Computer vision is a trend nowadays due to the latest developments in the field of deep learning. Researchers and developers are continuously proposing interesting applications of computer vision using deep learning frameworks. In the last article ‘Transfer Learning for Multi-Class Image Classification Using Deep Convolutional Network’, we used the VGG19 model as a transfer learning framework to classify CIFAR-10 images into 10 classes. Now we will explore the other popular transfer learning architectures in the same task and compare their classification performance.

In this article, we will compare the multi-class classification performance of three popular transfer learning architectures – VGG16, VGG19 and ResNet50. These all three models that we will use are pre-trained on ImageNet dataset. For the experiment, we have taken the CIFAR-10 image dataset that is a popular benchmark in image classification. The performances of all the three models will be compared using the confusion matrices and their average accuracies.

**The Dataset**

In this experiment, we will be using the CIFAR-10 dataset that is a publically available image data set provided by the Canadian Institute for Advanced Research (CIFAR). It consists of 60000 32×32 colour images in 10 classes, with 6000 images per class. The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. There are 50000 training images and 10000 test images in this dataset.

**VGG16 and VGG19**

VGG16 and VGG 19 are the variants of the VGGNet. VGGNet is a Deep Convolutional Neural Network that was proposed by Karen Simonyan and Andrew Zisserman of the University of Oxford in their research work ‘Very Deep Convolutional Neural Networks for Large-Scale Image Recognition’. The name of this model was inspired by the name of their research group ‘Visual Geometry Group (VGG)’. The VGG16 has 16 layers in its architecture while the VGG19 has 19 layers.

**ResNet50**

ResNet is the short name for Residual Networks and ResNet50 is a variant of this having 50 layers. It is a deep convolutional neural network used as a transfer learning framework where it uses the weights of pre-trained ImageNet.

**Implementation of Transfer Learning Models in Python**

Here, we are going to import all the required libraries. Make sure that you have installed the TensorFlow if you are working on your local system. For the implementation of transfer learning, three models VGG19, VGG16 and ResNet50 are also imported here.

#importing other required libraries import numpy as np import pandas as pd from sklearn.utils.multiclass import unique_labels import matplotlib.pyplot as plt import matplotlib.image as mpimg import seaborn as sns import itertools from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix from keras import Sequential from keras.applications import VGG19, VGG16, ResNet50 from keras.preprocessing.image import ImageDataGenerator from keras.optimizers import SGD,Adam from keras.callbacks import ReduceLROnPlateau from keras.layers import Flatten, Dense, BatchNormalization, Activation,Dropout from keras.utils import to_categorical import tensorflow as tf import random

Once the libraries are imported successfully, we will download the CIFAR-10 dataset that is a publicly available dataset with Keras.

#Keras library for CIFAR dataset from keras.datasets import cifar10 (x_train, y_train),(x_test, y_test)=cifar10.load_data()

After downloading the dataset, we will plot some random images from the dataset CIFAR-10 dataset to verify whether it has been downloaded correctly or not.

W_grid=5 L_grid=5 fig,axes = plt.subplots(L_grid,W_grid,figsize=(10,10)) axes=axes.ravel() n_training=len(x_train) for i in np.arange(0,L_grid * W_grid): index=np.random.randint(0,n_training) axes[i].imshow(x_train[index]) axes[i].set_title(y_train[index]) axes[i].axis('off') plt.subplots_adjust(hspace=0.4)

We will split our dataset into training and validation sets. Training and validation sets will be used during the training and the test set will be used in final prediction on the new image dataset.

#Train-validation-test split x_train,x_val,y_train,y_val=train_test_split(x_train,y_train,test_size=.3)

After the split, we will perform one-hot encoding on the dataset because our output has 10 classes. First, we will print the shape and after one-hot encoding, we will verify the final shape of the dataset.

#Dimension of the CIFAR10 dataset print((x_train.shape,y_train.shape)) print((x_val.shape,y_val.shape)) print((x_test.shape,y_test.shape)) #Onehot Encoding the labels. #Since we have 10 classes we should expect the shape[1] of y_train,y_val and y_test to change from 1 to 10 y_train=to_categorical(y_train) y_val=to_categorical(y_val) y_test=to_categorical(y_test) #Verifying the dimension after one hot encoding print((x_train.shape,y_train.shape)) print((x_val.shape,y_val.shape)) print((x_test.shape,y_test.shape))

In order to preprocess the image dataset to make it available for training the deep learning model, the below image data augmentation steps will be performed.

#Image Data Augmentation train_generator = ImageDataGenerator(rotation_range=2, horizontal_flip=True, zoom_range=.1 ) val_generator = ImageDataGenerator(rotation_range=2, horizontal_flip=True,zoom_range=.1) test_generator = ImageDataGenerator(rotation_range=2, horizontal_flip= True, zoom_range=.1) #Fitting the augmentation defined above to the data train_generator.fit(x_train) val_generator.fit(x_val) test_generator.fit(x_test)

Now, we will define the learning rate annealer. As we have discussed in the previous article, the learning rate annealer decreases the learning rate after a certain number of epochs if the error rate does not change.

#Learning Rate Annealer lrr= ReduceLROnPlateau(monitor='val_acc', factor=.01, patience=3, min_lr=1e-5)

**VGG19 Transfer Learning Model**

In the next step, we will initialize our VGG19 model. As we are going to use the VGG10 as a transfer learning framework, we will use the pre-trained ImageNet weights with this model.

`base_model_VGG19 = VGG19(include_top=False, weights='imagenet', input_shape=(32,32,3), classes=y_train.shape[1])`

Now we will add the layers to the VGG19 network that we have initialized above.

#Adding the final layers to the above base models where the actual classification is done in the dense layers model_vgg19 = Sequential() model_vgg19.add(base_model_VGG19) model_vgg19.add(Flatten()) model_vgg19.add(Dense(1024,activation=('relu'),input_dim=512)) model_vgg19.add(Dense(512,activation=('relu'))) model_vgg19.add(Dense(256,activation=('relu'))) #model_vgg19.add(Dropout(.3)) model_vgg19.add(Dense(128,activation=('relu'))) #model_vgg19.add(Dropout(.2)) model_vgg19.add(Dense(10,activation=('softmax')))

After adding all the layers, we will check the model’s summary.

```
#VGG19 Model Summary
model_vgg19.summary()
```

Next, we will define the training hyperparameters and compile our model. For error optimization, we will be using stochastic gradient descent.

#Defining the hyperparameters batch_size= 100 epochs=50 learn_rate=.001 sgd=SGD(lr=learn_rate,momentum=.9,nesterov=False) #Compiling the VGG19 model model_vgg19.compile(optimizer=sgd,loss='categorical_crossentropy',metrics=['accuracy'])

After defining all the hyperparameters, we will train our model in 20 epochs.

```
model_vgg19.fit_generator(train_generator.flow(x_train, y_train, batch_size = batch_size), epochs=epochs, steps_per_epoch = x_train.shape[0]//batch_size, validation_data = val_generator.flow(x_val, y_val, batch_size = batch_size), validation_steps = 250, callbacks = [lrr], verbose = 1)
```

The training performance will be visualized now in terms of loss and accuracy during the training and the validation.

#Plotting the training and validation loss f,ax=plt.subplots(2,1) #Creates 2 subplots under 1 column #Training loss and validation loss ax[0].plot(model_vgg19.history.history['loss'],color='b',label='Training Loss') ax[0].plot(model_vgg19.history.history['val_loss'],color='r',label='Validation Loss') #Training accuracy and validation accuracy ax[1].plot(model_vgg19.history.history['acc'],color='b',label='Training Accuracy') ax[1].plot(model_vgg19.history.history['val_acc'],color='r',label='Validation Accuracy')

To plot the confusion matrix, we will define a function here.

#Defining function for confusion matrix plot def plot_confusion_matrix(y_true, y_pred, classes, normalize=False, title=None, cmap=plt.cm.Blues): if not title: if normalize: title = 'Normalized confusion matrix' else: title = 'Confusion matrix, without normalization' # Computing confusion matrix cm = confusion_matrix(y_true, y_pred) if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] print("Normalized confusion matrix") else: print('Confusion matrix, without normalization') # Visualizing fig, ax = plt.subplots(figsize=(7,7)) im = ax.imshow(cm, interpolation='nearest', cmap=cmap) ax.figure.colorbar(im, ax=ax) # We want to show all ticks... ax.set(xticks=np.arange(cm.shape[1]), yticks=np.arange(cm.shape[0]), xticklabels=classes, yticklabels=classes, title=title, ylabel='True label', xlabel='Predicted label') # Rotating the tick labels and setting their alignment. plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") # Looping over data dimensions and create text annotations. fmt = '.2f' if normalize else 'd' thresh = cm.max() / 2. for i in range(cm.shape[0]): for j in range(cm.shape[1]): ax.text(j, i, format(cm[i, j], fmt), ha="center", va="center", color="white" if cm[i, j] > thresh else "black") fig.tight_layout() return ax np.set_printoptions(precision=2)

We will make the predictions through the trained VGG19 model using the test image dataset.

#Making prediction y_pred1 = model_vgg19.predict_classes(x_test) y_true = np.argmax(y_test,axis=1)

Now, we will plot the non-normalized confusion matrix to visualize the exact number of classifications and normalized confusion matrix to visualize the percentage of classifications.

#Plotting the confusion matrix confusion_mtx=confusion_matrix(y_true,y_pred) class_names=['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] #Plotting non-normalized confusion matrix plot_confusion_matrix(y_true, y_pred1, classes = class_names, title = 'Non-Normalized VGG19 Confusion Matrix') #Plotting normalized confusion matrix plot_confusion_matrix(y_true, y_pred1, classes = class_names, normalize = True, title = 'Normalized VGG19 Confusion matrix')

Finally, we will see the average classification accuracy of VGG19.

#Accuracy of VGG19 from sklearn.metrics import accuracy_score accuracy_score(y_true, y_pred1)

**VGG16 ****Transfer Learning Model**

As the next model, we will repeat the above steps for the VGG16 model.

#VGG16 Model base_model_vgg16 = VGG16(include_top = False, weights= 'imagenet', input_shape = (32,32,3), classes = y_train.shape[1]) #Adding the final layers to the above base models where the actual classification is done in the dense layers model_vgg16= Sequential() model_vgg16.add(base_model_vgg16) model_vgg16.add(Flatten()) #Adding the Dense layers along with activation and batch normalization model_vgg16.add(Dense(1024,activation=('relu'),input_dim=512)) model_vgg16.add(Dense(512,activation=('relu'))) model_vgg16.add(Dense(256,activation=('relu'))) #model.add(Dropout(.3)) model_vgg16.add(Dense(128,activation=('relu'))) #model.add(Dropout(.2)) model_vgg16.add(Dense(10,activation=('softmax'))) #Checking the final VGG16 model summary model_vgg16.summary() #Compiling VGG16 model_vgg16.compile(optimizer = sgd, loss = 'categorical_crossentropy', metrics = ['accuracy']) #Training VGG16 model_vgg16.fit_generator(train_generator.flow(x_train, y_train, batch_size = batch_size), epochs = epochs, steps_per_epoch = x_train.shape[0]//batch_size, validation_data = val_generator.flow(x_val, y_val, batch_size = batch_size), validation_steps=250, callbacks=[lrr], verbose=1) #Plotting the VGG16 training and validation loss f,ax=plt.subplots(2,1) #Creates 2 subplots under 1 column #Training loss and validation loss ax[0].plot(model_vgg16.history.history['loss'],color='b',label='Training Loss') ax[0].plot(model_vgg16.history.history['val_loss'],color='r',label='Validation Loss') #Training accuracy and validation accuracy ax[1].plot(model_vgg16.history.history['accuracy'],color='b',label='Training Accuracy') ax[1].plot(model_vgg16.history.history['val_accuracy'],color='r',label='Validation Accuracy') #Making prediction y_pred2=model_vgg16.predict_classes(x_test) y_true=np.argmax(y_test,axis=1) #Plotting the confusion matrix confusion_mtx=confusion_matrix(y_true,y_pred2) #Plotting non-normalized confusion matrix plot_confusion_matrix(y_true, y_pred2, classes = class_names,title = 'Non-Normalized VGG16 Confusion Matrix') #Plotting normalized confusion matrix plot_confusion_matrix(y_true, y_pred2, classes = class_names, normalize = True, title= 'Normalized VGG16 Confusion matrix') #Accuracy of VGG16 from sklearn.metrics import accuracy_score accuracy_score(y_true, y_pred2)

**ResNet50 ****Transfer Learning Model**

In the next step, we will perform the same steps with the ResNet50 model.

#Initializing ResNet50 base_model_resnet = ResNet50(include_top = False, weights = 'imagenet', input_shape = (32,32,3), classes = y_train.shape[1])

#Adding layers to the ResNet50 model_resnet=Sequential() #Add the Dense layers along with activation and batch normalization model_resnet.add(base_model_resnet) model_resnet.add(Flatten()) #Add the Dense layers along with activation and batch normalization model_resnet.add(Dense(1024,activation=('relu'),input_dim=512)) model_resnet.add(Dense(512,activation=('relu'))) model_resnet.add(Dropout(.4)) model_resnet.add(Dense(256,activation=('relu'))) model_resnet.add(Dropout(.3)) model_resnet.add(Dense(128,activation=('relu'))) model_resnet.add(Dropout(.2)) model_resnet.add(Dense(10,activation=('softmax'))) #Summary of ResNet50 Model model_resnet.summary() #Compiling ResNet50 model_resnet.compile(optimizer = sgd, loss = 'categorical_crossentropy', metrics = ['accuracy']) #Training the ResNet50 model model_resnet.fit_generator(train_generator.flow(x_train, y_train, batch_size=batch_size), epochs=epochs, steps_per_epoch = x_train.shape[0]//batch_size, validation_data = val_generator.flow(x_val, y_val, batch_size = batch_size), validation_steps = 250, callbacks = [lrr], verbose=1) #Plotting the training and validation loss f,ax=plt.subplots(2,1) #Creates 2 subplots under 1 column #Training loss and validation loss ax[0].plot(model_resnet.history.history['loss'],color='b',label='Training Loss') ax[0].plot(model_resnet.history.history['val_loss'],color='r',label='Validation Loss') #Training accuracy and validation accuracy ax[1].plot(model_resnet.history.history['accuracy'],color='b',label='Training Accuracy') ax[1].plot(model_resnet.history.history['val_accuracy'],color='r',label='Validation Accuracy') #Making prediction y_pred3=model_resnet.predict_classes(x_test) y_true=np.argmax(y_test,axis=1) #Plotting the non normalized confusion matrix confusion_mtx=confusion_matrix(y_true,y_pred3) #Plotting non-normalized confusion matrix plot_confusion_matrix(y_true, y_pred3, classes = class_names, title = 'Non-Normalized ResNet50 Confusion Matrix') #Plotting normalized confusion matrix plot_confusion_matrix(y_true, y_pred3, classes=class_names, normalize = True, title = 'Normalized ResNet50 Confusion Matrix') #ResNet50 Classification accuracy from sklearn.metrics import accuracy_score accuracy_score(y_true, y_pred3)

Hence, the accuracy scores of all the three models are:-

Model |
VGG19 |
VGG16 |
ResNet50 |

Accuracy(%) |
85.08 | 84.54 | 79.48 |

Finally, we are ready with all the evaluation matrices to analyze the three transfer learning-based deep convolutional neural network models. By analyzing accuracy scores and confusion matrices of all the tree models – VGG19, VGG16 and the ResNet50, we can conclude that the VGG19 has the best performance among all. The above scores are obtained in 20 epochs of training. It is possible that the score may be improved if we train the models in more epochs.

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Get the latest updates and relevant offers by sharing your email.Vaibhav Kumar has experience in the field of Data Science and Machine Learning, including research and development. He holds a PhD degree in which he has worked in the area of Deep Learning for Stock Market Prediction. He has published/presented more than 15 research papers in international journals and conferences. He has an interest in writing articles related to data science, machine learning and artificial intelligence.