tf.keras.layers.ConvLSTM2D

Convolutional LSTM.

View aliases

Compat aliases for migration

tf.compat.v1.keras.layers.ConvLSTM2D, `tf.compat.v2.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_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:

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting The 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`