# Artificial Neural Network

# Installing Theano
# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git

# Installing Tensorflow
# Install Tensorflow from the website: https://www.tensorflow.org/versions/r0.12/get_started/os_setup.html

# Installing Keras
# pip install --upgrade keras

# Part 1 - Data Preprocessing

# Importing the libraries
import numpy as np
import keras
from keras.models import Sequential
#from keras.models import load_model
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import AveragePooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers import Dropout


#Importing the dataset
with open("fer2013/fer2013.csv") as f:
    content = f.readlines()

lines = np.array(content)

num_of_instances = lines.size
print("number of instances: ",num_of_instances)

x_train, y_train, x_test, y_test = [], [], [], []

for i in range(1,num_of_instances):
    try:
        emotion, img, usage = lines[i].split(",")
        val = img.split(" ")
        pixels = np.array(val, 'float32')
        emotion = keras.utils.to_categorical(emotion, 6)
        if 'Training' in usage:
            x_train.append(pixels)
            y_train.append(emotion)
        elif 'PublicTest' in usage:
            x_test.append(pixels)
            y_test.append(emotion)
    except:
        print("", end="")

print(x_train)
model = Sequential()

#1st convolution layer
model.add(Convolution2D(64, (5, 5), input_shape=(48,48,1), activation='relu'))
model.add(MaxPooling2D(pool_size=(5,5), strides=(2, 2)))

#2nd convolution layer
model.add(Convolution2D(64, (3, 3), activation='relu'))
model.add(Convolution2D(64, (3, 3), activation='relu'))
model.add(AveragePooling2D(pool_size=(3,3), strides=(2, 2)))

#3rd convolution layer
model.add(Convolution2D(128, (3, 3), activation='relu'))
model.add(Convolution2D(128, (3, 3), activation='relu'))
model.add(AveragePooling2D(pool_size=(3,3), strides=(2, 2)))

model.add(Flatten())

#fully connected neural networks
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.2))

model.add(Dense(6, activation='softmax'))

from keras.preprocessing.image import ImageDataGenerator
#from keras import utils as np_utils

gen = ImageDataGenerator()
train_generator = gen.flow(x_train, y_train, batch_size=32)

#model.compile(loss='categorical_crossentropy', optimizer=keras.optimizers.Adam(), metrics=['accuracy'])

model.fit_generator(train_generator, steps_per_epoch=32, epochs=25)