tf.keras.layers.SimpleRNN     View source on GitHub    Fully-connected RNN where the output is to be fed back to input. Inherits From: RNN, Layer, Module  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.keras.layers.SimpleRNN  
tf.keras.layers.SimpleRNN(
    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=0.0, recurrent_dropout=0.0, return_sequences=False, return_state=False,
    go_backwards=False, stateful=False, unroll=False, **kwargs
)
 See the Keras RNN API guide 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: 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)

 


 Attributes
  activation  
 
  bias_constraint  
 
  bias_initializer  
 
  bias_regularizer  
 
  dropout  
 
  kernel_constraint  
 
  kernel_initializer  
 
  kernel_regularizer  
 
  recurrent_constraint  
 
  recurrent_dropout  
 
  recurrent_initializer  
 
  recurrent_regularizer  
 
  states  
 
  units  
 
  use_bias  
   Methods reset_states View source 
reset_states(
    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.