model.py

import os
import cv2
import tensorflow as tf
from utils import *

EMOTIONS = ['angry', 'disgusted', 'fearful', 'happy', 'sad', 'surprised', 'neutral']


def deepnn(x):
    x_image = tf.reshape(x, [-1, 48, 48, 1])
    # conv1
    W_conv1 = weight_variables([5, 5, 1, 64])
    b_conv1 = bias_variable([64])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    # pool1
    h_pool1 = maxpool(h_conv1)
    # norm1
    norm1 = tf.nn.lrn(h_pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)

    # conv2
    W_conv2 = weight_variables([3, 3, 64, 64])
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    norm2 = tf.nn.lrn(h_conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
    h_pool2 = maxpool(norm2)

    # Fully connected layer
    W_fc1 = weight_variables([12 * 12 * 64, 384])
    b_fc1 = bias_variable([384])
    h_conv3_flat = tf.reshape(h_pool2, [-1, 12 * 12 * 64])
    h_fc1 = tf.nn.relu(tf.matmul(h_conv3_flat, W_fc1) + b_fc1)

    # Fully connected layer
    W_fc2 = weight_variables([384, 192])
    b_fc2 = bias_variable([192])
    h_fc2 = tf.matmul(h_fc1, W_fc2) + b_fc2

    # linear
    W_fc3 = weight_variables([192, 7])
    b_fc3 = bias_variable([7])
    y_conv = tf.add(tf.matmul(h_fc2, W_fc3), b_fc3)

    return y_conv


def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def maxpool(x):
    return tf.nn.max_pool(x, ksize=[1, 3, 3, 1],
                        strides=[1, 2, 2, 1], padding='SAME')


def weight_variables(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)


def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)


def train_model(train_data):
    fer2013 = input_data(train_data)
    max_train_steps = 30001

    x = tf.placeholder(tf.float32, [None, 2304])
    y_ = tf.placeholder(tf.float32, [None, 7])

    y_conv = deepnn(x)

    cross_entropy = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    with tf.Session() as sess:
        saver = tf.train.Saver()
        sess.run(tf.global_variables_initializer())

        for step in range(max_train_steps):
            batch = fer2013.train.next_batch(25)
            if step % 100 == 0:
                train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1]})
                print('step %d, training accuracy %g' % (step, train_accuracy))
            if step + 1 == max_train_steps:
                saver.save(sess, './models/emotion_model', global_step=step + 1)
            train_step.run(feed_dict={x: batch[0], y_: batch[1]})


def image_to_tensor(image):
    tensor = np.asarray(image).reshape(-1, 2304) * 1 / 255.0
    return tensor