ConvLSTM2D layer

[source]

ConvLSTM2D class

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,
    return_state=False,
    go_backwards=False,
    stateful=False,
    dropout=0.0,
    recurrent_dropout=0.0,
    **kwargs
)

2D Convolutional LSTM.

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). "valid" means no padding. "same" results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input.
• 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, ...). When unspecified, uses image_data_format value found in your TF-Keras config file at ~/.keras/keras.json (if exists) else 'channels_last'. Defaults to '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., 2015
• 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. (default False)
• return_state: Boolean Whether to return the last state in addition to the output. (default False)
• 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_state: a list of tensors. The first tensor is the output. The remaining tensors are the last states, each 4D tensor with shape: (samples, filters, new_rows, new_cols) if data_format='channels_first' or shape: (samples, new_rows, new_cols, filters) if data_format='channels_last'. rows and cols values might have changed due to padding.
• If return_sequences: 5D tensor with shape: (samples, timesteps, filters, new_rows, new_cols) if data_format='channels_first' or shape: (samples, timesteps, new_rows, new_cols, filters) if data_format='channels_last'.
• Else, 4D tensor with shape: (samples, filters, new_rows, new_cols) if data_format='channels_first' or shape: (samples, new_rows, new_cols, filters) if data_format='channels_last'.

Raises

• ValueError: in case of invalid constructor arguments.

References

• Shi et al., 2015 (the current implementation does not include the feedback loop on the cells output).