Vaibhav Kumar has experience in the field of Data Science…

Image classification has become more interesting in the research field due to the development of new and high performing machine learning frameworks. With the advancement of artificial neural networks and the development of deep learning architectures such as the convolutional neural network, that is based on artificial neural networks has triggered the application of multiclass image classification and recognition of objects belonging to the multiple categories. Every latest machine learning framework has a comparative advantage over the older ones in terms of performance and complexity.

In this article, we will implement the multiclass image classification using the VGG-19 Deep Convolutional Network used as a Transfer Learning framework where the VGGNet comes pre-trained on the ImageNet dataset. For the experiment, we will use the CIFAR-10 dataset and classify the image objects into 10 classes. The classification accuracies of the VGG-19 model will be visualized using the non-normalized and normalized confusion matrices.

**What is Transfer Learning?**

Transfer learning is a research problem in the field of machine learning. It stores the knowledge gained while solving one problem and applies it to a different but related problem. For example, the knowledge gained while learning to recognize cats could apply when trying to recognize cheetahs. In deep learning, transfer learning is a technique whereby a neural network model is first trained on a problem similar to the problem that is being solved. Transfer learning has the advantage of decreasing the training time for a learning model and can result in lower generalization error.

**VGGNet – The Deep Convolutional Network**

VGGNet is 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)’. As this convolutional neural network has 19 layers in its architecture, it was named VGG-19. This model was proposed to reduce the number of parameters in a convolutional neural network with improved training time. Below is the block diagram of VGG-19 that illustrates its architecture.

(Image source: mc.ai)

The biggest advantage of this network is that You can load a pre-trained version of the network trained on more than a million images from the ImageNet database. A pre-trained network can classify images into thousands of object categories. Due to this advantage, we are going to apply this model on the CIFAR-10 image dataset that has 10 object categories.

**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.

**Implementation in Python**

We will import the library to download the CIFAR-10 data set.

#Keras library for CIFAR-10 dataset from keras.datasets import cifar10 #Downloading the CIFAR dataset (x_train,y_train),(x_test,y_test)=cifar10.load_data()

We will import the remaining libraries that are going to be required in our experiment.

#importing other required libraries import numpy as np import pandas as pd from sklearn.utils.multiclass import unique_labels import os 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 #For Transfer Learning 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

Here, we will split the downloaded dataset into training, test and validation sets.

#defining training and test sets x_train,x_val,y_train,y_val=train_test_split(x_train,y_train,test_size=.3)

Once split, we will see the shape of our data. It should be same as given in the dataset description at its parent website.

#Dimension of the dataset print((x_train.shape,y_train.shape)) print((x_val.shape,y_val.shape)) print((x_test.shape,y_test.shape))

We need to do one hot encoding here because we have 10 classes and we should expect the shape[1] of y_train,y_val and y_test to change from 1 to 10

#One Hot Encoding y_train=to_categorical(y_train) y_val=to_categorical(y_val) y_test=to_categorical(y_test)

After one hot encoding, we will ensure that we have obtained the required shape.

#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))

Here, we will perform the image data augmentation. This is the technique that is used to expand the size of a training dataset by creating modified versions of images in the dataset. First, we will define individual instances of ImageDataGenerator for augmentation and then we will fit them with each of the training, test and validation datasets.

#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)

We will use the learning rate annealer in this experiment. The learning rate annealer decreases the learning rate after a certain number of epochs if the error rate does not change. Here, through this technique, we will monitor the validation accuracy and if it seems to be a plateau in 3 epochs, it will reduce the learning rate by 0.01.

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

Now, we will instantiate the VGG19 that is a deep convolutional neural network as a transfer learning model.

##### Defining VGG19 as a Deep Convolutional Neural Network

#Defining the VGG Convolutional Neural Net base_model = VGG19(include_top = False, weights = 'imagenet', input_shape = (32,32,3), classes = y_train.shape[1])

Now, we will define VGG19 as a deep learning architecture. For this purpose, it will be defined as a Keras Sequential model with several dense layers.

Home » Transfer Learning For Multi-Class Image Classification Using Deep Convolutional Neural Network

#Adding the final layers to the above base models where the actual classification is done in the dense layers model= Sequential() model.add(base_model) model.add(Flatten())

Now, to add further layers, we need to see the dimension of our model.

#Model summary model.summary()

#Adding the Dense layers along with activation and batch normalization model.add(Dense(1024,activation=('relu'),input_dim=512)) model.add(Dense(512,activation=('relu'))) model.add(Dense(256,activation=('relu'))) model.add(Dropout(.3))ense(128,activation=('relu'))) #model.add(Dropout(.2)) model.add(Dense(10,activation=('softmax'))) #Checking the final model summary model.summary()

As we have defined our model, now we need to initialize the hyperparameters that are required to train the model and then finally, we will compile our model.

#Initializing the hyperparameters batch_size= 100 epochs=50 learn_rate=.001 sgd=SGD(lr=learn_rate,momentum=.9,nesterov=False) adam=Adam(lr=learn_rate, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) model.compile(optimizer=sgd,loss='categorical_crossentropy',metrics=['accuracy'])

Now, we start training our VGG10, the deep convolutional neural network model.

#Training the model model.fit_generator(train_generator.flow(x_train, y_train, batch_siz e= 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)

As we can see in the above picture, we have achieved the training accuracy by 99.22% and validation accuracy by 85.41%. Now we will visualize the accuracy and loss during training.

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

We will make image class predictions through this model using the test data set.

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

##### Performance of VGG19 – The Deep Convolutional Neural Network

Finally, we will visualize the classification performance on test data using confusion matrices.

#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' #Compute 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') #print(cm) 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]), # ... and label them with the respective list entries xticklabels=classes, yticklabels=classes, title=title, ylabel='True label', xlabel='Predicted label') #Rotate the tick labels and set their alignment. plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") # Loop 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)

First, we will see the exact number of correct and incorrect classification using the non-normalized confusion matrix and then we will see the same in percentage using the normalized confusion matrix.

#Plotting the confusion matrix confusion_mtx = confusion_matrix(y_true, y_pred) #Defining the class labels class_names=['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] # Plotting non-normalized confusion matrix plot_confusion_matrix(y_true, y_pred, classes = class_names, title='Confusion matrix, without normalization')

#Plotting normalized confusion matrix plot_confusion_matrix(y_true, y_pred, classes = class_names, normalize = True, title = 'Normalized confusion matrix')

As we can see by classifying images into 10 classes, the model has given a minimum accuracy of 72% and a maximum accuracy of 95%. We can further tune the training parameters and re-train our model to see any possible upscaling in the classification. But what we have got in this experiment is the standard one. Out of 10 classes, it has given less than 80% accuracy in classifying only for 3 classes and has given more than 90% accuracy in classifying images of 5 classes.

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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.