Convolutional Recurrent Networks
This module belongs to the " Computer Vision" category .
Description
The Convolutional Recurrent Network (CRNN or ConVNet) module is used to initiate a RNN estimator based on SmartPredict library.
This is a particularly popular function for treating visual data.
Parameters
As parameters, we can input shape ourselves right from the beginning. Then , we can adjust the optimization parameters and finally set the features selector.
Optimization parameters
The CRNN parameters are as follows:
Learning rate
Learning rate Reduction Factor
Beta 1 and 2
Gradient Clipping by Norm/Value
Epsilon
Momentum
Rho
Optimizers
As optimizers :
adam
rmsprop
adagrad
adamax
sgd (stochastic gradient descent)
Features extractor
The CRNN module parameters allow to implement different kinds of features extractor:
fcn
darknet
mobilenet_v2
resnet50_v2
resnet101_v2
resnet152_v2
nasnet
xception
The output decoder can be ctc or simple and imagenet is available as a features extractor weight.
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