Image Classifier: An Application of Deep Learning

Convolutional Neural Networks (CNNs) solve an image classification problem, that is to which class the input image belongs to.

You can use Keras deep learning library. Note that we are using the training and testing data set of images of cats and dogs from following link https://www.kaggle.com/c/dogs-vs-cats/data.

Import the important keras libraries and packages as shown:

The following package called sequential will initialize the neural networks as sequential network.

from keras.models import Sequential

The following package called Conv2D is used to perform the convolution operation, the first step of CNN.

from keras.layers import Conv2D

The following package called MaxPoling2D is used to perform the pooling operation, the second step of CNN.

from keras.layers import MaxPooling2D

The following package called Flatten is the process of converting all the resultant 2D arrays into a single long continuous linear vector.

from keras.layers import Flatten

The following package called Dense is used to perform the full connection of the neural network, the fourth step of CNN.

from keras.layers import Dense

Now, create an object of the sequential class.

S_classifier = Sequential()

Now, next step is coding the convolution part.

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

Here relu is the rectifier function.

Now, the next step of CNN is the pooling operation on the resultant feature maps after convolution part.

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

Now, convert all the pooled images into a continuous vector by using flattering

S_classifier.add(Flatten())

Next, create a fully connected layer.

S_classifier.add(Dense(units = 128, activation = 'relu'))

Here, 128 is the number of hidden units. It is a common practice to define the number of hidden units as the power of 2.

Now, initialize the output layer as follows:

S_classifier.add(Dense(units = 1, activation = 'sigmoid'))

Now, compile the CNN, we have built:

S_classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

Here optimizer parameter is to choose the stochastic gradient descent algorithm, loss parameter is to choose the loss function and metrics parameter is to choose the performance metric.

Now, perform image augmentations and then fit the images to the neural networks:

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(”/Users/admin/training_set”,target_size = (64, 64),batch_size = 32,class_mode = 'binary')
test_set = test_datagen.flow_from_directory('test_set',target_size = (64, 64),batch_size = 32,class_mode = 'binary')

Now, fit the data to the model we have created:

classifier.fit_generator(training_set,steps_per_epoch = 8000,epochs = 25,validation_data = test_set,validation_steps = 2000)

Here steps_per_epoch have the number of training images.

Now as the model has been trained, we can use it for prediction as follows:

from keras.preprocessing import image
test_image = image.load_img('dataset/single_prediction/cat_or_dog_1.jpg', target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)

training_set.class_indices
if result[0][0] == 1:
prediction = 'dog'
else:
prediction = 'cat'