conv_block: Apply BatchNorm, ...
In dfalbel/densenet: Densely Connected Convolutional Networks in Keras
Description
Usage
Arguments
References
See Also
Description
... Relu, 3x3 Conv2D, optional bottleneck block and dropout
Usage
1
2
conv_block(ip, nb_filter, bottleneck = FALSE, dropout_rate = NULL,
weight_decay = 1e-04)
Arguments
ip
Input keras tensor
nb_filter
number of filters
bottleneck
add bottleneck block
dropout_rate
dropout rate
weight_decay
weight decay factor
References
https://github.com/titu1994/DenseNet/blob/master/densenet.py
See Also
Other internal: create_dense_net,
dense_block,
transition_up_block
dfalbel/densenet documentation built on May 31, 2019, 11:49 p.m.
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Description Usage Arguments References See Also
Description
... Relu, 3x3 Conv2D, optional bottleneck block and dropout
Usage
1 2 | conv_block(ip, nb_filter, bottleneck = FALSE, dropout_rate = NULL,
weight_decay = 1e-04)
|
Arguments
ip |
Input keras tensor |
nb_filter |
number of filters |
bottleneck |
add bottleneck block |
dropout_rate |
dropout rate |
weight_decay |
weight decay factor |
References
https://github.com/titu1994/DenseNet/blob/master/densenet.py
See Also
Other internal: create_dense_net,
dense_block,
transition_up_block
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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