Let's plot the validation accuracy per epoch for the three different models. First, we plot for the model trained using the sgd optimizer:
from matplotlib import pyplot as plt
plt.plot(range(1,11), SGD_score.history['acc'], label='Training Accuracy')
plt.plot(range(1,11), SGD_score.history['val_acc'],
label='Validation Accuracy')
plt.axis([1, 10, 0, 1])
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.title('Train and Validation Accuracy using SGD Optimizer')
plt.legend()
plt.show()
We get the following output:
Did you notice anything wrong? The training and validation accuracy is stuck at 50%! Essentially, this shows that the training has failed and our neural network performs no better than a random coin toss for this binary classification task. Clearly, the sgd optimizer is not...
