Python Deep Learning Projects

Executing the model 

Now it's time to train our model with the provided dataset and the pre-trained embedding model. A few hyperparameters will need fine-tuning to achieve good results. But once we have executed the train.py file with reasonably good configurations, we can demonstrate that the model is able to distinguish well between the positive and negative sentences when classifying.

As we can see in the following graph, the performance metric of accuracy is tending towards 1 and the loss factor is reducing towards 0 over each iteration:

A plot of the performance metrics accuracy and loss of the CNN model during the training process