Previous weeks - Training the models

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Training Networks

Now that we have the proper datasets created to train different models of Neural Networks with Deep Learning is time to check if the conclusions made by Nuria are the same with my models. I keeped the models which give her the least mean realative error after making the test.

For training the network I have been using the scripts main_train.py and net_train_config.yml located in link

As you can see the code is very similar to the generator script, where you can chenge some basics like:

  • the complexity of the model (simple, complex, convLSTM, complex_convLSTM)
  • type of Net (Recurrent or Non recurrent), the activation function which decides, whether a neuron should be activated or not by calculating weighted sum and further adding bias with it
  • loss function (generate predictions, compare them with the actual values and then compute what is known as a loss, it has to be as low as possible)
  • batch data: taking data by groups, for large datasets
  • number of epochs
  • number of samples
  • patience: how many epochs are needed to determinate that the train have finished if there is no improve between epochs.

Version

version: 1

Complexity: simple, complex, convLSTM, complex_convLSTM

complexity: complex

Root to save the model

root: /Users/Martin/Desktop/TFG/Proyecto Github/2020-tfg-alvaro-martin/Generator & Train_Test/Models/

root: C:/Users/optiva/Desktop/TFG/2020-tfg-alvaro-martin/Generator & Train_Test/Models/

Type of the net to train (NoRec, Rec)

net_type: Rec

Activation function

activation: relu modeled_activation: tanh

Loss function

raw_frame_loss: categorical_crossentropy modeled_frame_loss: mean_squared_error

Dropout options

dropout: flag: False percentage: 0.2

Epochs

n_epochs: 5000

Batch size

batch_size: 10

Patience

patience: 15

Data

data_dir: /Users/Martin/Desktop/30/Frames_dataset/linear_var_5000_80_120/linear_30[None]_ ###data_dir: /Users/Martin/Desktop/Generator_10/Frames_dataset/parabolic_point_255var_1_6000_80_120/parabolic_10[None]_ ###data_dir: /Users/Martin/Desktop/Generator_10/Frames_dataset/sinusoidal_point_255fix_1000_80_120/sinusoidal_10[None]_

WINDOWS ······················································

Data

###data_dir: C:/Users/optiva/Desktop/TFG/2020-tfg-alvaro-martin/Generator & Train_Test/Dataset_10/Frames_dataset/linear_point_255var_1_20000_80_120/linear_10[None]_ ###data_dir: C:/Users/optiva/Desktop/TFG/2020-tfg-alvaro-martin/Generator & Train_Test/Dataset_10/Frames_dataset/parabolic_point_255var_1_6000_80_120/parabolic_10[None]_ ###data_dir: C:/Users/optiva/Desktop/TFG/2020-tfg-alvaro-martin/Generator & Train_Test/Dataset_10/Frames_dataset/sinusoidal_point_255fix_1000_80_120/sinusoidal_10[None]_

batch_data: False ###True or False data_model: modeled ###raw or modeled gauss_pixel: False

So I started to understand how the code write in Python works and modified it for my own tests. I created my firsts Neural Networks models with LSMT 1 layer (Rec-simple) and LSMT 4 layers(Rec-complex), which are the ones with better performance.

All the training models are located in link

LSTM1

NET1
Resultados_LSTM1

LSTM4

NET4
Resultados_LSTM4

The extension of the files is .h5

10_False_tanh_mean_squared_error_10.h5

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