TensorFlow和Keras完成JAFFE人脸表情识别

cut_save_face.py

#!/usr/bin/python

# coding:utf8

import cv2

import os

import numpy as np

import csv

def detect(img, cascade):

    """

    使用Haar特征检测分类器完成人脸检测

    :param img:

    :param cascade:

    :return:

    """

    # detectMultiScale检测出图片中所有的人脸，并将人脸用vector保存各个人脸的坐标、大小（用矩形表示），返回坐标。

    rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30),

                                     flags=cv2.CASCADE_SCALE_IMAGE)

    if len(rects) == 0:

        return []

    rects[:, 2:] += rects[:, :2]

    return rects

cascade = cv2.CascadeClassifier(

    "E:/Anaconda3/envs/sklearn/Library/etc/haarcascades/haarcascade_frontalface_alt.xml")

f = "jaffe/"

fs = os.listdir(f)

data = np.zeros([213, 48 * 48], dtype=np.uint8)

label = np.zeros([213], dtype=int)

i = 0

for f1 in fs:

    tmp_path = os.path.join(f, f1)

    if not os.path.isdir(tmp_path):

        # print(tmp_path[len(f):])

        img = cv2.imread(tmp_path, 1)

        # BGR转换为灰度图

        dst = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

        rects = detect(dst, cascade)

        for x1, y1, x2, y2 in rects:

            cv2.rectangle(img, (x1 + 10, y1 + 20), (x2 - 10, y2), (0, 255, 255), 2)

            # 调整截取脸部区域大小

            img_roi = np.uint8([y2 - (y1 + 20), (x2 - 10) - (x1 + 10)])

            roi = dst[y1 + 20:y2, x1 + 10:x2 - 10]

            img_roi = roi

            re_roi = cv2.resize(img_roi, (48, 48))

            # 获得表情label

            img_label = tmp_path[len(f) + 3:len(f) + 5]

            # print(img_label)

            if img_label == 'AN':

                label[i] = 0

            elif img_label == 'DI':

                label[i] = 1

            elif img_label == 'FE':

                label[i] = 2

            elif img_label == 'HA':

                label[i] = 3

            elif img_label == 'SA':

                label[i] = 4

            elif img_label == 'SU':

                label[i] = 5

            elif img_label == 'NE':

                label[i] = 6

            else:

                print("get label error.......\n")

            # flatten返回一个折叠成一维的数组。但是该函数只能适用于numpy对象，即array或者mat，普通的list列表是不行的。

            data[i][0:48 * 48] = np.ndarray.flatten(re_roi)

            i = i + 1

            # cv2.imshow("src", dst)

            # cv2.imshow("img", img)

            # if cv2.waitKey() == 32:

            #     continue

with open(r"face.csv", "w") as csvfile:

    writer = csv.writer(csvfile)

    writer.writerow(['emotion', 'pixels'])

    for i in range(len(label)):

        data_list = list(data[i])

        b = " ".join(str(x) for x in data_list)

        # 在水平方向上平铺

        l = np.hstack([label[i], b])

        writer.writerow(l)

detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30),

                                     flags=cv2.CASCADE_SCALE_IMAGE)

参数image--待检测图片，一般为灰度图像加快检测速度；
参数scaleFactor--表示在前后两次相继的扫描中，搜索窗口的比例系数。默认为1.1即每次搜索窗口依次扩大10%;
参数minNeighbors--表示构成检测目标的相邻矩形的最小个数(默认为3个)。如果组成检测目标的小矩形的个数和小于 min_neighbors - 1 都会被排除。如果min_neighbors 为 0, 则函数不做任何操作就返回所有的被检候选矩形框，这种设定值一般用在用户自定义对检测结果的组合程序上；
参数flags--flags=0：可以取如下这些值：CASCADE_DO_CANNY_PRUNING=1,利用canny边缘检测来排除一些边缘很少或者很多的图像区域，CASCADE_SCALE_IMAGE=2,正常比例检测，CASCADE_FIND_BIGGEST_OBJECT=4,只检测最大的物体，CASCADE_DO_ROUGH_SEARCH=8 初略的检测

show_facial_expression.py

#!/usr/bin/python

# coding:utf8

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

emotion = {0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'}

data = pd.read_csv(r'face.csv', dtype='a')

label = np.array(data['emotion'])

img_data = np.array(data['pixels'])

N_sample = label.size

Face_data = np.zeros((N_sample, 48 * 48))

Face_label = np.zeros((N_sample, 7), dtype=int)

# 显示人脸以及对应表情

for i in range(25):

    x = img_data[i]

    # 使用字符串创建矩阵。

    x = np.fromstring(x, dtype=float, sep=' ')

    x = x / x.max()

    img_x = np.reshape(x, (48, 48))

    plt.subplot(5, 5, i + 1)

    plt.axis('off')

    plt.title(emotion[int(label[i])])

    plt.imshow(img_x, plt.cm.gray)

plt.show()

count_facial_expression.py

#!/usr/bin/python

# coding:utf8

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

emotion = {0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'}

data = pd.read_csv(r'face.csv', dtype='a')

label = np.array(data['emotion'])

img_data = np.array(data['pixels'])

N_sample = label.size

emotions = np.zeros(7)

for i in label:

    for j in range(7):

        if int(i) == j:

            emotions[j] = emotions[j] + 1

print(emotions)

plt.bar(range(7), emotions, 0.5, color=['red', 'green', 'blue'])

plt.xlabel('emotions')

plt.xticks(range(7), ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral'], rotation=0)

plt.ylabel('number')

plt.grid()

plt.show()

train_keras.py

#!/usr/bin/python

# coding:utf8

import numpy as np

import pandas as pd

from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten

from keras.models import Sequential

from keras.preprocessing.image import ImageDataGenerator

# 表情类别

emotion = {0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'}

# 读取数据

data = pd.read_csv(r'face.csv', dtype='a')

# 读取标签列表

label = np.array(data['emotion'])

# 图像列表

img_data = np.array(data['pixels'])

# 图像数量

N_sample = label.size

# (213, 2304)

Face_data = np.zeros((N_sample, 48 * 48))

# (213, 7)

Face_label = np.zeros((N_sample, 7), dtype=np.float)

for i in range(N_sample):

    x = img_data[i]

    x = np.fromstring(x, dtype=float, sep=' ')

    x = x / x.max()

    Face_data[i] = x

    Face_label[i, int(label[i])] = 1.0

# 训练数据数量

train_num = 200

# 测试数据数量

test_num = 13

# 训练数据

train_x = Face_data[0:train_num, :]

train_y = Face_label[0:train_num, :]

train_x = train_x.reshape(-1, 48, 48, 1)  # reshape

# 测试数据

test_x = Face_data[train_num: train_num + test_num, :]

test_y = Face_label[train_num: train_num + test_num, :]

test_x = test_x.reshape(-1, 48, 48, 1)  # reshape

# 序贯模型

model = Sequential()

model.add(Conv2D(32, (5, 5), activation='relu', input_shape=(48, 48, 1)))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (5, 5), activation='relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())

model.add(Dense(1024, activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(7, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

# 扩增数据

datagen = ImageDataGenerator(

    featurewise_center=True,

    featurewise_std_normalization=True,

    rotation_range=20,

    width_shift_range=0.2,

    height_shift_range=0.2,

    horizontal_flip=True)

datagen.fit(train_x)

model.fit_generator(datagen.flow(train_x, train_y, batch_size=10), steps_per_epoch=len(train_x), epochs=20)

model.fit(train_x, train_y, batch_size=10, epochs=100)

score = model.evaluate(test_x, test_y, batch_size=10)

print("score:", score)

model.summary()

输出结果

Epoch 50/50

 10/200 [>.............................] - ETA: 1s - loss: 0.0015 - acc: 1.0000

 20/200 [==>...........................] - ETA: 1s - loss: 7.8377e-04 - acc: 1.0000

 30/200 [===>..........................] - ETA: 1s - loss: 0.0013 - acc: 1.0000

 40/200 [=====>........................] - ETA: 1s - loss: 0.0012 - acc: 1.0000

 50/200 [======>.......................] - ETA: 1s - loss: 0.0013 - acc: 1.0000

 60/200 [========>.....................] - ETA: 1s - loss: 0.0011 - acc: 1.0000

 70/200 [=========>....................] - ETA: 1s - loss: 0.0013 - acc: 1.0000

 80/200 [===========>..................] - ETA: 1s - loss: 0.0012 - acc: 1.0000

 90/200 [============>.................] - ETA: 0s - loss: 0.0011 - acc: 1.0000

100/200 [==============>...............] - ETA: 0s - loss: 0.0011 - acc: 1.0000

110/200 [===============>..............] - ETA: 0s - loss: 9.7286e-04 - acc: 1.0000

120/200 [=================>............] - ETA: 0s - loss: 8.9958e-04 - acc: 1.0000

130/200 [==================>...........] - ETA: 0s - loss: 8.3767e-04 - acc: 1.0000

140/200 [====================>.........] - ETA: 0s - loss: 9.1249e-04 - acc: 1.0000

150/200 [=====================>........] - ETA: 0s - loss: 9.3190e-04 - acc: 1.0000

160/200 [=======================>......] - ETA: 0s - loss: 9.0101e-04 - acc: 1.0000

170/200 [========================>.....] - ETA: 0s - loss: 8.6291e-04 - acc: 1.0000

180/200 [==========================>...] - ETA: 0s - loss: 8.2539e-04 - acc: 1.0000

190/200 [===========================>..] - ETA: 0s - loss: 7.8394e-04 - acc: 1.0000

200/200 [==============================] - 2s 9ms/step - loss: 7.8767e-04 - acc: 1.0000

10/13 [======================>.......] - ETA: 0s

13/13 [==============================] - 0s 5ms/step

_________________________________________________________________

Layer (type)                 Output Shape              Param #

=================================================================

conv2d_1 (Conv2D)            (None, 44, 44, 32)        832

_________________________________________________________________

max_pooling2d_1 (MaxPooling2 (None, 22, 22, 32)        0

_________________________________________________________________

conv2d_2 (Conv2D)            (None, 18, 18, 64)        51264

_________________________________________________________________

max_pooling2d_2 (MaxPooling2 (None, 9, 9, 64)          0

_________________________________________________________________

flatten_1 (Flatten)          (None, 5184)              0

_________________________________________________________________

dense_1 (Dense)              (None, 1024)              5309440

_________________________________________________________________

dropout_1 (Dropout)          (None, 1024)              0

_________________________________________________________________

dense_2 (Dense)              (None, 7)                 7175

=================================================================

Total params: 5,368,711

Trainable params: 5,368,711

Non-trainable params: 0

_________________________________________________________________

train_tensorflow.py

#!/usr/bin/python

# coding:utf8

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

import tensorflow as tf

def conv_pool_layer(data, weights_size, biases_size):

    """

    卷积神经网络的层

    :param data:

    :param weights_size:

    :param biases_size:

    :return:

    """

    weights = tf.Variable(tf.truncated_normal(weights_size, stddev=0.1))

    biases = tf.Variable(tf.constant(0.1, shape=biases_size))

    conv2d = tf.nn.conv2d(data, weights, strides=[1, 1, 1, 1], padding='SAME')

    relu = tf.nn.relu(conv2d + biases)

    return tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

def linear_layer(data, weights_size, biases_size):

    weights = tf.Variable(tf.truncated_normal(weights_size, stddev=0.1))

    biases = tf.Variable(tf.constant(0.1, shape=biases_size))

    return tf.add(tf.matmul(data, weights), biases)

def convolutional_neural_network(x, keep_prob):

    """

    卷积神经网络

    :param x:

    :param keep_prob:

    :return:

    """

    x_image = tf.reshape(x, [-1, 48, 48, 1])

    h_pool1 = conv_pool_layer(x_image, [5, 5, 1, 32], [32])

    h_pool2 = conv_pool_layer(h_pool1, [5, 5, 32, 64], [64])

    h_pool2_flat = tf.reshape(h_pool2, [-1, 12 * 12 * 64])

    h_fc1 = tf.nn.relu(linear_layer(h_pool2_flat, [12 * 12 * 64, 1024], [1024]))

    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    return tf.nn.softmax(linear_layer(h_fc1_drop, [1024, class_sum], [class_sum]))

def batch_data(x, y, batch, num):

    ind = np.arange(num)

    index = ind[batch * batch_size:(batch + 1) * batch_size]

    batch_x = x[index, :]

    batch_y = y[index, :]

    return batch_x, batch_y

if __name__ == "__main__":

    # 表情类别

    emotion = {0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'}

    # 数据

    data = pd.read_csv(r'face.csv', dtype='a')

    # 表情标签

    label = np.array(data['emotion'])

    # 图片数据

    img_data = np.array(data['pixels'])

    # 图片数量

    N_sample = label.size

    # 图片矩阵

    Face_data = np.zeros((N_sample, 48 * 48))

    # 标签矩阵

    Face_label = np.zeros((N_sample, 7), dtype=int)

    # 遍历将图片和标签构建为矩阵形式

    for i in range(N_sample):

        x = img_data[i]

        x = np.fromstring(x, dtype=float, sep=' ')

        x = x / x.max()

        Face_data[i] = x

        Face_label[i, int(label[i])] = 1

    # 参数

    dropout = 0.5

    class_sum = 7

    # dropout减轻过拟合问题

    keep_prob = tf.placeholder(tf.float32)

    x = tf.placeholder(tf.float32, [None, 48 * 48])

    y = tf.placeholder(tf.float32, [None, class_sum])

    # 构建卷积神经网络

    pred = convolutional_neural_network(x, keep_prob)

    # 取前200个作为训练数据，后13个为测试数据

    train_num = 200

    test_num = 13

    train_x = Face_data[0:train_num, :]

    train_y = Face_label[0:train_num, :]

    test_x = Face_data[train_num: train_num + test_num, :]

    test_y = Face_label[train_num: train_num + test_num, :]

    batch_size = 20

    train_batch_num = int(train_num / batch_size)

    test_batch_num = test_num / batch_size

    # 训练和评估模型

    cross_entropy = -tf.reduce_sum(y * tf.log(pred))

    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

    correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))

    accuracy = tf.reduce_mean(tf.cast(correct_pred, "float"))

    total_train_loss = []

    total_train_acc = []

    total_test_loss = []

    total_test_acc = []

    train_epoch = 50

    with tf.Session() as sess:

        sess.run(tf.initialize_all_variables())

        for epoch in range(0, train_epoch):

            Total_train_loss = 0

            Total_train_acc = 0

            for train_batch in range(0, train_batch_num):

                batch_x, batch_y = batch_data(train_x, train_y, train_batch, train_num)

                # 优化操作

                sess.run(train_step, feed_dict={x: batch_x, y: batch_y, keep_prob: dropout})

                if train_batch % batch_size == 0:

                    # 计算损失和准确率

                    loss, acc = sess.run([cross_entropy, accuracy], feed_dict={x: batch_x, y: batch_y, keep_prob: 1.})

                    print("Epoch: " + str(epoch + 1) + ", Batch: " + str(train_batch) + ", Loss= " + "{:.3f}".format(

                        loss) + ", Training Accuracy= " + "{:.3f}".format(acc))

                    Total_train_loss = Total_train_loss + loss

                    Total_train_acc = Total_train_acc + acc

            total_train_loss.append(Total_train_loss)

            total_train_acc.append(Total_train_acc)

    plt.subplot(2, 1, 1)

    plt.ylabel('Train loss')

    plt.plot(total_train_loss, 'r')

    plt.subplot(2, 1, 2)

    plt.ylabel('Train accuracy')

    plt.plot(total_train_acc, 'r')

    plt.savefig("loss_acc.png")

    plt.show()

输出结果

Epoch: 1, Batch: 0, Loss= 119.417, Training Accuracy= 0.150

Epoch: 2, Batch: 0, Loss= 49.221, Training Accuracy= 0.250

Epoch: 3, Batch: 0, Loss= 39.813, Training Accuracy= 0.200

Epoch: 4, Batch: 0, Loss= 43.067, Training Accuracy= 0.300

Epoch: 5, Batch: 0, Loss= 28.889, Training Accuracy= 0.500

Epoch: 6, Batch: 0, Loss= 18.173, Training Accuracy= 0.700

Epoch: 7, Batch: 0, Loss= 18.474, Training Accuracy= 0.600

Epoch: 8, Batch: 0, Loss= 15.813, Training Accuracy= 0.800

Epoch: 9, Batch: 0, Loss= 12.878, Training Accuracy= 0.850

Epoch: 10, Batch: 0, Loss= 13.250, Training Accuracy= 0.800

Epoch: 11, Batch: 0, Loss= 8.750, Training Accuracy= 0.950

Epoch: 12, Batch: 0, Loss= 10.424, Training Accuracy= 0.850

Epoch: 13, Batch: 0, Loss= 7.592, Training Accuracy= 1.000

Epoch: 14, Batch: 0, Loss= 7.146, Training Accuracy= 0.950

Epoch: 15, Batch: 0, Loss= 7.377, Training Accuracy= 1.000

Epoch: 16, Batch: 0, Loss= 5.423, Training Accuracy= 1.000

Epoch: 17, Batch: 0, Loss= 6.173, Training Accuracy= 1.000

Epoch: 18, Batch: 0, Loss= 4.069, Training Accuracy= 1.000

Epoch: 19, Batch: 0, Loss= 4.163, Training Accuracy= 1.000

Epoch: 20, Batch: 0, Loss= 3.650, Training Accuracy= 1.000

Epoch: 21, Batch: 0, Loss= 3.317, Training Accuracy= 1.000

Epoch: 22, Batch: 0, Loss= 4.195, Training Accuracy= 1.000

Epoch: 23, Batch: 0, Loss= 2.729, Training Accuracy= 1.000

Epoch: 24, Batch: 0, Loss= 2.448, Training Accuracy= 1.000

Epoch: 25, Batch: 0, Loss= 2.614, Training Accuracy= 1.000

Epoch: 26, Batch: 0, Loss= 2.424, Training Accuracy= 1.000

Epoch: 27, Batch: 0, Loss= 2.707, Training Accuracy= 1.000

Epoch: 28, Batch: 0, Loss= 2.072, Training Accuracy= 1.000

Epoch: 29, Batch: 0, Loss= 1.726, Training Accuracy= 1.000

Epoch: 30, Batch: 0, Loss= 1.701, Training Accuracy= 1.000

Epoch: 31, Batch: 0, Loss= 1.598, Training Accuracy= 1.000

Epoch: 32, Batch: 0, Loss= 1.381, Training Accuracy= 1.000

Epoch: 33, Batch: 0, Loss= 1.826, Training Accuracy= 1.000

Epoch: 34, Batch: 0, Loss= 1.227, Training Accuracy= 1.000

Epoch: 35, Batch: 0, Loss= 1.320, Training Accuracy= 1.000

Epoch: 36, Batch: 0, Loss= 1.110, Training Accuracy= 1.000

Epoch: 37, Batch: 0, Loss= 0.875, Training Accuracy= 1.000

Epoch: 38, Batch: 0, Loss= 1.214, Training Accuracy= 1.000

Epoch: 39, Batch: 0, Loss= 0.982, Training Accuracy= 1.000

Epoch: 40, Batch: 0, Loss= 0.982, Training Accuracy= 1.000

Epoch: 41, Batch: 0, Loss= 0.681, Training Accuracy= 1.000

Epoch: 42, Batch: 0, Loss= 0.839, Training Accuracy= 1.000

Epoch: 43, Batch: 0, Loss= 0.777, Training Accuracy= 1.000

Epoch: 44, Batch: 0, Loss= 0.671, Training Accuracy= 1.000

Epoch: 45, Batch: 0, Loss= 0.859, Training Accuracy= 1.000

Epoch: 46, Batch: 0, Loss= 0.529, Training Accuracy= 1.000

Epoch: 47, Batch: 0, Loss= 0.707, Training Accuracy= 1.000

Epoch: 48, Batch: 0, Loss= 0.491, Training Accuracy= 1.000

Epoch: 49, Batch: 0, Loss= 0.500, Training Accuracy= 1.000

Epoch: 50, Batch: 0, Loss= 0.415, Training Accuracy= 1.000

train_tensorboard.py

#!/usr/bin/python

# coding:utf8

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

import tensorflow as tf

def conv_pool_layer(data, weights_size, biases_size):

    weights = tf.Variable(tf.truncated_normal(weights_size, stddev=0.1))

    biases = tf.Variable(tf.constant(0.1, shape=biases_size))

    conv2d = tf.nn.conv2d(data, weights, strides=[1, 1, 1, 1], padding='SAME')

    relu = tf.nn.relu(conv2d + biases)

    return tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

def linear_layer(data, weights_size, biases_size):

    weights = tf.Variable(tf.truncated_normal(weights_size, stddev=0.1))

    biases = tf.Variable(tf.constant(0.1, shape=biases_size))

    return tf.add(tf.matmul(data, weights), biases)

def convolutional_neural_network(x, keep_prob):

    with tf.name_scope('input'):

        x_image = tf.reshape(x, [-1, 48, 48, 1])

    with tf.name_scope('conv1'):

        h_pool1 = conv_pool_layer(x_image, [5, 5, 1, 32], [32])

    with tf.name_scope('conv2'):

        h_pool2 = conv_pool_layer(h_pool1, [5, 5, 32, 64], [64])

    h_pool2_flat = tf.reshape(h_pool2, [-1, 12 * 12 * 64])

    with tf.name_scope('fc3'):

        h_fc1 = tf.nn.relu(linear_layer(h_pool2_flat, [12 * 12 * 64, 1024], [1024]))

    with tf.name_scope('dropout4'):

        h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    with tf.name_scope('softmax5'):

        out = tf.nn.softmax(linear_layer(h_fc1_drop, [1024, class_sum], [class_sum]))

    return out

def batch_data(x, y, batch, num):

    ind = np.arange(num)

    index = ind[batch * batch_size:(batch + 1) * batch_size]

    batch_x = x[index, :]

    batch_y = y[index, :]

    return batch_x, batch_y

if __name__ == "__main__":

    emotion = {0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'}

    data = pd.read_csv(r'face.csv', dtype='a')

    label = np.array(data['emotion'])

    img_data = np.array(data['pixels'])

    N_sample = label.size

    Face_data = np.zeros((N_sample, 48 * 48))

    Face_label = np.zeros((N_sample, 7), dtype=int)

    for i in range(N_sample):

        x = img_data[i]

        x = np.fromstring(x, dtype=float, sep=' ')

        x = x / x.max()

        Face_data[i] = x

        Face_label[i, int(label[i])] = 1

    # 参数

    dropout = 0.5

    class_sum = 7

    # dropout减轻过拟合问题

    keep_prob = tf.placeholder(tf.float32)

    x = tf.placeholder(tf.float32, [None, 48 * 48])

    y = tf.placeholder(tf.float32, [None, class_sum])

    pred = convolutional_neural_network(x, keep_prob)

    # 取前200个作为训练数据，后13个为测试数据

    train_num = 200

    test_num = 13

    train_x = Face_data[0:train_num, :]

    train_y = Face_label[0:train_num, :]

    test_x = Face_data[train_num: train_num + test_num, :]

    test_y = Face_label[train_num: train_num + test_num, :]

    batch_size = 20

    train_batch_num = int(train_num / batch_size)

    test_batch_num = test_num / batch_size

    # 训练和评估模型

    with tf.name_scope('cross_entropy'):

        cross_entropy = -tf.reduce_sum(y * tf.log(pred))

        tf.summary.histogram("cross_entropy", cross_entropy)

        # tf.summary.scalar("cross_entropy", cross_entropy)

    with tf.name_scope('minimize'):

        train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

    with tf.name_scope('accuracy'):

        with tf.name_scope('correct_pred'):

            correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))

        with tf.name_scope('accuracy'):

            accuracy = tf.reduce_mean(tf.cast(correct_pred, "float"))

            tf.summary.histogram("accuracy", accuracy)

        # 输出包含单个标量值的摘要协议缓冲区

        tf.summary.scalar('accuracy', accuracy)

    total_train_loss = []

    total_train_acc = []

    total_test_loss = []

    total_test_acc = []

    train_epoch = 50

    # 合并在默认图形中收集的所有摘要

    merged = tf.summary.merge_all()

    with tf.Session() as sess:

        writer = tf.summary.FileWriter("tmp/logs", sess.graph)

        sess.run(tf.global_variables_initializer())

        for epoch in range(0, train_epoch):

            Total_train_loss = 0

            Total_train_acc = 0

            for train_batch in range(0, train_batch_num):

                batch_x, batch_y = batch_data(train_x, train_y, train_batch, train_num)

                # 优化操作

                # sess.run(train_step, feed_dict={x: batch_x, y: batch_y, keep_prob: dropout})

                summary, _ = sess.run([merged, train_step], feed_dict={x: batch_x, y: batch_y, keep_prob: dropout})

                writer.add_summary(summary, train_batch)

                if train_batch % batch_size == 0:

                    # 计算损失和准确率

                    loss, acc = sess.run([cross_entropy, accuracy], feed_dict={x: batch_x, y: batch_y, keep_prob: 1.})

                    print("Epoch: " + str(epoch + 1) + ", Batch: " + str(train_batch) +

                          ", Loss= " + "{:.3f}".format(loss) +

                          ", Training Accuracy= " + "{:.3f}".format(acc))

                    Total_train_loss = Total_train_loss + loss

                    Total_train_acc = Total_train_acc + acc

            total_train_loss.append(Total_train_loss)

            total_train_acc.append(Total_train_acc)

        writer.close()

    plt.subplot(2, 1, 1)

    plt.ylabel('Train loss')

    plt.plot(total_train_loss, 'r')

    plt.subplot(2, 1, 2)

    plt.ylabel('Train accuracy')

    plt.plot(total_train_acc, 'r')

    plt.savefig("face_loss_acc.png")

    plt.show()

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