# 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()`