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tf.keras.layers.ConvLSTM2D

TensorFlow 2.0 version View source on GitHub

Class ConvLSTM2D

Convolutional LSTM.

Aliases:

  • Class tf.compat.v1.keras.layers.ConvLSTM2D
  • Class tf.compat.v2.keras.layers.ConvLSTM2D

It is similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.

Arguments:

  • filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
  • kernel_size: An integer or tuple/list of n integers, specifying the dimensions of the convolution window.
  • strides: An integer or tuple/list of n integers, specifying the strides of the convolution. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.
  • padding: One of "valid" or "same" (case-insensitive).
  • data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, time, ..., channels) while channels_first corresponds to inputs with shape (batch, time, channels, ...). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".
  • dilation_rate: An integer or tuple/list of n integers, specifying the dilation rate to use for dilated convolution. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any strides value != 1.
  • activation: Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x).
  • recurrent_activation: Activation function to use for the recurrent step.
  • 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. Use in combination with bias_initializer="zeros". This is recommended in Jozefowicz et al.
  • 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.
  • 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.
  • return_sequences: Boolean. Whether to return the last output in the output sequence, or the full sequence.
  • go_backwards: Boolean (default False). If True, process the input sequence backwards.
  • 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.
  • 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 5D 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 are set.
  • initial_state: List of initial state tensors to be passed to the first call of the cell.

Input shape:

  • If data_format='channels_first' 5D tensor with shape: (samples, time, channels, rows, cols)
  • If data_format='channels_last' 5D tensor with shape: (samples, time, rows, cols, channels)

Output shape:

  • If return_sequences
    • If data_format='channels_first' 5D tensor with shape: (samples, time, filters, output_row, output_col)
    • If data_format='channels_last' 5D tensor with shape: (samples, time, output_row, output_col, filters)
  • Else
    • If data_format ='channels_first' 4D tensor with shape: (samples, filters, output_row, output_col)
    • If data_format='channels_last' 4D tensor with shape: (samples, output_row, output_col, filters) where o_row and o_col depend on the shape of the filter and the padding

Raises:

  • ValueError: in case of invalid constructor arguments.

References:

__init__

View source

__init__(
    filters,
    kernel_size,
    strides=(1, 1),
    padding='valid',
    data_format=None,
    dilation_rate=(1, 1),
    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,
    return_sequences=False,
    go_backwards=False,
    stateful=False,
    dropout=0.0,
    recurrent_dropout=0.0,
    **kwargs
)

Properties

activation

bias_constraint

bias_initializer

bias_regularizer

data_format

dilation_rate

dropout

filters

kernel_constraint

kernel_initializer

kernel_regularizer

kernel_size

padding

recurrent_activation

recurrent_constraint

recurrent_dropout

recurrent_initializer

recurrent_regularizer

states

strides

unit_forget_bias

use_bias

Methods

get_initial_state

View source

get_initial_state(inputs)

reset_states

View source

reset_states(states=None)