s in state_name_flat]) else: initial_state = state_saver.state(state_name) (outputs, state) = static_rnn( cell, inputs, initial_state=initial_state, sequence_length=sequence_length, scope=scope) if state_is_tuple: flat_state = nest.flatten(state) state_name = nest.flatten(state_name) save_state = [ state_saver.save_state(name, substate) for name, substate in zip(state_name, flat_state) ] else: save_state = [state_saver.save_state(state_name, state)] with ops.control_dependencies(save_state): last_output = outputs[-1] flat_last_output = nest.flatten(last_output) flat_last_output = [ array_ops.identity(output) for output in flat_last_output ] outputs[-1] = nest.pack_sequence_as( structure=last_output, flat_sequence=flat_last_output) if state_is_tuple: state = nest.pack_sequence_as( structure=state, flat_sequence=[array_ops.identity(s) for s in flat_state]) else: state = array_ops.identity(state) return (outputs, state) @deprecation.deprecated(None, "Please use `keras.layers.Bidirectional(" "keras.layers.RNN(cell, unroll=True))`, which is " "equivalent to this API") @tf_export(v1=["nn.static_bidirectional_rnn"]) @dispatch.add_dispatch_support def static_bidirectional_rnn(cell_fw, cell_bw, inputs, initial_state_fw=None, initial_state_bw=None, dtype=None, sequence_length=None, scope=None): """Creates a bidirectional recurrent neural network. Similar to the unidirectional case above (rnn) but takes input and builds independent forward and backward RNNs with the final forward and backward outputs depth-concatenated, such that the output will have the format [time][batch][cell_fw.output_size + cell_bw.output_size]. The input_size of forward and backward cell must match. The initial state for both directions is zero by default (but can be set optionally) and no intermediate states are ever returned -- the network is fully unrolled for the given (passed in) length(s) of the sequence(s) or completely unrolled if length(s) is not given. Args: cell_fw: An instance of RNNCell, to be used for forward direction. cell_bw: An instance of RNNCell, to be used for backward direction. inputs: A length T list of inputs, each a tensor of shape [batch_size, input_size], or a nested tuple of such elements. initial_state_fw: (optional) An initial state for the forward RNN. This must be a tensor of appropriate type and shape `[batch_size, cell_fw.state_size]`. If `cell_fw.state_size` is a tuple, this should be a tuple of tensors having shapes `[batch_size, s] for s in cell_fw.state_size`. initial_state_bw: (optional) Same as for `initial_state_fw`, but using the corresponding properties of `cell_bw`. dtype: (optional) The data type for the initial state. Required if 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 "bidirectional_rnn" Returns: A tuple (outputs, output_state_fw, output_state_bw) where: outputs is a length `T` list of outputs (one for each input), which are depth-concatenated forward and backward outputs. output_state_fw is the final state of the forward rnn. output_state_bw is the final state of the backward rnn. Raises: TypeError: If `cell_fw` or `cell_bw` is not an instance of `RNNCell`. ValueError: If inputs is None or an empty list. """ rnn_cell_impl.assert_like_rnncell("cell_fw", cell_fw) rnn_cell_impl.assert_like_rnncell("cell_bw", cell_bw) if not nest.is_nested(inputs): raise TypeError(f"Argument `inputs` must be a sequence. Received: {inputs}") if not inputs: raise ValueError("Argument `inputs` must not be empty.") with vs.variable_scope(scope or "bidirectional_rnn"): # Forward direction with vs.variable_scope("fw") as fw_scope: output_fw, output_state_fw = static_rnn( cell_fw, inputs, initial_state_fw, dtype, sequence_length, scope=fw_scope) # Backward direction with vs.variable_scope("bw") as bw_scope: reversed_inputs = _reverse_seq(inputs, sequence_length) tmp, output_state_bw = static_rnn( cell_bw, reversed_inputs, initial_state_bw, dtype, sequence_length, scope=bw_scope) output_bw = _reverse_seq(tmp, sequence_length) # Concat each of the forward/backward outputs flat_output_fw = nest.flatten(output_fw) flat_output_bw = nest.flatten(output_bw) flat_outputs = tuple( array_ops.concat([fw, bw], 1) for fw, bw in zip(flat_output_fw, flat_output_bw)) outputs = nest.pack_sequence_as( structure=output_fw, flat_sequence=flat_outputs) return (outputs, output_state_fw, output_state_bw)