layer_conv_lstm_2d
Convolutional LSTM.
Description
It is similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.
Usage
layer_conv_lstm_2d(
object,
filters,
kernel_size,
strides = c(1L, 1L),
padding = "valid",
data_format = NULL,
dilation_rate = c(1L, 1L),
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 = NULL,
recurrent_regularizer = NULL,
bias_regularizer = NULL,
activity_regularizer = NULL,
kernel_constraint = NULL,
recurrent_constraint = NULL,
bias_constraint = NULL,
return_sequences = FALSE,
return_state = FALSE,
go_backwards = FALSE,
stateful = FALSE,
dropout = 0,
recurrent_dropout = 0,
batch_size = NULL,
name = NULL,
trainable = NULL,
weights = NULL,
input_shape = NULL
) Arguments
| Arguments | Description |
|---|---|
| object | What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is: - missing or NULL, the Layer instance is returned. - a Sequential model, the model with an additional layer is returned. - a Tensor, the output tensor from layer_instance(object) is returned. |
| filters | Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). |
| kernel_size | An integer or list of n integers, specifying the dimensions of the convolution window. |
| strides | An integer or 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 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. If you don’t specify anything, no activation is applied (ie. “linear” activation: a(x) = 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 the output of the layer (its “activation”).. |
| 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. |
| return_state | Boolean. Whether to return the last state in addition to the output. |
| go_backwards | Boolean (default FALSE). If TRUE, rocess 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. |
| batch_size | Fixed batch size for layer |
| name | An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn’t provided. |
| trainable | Whether the layer weights will be updated during training. |
| weights | Initial weights for layer. |
| input_shape | Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model. |
Section
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)
References
- Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting The current implementation does not include the feedback loop on the cells output
See Also
Other convolutional layers: layer_conv_1d_transpose(), layer_conv_1d(), layer_conv_2d_transpose(), layer_conv_2d(), layer_conv_3d_transpose(), layer_conv_3d(), layer_cropping_1d(), layer_cropping_2d(), layer_cropping_3d(), layer_depthwise_conv_1d(), layer_depthwise_conv_2d(), layer_separable_conv_1d(), layer_separable_conv_2d(), layer_upsampling_1d(), layer_upsampling_2d(), layer_upsampling_3d(), layer_zero_padding_1d(), layer_zero_padding_2d(), layer_zero_padding_3d()