6bi9"LSrSSKJr SSKJr \"S5"SS\55rg)z1D Convolutional LSTM layer.)ConvLSTM) keras_exportzkeras.layers.ConvLSTM1Dc`^\rSrSrSrSU4SjjrSrU=r$) ConvLSTM1Da1D Convolutional LSTM. Similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional. Args: 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]( http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf) 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 4D 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' 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'. Raises: ValueError: in case of invalid constructor arguments. References: - [Shi et al., 2015](http://arxiv.org/abs/1506.04214v1) (the current implementation does not include the feedback loop on the cells output). c >[TU]"S0SS_SU_SU_SU_SU_SU_SU_S U_S U_S U _S U _S U _SU _SU _SU_SU_SU_SU_SU_SU_SU_SU_SU_SU_SU_SU_SU_UD6 g)Nrankfilters kernel_sizestridespadding data_format dilation_rate activationrecurrent_activationuse_biaskernel_initializerrecurrent_initializerbias_initializerunit_forget_biaskernel_regularizerrecurrent_regularizerbias_regularizeractivity_regularizerkernel_constraintrecurrent_constraintbias_constraintreturn_sequences return_state go_backwardsstatefuldropoutrecurrent_dropout)super__init__)selfr r r rrrrrrrrrrrrrrrrrrr r!r"r#r$kwargs __class__s ]/home/james-whalen/.local/lib/python3.13/site-packages/tf_keras/src/layers/rnn/conv_lstm1d.pyr'ConvLSTM1D.__init__s<    $     $  ( " "6   2 #8 . .  2 #8! 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