cnn.py

# Importing the Keras libraries and packages
from keras.models import Sequential, load_model
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense

# Initialising the CNN
classifier = Sequential()

# Step 1 - Convolution
classifier.add(Convolution2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))

# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Adding a second convolutional layer
classifier.add(Convolution2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Step 3 - Flattening
classifier.add(Flatten())

# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 4, activation = 'softmax'))

# Compiling the CNN
classifier.compile(
  optimizer = 'adam',
  loss = 'categorical_crossentropy', 
  metrics = ['accuracy']
)

# Part 2 - Fitting the CNN to the images
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('dataset/training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'categorical')

test_set = test_datagen.flow_from_directory('dataset/testing_set',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'categorical')

classifier.fit_generator(training_set,
                         samples_per_epoch = 1000,
                         nb_epoch = 10,
                         validation_data = test_set,
                         nb_val_samples = 500)

classifier.save("darth.h5")