ConvLSTM1D layer

[source]

ConvLSTM1D class

keras.layers.ConvLSTM1D(
    filters,
    kernel_size,
    strides=1,
    padding="valid",
    data_format=None,
    dilation_rate=1,
    activation="tanh",
    recurrent_activation="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,
    dropout=0.0,
    recurrent_dropout=0.0,
    seed=None,
    return_sequences=False,
    return_state=False,
    go_backwards=False,
    stateful=False,
    **kwargs
)

1D Convolutional LSTM.

Similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.

Arguments

• filters: int, the dimension of the output space (the number of filters in the convolution).
• kernel_size: int or tuple/list of 1 integer, specifying the size of the convolution window.
• strides: int or tuple/list of 1 integer, specifying the stride length of the convolution. strides > 1 is incompatible with dilation_rate > 1.
• padding: string, "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: string, either "channels_last" or "channels_first". The ordering of the dimensions in the inputs. "channels_last" corresponds to inputs with shape (batch, steps, features) while "channels_first" corresponds to inputs with shape (batch, features, steps). 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: int or tuple/list of 1 integers, specifying the dilation rate to use for dilated convolution.
• 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.
• 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.
• seed: Random seed for dropout.
• 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 and return the reversed sequence.
• 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.
• unroll: Boolean (default: False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences.

Call arguments

• inputs: A 4D tensor.
• initial_state: List of initial state tensors to be passed to the first call of the cell.
• 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 is only relevant if dropout or recurrent_dropout are set.

Input shape

• If data_format="channels_first": 4D tensor with shape: (samples, time, channels, rows)
• If data_format="channels_last": 4D tensor with shape: (samples, time, rows, channels)

Output shape

• If return_state: a list of tensors. The first tensor is the output. The remaining tensors are the last states, each 3D tensor with shape: (samples, filters, new_rows) if data_format='channels_first' or shape: (samples, new_rows, filters) if data_format='channels_last'. rows values might have changed due to padding.
• If return_sequences: 4D tensor with shape: (samples, timesteps, filters, new_rows) if data_format='channels_first' or shape: (samples, timesteps, new_rows, filters) if data_format='channels_last'.
• Else, 3D tensor with shape: (samples, filters, new_rows) if data_format='channels_first' or shape: (samples, new_rows, filters) if data_format='channels_last'.

References

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