Confusion matrix 

We use a confusion matrix to validate the performance of a classification model on the test data for which the true values are known. 

To view the confusion matrix of the model, execute the following code:

y_pred=model.predict(x_test)
y_pred=np.argmax(y_pred, axis=1)
y_test=np.argmax(y_test, axis=1)
from sklearn.metrics import confusion_matrix
confusion_matrix = confusion_matrix(y_test, y_pred)
confusion_matrix

The confusion matrix of the model looks as follows:

You can create the confusion matrix in a more advanced way with the following code:

# Confussion matrix 
import itertools
from sklearn.metrics import confusion_matrix

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks...