更新时间：2018-07-05
前言
项目是好久之前做的，现在可能类似的demo也有很多了，已知忘记发博客和上传GitHub了，刚传上去，地址：https://github.com/roguesir/DL-ML-project/tree/master/Expression-Recognition
结构设计
采用fer2013人脸表情数据集，使用裁剪和旋转变换进行数据增强，处理后的数据量达到105000张，大小为48x48的黑白图片。 基本结构如下：
训练结果
模型准确率（%）
model-basic90.11model-899.27model-1399.80model-13-conv99.86
部分代码
from keras.models
import Sequential
from keras.layers.core
import Dense, Dropout, Flatten, Activation
from keras.layers.convolutional
import Conv2D, MaxPooling2D
from keras.preprocessing.image
import load_img, img_to_array
from keras.utils
import np_utils
import os
import numpy
as np
def load_dataset(filedir):
"""
读取数据
:param filedir:
:return:
"""
image_data_list = []
label = []
train_image_list = os.listdir(filedir +
'/train_data')
for img
in train_image_list:
url = os.path.join(filedir +
'/train_data/' + img)
image = load_img(url, grayscale=
True, target_size=(
48,
48))
image_data_list.append(img_to_array(image))
label.append(img.split(
'.')[
0].split(
"-")[
1])
img_data = np.array(image_data_list)
img_data = img_data.astype(
'float32')
img_data /=
255
return img_data, label
def make_network():
model = Sequential()
model.add(Conv2D(
32, (
3,
3), padding=
'same', input_shape=(
48,
48,
1)))
model.add(Activation(
'relu'))
model.add(Conv2D(
32, (
3,
3)))
model.add(Activation(
'relu'))
model.add(MaxPooling2D(pool_size=(
2,
2)))
model.add(Dropout(
0.5))
model.add(Flatten())
model.add(Dense(
128))
model.add(Activation(
'relu'))
model.add(Dropout(
0.5))
model.add(Dense(
7))
model.add(Activation(
'softmax'))
model.save(
'./data/model/model-basic.h5')
return model
if __name__ ==
'__main__':
train_loss = []
train_accuracy = []
train_x, train_y = load_dataset(
'data')
train_y = np_utils.to_categorical(train_y)
model = make_network()
model.compile(loss=
'categorical_crossentropy', optimizer=
'adadelta', metrics=[
'accuracy'])
model.fit(train_x, train_y, batch_size=
500, epochs=
200, verbose=
1)
