In [8]:
print('Mean value of accuracy during training time is : %.2f' %np.mean(results.history["val_acc"]))

Mean value of accuracy during training time is : 0.97
In [9]:
prediction_values = model.predict_classes(X_test)
prediction_values.shape # our neural network outputs predictions of labels for training set
Out[9]:
(1980, 1)
In [10]:
print(model.metrics_names[:])

['loss', 'acc']
In [11]:
# evaluate the model
scores = model.evaluate(X_train,y_train.T)
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

4020/4020 [==============================] - 0s 12us/step

acc: 100.00%
In [12]:
scores
Out[12]:
[0.0003365136498228568, 1.0]