2Vha HddlmZddlmZedGddeZy)) keras_export)ConvLSTMzkeras.layers.ConvLSTM3DcVeZdZdZ dfd ZxZS) ConvLSTM3Da3D Convolutional LSTM. Similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional. Args: filters: int, the dimension of the output space (the number of filters in the convolution). kernel_size: int or tuple/list of 3 integers, specifying the size of the convolution window. strides: int or tuple/list of 3 integers, 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 3 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]( 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. 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 6D 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 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, *spatial_dims)` - If `data_format='channels_last'`: 5D tensor with shape: `(samples, time, *spatial_dims, 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, *spatial_dims)` if `data_format='channels_first'` or shape: `(samples, *spatial_dims, filters)` if `data_format='channels_last'`. - If `return_sequences`: 5D tensor with shape: `(samples, timesteps, filters, *spatial_dims)` if data_format='channels_first' or shape: `(samples, timesteps, *spatial_dims, filters)` if `data_format='channels_last'`. - Else, 4D tensor with shape: `(samples, filters, *spatial_dims)` if `data_format='channels_first'` or shape: `(samples, *spatial_dims, filters)` if `data_format='channels_last'`. 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 t|diddd|d|d|d|d|d|d |d |d | d | d | d| d| d|d|d|d|d|d|d|d|d|d|d|d|d|d||y)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_dropoutseed)super__init__)selfr r r r rrrrrrrrrrrrrrrrr"r#r$rrr r!kwargs __class__s P/home/dcms/DCMS/lib/python3.12/site-packages/keras/src/layers/rnn/conv_lstm3d.pyr'zConvLSTM3D.__init__zs)>    $     $  ( " "6   2 #8 . .  2 #8! 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