# 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. # ============================================================================== # pylint: disable=protected-access """Recurrent layers and their base classes. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import warnings import numpy as np from tensorflow.python.distribute import distribution_strategy_context as ds_context from tensorflow.python.eager import context from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.keras import activations from tensorflow.python.keras import backend as K from tensorflow.python.keras import constraints from tensorflow.python.keras import initializers from tensorflow.python.keras import regularizers from tensorflow.python.keras.engine.base_layer import Layer from tensorflow.python.keras.engine.input_spec import InputSpec from tensorflow.python.keras.saving.saved_model import layer_serialization from tensorflow.python.keras.utils import control_flow_util from tensorflow.python.keras.utils import generic_utils from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import state_ops from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training.tracking import base as trackable from tensorflow.python.training.tracking import data_structures from tensorflow.python.util import nest from tensorflow.python.util.compat import collections_abc from tensorflow.python.util.tf_export import keras_export from tensorflow.tools.docs import doc_controls RECURRENT_DROPOUT_WARNING_MSG = ( 'RNN `implementation=2` is not supported when `recurrent_dropout` is set. ' 'Using `implementation=1`.') @keras_export('keras.layers.StackedRNNCells') class StackedRNNCells(Layer): """Wrapper allowing a stack of RNN cells to behave as a single cell. Used to implement efficient stacked RNNs. Arguments: cells: List of RNN cell instances. Examples: ```python batch_size = 3 sentence_max_length = 5 n_features = 2 new_shape = (batch_size, sentence_max_length, n_features) x = tf.constant(np.reshape(np.arange(30), new_shape), dtype = tf.float32) rnn_cells = [tf.keras.layers.LSTMCell(128) for _ in range(2)] stacked_lstm = tf.keras.layers.StackedRNNCells(rnn_cells) lstm_layer = tf.keras.layers.RNN(stacked_lstm) result = lstm_layer(x) ``` """ def __init__(self, cells, **kwargs): for cell in cells: if not 'call' in dir(cell): raise ValueError('All cells must have a `call` method. ' 'received cells:', cells) if not 'state_size' in dir(cell): raise ValueError('All cells must have a ' '`state_size` attribute. ' 'received cells:', cells) self.cells = cells # reverse_state_order determines whether the state size will be in a reverse # order of the cells' state. User might want to set this to True to keep the # existing behavior. This is only useful when use RNN(return_state=True) # since the state will be returned as the same order of state_size. self.reverse_state_order = kwargs.pop('reverse_state_order', False) if self.reverse_state_order: logging.warning('reverse_state_order=True in StackedRNNCells will soon ' 'be deprecated. Please update the code to work with the ' 'natural order of states if you rely on the RNN states, ' 'eg RNN(return_state=True).') super(StackedRNNCells, self).__init__(**kwargs) @property def state_size(self): return tuple(c.state_size for c in (self.cells[::-1] if self.reverse_state_order else self.cells)) @property def output_size(self): if getattr(self.cells[-1], 'output_size', None) is not None: return self.cells[-1].output_size elif _is_multiple_state(self.cells[-1].state_size): return self.cells[-1].state_size[0] else: return self.cells[-1].state_size def get_initial_state(self, inputs=None, batch_size=None, dtype=None): initial_states = [] for cell in self.cells[::-1] if self.reverse_state_order else self.cells: get_initial_state_fn = getattr(cell, 'get_initial_state', None) if get_initial_state_fn: initial_states.append(get_initial_state_fn( inputs=inputs, batch_size=batch_size, dtype=dtype)) else: initial_states.append(_generate_zero_filled_state_for_cell( cell, inputs, batch_size, dtype)) return tuple(initial_states) def call(self, inputs, states, constants=None, training=None, **kwargs): # Recover per-cell states. state_size = (self.state_size[::-1] if self.reverse_state_order else self.state_size) nested_states = nest.pack_sequence_as(state_size, nest.flatten(states)) # Call the cells in order and store the returned states. new_nested_states = [] for cell, states in zip(self.cells, nested_states): states = states if nest.is_nested(states) else [states] # TF cell does not wrap the state into list when there is only one state. is_tf_rnn_cell = getattr(cell, '_is_tf_rnn_cell', None) is not None states = states[0] if len(states) == 1 and is_tf_rnn_cell else states if generic_utils.has_arg(cell.call, 'training'): kwargs['training'] = training else: kwargs.pop('training', None) # Use the __call__ function for callable objects, eg layers, so that it # will have the proper name scopes for the ops, etc. cell_call_fn = cell.__call__ if callable(cell) else cell.call if generic_utils.has_arg(cell.call, 'constants'): inputs, states = cell_call_fn(inputs, states, constants=constants, **kwargs) else: inputs, states = cell_call_fn(inputs, states, **kwargs) new_nested_states.append(states) return inputs, nest.pack_sequence_as(state_size, nest.flatten(new_nested_states)) @tf_utils.shape_type_conversion def build(self, input_shape): if isinstance(input_shape, list): input_shape = input_shape[0] for cell in self.cells: if isinstance(cell, Layer) and not cell.built: with K.name_scope(cell.name): cell.build(input_shape) cell.built = True if getattr(cell, 'output_size', None) is not None: output_dim = cell.output_size elif _is_multiple_state(cell.state_size): output_dim = cell.state_size[0] else: output_dim = cell.state_size input_shape = tuple([input_shape[0]] + tensor_shape.TensorShape(output_dim).as_list()) self.built = True def get_config(self): cells = [] for cell in self.cells: cells.append(generic_utils.serialize_keras_object(cell)) config = {'cells': cells} base_config = super(StackedRNNCells, self).get_config() return dict(list(base_config.items()) + list(config.items())) @classmethod def from_config(cls, config, custom_objects=None): from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top cells = [] for cell_config in config.pop('cells'): cells.append( deserialize_layer(cell_config, custom_objects=custom_objects)) return cls(cells, **config) @keras_export('keras.layers.RNN') class RNN(Layer): """Base class for recurrent layers. See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn) for details about the usage of RNN API. Arguments: cell: A RNN cell instance or a list of RNN cell instances. A RNN cell is a class that has: - A `call(input_at_t, states_at_t)` method, returning `(output_at_t, states_at_t_plus_1)`. The call method of the cell can also take the optional argument `constants`, see section "Note on passing external constants" below. - A `state_size` attribute. This can be a single integer (single state) in which case it is the size of the recurrent state. This can also be a list/tuple of integers (one size per state). The `state_size` can also be TensorShape or tuple/list of TensorShape, to represent high dimension state. - A `output_size` attribute. This can be a single integer or a TensorShape, which represent the shape of the output. For backward compatible reason, if this attribute is not available for the cell, the value will be inferred by the first element of the `state_size`. - A `get_initial_state(inputs=None, batch_size=None, dtype=None)` method that creates a tensor meant to be fed to `call()` as the initial state, if the user didn't specify any initial state via other means. The returned initial state should have a shape of [batch_size, cell.state_size]. The cell might choose to create a tensor full of zeros, or full of other values based on the cell's implementation. `inputs` is the input tensor to the RNN layer, which should contain the batch size as its shape[0], and also dtype. Note that the shape[0] might be `None` during the graph construction. Either the `inputs` or the pair of `batch_size` and `dtype` are provided. `batch_size` is a scalar tensor that represents the batch size of the inputs. `dtype` is `tf.DType` that represents the dtype of the inputs. For backward compatible reason, if this method is not implemented by the cell, the RNN layer will create a zero filled tensor with the size of [batch_size, cell.state_size]. In the case that `cell` is a list of RNN cell instances, the cells will be stacked on top of each other in the RNN, resulting in an efficient stacked RNN. return_sequences: Boolean (default `False`). Whether to return the last output in the output sequence, or the full sequence. return_state: Boolean (default `False`). Whether to return the last state in addition to the output. go_backwards: Boolean (default `False`). If True, process the input sequence backwards and return the reversed sequence. stateful: Boolean (default `False`). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch. unroll: Boolean (default `False`). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences. time_major: The shape format of the `inputs` and `outputs` tensors. If True, the inputs and outputs will be in shape `(timesteps, batch, ...)`, whereas in the False case, it will be `(batch, timesteps, ...)`. 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. zero_output_for_mask: Boolean (default `False`). Whether the output should use zeros for the masked timesteps. Note that this field is only used when `return_sequences` is True and mask is provided. It can useful if you want to reuse the raw output sequence of the RNN without interference from the masked timesteps, eg, merging bidirectional RNNs. Call arguments: inputs: Input tensor. mask: Binary tensor of shape `[batch_size, timesteps]` indicating whether a given timestep should be masked. training: Python boolean indicating whether the layer should behave in training mode or in inference mode. This argument is passed to the cell when calling it. This is for use with cells that use dropout. initial_state: List of initial state tensors to be passed to the first call of the cell. constants: List of constant tensors to be passed to the cell at each timestep. Input shape: N-D tensor with shape `[batch_size, timesteps, ...]` or `[timesteps, batch_size, ...]` when time_major is True. Output shape: - If `return_state`: a list of tensors. The first tensor is the output. The remaining tensors are the last states, each with shape `[batch_size, state_size]`, where `state_size` could be a high dimension tensor shape. - If `return_sequences`: N-D tensor with shape `[batch_size, timesteps, output_size]`, where `output_size` could be a high dimension tensor shape, or `[timesteps, batch_size, output_size]` when `time_major` is True. - Else, N-D tensor with shape `[batch_size, output_size]`, where `output_size` could be a high dimension tensor shape. Masking: This layer supports masking for input data with a variable number of timesteps. To introduce masks to your data, use an [tf.keras.layers.Embedding] layer with the `mask_zero` parameter set to `True`. Note on using statefulness in RNNs: You can set RNN layers to be 'stateful', which means that the states computed for the samples in one batch will be reused as initial states for the samples in the next batch. This assumes a one-to-one mapping between samples in different successive batches. To enable statefulness: - Specify `stateful=True` in the layer constructor. - Specify a fixed batch size for your model, by passing If sequential model: `batch_input_shape=(...)` to the first layer in your model. Else for functional model with 1 or more Input layers: `batch_shape=(...)` to all the first layers in your model. This is the expected shape of your inputs *including the batch size*. It should be a tuple of integers, e.g. `(32, 10, 100)`. - Specify `shuffle=False` when calling `fit()`. To reset the states of your model, call `.reset_states()` on either a specific layer, or on your entire model. Note on specifying the initial state of RNNs: You can specify the initial state of RNN layers symbolically by calling them with the keyword argument `initial_state`. The value of `initial_state` should be a tensor or list of tensors representing the initial state of the RNN layer. You can specify the initial state of RNN layers numerically by calling `reset_states` with the keyword argument `states`. The value of `states` should be a numpy array or list of numpy arrays representing the initial state of the RNN layer. Note on passing external constants to RNNs: You can pass "external" constants to the cell using the `constants` keyword argument of `RNN.__call__` (as well as `RNN.call`) method. This requires that the `cell.call` method accepts the same keyword argument `constants`. Such constants can be used to condition the cell transformation on additional static inputs (not changing over time), a.k.a. an attention mechanism. Examples: ```python # First, let's define a RNN Cell, as a layer subclass. class MinimalRNNCell(keras.layers.Layer): def __init__(self, units, **kwargs): self.units = units self.state_size = units super(MinimalRNNCell, self).__init__(**kwargs) def build(self, input_shape): self.kernel = self.add_weight(shape=(input_shape[-1], self.units), initializer='uniform', name='kernel') self.recurrent_kernel = self.add_weight( shape=(self.units, self.units), initializer='uniform', name='recurrent_kernel') self.built = True def call(self, inputs, states): prev_output = states[0] h = K.dot(inputs, self.kernel) output = h + K.dot(prev_output, self.recurrent_kernel) return output, [output] # Let's use this cell in a RNN layer: cell = MinimalRNNCell(32) x = keras.Input((None, 5)) layer = RNN(cell) y = layer(x) # Here's how to use the cell to build a stacked RNN: cells = [MinimalRNNCell(32), MinimalRNNCell(64)] x = keras.Input((None, 5)) layer = RNN(cells) y = layer(x) ``` """ def __init__(self, cell, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False, time_major=False, **kwargs): if isinstance(cell, (list, tuple)): cell = StackedRNNCells(cell) if not 'call' in dir(cell): raise ValueError('`cell` should have a `call` method. ' 'The RNN was passed:', cell) if not 'state_size' in dir(cell): raise ValueError('The RNN cell should have ' 'an attribute `state_size` ' '(tuple of integers, ' 'one integer per RNN state).') # If True, the output for masked timestep will be zeros, whereas in the # False case, output from previous timestep is returned for masked timestep. self.zero_output_for_mask = kwargs.pop('zero_output_for_mask', False) if 'input_shape' not in kwargs and ( 'input_dim' in kwargs or 'input_length' in kwargs): input_shape = (kwargs.pop('input_length', None), kwargs.pop('input_dim', None)) kwargs['input_shape'] = input_shape super(RNN, self).__init__(**kwargs) self.cell = cell self.return_sequences = return_sequences self.return_state = return_state self.go_backwards = go_backwards self.stateful = stateful self.unroll = unroll self.time_major = time_major self.supports_masking = True # The input shape is unknown yet, it could have nested tensor inputs, and # the input spec will be the list of specs for nested inputs, the structure # of the input_spec will be the same as the input. self.input_spec = None self.state_spec = None self._states = None self.constants_spec = None self._num_constants = 0 if stateful: if ds_context.has_strategy(): raise ValueError('RNNs with stateful=True not yet supported with ' 'tf.distribute.Strategy.') @property def states(self): if self._states is None: state = nest.map_structure(lambda _: None, self.cell.state_size) return state if nest.is_nested(self.cell.state_size) else [state] return self._states @states.setter # Automatic tracking catches "self._states" which adds an extra weight and # breaks HDF5 checkpoints. @trackable.no_automatic_dependency_tracking def states(self, states): self._states = states def compute_output_shape(self, input_shape): if isinstance(input_shape, list): input_shape = input_shape[0] # Check whether the input shape contains any nested shapes. It could be # (tensor_shape(1, 2), tensor_shape(3, 4)) or (1, 2, 3) which is from numpy # inputs. try: input_shape = tensor_shape.TensorShape(input_shape) except (ValueError, TypeError): # A nested tensor input input_shape = nest.flatten(input_shape)[0] batch = input_shape[0] time_step = input_shape[1] if self.time_major: batch, time_step = time_step, batch if _is_multiple_state(self.cell.state_size): state_size = self.cell.state_size else: state_size = [self.cell.state_size] def _get_output_shape(flat_output_size): output_dim = tensor_shape.TensorShape(flat_output_size).as_list() if self.return_sequences: if self.time_major: output_shape = tensor_shape.TensorShape( [time_step, batch] + output_dim) else: output_shape = tensor_shape.TensorShape( [batch, time_step] + output_dim) else: output_shape = tensor_shape.TensorShape([batch] + output_dim) return output_shape if getattr(self.cell, 'output_size', None) is not None: # cell.output_size could be nested structure. output_shape = nest.flatten(nest.map_structure( _get_output_shape, self.cell.output_size)) output_shape = output_shape[0] if len(output_shape) == 1 else output_shape else: # Note that state_size[0] could be a tensor_shape or int. output_shape = _get_output_shape(state_size[0]) if self.return_state: def _get_state_shape(flat_state): state_shape = [batch] + tensor_shape.TensorShape(flat_state).as_list() return tensor_shape.TensorShape(state_shape) state_shape = nest.map_structure(_get_state_shape, state_size) return generic_utils.to_list(output_shape) + nest.flatten(state_shape) else: return output_shape def compute_mask(self, inputs, mask): # Time step masks must be the same for each input. # This is because the mask for an RNN is of size [batch, time_steps, 1], # and specifies which time steps should be skipped, and a time step # must be skipped for all inputs. # TODO(scottzhu): Should we accept multiple different masks? mask = nest.flatten(mask)[0] output_mask = mask if self.return_sequences else None if self.return_state: state_mask = [None for _ in self.states] return [output_mask] + state_mask else: return output_mask def build(self, input_shape): if isinstance(input_shape, list): input_shape = input_shape[0] # The input_shape here could be a nest structure. # do the tensor_shape to shapes here. The input could be single tensor, or a # nested structure of tensors. def get_input_spec(shape): """Convert input shape to InputSpec.""" if isinstance(shape, tensor_shape.TensorShape): input_spec_shape = shape.as_list() else: input_spec_shape = list(shape) batch_index, time_step_index = (1, 0) if self.time_major else (0, 1) if not self.stateful: input_spec_shape[batch_index] = None input_spec_shape[time_step_index] = None return InputSpec(shape=tuple(input_spec_shape)) def get_step_input_shape(shape): if isinstance(shape, tensor_shape.TensorShape): shape = tuple(shape.as_list()) # remove the timestep from the input_shape return shape[1:] if self.time_major else (shape[0],) + shape[2:] # Check whether the input shape contains any nested shapes. It could be # (tensor_shape(1, 2), tensor_shape(3, 4)) or (1, 2, 3) which is from numpy # inputs. try: input_shape = tensor_shape.TensorShape(input_shape) except (ValueError, TypeError): # A nested tensor input pass if not nest.is_nested(input_shape): # This indicates the there is only one input. if self.input_spec is not None: self.input_spec[0] = get_input_spec(input_shape) else: self.input_spec = [get_input_spec(input_shape)] step_input_shape = get_step_input_shape(input_shape) else: if self.input_spec is not None: self.input_spec[0] = nest.map_structure(get_input_spec, input_shape) else: self.input_spec = generic_utils.to_list( nest.map_structure(get_input_spec, input_shape)) step_input_shape = nest.map_structure(get_step_input_shape, input_shape) # allow cell (if layer) to build before we set or validate state_spec. if isinstance(self.cell, Layer) and not self.cell.built: with K.name_scope(self.cell.name): self.cell.build(step_input_shape) self.cell.built = True # set or validate state_spec if _is_multiple_state(self.cell.state_size): state_size = list(self.cell.state_size) else: state_size = [self.cell.state_size] if self.state_spec is not None: # initial_state was passed in call, check compatibility self._validate_state_spec(state_size, self.state_spec) else: self.state_spec = [ InputSpec(shape=[None] + tensor_shape.TensorShape(dim).as_list()) for dim in state_size ] if self.stateful: self.reset_states() self.built = True @staticmethod def _validate_state_spec(cell_state_sizes, init_state_specs): """Validate the state spec between the initial_state and the state_size. Args: cell_state_sizes: list, the `state_size` attribute from the cell. init_state_specs: list, the `state_spec` from the initial_state that is passed in `call()`. Raises: ValueError: When initial state spec is not compatible with the state size. """ validation_error = ValueError( 'An `initial_state` was passed that is not compatible with ' '`cell.state_size`. Received `state_spec`={}; ' 'however `cell.state_size` is ' '{}'.format(init_state_specs, cell_state_sizes)) flat_cell_state_sizes = nest.flatten(cell_state_sizes) flat_state_specs = nest.flatten(init_state_specs) if len(flat_cell_state_sizes) != len(flat_state_specs): raise validation_error for cell_state_spec, cell_state_size in zip(flat_state_specs, flat_cell_state_sizes): if not tensor_shape.TensorShape( # Ignore the first axis for init_state which is for batch cell_state_spec.shape[1:]).is_compatible_with( tensor_shape.TensorShape(cell_state_size)): raise validation_error @doc_controls.do_not_doc_inheritable def get_initial_state(self, inputs): get_initial_state_fn = getattr(self.cell, 'get_initial_state', None) if nest.is_nested(inputs): # The input are nested sequences. Use the first element in the seq to get # batch size and dtype. inputs = nest.flatten(inputs)[0] input_shape = array_ops.shape(inputs) batch_size = input_shape[1] if self.time_major else input_shape[0] dtype = inputs.dtype if get_initial_state_fn: init_state = get_initial_state_fn( inputs=None, batch_size=batch_size, dtype=dtype) else: init_state = _generate_zero_filled_state(batch_size, self.cell.state_size, dtype) # Keras RNN expect the states in a list, even if it's a single state tensor. if not nest.is_nested(init_state): init_state = [init_state] # Force the state to be a list in case it is a namedtuple eg LSTMStateTuple. return list(init_state) def __call__(self, inputs, initial_state=None, constants=None, **kwargs): inputs, initial_state, constants = _standardize_args(inputs, initial_state, constants, self._num_constants) if initial_state is None and constants is None: return super(RNN, self).__call__(inputs, **kwargs) # If any of `initial_state` or `constants` are specified and are Keras # tensors, then add them to the inputs and temporarily modify the # input_spec to include them. additional_inputs = [] additional_specs = [] if initial_state is not None: additional_inputs += initial_state self.state_spec = nest.map_structure( lambda s: InputSpec(shape=K.int_shape(s)), initial_state) additional_specs += self.state_spec if constants is not None: additional_inputs += constants self.constants_spec = [ InputSpec(shape=K.int_shape(constant)) for constant in constants ] self._num_constants = len(constants) additional_specs += self.constants_spec # additional_inputs can be empty if initial_state or constants are provided # but empty (e.g. the cell is stateless). flat_additional_inputs = nest.flatten(additional_inputs) is_keras_tensor = K.is_keras_tensor( flat_additional_inputs[0]) if flat_additional_inputs else True for tensor in flat_additional_inputs: if K.is_keras_tensor(tensor) != is_keras_tensor: raise ValueError('The initial state or constants of an RNN' ' layer cannot be specified with a mix of' ' Keras tensors and non-Keras tensors' ' (a "Keras tensor" is a tensor that was' ' returned by a Keras layer, or by `Input`)') if is_keras_tensor: # Compute the full input spec, including state and constants full_input = [inputs] + additional_inputs if self.built: # Keep the input_spec since it has been populated in build() method. full_input_spec = self.input_spec + additional_specs else: # The original input_spec is None since there could be a nested tensor # input. Update the input_spec to match the inputs. full_input_spec = generic_utils.to_list( nest.map_structure(lambda _: None, inputs)) + additional_specs # Perform the call with temporarily replaced input_spec self.input_spec = full_input_spec output = super(RNN, self).__call__(full_input, **kwargs) # Remove the additional_specs from input spec and keep the rest. It is # important to keep since the input spec was populated by build(), and # will be reused in the stateful=True. self.input_spec = self.input_spec[:-len(additional_specs)] return output else: if initial_state is not None: kwargs['initial_state'] = initial_state if constants is not None: kwargs['constants'] = constants return super(RNN, self).__call__(inputs, **kwargs) def call(self, inputs, mask=None, training=None, initial_state=None, constants=None): # The input should be dense, padded with zeros. If a ragged input is fed # into the layer, it is padded and the row lengths are used for masking. inputs, row_lengths = K.convert_inputs_if_ragged(inputs) is_ragged_input = (row_lengths is not None) self._validate_args_if_ragged(is_ragged_input, mask) inputs, initial_state, constants = self._process_inputs( inputs, initial_state, constants) self._maybe_reset_cell_dropout_mask(self.cell) if isinstance(self.cell, StackedRNNCells): for cell in self.cell.cells: self._maybe_reset_cell_dropout_mask(cell) if mask is not None: # Time step masks must be the same for each input. # TODO(scottzhu): Should we accept multiple different masks? mask = nest.flatten(mask)[0] if nest.is_nested(inputs): # In the case of nested input, use the first element for shape check. input_shape = K.int_shape(nest.flatten(inputs)[0]) else: input_shape = K.int_shape(inputs) timesteps = input_shape[0] if self.time_major else input_shape[1] if self.unroll and timesteps is None: raise ValueError('Cannot unroll a RNN if the ' 'time dimension is undefined. \n' '- If using a Sequential model, ' 'specify the time dimension by passing ' 'an `input_shape` or `batch_input_shape` ' 'argument to your first layer. If your ' 'first layer is an Embedding, you can ' 'also use the `input_length` argument.\n' '- If using the functional API, specify ' 'the time dimension by passing a `shape` ' 'or `batch_shape` argument to your Input layer.') kwargs = {} if generic_utils.has_arg(self.cell.call, 'training'): kwargs['training'] = training # TF RNN cells expect single tensor as state instead of list wrapped tensor. is_tf_rnn_cell = getattr(self.cell, '_is_tf_rnn_cell', None) is not None # Use the __call__ function for callable objects, eg layers, so that it # will have the proper name scopes for the ops, etc. cell_call_fn = self.cell.__call__ if callable(self.cell) else self.cell.call if constants: if not generic_utils.has_arg(self.cell.call, 'constants'): raise ValueError('RNN cell does not support constants') def step(inputs, states): constants = states[-self._num_constants:] # pylint: disable=invalid-unary-operand-type states = states[:-self._num_constants] # pylint: disable=invalid-unary-operand-type states = states[0] if len(states) == 1 and is_tf_rnn_cell else states output, new_states = cell_call_fn( inputs, states, constants=constants, **kwargs) if not nest.is_nested(new_states): new_states = [new_states] return output, new_states else: def step(inputs, states): states = states[0] if len(states) == 1 and is_tf_rnn_cell else states output, new_states = cell_call_fn(inputs, states, **kwargs) if not nest.is_nested(new_states): new_states = [new_states] return output, new_states last_output, outputs, states = K.rnn( step, inputs, initial_state, constants=constants, go_backwards=self.go_backwards, mask=mask, unroll=self.unroll, input_length=row_lengths if row_lengths is not None else timesteps, time_major=self.time_major, zero_output_for_mask=self.zero_output_for_mask) if self.stateful: updates = [ state_ops.assign(self_state, state) for self_state, state in zip( nest.flatten(self.states), nest.flatten(states)) ] self.add_update(updates) if self.return_sequences: output = K.maybe_convert_to_ragged(is_ragged_input, outputs, row_lengths) else: output = last_output if self.return_state: if not isinstance(states, (list, tuple)): states = [states] else: states = list(states) return generic_utils.to_list(output) + states else: return output def _process_inputs(self, inputs, initial_state, constants): # input shape: `(samples, time (padded with zeros), input_dim)` # note that the .build() method of subclasses MUST define # self.input_spec and self.state_spec with complete input shapes. if (isinstance(inputs, collections_abc.Sequence) and not isinstance(inputs, tuple)): # get initial_state from full input spec # as they could be copied to multiple GPU. if not self._num_constants: initial_state = inputs[1:] else: initial_state = inputs[1:-self._num_constants] constants = inputs[-self._num_constants:] if len(initial_state) == 0: initial_state = None inputs = inputs[0] if self.stateful: if initial_state is not None: # When layer is stateful and initial_state is provided, check if the # recorded state is same as the default value (zeros). Use the recorded # state if it is not same as the default. non_zero_count = math_ops.add_n([math_ops.count_nonzero_v2(s) for s in nest.flatten(self.states)]) # Set strict = True to keep the original structure of the state. initial_state = control_flow_ops.cond(non_zero_count > 0, true_fn=lambda: self.states, false_fn=lambda: initial_state, strict=True) else: initial_state = self.states elif initial_state is None: initial_state = self.get_initial_state(inputs) if len(initial_state) != len(self.states): raise ValueError('Layer has ' + str(len(self.states)) + ' states but was passed ' + str(len(initial_state)) + ' initial states.') return inputs, initial_state, constants def _validate_args_if_ragged(self, is_ragged_input, mask): if not is_ragged_input: return if mask is not None: raise ValueError('The mask that was passed in was ' + str(mask) + ' and cannot be applied to RaggedTensor inputs. Please ' 'make sure that there is no mask passed in by upstream ' 'layers.') if self.unroll: raise ValueError('The input received contains RaggedTensors and does ' 'not support unrolling. Disable unrolling by passing ' '`unroll=False` in the RNN Layer constructor.') def _maybe_reset_cell_dropout_mask(self, cell): if isinstance(cell, DropoutRNNCellMixin): cell.reset_dropout_mask() cell.reset_recurrent_dropout_mask() def reset_states(self, states=None): """Reset the recorded states for the stateful RNN layer. Can only be used when RNN layer is constructed with `stateful` = `True`. Args: states: Numpy arrays that contains the value for the initial state, which will be feed to cell at the first time step. When the value is None, zero filled numpy array will be created based on the cell state size. Raises: AttributeError: When the RNN layer is not stateful. ValueError: When the batch size of the RNN layer is unknown. ValueError: When the input numpy array is not compatible with the RNN layer state, either size wise or dtype wise. """ if not self.stateful: raise AttributeError('Layer must be stateful.') spec_shape = None if self.input_spec is not None: spec_shape = nest.flatten(self.input_spec[0])[0].shape if spec_shape is None: # It is possible to have spec shape to be None, eg when construct a RNN # with a custom cell, or standard RNN layers (LSTM/GRU) which we only know # it has 3 dim input, but not its full shape spec before build(). batch_size = None else: batch_size = spec_shape[1] if self.time_major else spec_shape[0] if not batch_size: raise ValueError('If a RNN is stateful, it needs to know ' 'its batch size. Specify the batch size ' 'of your input tensors: \n' '- If using a Sequential model, ' 'specify the batch size by passing ' 'a `batch_input_shape` ' 'argument to your first layer.\n' '- If using the functional API, specify ' 'the batch size by passing a ' '`batch_shape` argument to your Input layer.') # initialize state if None if nest.flatten(self.states)[0] is None: if getattr(self.cell, 'get_initial_state', None): flat_init_state_values = nest.flatten(self.cell.get_initial_state( inputs=None, batch_size=batch_size, dtype=self.dtype or K.floatx())) else: flat_init_state_values = nest.flatten(_generate_zero_filled_state( batch_size, self.cell.state_size, self.dtype or K.floatx())) flat_states_variables = nest.map_structure( K.variable, flat_init_state_values) self.states = nest.pack_sequence_as(self.cell.state_size, flat_states_variables) if not nest.is_nested(self.states): self.states = [self.states] elif states is None: for state, size in zip(nest.flatten(self.states), nest.flatten(self.cell.state_size)): K.set_value(state, np.zeros([batch_size] + tensor_shape.TensorShape(size).as_list())) else: flat_states = nest.flatten(self.states) flat_input_states = nest.flatten(states) if len(flat_input_states) != len(flat_states): raise ValueError('Layer ' + self.name + ' expects ' + str(len(flat_states)) + ' states, ' 'but it received ' + str(len(flat_input_states)) + ' state values. Input received: ' + str(states)) set_value_tuples = [] for i, (value, state) in enumerate(zip(flat_input_states, flat_states)): if value.shape != state.shape: raise ValueError( 'State ' + str(i) + ' is incompatible with layer ' + self.name + ': expected shape=' + str( (batch_size, state)) + ', found shape=' + str(value.shape)) set_value_tuples.append((state, value)) K.batch_set_value(set_value_tuples) def get_config(self): config = { 'return_sequences': self.return_sequences, 'return_state': self.return_state, 'go_backwards': self.go_backwards, 'stateful': self.stateful, 'unroll': self.unroll, 'time_major': self.time_major } if self._num_constants: config['num_constants'] = self._num_constants if self.zero_output_for_mask: config['zero_output_for_mask'] = self.zero_output_for_mask config['cell'] = generic_utils.serialize_keras_object(self.cell) base_config = super(RNN, self).get_config() return dict(list(base_config.items()) + list(config.items())) @classmethod def from_config(cls, config, custom_objects=None): from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top cell = deserialize_layer(config.pop('cell'), custom_objects=custom_objects) num_constants = config.pop('num_constants', 0) layer = cls(cell, **config) layer._num_constants = num_constants return layer @property def _trackable_saved_model_saver(self): return layer_serialization.RNNSavedModelSaver(self) @keras_export('keras.layers.AbstractRNNCell') class AbstractRNNCell(Layer): """Abstract object representing an RNN cell. See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn) for details about the usage of RNN API. This is the base class for implementing RNN cells with custom behavior. Every `RNNCell` must have the properties below and implement `call` with the signature `(output, next_state) = call(input, state)`. Examples: ```python class MinimalRNNCell(AbstractRNNCell): def __init__(self, units, **kwargs): self.units = units super(MinimalRNNCell, self).__init__(**kwargs) @property def state_size(self): return self.units def build(self, input_shape): self.kernel = self.add_weight(shape=(input_shape[-1], self.units), initializer='uniform', name='kernel') self.recurrent_kernel = self.add_weight( shape=(self.units, self.units), initializer='uniform', name='recurrent_kernel') self.built = True def call(self, inputs, states): prev_output = states[0] h = K.dot(inputs, self.kernel) output = h + K.dot(prev_output, self.recurrent_kernel) return output, output ``` This definition of cell differs from the definition used in the literature. In the literature, 'cell' refers to an object with a single scalar output. This definition refers to a horizontal array of such units. An RNN cell, in the most abstract setting, is anything that has a state and performs some operation that takes a matrix of inputs. This operation results in an output matrix with `self.output_size` columns. If `self.state_size` is an integer, this operation also results in a new state matrix with `self.state_size` columns. If `self.state_size` is a (possibly nested tuple of) TensorShape object(s), then it should return a matching structure of Tensors having shape `[batch_size].concatenate(s)` for each `s` in `self.batch_size`. """ def call(self, inputs, states): """The function that contains the logic for one RNN step calculation. Args: inputs: the input tensor, which is a slide from the overall RNN input by the time dimension (usually the second dimension). states: the state tensor from previous step, which has the same shape as `(batch, state_size)`. In the case of timestep 0, it will be the initial state user specified, or zero filled tensor otherwise. Returns: A tuple of two tensors: 1. output tensor for the current timestep, with size `output_size`. 2. state tensor for next step, which has the shape of `state_size`. """ raise NotImplementedError('Abstract method') @property def state_size(self): """size(s) of state(s) used by this cell. It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes. """ raise NotImplementedError('Abstract method') @property def output_size(self): """Integer or TensorShape: size of outputs produced by this cell.""" raise NotImplementedError('Abstract method') def get_initial_state(self, inputs=None, batch_size=None, dtype=None): return _generate_zero_filled_state_for_cell(self, inputs, batch_size, dtype) @doc_controls.do_not_generate_docs class DropoutRNNCellMixin(object): """Object that hold dropout related fields for RNN Cell. This class is not a standalone RNN cell. It suppose to be used with a RNN cell by multiple inheritance. Any cell that mix with class should have following fields: dropout: a float number within range [0, 1). The ratio that the input tensor need to dropout. recurrent_dropout: a float number within range [0, 1). The ratio that the recurrent state weights need to dropout. This object will create and cache created dropout masks, and reuse them for the incoming data, so that the same mask is used for every batch input. """ def __init__(self, *args, **kwargs): self._create_non_trackable_mask_cache() super(DropoutRNNCellMixin, self).__init__(*args, **kwargs) @trackable.no_automatic_dependency_tracking def _create_non_trackable_mask_cache(self): """Create the cache for dropout and recurrent dropout mask. Note that the following two masks will be used in "graph function" mode, e.g. these masks are symbolic tensors. In eager mode, the `eager_*_mask` tensors will be generated differently than in the "graph function" case, and they will be cached. Also note that in graph mode, we still cache those masks only because the RNN could be created with `unroll=True`. In that case, the `cell.call()` function will be invoked multiple times, and we want to ensure same mask is used every time. Also the caches are created without tracking. Since they are not picklable by python when deepcopy, we don't want `layer._obj_reference_counts_dict` to track it by default. """ self._dropout_mask_cache = K.ContextValueCache(self._create_dropout_mask) self._recurrent_dropout_mask_cache = K.ContextValueCache( self._create_recurrent_dropout_mask) def reset_dropout_mask(self): """Reset the cached dropout masks if any. This is important for the RNN layer to invoke this in it `call()` method so that the cached mask is cleared before calling the `cell.call()`. The mask should be cached across the timestep within the same batch, but shouldn't be cached between batches. Otherwise it will introduce unreasonable bias against certain index of data within the batch. """ self._dropout_mask_cache.clear() def reset_recurrent_dropout_mask(self): """Reset the cached recurrent dropout masks if any. This is important for the RNN layer to invoke this in it call() method so that the cached mask is cleared before calling the cell.call(). The mask should be cached across the timestep within the same batch, but shouldn't be cached between batches. Otherwise it will introduce unreasonable bias against certain index of data within the batch. """ self._recurrent_dropout_mask_cache.clear() def _create_dropout_mask(self, inputs, training, count=1): return _generate_dropout_mask( array_ops.ones_like(inputs), self.dropout, training=training, count=count) def _create_recurrent_dropout_mask(self, inputs, training, count=1): return _generate_dropout_mask( array_ops.ones_like(inputs), self.recurrent_dropout, training=training, count=count) def get_dropout_mask_for_cell(self, inputs, training, count=1): """Get the dropout mask for RNN cell's input. It will create mask based on context if there isn't any existing cached mask. If a new mask is generated, it will update the cache in the cell. Args: inputs: The input tensor whose shape will be used to generate dropout mask. training: Boolean tensor, whether its in training mode, dropout will be ignored in non-training mode. count: Int, how many dropout mask will be generated. It is useful for cell that has internal weights fused together. Returns: List of mask tensor, generated or cached mask based on context. """ if self.dropout == 0: return None init_kwargs = dict(inputs=inputs, training=training, count=count) return self._dropout_mask_cache.setdefault(kwargs=init_kwargs) def get_recurrent_dropout_mask_for_cell(self, inputs, training, count=1): """Get the recurrent dropout mask for RNN cell. It will create mask based on context if there isn't any existing cached mask. If a new mask is generated, it will update the cache in the cell. Args: inputs: The input tensor whose shape will be used to generate dropout mask. training: Boolean tensor, whether its in training mode, dropout will be ignored in non-training mode. count: Int, how many dropout mask will be generated. It is useful for cell that has internal weights fused together. Returns: List of mask tensor, generated or cached mask based on context. """ if self.recurrent_dropout == 0: return None init_kwargs = dict(inputs=inputs, training=training, count=count) return self._recurrent_dropout_mask_cache.setdefault(kwargs=init_kwargs) def __getstate__(self): # Used for deepcopy. The caching can't be pickled by python, since it will # contain tensor and graph. state = super(DropoutRNNCellMixin, self).__getstate__() state.pop('_dropout_mask_cache', None) state.pop('_recurrent_dropout_mask_cache', None) return state def __setstate__(self, state): state['_dropout_mask_cache'] = K.ContextValueCache( self._create_dropout_mask) state['_recurrent_dropout_mask_cache'] = K.ContextValueCache( self._create_recurrent_dropout_mask) super(DropoutRNNCellMixin, self).__setstate__(state) @keras_export('keras.layers.SimpleRNNCell') class SimpleRNNCell(DropoutRNNCellMixin, Layer): """Cell class for SimpleRNN. See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn) for details about the usage of RNN API. This class processes one step within the whole time sequence input, whereas `tf.keras.layer.SimpleRNN` processes the whole sequence. Arguments: units: Positive integer, dimensionality of the output space. activation: Activation function to use. Default: hyperbolic tangent (`tanh`). If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, (default `True`), whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. Default: `glorot_uniform`. recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. Default: `orthogonal`. bias_initializer: Initializer for the bias vector. Default: `zeros`. kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. Default: `None`. recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix. Default: `None`. bias_regularizer: Regularizer function applied to the bias vector. Default: `None`. kernel_constraint: Constraint function applied to the `kernel` weights matrix. Default: `None`. recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix. Default: `None`. bias_constraint: Constraint function applied to the bias vector. Default: `None`. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. Default: 0. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Default: 0. Call arguments: inputs: A 2D tensor, with shape of `[batch, feature]`. states: A 2D tensor with shape of `[batch, units]`, which is the state from the previous time step. For timestep 0, the initial state provided by user will be feed to cell. training: Python boolean indicating whether the layer should behave in training mode or in inference mode. Only relevant when `dropout` or `recurrent_dropout` is used. Examples: ```python inputs = np.random.random([32, 10, 8]).astype(np.float32) rnn = tf.keras.layers.RNN(tf.keras.layers.SimpleRNNCell(4)) output = rnn(inputs) # The output has shape `[32, 4]`. rnn = tf.keras.layers.RNN( tf.keras.layers.SimpleRNNCell(4), return_sequences=True, return_state=True) # whole_sequence_output has shape `[32, 10, 4]`. # final_state has shape `[32, 4]`. whole_sequence_output, final_state = rnn(inputs) ``` """ def __init__(self, units, activation='tanh', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., **kwargs): # By default use cached variable under v2 mode, see b/143699808. if ops.executing_eagerly_outside_functions(): self._enable_caching_device = kwargs.pop('enable_caching_device', True) else: self._enable_caching_device = kwargs.pop('enable_caching_device', False) super(SimpleRNNCell, self).__init__(**kwargs) self.units = units self.activation = activations.get(activation) self.use_bias = use_bias self.kernel_initializer = initializers.get(kernel_initializer) self.recurrent_initializer = initializers.get(recurrent_initializer) self.bias_initializer = initializers.get(bias_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.recurrent_regularizer = regularizers.get(recurrent_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.recurrent_constraint = constraints.get(recurrent_constraint) self.bias_constraint = constraints.get(bias_constraint) self.dropout = min(1., max(0., dropout)) self.recurrent_dropout = min(1., max(0., recurrent_dropout)) self.state_size = self.units self.output_size = self.units @tf_utils.shape_type_conversion def build(self, input_shape): default_caching_device = _caching_device(self) self.kernel = self.add_weight( shape=(input_shape[-1], self.units), name='kernel', initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint, caching_device=default_caching_device) self.recurrent_kernel = self.add_weight( shape=(self.units, self.units), name='recurrent_kernel', initializer=self.recurrent_initializer, regularizer=self.recurrent_regularizer, constraint=self.recurrent_constraint, caching_device=default_caching_device) if self.use_bias: self.bias = self.add_weight( shape=(self.units,), name='bias', initializer=self.bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint, caching_device=default_caching_device) else: self.bias = None self.built = True def call(self, inputs, states, training=None): prev_output = states[0] if nest.is_nested(states) else states dp_mask = self.get_dropout_mask_for_cell(inputs, training) rec_dp_mask = self.get_recurrent_dropout_mask_for_cell( prev_output, training) if dp_mask is not None: h = K.dot(inputs * dp_mask, self.kernel) else: h = K.dot(inputs, self.kernel) if self.bias is not None: h = K.bias_add(h, self.bias) if rec_dp_mask is not None: prev_output = prev_output * rec_dp_mask output = h + K.dot(prev_output, self.recurrent_kernel) if self.activation is not None: output = self.activation(output) new_state = [output] if nest.is_nested(states) else output return output, new_state def get_initial_state(self, inputs=None, batch_size=None, dtype=None): return _generate_zero_filled_state_for_cell(self, inputs, batch_size, dtype) def get_config(self): config = { 'units': self.units, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout } config.update(_config_for_enable_caching_device(self)) base_config = super(SimpleRNNCell, self).get_config() return dict(list(base_config.items()) + list(config.items())) @keras_export('keras.layers.SimpleRNN') class SimpleRNN(RNN): """Fully-connected RNN where the output is to be fed back to input. See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn) for details about the usage of RNN API. Arguments: units: Positive integer, dimensionality of the output space. activation: Activation function to use. Default: hyperbolic tangent (`tanh`). If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, (default `True`), whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. Default: `glorot_uniform`. recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. Default: `orthogonal`. bias_initializer: Initializer for the bias vector. Default: `zeros`. kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. Default: `None`. recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix. Default: `None`. bias_regularizer: Regularizer function applied to the bias vector. Default: `None`. activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). Default: `None`. kernel_constraint: Constraint function applied to the `kernel` weights matrix. Default: `None`. recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix. Default: `None`. bias_constraint: Constraint function applied to the bias vector. Default: `None`. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. Default: 0. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Default: 0. return_sequences: Boolean. Whether to return the last output in the output sequence, or the full sequence. Default: `False`. return_state: Boolean. Whether to return the last state in addition to the output. Default: `False` go_backwards: Boolean (default False). If True, process the input sequence backwards and return the reversed sequence. stateful: Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch. unroll: Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences. Call arguments: inputs: A 3D tensor, with shape `[batch, timesteps, feature]`. mask: Binary tensor of shape `[batch, timesteps]` indicating whether a given timestep should be masked. training: Python boolean indicating whether the layer should behave in training mode or in inference mode. This argument is passed to the cell when calling it. This is only relevant if `dropout` or `recurrent_dropout` is used. initial_state: List of initial state tensors to be passed to the first call of the cell. Examples: ```python inputs = np.random.random([32, 10, 8]).astype(np.float32) simple_rnn = tf.keras.layers.SimpleRNN(4) output = simple_rnn(inputs) # The output has shape `[32, 4]`. simple_rnn = tf.keras.layers.SimpleRNN( 4, return_sequences=True, return_state=True) # whole_sequence_output has shape `[32, 10, 4]`. # final_state has shape `[32, 4]`. whole_sequence_output, final_state = simple_rnn(inputs) ``` """ def __init__(self, units, activation='tanh', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False, **kwargs): if 'implementation' in kwargs: kwargs.pop('implementation') logging.warning('The `implementation` argument ' 'in `SimpleRNN` has been deprecated. ' 'Please remove it from your layer call.') if 'enable_caching_device' in kwargs: cell_kwargs = {'enable_caching_device': kwargs.pop('enable_caching_device')} else: cell_kwargs = {} cell = SimpleRNNCell( units, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, recurrent_initializer=recurrent_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, recurrent_regularizer=recurrent_regularizer, bias_regularizer=bias_regularizer, kernel_constraint=kernel_constraint, recurrent_constraint=recurrent_constraint, bias_constraint=bias_constraint, dropout=dropout, recurrent_dropout=recurrent_dropout, dtype=kwargs.get('dtype'), trainable=kwargs.get('trainable', True), **cell_kwargs) super(SimpleRNN, self).__init__( cell, return_sequences=return_sequences, return_state=return_state, go_backwards=go_backwards, stateful=stateful, unroll=unroll, **kwargs) self.activity_regularizer = regularizers.get(activity_regularizer) self.input_spec = [InputSpec(ndim=3)] def call(self, inputs, mask=None, training=None, initial_state=None): self._maybe_reset_cell_dropout_mask(self.cell) return super(SimpleRNN, self).call( inputs, mask=mask, training=training, initial_state=initial_state) @property def units(self): return self.cell.units @property def activation(self): return self.cell.activation @property def use_bias(self): return self.cell.use_bias @property def kernel_initializer(self): return self.cell.kernel_initializer @property def recurrent_initializer(self): return self.cell.recurrent_initializer @property def bias_initializer(self): return self.cell.bias_initializer @property def kernel_regularizer(self): return self.cell.kernel_regularizer @property def recurrent_regularizer(self): return self.cell.recurrent_regularizer @property def bias_regularizer(self): return self.cell.bias_regularizer @property def kernel_constraint(self): return self.cell.kernel_constraint @property def recurrent_constraint(self): return self.cell.recurrent_constraint @property def bias_constraint(self): return self.cell.bias_constraint @property def dropout(self): return self.cell.dropout @property def recurrent_dropout(self): return self.cell.recurrent_dropout def get_config(self): config = { 'units': self.units, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout } base_config = super(SimpleRNN, self).get_config() config.update(_config_for_enable_caching_device(self.cell)) del base_config['cell'] return dict(list(base_config.items()) + list(config.items())) @classmethod def from_config(cls, config): if 'implementation' in config: config.pop('implementation') return cls(**config) @keras_export(v1=['keras.layers.GRUCell']) class GRUCell(DropoutRNNCellMixin, Layer): """Cell class for the GRU layer. Arguments: units: Positive integer, dimensionality of the output space. activation: Activation function to use. Default: hyperbolic tangent (`tanh`). If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use for the recurrent step. Default: hard sigmoid (`hard_sigmoid`). If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. bias_initializer: Initializer for the bias vector. kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix. bias_regularizer: Regularizer function applied to the bias vector. kernel_constraint: Constraint function applied to the `kernel` weights matrix. recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix. bias_constraint: Constraint function applied to the bias vector. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. reset_after: GRU convention (whether to apply reset gate after or before matrix multiplication). False = "before" (default), True = "after" (CuDNN compatible). Call arguments: inputs: A 2D tensor. states: List of state tensors corresponding to the previous timestep. training: Python boolean indicating whether the layer should behave in training mode or in inference mode. Only relevant when `dropout` or `recurrent_dropout` is used. """ def __init__(self, units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., reset_after=False, **kwargs): # By default use cached variable under v2 mode, see b/143699808. if ops.executing_eagerly_outside_functions(): self._enable_caching_device = kwargs.pop('enable_caching_device', True) else: self._enable_caching_device = kwargs.pop('enable_caching_device', False) super(GRUCell, self).__init__(**kwargs) self.units = units self.activation = activations.get(activation) self.recurrent_activation = activations.get(recurrent_activation) self.use_bias = use_bias self.kernel_initializer = initializers.get(kernel_initializer) self.recurrent_initializer = initializers.get(recurrent_initializer) self.bias_initializer = initializers.get(bias_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.recurrent_regularizer = regularizers.get(recurrent_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.recurrent_constraint = constraints.get(recurrent_constraint) self.bias_constraint = constraints.get(bias_constraint) self.dropout = min(1., max(0., dropout)) self.recurrent_dropout = min(1., max(0., recurrent_dropout)) implementation = kwargs.pop('implementation', 1) if self.recurrent_dropout != 0 and implementation != 1: logging.debug(RECURRENT_DROPOUT_WARNING_MSG) self.implementation = 1 else: self.implementation = implementation self.reset_after = reset_after self.state_size = self.units self.output_size = self.units @tf_utils.shape_type_conversion def build(self, input_shape): input_dim = input_shape[-1] default_caching_device = _caching_device(self) self.kernel = self.add_weight( shape=(input_dim, self.units * 3), name='kernel', initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint, caching_device=default_caching_device) self.recurrent_kernel = self.add_weight( shape=(self.units, self.units * 3), name='recurrent_kernel', initializer=self.recurrent_initializer, regularizer=self.recurrent_regularizer, constraint=self.recurrent_constraint, caching_device=default_caching_device) if self.use_bias: if not self.reset_after: bias_shape = (3 * self.units,) else: # separate biases for input and recurrent kernels # Note: the shape is intentionally different from CuDNNGRU biases # `(2 * 3 * self.units,)`, so that we can distinguish the classes # when loading and converting saved weights. bias_shape = (2, 3 * self.units) self.bias = self.add_weight(shape=bias_shape, name='bias', initializer=self.bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint, caching_device=default_caching_device) else: self.bias = None self.built = True def call(self, inputs, states, training=None): h_tm1 = states[0] if nest.is_nested(states) else states # previous memory dp_mask = self.get_dropout_mask_for_cell(inputs, training, count=3) rec_dp_mask = self.get_recurrent_dropout_mask_for_cell( h_tm1, training, count=3) if self.use_bias: if not self.reset_after: input_bias, recurrent_bias = self.bias, None else: input_bias, recurrent_bias = array_ops.unstack(self.bias) if self.implementation == 1: if 0. < self.dropout < 1.: inputs_z = inputs * dp_mask[0] inputs_r = inputs * dp_mask[1] inputs_h = inputs * dp_mask[2] else: inputs_z = inputs inputs_r = inputs inputs_h = inputs x_z = K.dot(inputs_z, self.kernel[:, :self.units]) x_r = K.dot(inputs_r, self.kernel[:, self.units:self.units * 2]) x_h = K.dot(inputs_h, self.kernel[:, self.units * 2:]) if self.use_bias: x_z = K.bias_add(x_z, input_bias[:self.units]) x_r = K.bias_add(x_r, input_bias[self.units: self.units * 2]) x_h = K.bias_add(x_h, input_bias[self.units * 2:]) if 0. < self.recurrent_dropout < 1.: h_tm1_z = h_tm1 * rec_dp_mask[0] h_tm1_r = h_tm1 * rec_dp_mask[1] h_tm1_h = h_tm1 * rec_dp_mask[2] else: h_tm1_z = h_tm1 h_tm1_r = h_tm1 h_tm1_h = h_tm1 recurrent_z = K.dot(h_tm1_z, self.recurrent_kernel[:, :self.units]) recurrent_r = K.dot(h_tm1_r, self.recurrent_kernel[:, self.units:self.units * 2]) if self.reset_after and self.use_bias: recurrent_z = K.bias_add(recurrent_z, recurrent_bias[:self.units]) recurrent_r = K.bias_add(recurrent_r, recurrent_bias[self.units:self.units * 2]) z = self.recurrent_activation(x_z + recurrent_z) r = self.recurrent_activation(x_r + recurrent_r) # reset gate applied after/before matrix multiplication if self.reset_after: recurrent_h = K.dot(h_tm1_h, self.recurrent_kernel[:, self.units * 2:]) if self.use_bias: recurrent_h = K.bias_add(recurrent_h, recurrent_bias[self.units * 2:]) recurrent_h = r * recurrent_h else: recurrent_h = K.dot(r * h_tm1_h, self.recurrent_kernel[:, self.units * 2:]) hh = self.activation(x_h + recurrent_h) else: if 0. < self.dropout < 1.: inputs = inputs * dp_mask[0] # inputs projected by all gate matrices at once matrix_x = K.dot(inputs, self.kernel) if self.use_bias: # biases: bias_z_i, bias_r_i, bias_h_i matrix_x = K.bias_add(matrix_x, input_bias) x_z, x_r, x_h = array_ops.split(matrix_x, 3, axis=-1) if self.reset_after: # hidden state projected by all gate matrices at once matrix_inner = K.dot(h_tm1, self.recurrent_kernel) if self.use_bias: matrix_inner = K.bias_add(matrix_inner, recurrent_bias) else: # hidden state projected separately for update/reset and new matrix_inner = K.dot(h_tm1, self.recurrent_kernel[:, :2 * self.units]) recurrent_z, recurrent_r, recurrent_h = array_ops.split( matrix_inner, [self.units, self.units, -1], axis=-1) z = self.recurrent_activation(x_z + recurrent_z) r = self.recurrent_activation(x_r + recurrent_r) if self.reset_after: recurrent_h = r * recurrent_h else: recurrent_h = K.dot(r * h_tm1, self.recurrent_kernel[:, 2 * self.units:]) hh = self.activation(x_h + recurrent_h) # previous and candidate state mixed by update gate h = z * h_tm1 + (1 - z) * hh new_state = [h] if nest.is_nested(states) else h return h, new_state def get_config(self): config = { 'units': self.units, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation, 'reset_after': self.reset_after } config.update(_config_for_enable_caching_device(self)) base_config = super(GRUCell, self).get_config() return dict(list(base_config.items()) + list(config.items())) def get_initial_state(self, inputs=None, batch_size=None, dtype=None): return _generate_zero_filled_state_for_cell(self, inputs, batch_size, dtype) @keras_export(v1=['keras.layers.GRU']) class GRU(RNN): """Gated Recurrent Unit - Cho et al. 2014. There are two variants. The default one is based on 1406.1078v3 and has reset gate applied to hidden state before matrix multiplication. The other one is based on original 1406.1078v1 and has the order reversed. The second variant is compatible with CuDNNGRU (GPU-only) and allows inference on CPU. Thus it has separate biases for `kernel` and `recurrent_kernel`. Use `'reset_after'=True` and `recurrent_activation='sigmoid'`. Arguments: units: Positive integer, dimensionality of the output space. activation: Activation function to use. Default: hyperbolic tangent (`tanh`). If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use for the recurrent step. Default: hard sigmoid (`hard_sigmoid`). If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. bias_initializer: Initializer for the bias vector. kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix. bias_regularizer: Regularizer function applied to the bias vector. activity_regularizer: Regularizer function applied to the output of the layer (its "activation").. kernel_constraint: Constraint function applied to the `kernel` weights matrix. recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix. bias_constraint: Constraint function applied to the bias vector. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. return_sequences: Boolean. Whether to return the last output in the output sequence, or the full sequence. return_state: Boolean. Whether to return the last state in addition to the output. go_backwards: Boolean (default False). If True, process the input sequence backwards and return the reversed sequence. stateful: Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch. unroll: Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences. time_major: The shape format of the `inputs` and `outputs` tensors. If True, the inputs and outputs will be in shape `(timesteps, batch, ...)`, whereas in the False case, it will be `(batch, timesteps, ...)`. 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. reset_after: GRU convention (whether to apply reset gate after or before matrix multiplication). False = "before" (default), True = "after" (CuDNN compatible). Call arguments: inputs: A 3D tensor. mask: Binary tensor of shape `(samples, timesteps)` indicating whether a given timestep should be masked. training: Python boolean indicating whether the layer should behave in training mode or in inference mode. This argument is passed to the cell when calling it. This is only relevant if `dropout` or `recurrent_dropout` is used. initial_state: List of initial state tensors to be passed to the first call of the cell. """ def __init__(self, units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False, reset_after=False, **kwargs): implementation = kwargs.pop('implementation', 1) if implementation == 0: logging.warning('`implementation=0` has been deprecated, ' 'and now defaults to `implementation=1`.' 'Please update your layer call.') if 'enable_caching_device' in kwargs: cell_kwargs = {'enable_caching_device': kwargs.pop('enable_caching_device')} else: cell_kwargs = {} cell = GRUCell( units, activation=activation, recurrent_activation=recurrent_activation, use_bias=use_bias, kernel_initializer=kernel_initializer, recurrent_initializer=recurrent_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, recurrent_regularizer=recurrent_regularizer, bias_regularizer=bias_regularizer, kernel_constraint=kernel_constraint, recurrent_constraint=recurrent_constraint, bias_constraint=bias_constraint, dropout=dropout, recurrent_dropout=recurrent_dropout, implementation=implementation, reset_after=reset_after, dtype=kwargs.get('dtype'), trainable=kwargs.get('trainable', True), **cell_kwargs) super(GRU, self).__init__( cell, return_sequences=return_sequences, return_state=return_state, go_backwards=go_backwards, stateful=stateful, unroll=unroll, **kwargs) self.activity_regularizer = regularizers.get(activity_regularizer) self.input_spec = [InputSpec(ndim=3)] def call(self, inputs, mask=None, training=None, initial_state=None): self._maybe_reset_cell_dropout_mask(self.cell) return super(GRU, self).call( inputs, mask=mask, training=training, initial_state=initial_state) @property def units(self): return self.cell.units @property def activation(self): return self.cell.activation @property def recurrent_activation(self): return self.cell.recurrent_activation @property def use_bias(self): return self.cell.use_bias @property def kernel_initializer(self): return self.cell.kernel_initializer @property def recurrent_initializer(self): return self.cell.recurrent_initializer @property def bias_initializer(self): return self.cell.bias_initializer @property def kernel_regularizer(self): return self.cell.kernel_regularizer @property def recurrent_regularizer(self): return self.cell.recurrent_regularizer @property def bias_regularizer(self): return self.cell.bias_regularizer @property def kernel_constraint(self): return self.cell.kernel_constraint @property def recurrent_constraint(self): return self.cell.recurrent_constraint @property def bias_constraint(self): return self.cell.bias_constraint @property def dropout(self): return self.cell.dropout @property def recurrent_dropout(self): return self.cell.recurrent_dropout @property def implementation(self): return self.cell.implementation @property def reset_after(self): return self.cell.reset_after def get_config(self): config = { 'units': self.units, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation, 'reset_after': self.reset_after } config.update(_config_for_enable_caching_device(self.cell)) base_config = super(GRU, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items())) @classmethod def from_config(cls, config): if 'implementation' in config and config['implementation'] == 0: config['implementation'] = 1 return cls(**config) @keras_export(v1=['keras.layers.LSTMCell']) class LSTMCell(DropoutRNNCellMixin, Layer): """Cell class for the LSTM layer. Arguments: units: Positive integer, dimensionality of the output space. activation: Activation function to use. Default: hyperbolic tangent (`tanh`). If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use for the recurrent step. Default: hard sigmoid (`hard_sigmoid`). If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. bias_initializer: Initializer for the bias vector. unit_forget_bias: Boolean. If True, add 1 to the bias of the forget gate at initialization. Setting it to true will also force `bias_initializer="zeros"`. This is recommended in [Jozefowicz et al., 2015]( http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf) kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix. bias_regularizer: Regularizer function applied to the bias vector. kernel_constraint: Constraint function applied to the `kernel` weights matrix. recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix. bias_constraint: Constraint function applied to the bias vector. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Call arguments: inputs: A 2D tensor. states: List of state tensors corresponding to the previous timestep. training: Python boolean indicating whether the layer should behave in training mode or in inference mode. Only relevant when `dropout` or `recurrent_dropout` is used. """ def __init__(self, units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., **kwargs): # By default use cached variable under v2 mode, see b/143699808. if ops.executing_eagerly_outside_functions(): self._enable_caching_device = kwargs.pop('enable_caching_device', True) else: self._enable_caching_device = kwargs.pop('enable_caching_device', False) super(LSTMCell, self).__init__(**kwargs) self.units = units self.activation = activations.get(activation) self.recurrent_activation = activations.get(recurrent_activation) self.use_bias = use_bias self.kernel_initializer = initializers.get(kernel_initializer) self.recurrent_initializer = initializers.get(recurrent_initializer) self.bias_initializer = initializers.get(bias_initializer) self.unit_forget_bias = unit_forget_bias self.kernel_regularizer = regularizers.get(kernel_regularizer) self.recurrent_regularizer = regularizers.get(recurrent_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.recurrent_constraint = constraints.get(recurrent_constraint) self.bias_constraint = constraints.get(bias_constraint) self.dropout = min(1., max(0., dropout)) self.recurrent_dropout = min(1., max(0., recurrent_dropout)) implementation = kwargs.pop('implementation', 1) if self.recurrent_dropout != 0 and implementation != 1: logging.debug(RECURRENT_DROPOUT_WARNING_MSG) self.implementation = 1 else: self.implementation = implementation # tuple(_ListWrapper) was silently dropping list content in at least 2.7.10, # and fixed after 2.7.16. Converting the state_size to wrapper around # NoDependency(), so that the base_layer.__setattr__ will not convert it to # ListWrapper. Down the stream, self.states will be a list since it is # generated from nest.map_structure with list, and tuple(list) will work # properly. self.state_size = data_structures.NoDependency([self.units, self.units]) self.output_size = self.units @tf_utils.shape_type_conversion def build(self, input_shape): default_caching_device = _caching_device(self) input_dim = input_shape[-1] self.kernel = self.add_weight( shape=(input_dim, self.units * 4), name='kernel', initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint, caching_device=default_caching_device) self.recurrent_kernel = self.add_weight( shape=(self.units, self.units * 4), name='recurrent_kernel', initializer=self.recurrent_initializer, regularizer=self.recurrent_regularizer, constraint=self.recurrent_constraint, caching_device=default_caching_device) if self.use_bias: if self.unit_forget_bias: def bias_initializer(_, *args, **kwargs): return K.concatenate([ self.bias_initializer((self.units,), *args, **kwargs), initializers.get('ones')((self.units,), *args, **kwargs), self.bias_initializer((self.units * 2,), *args, **kwargs), ]) else: bias_initializer = self.bias_initializer self.bias = self.add_weight( shape=(self.units * 4,), name='bias', initializer=bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint, caching_device=default_caching_device) else: self.bias = None self.built = True def _compute_carry_and_output(self, x, h_tm1, c_tm1): """Computes carry and output using split kernels.""" x_i, x_f, x_c, x_o = x h_tm1_i, h_tm1_f, h_tm1_c, h_tm1_o = h_tm1 i = self.recurrent_activation( x_i + K.dot(h_tm1_i, self.recurrent_kernel[:, :self.units])) f = self.recurrent_activation(x_f + K.dot( h_tm1_f, self.recurrent_kernel[:, self.units:self.units * 2])) c = f * c_tm1 + i * self.activation(x_c + K.dot( h_tm1_c, self.recurrent_kernel[:, self.units * 2:self.units * 3])) o = self.recurrent_activation( x_o + K.dot(h_tm1_o, self.recurrent_kernel[:, self.units * 3:])) return c, o def _compute_carry_and_output_fused(self, z, c_tm1): """Computes carry and output using fused kernels.""" z0, z1, z2, z3 = z i = self.recurrent_activation(z0) f = self.recurrent_activation(z1) c = f * c_tm1 + i * self.activation(z2) o = self.recurrent_activation(z3) return c, o def call(self, inputs, states, training=None): h_tm1 = states[0] # previous memory state c_tm1 = states[1] # previous carry state dp_mask = self.get_dropout_mask_for_cell(inputs, training, count=4) rec_dp_mask = self.get_recurrent_dropout_mask_for_cell( h_tm1, training, count=4) if self.implementation == 1: if 0 < self.dropout < 1.: inputs_i = inputs * dp_mask[0] inputs_f = inputs * dp_mask[1] inputs_c = inputs * dp_mask[2] inputs_o = inputs * dp_mask[3] else: inputs_i = inputs inputs_f = inputs inputs_c = inputs inputs_o = inputs k_i, k_f, k_c, k_o = array_ops.split( self.kernel, num_or_size_splits=4, axis=1) x_i = K.dot(inputs_i, k_i) x_f = K.dot(inputs_f, k_f) x_c = K.dot(inputs_c, k_c) x_o = K.dot(inputs_o, k_o) if self.use_bias: b_i, b_f, b_c, b_o = array_ops.split( self.bias, num_or_size_splits=4, axis=0) x_i = K.bias_add(x_i, b_i) x_f = K.bias_add(x_f, b_f) x_c = K.bias_add(x_c, b_c) x_o = K.bias_add(x_o, b_o) if 0 < self.recurrent_dropout < 1.: h_tm1_i = h_tm1 * rec_dp_mask[0] h_tm1_f = h_tm1 * rec_dp_mask[1] h_tm1_c = h_tm1 * rec_dp_mask[2] h_tm1_o = h_tm1 * rec_dp_mask[3] else: h_tm1_i = h_tm1 h_tm1_f = h_tm1 h_tm1_c = h_tm1 h_tm1_o = h_tm1 x = (x_i, x_f, x_c, x_o) h_tm1 = (h_tm1_i, h_tm1_f, h_tm1_c, h_tm1_o) c, o = self._compute_carry_and_output(x, h_tm1, c_tm1) else: if 0. < self.dropout < 1.: inputs = inputs * dp_mask[0] z = K.dot(inputs, self.kernel) z += K.dot(h_tm1, self.recurrent_kernel) if self.use_bias: z = K.bias_add(z, self.bias) z = array_ops.split(z, num_or_size_splits=4, axis=1) c, o = self._compute_carry_and_output_fused(z, c_tm1) h = o * self.activation(c) return h, [h, c] def get_config(self): config = { 'units': self.units, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'unit_forget_bias': self.unit_forget_bias, 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation } config.update(_config_for_enable_caching_device(self)) base_config = super(LSTMCell, self).get_config() return dict(list(base_config.items()) + list(config.items())) def get_initial_state(self, inputs=None, batch_size=None, dtype=None): return list(_generate_zero_filled_state_for_cell( self, inputs, batch_size, dtype)) @keras_export('keras.experimental.PeepholeLSTMCell') class PeepholeLSTMCell(LSTMCell): """Equivalent to LSTMCell class but adds peephole connections. Peephole connections allow the gates to utilize the previous internal state as well as the previous hidden state (which is what LSTMCell is limited to). This allows PeepholeLSTMCell to better learn precise timings over LSTMCell. From [Gers et al., 2002]( http://www.jmlr.org/papers/volume3/gers02a/gers02a.pdf): "We find that LSTM augmented by 'peephole connections' from its internal cells to its multiplicative gates can learn the fine distinction between sequences of spikes spaced either 50 or 49 time steps apart without the help of any short training exemplars." The peephole implementation is based on: [Sak et al., 2014](https://research.google.com/pubs/archive/43905.pdf) Example: ```python # Create 2 PeepholeLSTMCells peephole_lstm_cells = [PeepholeLSTMCell(size) for size in [128, 256]] # Create a layer composed sequentially of the peephole LSTM cells. layer = RNN(peephole_lstm_cells) input = keras.Input((timesteps, input_dim)) output = layer(input) ``` """ def __init__(self, units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., **kwargs): warnings.warn('`tf.keras.experimental.PeepholeLSTMCell` is deprecated ' 'and will be removed in a future version. ' 'Please use tensorflow_addons.rnn.PeepholeLSTMCell ' 'instead.') super(PeepholeLSTMCell, self).__init__( units=units, activation=activation, recurrent_activation=recurrent_activation, use_bias=use_bias, kernel_initializer=kernel_initializer, recurrent_initializer=recurrent_initializer, bias_initializer=bias_initializer, unit_forget_bias=unit_forget_bias, kernel_regularizer=kernel_regularizer, recurrent_regularizer=recurrent_regularizer, bias_regularizer=bias_regularizer, kernel_constraint=kernel_constraint, recurrent_constraint=recurrent_constraint, bias_constraint=bias_constraint, dropout=dropout, recurrent_dropout=recurrent_dropout, implementation=kwargs.pop('implementation', 1), **kwargs) def build(self, input_shape): super(PeepholeLSTMCell, self).build(input_shape) # The following are the weight matrices for the peephole connections. These # are multiplied with the previous internal state during the computation of # carry and output. self.input_gate_peephole_weights = self.add_weight( shape=(self.units,), name='input_gate_peephole_weights', initializer=self.kernel_initializer) self.forget_gate_peephole_weights = self.add_weight( shape=(self.units,), name='forget_gate_peephole_weights', initializer=self.kernel_initializer) self.output_gate_peephole_weights = self.add_weight( shape=(self.units,), name='output_gate_peephole_weights', initializer=self.kernel_initializer) def _compute_carry_and_output(self, x, h_tm1, c_tm1): x_i, x_f, x_c, x_o = x h_tm1_i, h_tm1_f, h_tm1_c, h_tm1_o = h_tm1 i = self.recurrent_activation( x_i + K.dot(h_tm1_i, self.recurrent_kernel[:, :self.units]) + self.input_gate_peephole_weights * c_tm1) f = self.recurrent_activation(x_f + K.dot( h_tm1_f, self.recurrent_kernel[:, self.units:self.units * 2]) + self.forget_gate_peephole_weights * c_tm1) c = f * c_tm1 + i * self.activation(x_c + K.dot( h_tm1_c, self.recurrent_kernel[:, self.units * 2:self.units * 3])) o = self.recurrent_activation( x_o + K.dot(h_tm1_o, self.recurrent_kernel[:, self.units * 3:]) + self.output_gate_peephole_weights * c) return c, o def _compute_carry_and_output_fused(self, z, c_tm1): z0, z1, z2, z3 = z i = self.recurrent_activation(z0 + self.input_gate_peephole_weights * c_tm1) f = self.recurrent_activation(z1 + self.forget_gate_peephole_weights * c_tm1) c = f * c_tm1 + i * self.activation(z2) o = self.recurrent_activation(z3 + self.output_gate_peephole_weights * c) return c, o @keras_export(v1=['keras.layers.LSTM']) class LSTM(RNN): """Long Short-Term Memory layer - Hochreiter 1997. Note that this cell is not optimized for performance on GPU. Please use `tf.compat.v1.keras.layers.CuDNNLSTM` for better performance on GPU. Arguments: units: Positive integer, dimensionality of the output space. activation: Activation function to use. Default: hyperbolic tangent (`tanh`). If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use for the recurrent step. Default: hard sigmoid (`hard_sigmoid`). If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs.. recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. bias_initializer: Initializer for the bias vector. unit_forget_bias: Boolean. If True, add 1 to the bias of the forget gate at initialization. Setting it to true will also force `bias_initializer="zeros"`. This is recommended in [Jozefowicz et al., 2015]( http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf). kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix. bias_regularizer: Regularizer function applied to the bias vector. activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). kernel_constraint: Constraint function applied to the `kernel` weights matrix. recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix. bias_constraint: Constraint function applied to the bias vector. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. return_sequences: Boolean. Whether to return the last output. in the output sequence, or the full sequence. return_state: Boolean. Whether to return the last state in addition to the output. go_backwards: Boolean (default False). If True, process the input sequence backwards and return the reversed sequence. stateful: Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch. unroll: Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences. time_major: The shape format of the `inputs` and `outputs` tensors. If True, the inputs and outputs will be in shape `(timesteps, batch, ...)`, whereas in the False case, it will be `(batch, timesteps, ...)`. 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. Call arguments: inputs: A 3D tensor. mask: Binary tensor of shape `(samples, timesteps)` indicating whether a given timestep should be masked. training: Python boolean indicating whether the layer should behave in training mode or in inference mode. This argument is passed to the cell when calling it. This is only relevant if `dropout` or `recurrent_dropout` is used. initial_state: List of initial state tensors to be passed to the first call of the cell. """ def __init__(self, units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False, **kwargs): implementation = kwargs.pop('implementation', 1) if implementation == 0: logging.warning('`implementation=0` has been deprecated, ' 'and now defaults to `implementation=1`.' 'Please update your layer call.') if 'enable_caching_device' in kwargs: cell_kwargs = {'enable_caching_device': kwargs.pop('enable_caching_device')} else: cell_kwargs = {} cell = LSTMCell( units, activation=activation, recurrent_activation=recurrent_activation, use_bias=use_bias, kernel_initializer=kernel_initializer, recurrent_initializer=recurrent_initializer, unit_forget_bias=unit_forget_bias, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, recurrent_regularizer=recurrent_regularizer, bias_regularizer=bias_regularizer, kernel_constraint=kernel_constraint, recurrent_constraint=recurrent_constraint, bias_constraint=bias_constraint, dropout=dropout, recurrent_dropout=recurrent_dropout, implementation=implementation, dtype=kwargs.get('dtype'), trainable=kwargs.get('trainable', True), **cell_kwargs) super(LSTM, self).__init__( cell, return_sequences=return_sequences, return_state=return_state, go_backwards=go_backwards, stateful=stateful, unroll=unroll, **kwargs) self.activity_regularizer = regularizers.get(activity_regularizer) self.input_spec = [InputSpec(ndim=3)] def call(self, inputs, mask=None, training=None, initial_state=None): self._maybe_reset_cell_dropout_mask(self.cell) return super(LSTM, self).call( inputs, mask=mask, training=training, initial_state=initial_state) @property def units(self): return self.cell.units @property def activation(self): return self.cell.activation @property def recurrent_activation(self): return self.cell.recurrent_activation @property def use_bias(self): return self.cell.use_bias @property def kernel_initializer(self): return self.cell.kernel_initializer @property def recurrent_initializer(self): return self.cell.recurrent_initializer @property def bias_initializer(self): return self.cell.bias_initializer @property def unit_forget_bias(self): return self.cell.unit_forget_bias @property def kernel_regularizer(self): return self.cell.kernel_regularizer @property def recurrent_regularizer(self): return self.cell.recurrent_regularizer @property def bias_regularizer(self): return self.cell.bias_regularizer @property def kernel_constraint(self): return self.cell.kernel_constraint @property def recurrent_constraint(self): return self.cell.recurrent_constraint @property def bias_constraint(self): return self.cell.bias_constraint @property def dropout(self): return self.cell.dropout @property def recurrent_dropout(self): return self.cell.recurrent_dropout @property def implementation(self): return self.cell.implementation def get_config(self): config = { 'units': self.units, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'unit_forget_bias': self.unit_forget_bias, 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation } config.update(_config_for_enable_caching_device(self.cell)) base_config = super(LSTM, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items())) @classmethod def from_config(cls, config): if 'implementation' in config and config['implementation'] == 0: config['implementation'] = 1 return cls(**config) def _generate_dropout_mask(ones, rate, training=None, count=1): def dropped_inputs(): return K.dropout(ones, rate) if count > 1: return [ K.in_train_phase(dropped_inputs, ones, training=training) for _ in range(count) ] return K.in_train_phase(dropped_inputs, ones, training=training) def _standardize_args(inputs, initial_state, constants, num_constants): """Standardizes `__call__` to a single list of tensor inputs. When running a model loaded from a file, the input tensors `initial_state` and `constants` can be passed to `RNN.__call__()` as part of `inputs` instead of by the dedicated keyword arguments. This method makes sure the arguments are separated and that `initial_state` and `constants` are lists of tensors (or None). Arguments: inputs: Tensor or list/tuple of tensors. which may include constants and initial states. In that case `num_constant` must be specified. initial_state: Tensor or list of tensors or None, initial states. constants: Tensor or list of tensors or None, constant tensors. num_constants: Expected number of constants (if constants are passed as part of the `inputs` list. Returns: inputs: Single tensor or tuple of tensors. initial_state: List of tensors or None. constants: List of tensors or None. """ if isinstance(inputs, list): # There are several situations here: # In the graph mode, __call__ will be only called once. The initial_state # and constants could be in inputs (from file loading). # In the eager mode, __call__ will be called twice, once during # rnn_layer(inputs=input_t, constants=c_t, ...), and second time will be # model.fit/train_on_batch/predict with real np data. In the second case, # the inputs will contain initial_state and constants as eager tensor. # # For either case, the real input is the first item in the list, which # could be a nested structure itself. Then followed by initial_states, which # could be a list of items, or list of list if the initial_state is complex # structure, and finally followed by constants which is a flat list. assert initial_state is None and constants is None if num_constants: constants = inputs[-num_constants:] inputs = inputs[:-num_constants] if len(inputs) > 1: initial_state = inputs[1:] inputs = inputs[:1] if len(inputs) > 1: inputs = tuple(inputs) else: inputs = inputs[0] def to_list_or_none(x): if x is None or isinstance(x, list): return x if isinstance(x, tuple): return list(x) return [x] initial_state = to_list_or_none(initial_state) constants = to_list_or_none(constants) return inputs, initial_state, constants def _is_multiple_state(state_size): """Check whether the state_size contains multiple states.""" return (hasattr(state_size, '__len__') and not isinstance(state_size, tensor_shape.TensorShape)) def _generate_zero_filled_state_for_cell(cell, inputs, batch_size, dtype): if inputs is not None: batch_size = array_ops.shape(inputs)[0] dtype = inputs.dtype return _generate_zero_filled_state(batch_size, cell.state_size, dtype) def _generate_zero_filled_state(batch_size_tensor, state_size, dtype): """Generate a zero filled tensor with shape [batch_size, state_size].""" if batch_size_tensor is None or dtype is None: raise ValueError( 'batch_size and dtype cannot be None while constructing initial state: ' 'batch_size={}, dtype={}'.format(batch_size_tensor, dtype)) def create_zeros(unnested_state_size): flat_dims = tensor_shape.TensorShape(unnested_state_size).as_list() init_state_size = [batch_size_tensor] + flat_dims return array_ops.zeros(init_state_size, dtype=dtype) if nest.is_nested(state_size): return nest.map_structure(create_zeros, state_size) else: return create_zeros(state_size) def _caching_device(rnn_cell): """Returns the caching device for the RNN variable. This is useful for distributed training, when variable is not located as same device as the training worker. By enabling the device cache, this allows worker to read the variable once and cache locally, rather than read it every time step from remote when it is needed. Note that this is assuming the variable that cell needs for each time step is having the same value in the forward path, and only gets updated in the backprop. It is true for all the default cells (SimpleRNN, GRU, LSTM). If the cell body relies on any variable that gets updated every time step, then caching device will cause it to read the stall value. Args: rnn_cell: the rnn cell instance. """ if context.executing_eagerly(): # caching_device is not supported in eager mode. return None if not getattr(rnn_cell, '_enable_caching_device', False): return None # 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. if control_flow_util.IsInWhileLoop(ops.get_default_graph()): logging.warn('Variable read device caching has been disabled because the ' 'RNN is in tf.while_loop loop context, which will cause ' 'reading stalled value in forward path. This could slow down ' 'the training due to duplicated variable reads. Please ' 'consider updating your code to remove tf.while_loop if ' 'possible.') return None if (rnn_cell._dtype_policy.compute_dtype != rnn_cell._dtype_policy.variable_dtype): logging.warn('Variable read device caching has been disabled since it ' 'doesn\'t work with the mixed precision API. This is ' 'likely to cause a slowdown for RNN training due to ' 'duplicated read of variable for each timestep, which ' 'will be significant in a multi remote worker setting. ' 'Please consider disabling mixed precision API if ' 'the performance has been affected.') return None # Cache the value on the device that access the variable. return lambda op: op.device def _config_for_enable_caching_device(rnn_cell): """Return the dict config for RNN cell wrt to enable_caching_device field. Since enable_caching_device is a internal implementation detail for speed up the RNN variable read when running on the multi remote worker setting, we don't want this config to be serialized constantly in the JSON. We will only serialize this field when a none default value is used to create the cell. Args: rnn_cell: the RNN cell for serialize. Returns: A dict which contains the JSON config for enable_caching_device value or empty dict if the enable_caching_device value is same as the default value. """ default_enable_caching_device = ops.executing_eagerly_outside_functions() if rnn_cell._enable_caching_device != default_enable_caching_device: return {'enable_caching_device': rnn_cell._enable_caching_device} return {}