Update docstring for keras.layers.SimpleRNN and SimpleRNNCell.
PiperOrigin-RevId: 270153457
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Scott Zhu
TensorFlower Gardener
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@ -1131,41 +1131,66 @@ class DropoutRNNCellMixin(object):
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class SimpleRNNCell(DropoutRNNCellMixin, Layer):
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"""Cell class for SimpleRNN.
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This class processes one step within the whole time sequence input, whereas
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`tf.keras.layer.SimpleRNN` processes the whole sequence.
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Arguments:
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units: Positive integer, dimensionality of the output space.
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activation: Activation function to use.
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Default: hyperbolic tangent (`tanh`).
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If you pass `None`, no activation is applied
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(ie. "linear" activation: `a(x) = x`).
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use_bias: Boolean, whether the layer uses a bias vector.
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use_bias: Boolean, (default `True`), whether the layer uses a bias vector.
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kernel_initializer: Initializer for the `kernel` weights matrix,
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used for the linear transformation of the inputs.
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used for the linear transformation of the inputs. Default:
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`glorot_uniform`.
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recurrent_initializer: Initializer for the `recurrent_kernel`
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weights matrix, used for the linear transformation of the recurrent state.
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bias_initializer: Initializer for the bias vector.
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kernel_regularizer: Regularizer function applied to
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the `kernel` weights matrix.
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recurrent_regularizer: Regularizer function applied to
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the `recurrent_kernel` weights matrix.
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bias_regularizer: Regularizer function applied to the bias vector.
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kernel_constraint: Constraint function applied to
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the `kernel` weights matrix.
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recurrent_constraint: Constraint function applied to
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the `recurrent_kernel` weights matrix.
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bias_constraint: Constraint function applied to the bias vector.
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dropout: Float between 0 and 1.
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Fraction of the units to drop for
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the linear transformation of the inputs.
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recurrent_dropout: Float between 0 and 1.
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Fraction of the units to drop for
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the linear transformation of the recurrent state.
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Default: `orthogonal`.
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bias_initializer: Initializer for the bias vector. Default: `zeros`.
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kernel_regularizer: Regularizer function applied to the `kernel` weights
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matrix. Default: `None`.
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recurrent_regularizer: Regularizer function applied to the
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`recurrent_kernel` weights matrix. Default: `None`.
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bias_regularizer: Regularizer function applied to the bias vector. Default:
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`None`.
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kernel_constraint: Constraint function applied to the `kernel` weights
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matrix. Default: `None`.
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recurrent_constraint: Constraint function applied to the `recurrent_kernel`
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weights matrix. Default: `None`.
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bias_constraint: Constraint function applied to the bias vector. Default:
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`None`.
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dropout: Float between 0 and 1. Fraction of the units to drop for the linear
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transformation of the inputs. Default: 0.
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recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for
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the linear transformation of the recurrent state. Default: 0.
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Call arguments:
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inputs: A 2D tensor.
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states: List of state tensors corresponding to the previous timestep.
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inputs: A 2D tensor, with shape of `[batch, feature]`.
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states: A 2D tensor with shape of `[batch, units]`, which is the state from
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the previous time step. For timestep 0, the initial state provided by user
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will be feed to cell.
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training: Python boolean indicating whether the layer should behave in
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training mode or in inference mode. Only relevant when `dropout` or
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`recurrent_dropout` is used.
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Examples:
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```python
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inputs = np.random.random([32, 10, 8]).astype(np.float32)
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rnn = tf.keras.layers.RNN(tf.keras.layers.SimpleRNNCell(4))
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output = rnn(inputs) # The output has shape `[32, 4]`.
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rnn = tf.keras.layers.RNN(
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tf.keras.layers.SimpleRNNCell(4),
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return_sequences=True,
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return_state=True)
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# whole_sequence_output has shape `[32, 10, 4]`.
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# final_state has shape `[32, 4]`.
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whole_sequence_output, final_state = rnn(inputs)
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```
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"""
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def __init__(self,
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@ -1300,35 +1325,38 @@ class SimpleRNN(RNN):
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Default: hyperbolic tangent (`tanh`).
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If you pass None, no activation is applied
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(ie. "linear" activation: `a(x) = x`).
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use_bias: Boolean, whether the layer uses a bias vector.
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use_bias: Boolean, (default `True`), whether the layer uses a bias vector.
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kernel_initializer: Initializer for the `kernel` weights matrix,
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used for the linear transformation of the inputs.
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used for the linear transformation of the inputs. Default:
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`glorot_uniform`.
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recurrent_initializer: Initializer for the `recurrent_kernel`
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weights matrix,
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used for the linear transformation of the recurrent state.
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bias_initializer: Initializer for the bias vector.
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kernel_regularizer: Regularizer function applied to
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the `kernel` weights matrix.
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recurrent_regularizer: Regularizer function applied to
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the `recurrent_kernel` weights matrix.
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bias_regularizer: Regularizer function applied to the bias vector.
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activity_regularizer: Regularizer function applied to
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the output of the layer (its "activation")..
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kernel_constraint: Constraint function applied to
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the `kernel` weights matrix.
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recurrent_constraint: Constraint function applied to
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the `recurrent_kernel` weights matrix.
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bias_constraint: Constraint function applied to the bias vector.
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weights matrix, used for the linear transformation of the recurrent state.
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Default: `orthogonal`.
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bias_initializer: Initializer for the bias vector. Default: `zeros`.
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kernel_regularizer: Regularizer function applied to the `kernel` weights
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matrix. Default: `None`.
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recurrent_regularizer: Regularizer function applied to the
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`recurrent_kernel` weights matrix. Default: `None`.
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bias_regularizer: Regularizer function applied to the bias vector. Default:
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`None`.
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activity_regularizer: Regularizer function applied to the output of the
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layer (its "activation"). Default: `None`.
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kernel_constraint: Constraint function applied to the `kernel` weights
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matrix. Default: `None`.
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recurrent_constraint: Constraint function applied to the `recurrent_kernel`
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weights matrix. Default: `None`.
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bias_constraint: Constraint function applied to the bias vector. Default:
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`None`.
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dropout: Float between 0 and 1.
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Fraction of the units to drop for
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the linear transformation of the inputs.
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Fraction of the units to drop for the linear transformation of the inputs.
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Default: 0.
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recurrent_dropout: Float between 0 and 1.
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Fraction of the units to drop for
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the linear transformation of the recurrent state.
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Fraction of the units to drop for the linear transformation of the
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recurrent state. Default: 0.
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return_sequences: Boolean. Whether to return the last output
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in the output sequence, or the full sequence.
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in the output sequence, or the full sequence. Default: `False`.
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return_state: Boolean. Whether to return the last state
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in addition to the output.
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in addition to the output. Default: `False`
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go_backwards: Boolean (default False).
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If True, process the input sequence backwards and return the
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reversed sequence.
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@ -1343,8 +1371,8 @@ class SimpleRNN(RNN):
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Unrolling is only suitable for short sequences.
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Call arguments:
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inputs: A 3D tensor.
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mask: Binary tensor of shape `(samples, timesteps)` indicating whether
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inputs: A 3D tensor, with shape `[batch, timesteps, feature]`.
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mask: Binary tensor of shape `[batch, timesteps]` indicating whether
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a given timestep should be masked.
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training: Python boolean indicating whether the layer should behave in
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training mode or in inference mode. This argument is passed to the cell
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@ -1352,6 +1380,22 @@ class SimpleRNN(RNN):
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`recurrent_dropout` is used.
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initial_state: List of initial state tensors to be passed to the first
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call of the cell.
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Examples:
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```python
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inputs = np.random.random([32, 10, 8]).astype(np.float32)
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simple_rnn = tf.keras.layers.SimpleRNN(4)
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output = simple_rnn(inputs) # The output has shape `[32, 4]`.
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simple_rnn = tf.keras.layers.SimpleRNN(
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4, return_sequences=True, return_state=True)
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# whole_sequence_output has shape `[32, 10, 4]`.
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# final_state has shape `[32, 4]`.
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whole_sequence_output, final_state = simple_rnn(inputs)
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```
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"""
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def __init__(self,
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