emotion_model_train.py

import tensorflow as tf

import keras
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
from keras.layers import Dense, Activation, Dropout, Flatten

from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator

import numpy as np
import matplotlib.pyplot as plt



num_classes = 7 #angry, disgust, fear, happy, sad, surprise, neutral
batch_size = 512
epochs = 5


with open("fer2013.csv") as f:
    content = f.readlines()

lines = np.array(content)

num_of_instances = lines.size
print("number of instances: ",num_of_instances)
print("instance length: ",len(lines[1].split(",")[1].split(" ")))


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

#------------------------------
#transfer train and test set data
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, num_classes)
		if 'Training' in usage:
			y_train.append(emotion)
			x_train.append(pixels)
		elif 'PublicTest' in usage:
			y_test.append(emotion)
			x_test.append(pixels)
	except:
		print("",end="")

#------------------------------
#data transformation for train and test sets
x_train = np.array(x_train, 'float32')
y_train = np.array(y_train, 'float32')
x_test = np.array(x_test, 'float32')
y_test = np.array(y_test, 'float32')

x_train /= 255 #normalize inputs between [0, 1]
x_test /= 255

x_train = x_train.reshape(x_train.shape[0], 48, 48, 1)
x_train = x_train.astype('float32')
x_test = x_test.reshape(x_test.shape[0], 48, 48, 1)
x_test = x_test.astype('float32')

print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

#------------------------------
#construct CNN structure
model = Sequential()

#1st convolution layer
model.add(Conv2D(128, (5, 5), activation='relu', input_shape=(48,48,1)))
model.add(MaxPooling2D(pool_size=(5,5), strides=(2, 2)))

#2nd convolution layer
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(AveragePooling2D(pool_size=(3,3), strides=(2, 2)))

#3rd convolution layer
model.add(Conv2D(256, (3, 3), activation='relu'))
model.add(Conv2D(256, (3, 3), activation='relu'))
model.add(AveragePooling2D(pool_size=(3,3), strides=(2, 2)))

model.add(Flatten())

#fully connected neural networks
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.2))

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

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

#------------------------------

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

model.fit_generator(train_generator, steps_per_epoch=batch_size, epochs=epochs) #train for randomly selected one
model.save("model.h5")