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I am currently trying to implement a confusion matrix into my cnn model code. All the examples that I've been watching includes using x_train, x_test, y_train, y_test, but I don't know how to do that on my code, or if I will be able to do it from my_model.h5 file. Hoping you could help me. Thanks.

I leave my model code below here:

classifier = Sequential()

classifier.add(Conv2D(32, (3,  3), input_shape = (64, 64, 3), activation = 'relu'))

classifier.add(MaxPooling2D(pool_size =(2,2)))

classifier.add(Conv2D(32, (3,  3), activation = 'relu'))

classifier.add(MaxPooling2D(pool_size =(2,2)))

classifier.add(Conv2D(64, (3,  3), activation = 'relu'))

classifier.add(MaxPooling2D(pool_size =(2,2)))

classifier.add(Flatten())

classifier.add(Dense(256, activation = 'relu'))

classifier.add(Dropout(0.5))

classifier.add(Dense(26, activation = 'softmax'))



classifier.compile(

          optimizer = optimizers.SGD(lr = 0.01),

          loss = 'categorical_crossentropy',

          metrics = ['accuracy'])



classifier.summary()



from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(

    rescale=1./255,

    shear_range=0.2,

    zoom_range=0.2,

    horizontal_flip=True)



test_datagen = ImageDataGenerator(rescale=1./255)



training_set = train_datagen.flow_from_directory(

    'mydata/training_set',

    target_size=(64, 64),

    batch_size=32,

    class_mode='categorical')



test_set = test_datagen.flow_from_directory(

    'mydata/test_set',

    target_size=(64, 64),

    batch_size=32,

    class_mode='categorical')



model = classifier.fit_generator(

    training_set,

    steps_per_epoch=int(steps_per_epoch_user),

    epochs=int(epochs_user),

    validation_data = test_set,

    validation_steps = int(validation_steps_user)

  )



import h5py

classifier.save('my_model.h5')



print(model.history.keys()) 



import matplotlib.pyplot as plt



plt.plot(model.history['acc'])

plt.plot(model.history['val_acc'])

plt.title('model accuracy')

plt.ylabel('acc')

plt.xlabel('epoch')

plt.legend(['train', 'test'], loc='upper left')

plt.show()



plt.plot(model.history['loss'])

plt.plot(model.history['val_loss'])

plt.title('model loss')

plt.ylabel('loss')

plt.xlabel('epoch')

plt.legend(['train', 'test'], loc='upper left')

plt.show()

I would add the following code:

from sklearn.metrics import confusion_matrix

cm = confusion_matrix(Y_test,Y_test_predicted)

print('n','cm=','n',cm)

for more descriptive confusion matrix look at scikit-learn documentation in the following link:

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html