Model Performance Analysis

Refer: 16.2 LittleVGG Simpsons

import matplotlib.pyplot as plt
import sklearn
from sklearn.metrics import classification_report, confusion_matrix
import numpy as np

	
We need to recreate our validation generator with shuffle = false

validation_generator = validation_datagen.flow_from_directory(

        validation_data_dir,

        target_size=(img_rows, img_cols),

        batch_size=batch_size,

        class_mode=‘categorical’,

        shuffle=False)

class_labels = validation_generator.class_indices

class_labels = {v: k for k, v in class_labels.items()}

classes = list(class_labels.values())
nb_train_samples = 19548

nb_validation_samples = 990
#Confution Matrix and Classification Report

Y_pred = model.predict_generator(validation_generator, nb_validation_samples // batch_size+1)

y_pred = np.argmax(Y_pred, axis=1)
print(‘Confusion Matrix’)

print(confusion_matrix(validation_generator.classes, y_pred))

print(‘Classification Report’)

target_names = list(class_labels.values())

print(classification_report(validation_generator.classes, y_pred, target_names=target_names))
plt.figure(figsize=(8,8))

cnf_matrix = confusion_matrix(validation_generator.classes, y_pred)
plt.imshow(cnf_matrix, interpolation=‘nearest’)

plt.colorbar()

tick_marks = np.arange(len(classes))

_ = plt.xticks(tick_marks, classes, rotation=90)

_ = plt.yticks(tick_marks, classes)