# In[101]:
# Training the model
epochs_tot = 1000
epochs_step = 250
epochs_ratio = int(epochs_tot / epochs_step)
hist =np.array([])
for i in range(epochs_ratio):
history = model.fit(features, targets, epochs=epochs_step, batch_size=100, verbose=0)
# Evaluating the model on the training and testing set
print("Step : " , i * epochs_step, "/", epochs_tot)
score = model.evaluate(features, targets)
print("Training MSE:", score[1])
score = model.evaluate(features_validation, targets_validation)
print("Validation MSE:", score[1], "\n")
hist = np.concatenate((hist, np.array(history.history['mse'])), axis = 0)#mse: mean_square_error
# plot metrics
plt.plot(hist)
plt.show()
# In[102]:
y_pred = model.predict(features_validation, verbose=0)
