a /Sic$ @sdZddlmZddlmZddlmZddlmZddlmZddlmZddl m Z dd l m Z dd l m Z dd l m Z dd l mZdd l mZddl mZddl mZddlmZddlmZddlmZddlmZejZddZddZddZddZddZd>dd Zd!d"Z e!d#d$ed%gd&ej"d?d'd(Z#e!d#d)ed*gd&ej"d@d+d,Z$dAd-d.Z%ed/gd&ej"dBd0d1Z&e!d#d2ed3gd&ej"dCd4d5Z'e!d#d6ed7gd&ej"dDd8d9Z(e!d#d:ed;gd&ej"dEd= 2. Received input=z of rank rr)shaper ValueErrorrr r) flat_inputinput_r( batch_sizer%r%r&_best_effort_input_batch_sizeBs     r-cs~|dur |St|rtddt|Ds>td|dtfddD}|sltd|d d S|jSdS) aEInfer 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. NcSsg|] }|jqSr%dtype).0elementr%r%r& nz&_infer_state_dtype..z*Unable to infer dtype from argument state=.c3s|]}|dkVqdS)rNr%)r0r"Zinferred_dtypesr%r& qr3z%_infer_state_dtype..zArgument state=zk has tensors of different inferred dtypes. Unable to infer a single representative dtype. Dtypes received: r)r is_nestedflattenr)allr/)Zexplicit_dtypestateZall_samer%r5r&_infer_state_dtype]s r;cCs$t|tjrtt|S|SdSN) isinstancerTensorras_shaperconstant_value)r(r%r%r&_maybe_tensor_shape_from_tensor|s rAcCs@tr dSt}|}t|du}t|}| o>| S)zHReturns True if a default caching device should be set, otherwise False.FN) rexecuting_eagerlyrget_default_graph_get_control_flow_contextr GetContainingWhileContextr in_while_loop_defun)graphctxtZin_v1_while_loopZin_v2_while_loopr%r%r& _should_caches  rIFc stt |kfddfdd fdd} |r\} } t | t| t| } t| } | | } n fdd} t|k| | } t| ttkrtd td td t| d | d t}| td }t|D]\}}|| qt|D]&\}}t |t j sB|| qBtj |d}tj |d}||fS)aCalculate 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`. cs\t|tjr|S|jjdkr |St|t||WdS1sN0YdSNr r=r TensorArrayr(rr colocate_withrwhere)output new_output) copy_condr%r&_copy_one_throughs    z$_rnn_step.._copy_one_throughcs8fddt|D}fddt|D}||S)Ncsg|]\}}||qSr%r%)r0 zero_outputrPrRr%r&r2sz9_rnn_step.._copy_some_through..csg|]\}}||qSr%r%)r0r: new_staterTr%r&r2s)zipflat_new_outputflat_new_state)rR flat_stateflat_zero_outputr%r&_copy_some_throughs  z%_rnn_step.._copy_some_throughcs^\}}t|t|t|t|tkfddfddS)z9Run RNN step. Pass through either no or some past state.csSr<r%r%rWr%r&r3z=_rnn_step.._maybe_copy_some_through..cs Sr<r%r%)r\rXrYr%r&r]r3)rassert_same_structurer8r cond)rPrU)r\ call_cellmin_sequence_lengthr:timerSrWr&_maybe_copy_some_throughs      z+_rnn_step.._maybe_copy_some_throughcsSr<r%r%)rZr[r%r&r] r3z_rnn_step..zYInternal error: state and output were not concatenated correctly. Received state length: z, output length: z . Expected contatenated length: r4N structure flat_sequence)rr8r^r r_lenr)rVrrr=rrLpack_sequence_as)rbsequence_lengthramax_sequence_lengthrSr:r` state_sizeskip_conditionalsrcrPrUZfinal_output_and_stateZ empty_updateZ final_output final_staterO flat_outputsubstateZ flat_substater%) rRr\r`rQrZr[rar:rbrSr& _rnn_stepsR=          rpc Cs|durtt|Stdd|D}ddtt|D}t|D]}tj|dj d}|D]}| || |qbt |}t ||dd}t |} t| |D]\} } | || | qqDd dt||D} | S) awReverse 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 Ncss|]}t|VqdSr<)rr8r0r+r%r%r&r6:r3z_reverse_seq..cSsg|]}gqSr%r%r0_r%r%r&r2<r3z _reverse_seq..r)rrcSsg|]\}}tj||dqS)rd)rrh)r0r+ flat_resultr%r%r&r2Ns)listreversedtuplerrgrVr unknown_shaperrassert_is_compatible_withrrstackreverse_sequenceunstackappend) Z input_seqlengthsZflat_input_seq flat_resultssequence input_shaper+Zs_joinedZ s_reversedresultrrtresultsr%r%r& _reverse_seq+s&       rNz`Please use `keras.layers.Bidirectional(keras.layers.RNN(cell))`, which is equivalent to this APIznn.bidirectional_dynamic_rnn)v1c  s<td|td|t| p"dtd.} t||||||| | d \} } Wdn1sf0Y| s~ddnddd d td L}fd d }t||}t||||||| |d \}}Wdn1s0YWdn1s 0Y|d}| |f}| |f}||fS)aCreates 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`. cell_fwcell_bwbidirectional_rnnfw) cellinputsri initial_stater/parallel_iterations swap_memory time_majorscopeNrrcSs.|durtj||||dStj||gdSdS)N)input seq_lengthsseq_axis batch_axisr)rr{reverse)r+rrrr%r%r&_reversesz+bidirectional_dynamic_rnn.._reversebwcs|dS)Nrrrr%)inprrriZ time_axisr%r& _map_reverses z/bidirectional_dynamic_rnn.._map_reverser)r assert_like_rnncellvsr dynamic_rnnr map_structure)rrrriinitial_state_fwinitial_state_bwr/rrrrfw_scope output_fwoutput_state_fwbw_scoperZinputs_reversetmpoutput_state_bw output_bwoutputsZ output_statesr%rr&bidirectional_dynamic_rnnUsXZ   (   H rzDPlease use `keras.layers.RNN(cell)`, which is equivalent to this APIznn.dynamic_rnnc  Cstd|t|pd} tr<| jdur<| ddt|} |sjdd| D} t dd | D} |ppd }|durt |t j }|jd vrtd |d |tj|dd}t| } |dur|} n<|stdt|dddur|jd| |d} n || |} dd} tsn|durnt| || ggtj|dd}Wdn1sd0Ytj|| d}t||| ||||d\}}|stt|}||fWdS1s0YdS)a1!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 rrnnNcSs|jSr<deviceopr%r%r&r]r3zdynamic_rnn..cSsg|]}t|qSr%rconvert_to_tensorrqr%r%r&r2r3zdynamic_rnn..css|]}t|VqdSr<)r'rqr%r%r&r6r3zdynamic_rnn.. NrzYArgument sequence_length must be a vector of length batch_size. Received sequence_length=z of shape: rinameCIf no initial_state is provided, argument `dtype` must be specifiedget_initial_staterr,r/cSs<t|}t|}ttt||d|j|d|gS)Nz Expected shape for Tensor %s is z but saw shape: ) rr(rzr Assertr reduce_allequalr)r"r(x_shapeZ packed_shaper%r%r&_assert_has_shapes   z&dynamic_rnn.._assert_has_shapeZ CheckSeqLenrd)rrrir/)r rrrrIcaching_deviceset_caching_devicerr8rwr castrint32rrr)ridentityr-getattrr zero_staterrBrcontrol_dependenciesrh_dynamic_rnn_looprr')rrrirr/rrrrvarscoper*r,r:rrrmr%r%r&rsp8      &  rc s&|t|tsJdjt }tj}t|d} | dt|t dd|D d dd\} t D]\} } | dd st d|| d| jdj} | jd j}| | krt d | d || d|krt d |d || dqfd dt fdd|D}tjj|ddurbt t n tjdtjdd}td}|Wdn1s0Yfddt rt fddt |D}t fddt |Dt ddt|Dn"t fddtt|D}| f dd}rvttd  n t j! fdd|||f||d\}}}rt dd|D}t||D]&\}}t"| g|d d!} |#| qn|}tjj|d}st$jd"d|}||fS)#a{Internal implementation of Dynamic RNN. Args: cell: An instance of RNNCell. inputs: A `Tensor` of shape [time, batch_size, input_size], or a nested tuple of such elements. initial_state: A `Tensor` of shape `[batch_size, state_size]`, or if `cell.state_size` is a tuple, then this should be a tuple of tensors having shapes `[batch_size, s] for s in cell.state_size`. parallel_iterations: Positive Python int. swap_memory: A Python boolean sequence_length: (optional) An `int32` `Tensor` of shape [batch_size]. dtype: (optional) Expected dtype of output. If not specified, inferred from initial_state. Returns: Tuple `(final_outputs, final_state)`. final_outputs: A `Tensor` of shape `[time, batch_size, cell.output_size]`. If `cell.output_size` is a (possibly nested) tuple of ints or `TensorShape` objects, then this returns a (possibly nested) tuple of Tensors matching the corresponding shapes. final_state: A `Tensor`, or possibly nested tuple of Tensors, matching in length and shapes to `initial_state`. Raises: ValueError: If the input depth cannot be inferred via shape inference from the inputs. ValueError: If time_step is not the same for all the elements in the inputs. ValueError: If batch_size is not the same for all the elements in the inputs. zparallel_iterations must be intrcss|]}|dVqdS)N)rwith_rank_at_leastrqr%r%r&r63sz$_dynamic_rnn_loop..NrzbInput size (depth of inputs) must be accessible via shape inference, but saw value None for input=r4rz]Time steps is not the same for all the elements in the input in a batch. Received time steps=z for input=zRBatch_size is not the same for all the elements in the input. Received batch size=cs"t|}tt|tSr<)_concatrzerosrzr;)size)r,r/r:r%r&_create_zero_arraysJs z._dynamic_rnn_loop.._create_zero_arraysc3s|]}|VqdSr<r%r0rO)rr%r&r6Osrdrb)r/rrcstj|||dS)N)r/r element_shapetensor_array_name)rrL)rrr/) base_name time_stepsr%r& _create_ta_s z%_dynamic_rnn_loop.._create_tac3s<|]4\}}d|tgt|tdVqdS)z output_%drr/N)rrr!rAr;)r0iout_size)rconst_batch_sizer/r:r%r&r6hs c3s0|](\}}d||jdd|jdVqdS)zinput_%drNr)r(r/)r0rZ flat_input_i)rr%r&r6ps  css|]\}}||VqdSr<)r|)r0tar+r%r%r&r6vsc3s$|]}ddtDVqdS)cSsg|]}dqS)rr%rrr%r%r&r2yr3z/_dynamic_rnn_loop...N)rnumpy)r0r)rr%r&r6ysc srBtfddDtD]\}}||ddq$ntfddDtjdfdd} durt  | d d \}}n |\}}t|}rtfd dt||D}n t||D]\}} | |<qވd||fS) a4Take 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). c3s|]}|VqdSr<)readr0rrbr%r&r6r3z8_dynamic_rnn_loop.._time_step..rNc3s|]}|VqdSr<)rrrr%r&r6r3rdcs Sr<r%r%)rinput_tr:r%r&r]r3z7_dynamic_rnn_loop.._time_step..T) rbrirarjrSr:r`rkrlc3s|]\}}||VqdSr<write)r0routrr%r&r6s)rwrVrrrhrpr8r) rb output_ta_tr:r+r(r`rOrUrr) r in_graph_modeinput_tarinputs_got_shaperjrarirkrS)rr:rbr& _time_step}s8     z%_dynamic_rnn_loop.._time_stepcs|kSr<r%)rbrs) loop_boundr%r&r]r3z#_dynamic_rnn_loop..)r_body loop_varsrmaximum_iterationsrcss|]}|VqdSr<)rzrr%r%r&r6r3TstaticcSstj|ddS)Nrr)rrz)r"r%r%r&r]r3)%r=intrkrr8 output_sizerr(r-rwas_list enumerateis_fully_definedr)rr rhr reduce_min reduce_maxconstantrrr name_scoperrBrVrrgminimummaximumr while_looprrmap_structure_up_to)rrrrrrir/r*flat_output_sizerZconst_time_stepsrr(Zgot_time_stepsZgot_batch_sizer[rbr output_tarrsZoutput_final_tarm final_outputsrOrr%)rrrr,rrr/rrrrrrjrarir:rkrrSr&rs)         $     2    rz nn.raw_rnnc srtdts$tdd|p*d}t|p6d }tr\|jdur\|ddt j d t j d }|ddd\}}} } } t |} | dur| nt j d t j d } d d | D}t|d d }|D]}|t|d qt|durt| d d t | j| }t |}d d |D}t j||d}| durxt | }dd |D}dd |D}n$j} t | }|d jgt|}fdd tt||D}t j| |d}fdd t||D}t j| |ddd}fdd}tj|||||||| g||d}|dd\}}}| durDd}|||fWdS1sd0YdS)a!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`. rz1Argument `loop_fn` must be a callable. Received: r4rrNcSs|jSr<rrr%r%r&r]r3zraw_rnn..rr.cSsg|] }|qSr%)rrqr%r%r&r2r3zraw_rnn..cSsg|]}t|qSr%rr0sr%r%r&r2r3rdcSs&g|]}|jr|jnt|qSr%)r(rrr0emitr%r%r&r2scSsg|] }|jqSr%r.rr%r%r&r2r3c s>g|]6\}\}}tj|dtgt|dd|dqS)Trz rnn_output_%d)r/ dynamic_sizerrr)rrLrrr!rA)r0rdtype_isize_i)rr%r&r2s cs"g|]\}}tt||qSr%)rrr)r0rr)r,r%r&r2scWstt|Sr<)r logical_notr)Z unused_timeelements_finishedrsr%r%r& conditionszraw_rnn..conditioncs||\}}t||tj|d}||||\} } } } } t|| t|| t|| | durx|n| }fdd}|| } ||| } tfdd|| }t| || || |fS)aInternal 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. rNcsfdd}t|||S)z.Copy some tensors through via array_ops.where.cs\t|tjr|S|jjdkr |St|t||WdS1sN0YdSrJrK)Zcur_iZcand_irr%r&copy_fns    zBraw_rnn..body.._copy_some_through..copy_fn)rr)current candidaterrr%r&r\s z1raw_rnn..body.._copy_some_throughcs ||Sr<r)rrrr%r&r]r3z'raw_rnn..body..)rr^rrr logical_or)rbr current_inputemit_tar: loop_stateZ next_outputZ cell_stateZ next_timeZ next_finished next_inputZ next_stateZ emit_outputZnext_loop_stater\)rloop_fn zero_emit)rrbr&rs.          zraw_rnn..body)rrr)r rcallable TypeErrorrrrIrrrrrrrr8rdimension_at_indexrydimension_valuerr(r^rkrhrr/rgrrVr r)rrrrrrrbrrrZemit_structureZinit_loop_stater*rrstatic_batch_sizeZ input_shape_ir:rZZflat_emit_structureZflat_emit_sizeZflat_emit_dtypesZ flat_emit_tar Zflat_zero_emitrrreturnedrmZfinal_loop_stater%)r,rrrrr&raw_rnns(               ;   rzQPlease use `keras.layers.RNN(cell, unroll=True)`, which is equivalent to this APIz nn.static_rnnc stdt|s$td||s0tdg}t|p>d}trd|j durd| dd|}t|r||d}qh| j d kr$| d } | jdt|} | D]n} | d } t| d| d d} t| jD],\} }t|durtd | d | d qqn| d dtrNtnt|d|durl|n>sztdtdddurjddn |durPtj|dd}| j dvrtdd|dfddj}t|}tfdd|D}tj||d}t |t!j"}t#|}t$|}t|D]j\}|dkrr|%fdd}|durt&||||||j'd\}n |\}|(|qX|fWdS1s0YdS)aCreates 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. r0Argument `inputs` must be a sequence. Received: $Argument `inputs` must not be empty.rNcSs|jSr<rrr%r%r&r]lr3zstatic_rnn..rrrzInput size (dimension z of input z=) must be accessible via shape inference, but saw value None.rrrrirrz6Argument `sequence_length` must be a vector of length z. Received sequence_length=r4csJt|}tt|t}tt|dd}|t||S)NTr) rrrrzr;rrrr)rrrOr()r,r/fixed_batch_sizer:r%r&_create_zero_outputs z'static_rnn.._create_zero_outputc3s|]}|VqdSr<r%)r0r)rr%r&r6szstatic_rnn..rdcs Sr<r%r%)rr+r:r%r&r]r3)rbrirarjrSr:r`rk))r rrr7rr)rrrIrrrrrrr8rrryrrrr(rrrrrrrwrhr rrrrrreuse_variablesrprkr})rrrr/rirrr first_inputr flat_inputsr* input_sizerrrrr[rSrarjrbr`rOr%)rr,rr/rr+r:r& static_rnnsE                       r"zSPlease use `keras.layers.RNN(cell, stateful=True)`, which is equivalent to this APIznn.static_state_saving_rnnc s|j}t|}t|}||kr8td|d|d|rt|} t|} t| t| krtd|dt| d|jdt| d tj|fdd | Dd } n |} t||| ||d \} } |rt| }t|}fd d t ||D}n || g}t |l| d }t|}dd |D}tj||d | d <|rhtj| dd |Dd } n t | } Wdn1s0Y| | fS)aRNN 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`. zYArgument `state_name` should be the same type as `cell.state_size`. Received: state_name=z, cell.state_size=r4zfNumber of elements in argument `state_name` and `cell.state_size` are mismatched. Received state_name=z with z elements and cell.state_size=z elements.csg|]}|qSr%)r:r state_saverr%r&r2r3z+static_state_saving_rnn..rd)rrircsg|]\}}||qSr%) save_state)r0rror#r%r&r2scSsg|]}t|qSr%rrrr%r%r&r2 scSsg|]}t|qSr%r'rr%r%r&r2)r3N)rkrr7r)r8rgrhr:r"rVr%rrrr)rrr$Z state_namerirrkZstate_is_tupleZstate_name_tupleZstate_name_flatZstate_size_flatrrr:rZr% last_outputZflat_last_outputr%r#r&static_state_saving_rnnsr$               *r)zmPlease use `keras.layers.Bidirectional(keras.layers.RNN(cell, unroll=True))`, which is equivalent to this APIznn.static_bidirectional_rnnc CsFtd|td|t|s0td||s.rd)r rrr7rr)rrr"rr8rwrVrh)rrrrrr/rirrrrrZreversed_inputsrrrZflat_output_fwZflat_output_bw flat_outputsrr%r%r&static_bidirectional_rnn0sH6    (  F   r+)F)NNNNNFFN)NNNNFFN)NN)NFN)NNNN)NN)NNNNN)*__doc__tensorflow.python.eagerrtensorflow.python.frameworkrrrrrtensorflow.python.opsrr r r r r rrrtensorflow.python.utilrrr tensorflow.python.util.tf_exportrrr'r-r;rArIrpr deprecatedadd_dispatch_supportrrrrr"r)r+r%r%r%r&s                   *     Y E 0 ]