87 lines
3.2 KiB
Python
87 lines
3.2 KiB
Python
import numpy as np
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import cv2
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from keras.preprocessing.image import ImageDataGenerator
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from keras.layers import Dense, Dropout, Flatten
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from keras.layers import Conv2D
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from keras.optimizers import Adam
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from keras.layers import MaxPooling2D
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from keras.models import Sequential
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# 照片路径
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train_dir = 'D:\\my\\college\\31\\machinelearning\\archive\\train'
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val_dir = 'D:\\my\\college\\31\\machinelearning\\archive\\test'
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# 把灰度值从0-255映射到0-1
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train_datagen = ImageDataGenerator(rescale=1./255)
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val_datagen = ImageDataGenerator(rescale=1./255)
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# 图像增强
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train_generator = train_datagen.flow_from_directory(
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train_dir,
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target_size=(48,48),
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batch_size=64,
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color_mode="grayscale",
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class_mode='categorical')
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validation_generator = val_datagen.flow_from_directory(
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val_dir,
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target_size=(48,48),
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batch_size=64,
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color_mode="grayscale",
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class_mode='categorical')
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# 第2个
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emotion_model = Sequential()
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emotion_model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48,48,1)))
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emotion_model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
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emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
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emotion_model.add(Dropout(0.25))
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emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
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emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
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emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
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emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
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emotion_model.add(Dropout(0.25))
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emotion_model.add(Flatten())
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emotion_model.add(Dense(1024, activation='relu'))
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emotion_model.add(Dropout(0.5))
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emotion_model.add(Dense(7, activation='softmax'))
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# 第1个
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# # 第一层
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# emotion_model = Sequential()
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# emotion_model.add(Conv2D(input_shape=(48, 48, 1), filters=32, kernel_size=3, padding='same', activation='relu'))
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# emotion_model.add(Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'))
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# emotion_model.add(MaxPooling2D(pool_size=2, strides=2))
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#
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# # 第二层
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# emotion_model.add(Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))
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# emotion_model.add(Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))
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# emotion_model.add(MaxPooling2D(pool_size=2, strides=2))
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#
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# # 第三层
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# emotion_model.add(Conv2D(filters=128, kernel_size=3, padding='same', activation='relu'))
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# emotion_model.add(Conv2D(filters=128, kernel_size=3, padding='same', activation='relu'))
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# emotion_model.add(MaxPooling2D(pool_size=2, strides=2))
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#
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# emotion_model.add(Flatten())
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#
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# # 全连接层
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# emotion_model.add(Dense(64, activation='relu'))
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# emotion_model.add(Dropout(0.5))
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# emotion_model.add(Dense(7, activation='softmax'))
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cv2.ocl.setUseOpenCL(False)
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emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
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emotion_model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
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emotion_model_info = emotion_model.fit(
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train_generator,
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steps_per_epoch=28709 // 64,
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epochs=20,
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validation_data=validation_generator,
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validation_steps=7178 // 64)
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emotion_model.save_weights('emotion_model_3.h5')
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