tf.keras.layers.RNN

TensorFlow 2 version

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Base class for recurrent layers.

Inherits From: Layer

tf.keras.layers.RNN(
    cell, return_sequences=False, return_state=False, go_backwards=False,
    stateful=False, unroll=False, time_major=False, **kwargs
)

Call arguments:

• inputs: Input 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 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 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:

# 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)

Methods

get_initial_state

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get_initial_state(
    inputs
)

reset_states

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reset_states(
    states=None
)