The PBuilder type selects what backend to use for building. Cowbuilder
implements copy-on-write semantics, but can be very slow when building for
a foreign architecture. PBuilder can be much faster, but relies on
installing foreign packages into the chroot, so any packages you install
must have multiarch support.
This will default to using Cowbuilder if not set.


Yobe: SWAN President charges athletes to see Sports beyond recreation
Duku JOEL, Damaturu
The National President, Sports Writers Association of Nigeria (SWAN) Mr. Honour Sirawoo is in Yobe for a two day official visit as part of his administration’s commitment to building a stronger SWAN across the country.
Sirawoo who arrived Damaturu Thursday via Maiduguri went straight to the August 27 Stadium where he separately addressed various athletes including the State Female Hockey team, Yobe Desert Football fielder team, the state para-soccer team and the basketball team.
During his address to the different athletes, Mr. Sirawoo advised them to look at sports beyond pleasure and recreation but a money spinning venture that can change their life for good.
The number one sports journalist in Nigeria also called on Yobe state government to harness sports and curtail youth effectiveness.
Sirawoo who appreciated the level of engagement of young talents in the sports development architecture of the state said with determination, their dreams of becoming great players could be achieved.
READ ALSO: Yobe SUBEB Chair begins teaching at alma mater
According to him, “Sports is now a serious business, if you do it well you will attract wealth, you will attract respect to your people, you will serve as model to others and be useful to society, you will put food on your table and the table of others,” he said.
The SWAN President who charged members of the association in the state to be proactive equally called on government to upgrade facilities at the August 27 stadium to expand the fortunes of sports in Yobe State.
Mr. Sirawoo who was particularly happy with the state female hockey team preparing to represent Nigeria at an international hockey championship in Egypt charged them not to go for anything less than the ultimate prize..
The visit continues with a courtesy call on the Emir of Bade, Alhaji Abubakar Umar Suleiman II , who is a strong supporter of Sports and SWAN in Yobe State.


Try to answer the following code related questions within the time limit. Keep in mind incorrect answers will penalize your score/time by ten seconds.


--- title: Convolutional LSTM. type: docs repo: https://github.com/rstudio/keras menu: main: name: Convolutional LSTM. - keras parent: keras-reference aliases: - /reference/keras/layer_conv_lstm_2d.html - /keras/reference/layer_conv_lstm_2d.html - /guide/keras/reference/layer_conv_lstm_2d.html - /tools/tools/keras/reference/layer_conv_lstm_2d.html - /installation/keras/reference/layer_conv_lstm_2d.html - /tutorials/keras/reference/layer_conv_lstm_2d.html - /guide/tools/keras/reference/layer_conv_lstm_2d.html - /deploy/keras/reference/layer_conv_lstm_2d.html - /tools/keras/reference/layer_conv_lstm_2d.html - /tutorials/tools/keras/reference/layer_conv_lstm_2d.html ---
It is similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.
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,
go_backwards = FALSE,
stateful = FALSE,
dropout = 0,
recurrent_dropout = 0,
batch_size = NULL,
name = NULL,
trainable = NULL,
weights = NULL,
input_shape = NULL
)
| object | Model or layer object |
| 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 |
| padding | One of |
| data_format | A string, one of |
| dilation_rate | An integer or list of n integers, specifying the
dilation rate to use for dilated convolution. Currently, specifying any
|
| activation | Activation function to use. If you don't specify anything,
no activation is applied (ie. "linear" activation: |
| 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 |
| recurrent_initializer | Initializer for the |
| 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 |
| kernel_regularizer | Regularizer function applied to the |
| recurrent_regularizer | Regularizer function applied to the
|
| 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 |
| recurrent_constraint | Constraint function applied to the
|
| 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. |
| 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. |
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)
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting The current implementation does not include the feedback loop on the cells output
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_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()