tf.keras.layers.ConvLSTM2D

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Convolutional LSTM.

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

reset_states

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reset_states(
    states=None
)

Reset the recorded states for the stateful RNN layer.

Can only be used when RNN layer is constructed with stateful = True. Args: states: Numpy arrays that contains the value for the initial state, which will be feed to cell at the first time step. When the value is None, zero filled numpy array will be created based on the cell state size.

Raises:

• AttributeError: When the RNN layer is not stateful.
• ValueError: When the batch size of the RNN layer is unknown.
• ValueError: When the input numpy array is not compatible with the RNN layer state, either size wise or dtype wise.