Commit 129eb779 authored by Jan Kovář's avatar Jan Kovář
Browse files

Adding NN vs data dimension check

parent e30a6d7c

neuralNetwork.py

+4 −0
Original line number Diff line number Diff line
@@ -3,6 +3,7 @@ from dataset import additional_input 
import numpy as np

import os

import shutil

import sys



class NeuralNetwork:  

    errors = []
@@ -78,6 +79,9 @@ class NeuralNetwork: 
            self.layer[n].bias -= self.learning_rate*self.layer[n].gradient_bias



    def getResult(self, input):

        if len(input) != self.structure[0]:

            print("Difference in dimension! The input layer has dimension "+str(self.structure[0])+", while the data have dimension "+str(len(input))+".")

            sys.exit(1)

        self.activation(input)

        return self.layer[self.depth-1].neurons

train.py

+3 −3
Original line number Diff line number Diff line
@@ -24,8 +24,8 @@ structure = [data.input_data_dimension,8,8,8,data.output_data_dimension] 
#Initialization of new neural network

network = NeuralNetwork(structure)



#Loading of already trained neural network

network.loadModel("model/")

#Loading of already trained neural network - uncomment if you want to evaluate saved network or train it further

#network.loadModel("model/")



#Initialization of training controller where the neural network, dataset and the whole configuration is stored

training_controller = TrainingController(network, data, plot_progress=True)
@@ -47,4 +47,4 @@ print("Accuracy on the test data is "+str(test_data_accuracy)+" %.") 


#Saving the trained model: weights, training history, final state plot, and the final weights plot

#Warning: For now, if the folder already exists, the model will be overwritten! Do not save, if you just want to evaluate with loaded network

#training_controller.saveTrainedModel()
 
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training_controller.saveTrainedModel()
 
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