tf.keras.layers.ConvLSTM2D

Stay organized with collections Save and categorize content based on your preferences.

Convolutional LSTM.

View aliases

Compat aliases for migration

tf.compat.v1.keras.layers.ConvLSTM2D

tf.keras.layers.ConvLSTM2D(
    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
)

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. By default hyperbolic tangent activation function is applied (tanh(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_kernelweights 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_dropoutare 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:

Convolutional LSTM Network: A Machine Learning Approach for Precipitation NowcastingThe current implementation does not include the feedback loop on the cells output.

| Attributes | | | ---------------------- | | | 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`