CNN.py

import tensorflow as tf
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers import Dropout
from keras.preprocessing.image import ImageDataGenerator

#Create dataset

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('data/train',
target_size = (64, 64),
batch_size = 32,
class_mode = 'categorical')

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

# Initialising the CNN
classifier = Sequential()
# Step 1 - Convolution
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Adding a second axpooling
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Adding a third convolutional layer
classifier.add(Conv2D(64, (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(Dropout(0.5))
classifier.add(Dense(units = 8, activation = 'softmax'))
# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])
classifier.summary()

#Train model
classifier.fit_generator(training_set,
steps_per_epoch = 6589,
epochs = 5,
validation_data = test_set,
validation_steps = 20)

#Save model
classifier.save('my_model.h5')