train.py

from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import SGD

batch_siz = 128
num_classes = 7
nb_epoch = 100
img_size = 48
data_path = './data'
model_path = './model'


class Model:
    def __init__(self):
        self.model = None

    def build_model(self):
        self.model = Sequential()

        self.model.add(Conv2D(32, (1, 1), strides=1, padding='same', input_shape=(img_size, img_size, 1)))
        self.model.add(Activation('relu'))
        self.model.add(Conv2D(32, (5, 5), padding='same'))
        self.model.add(Activation('relu'))
        self.model.add(MaxPooling2D(pool_size=(2, 2)))

        self.model.add(Conv2D(32, (3, 3), padding='same'))
        self.model.add(Activation('relu'))
        self.model.add(MaxPooling2D(pool_size=(2, 2)))

        self.model.add(Conv2D(64, (5, 5), padding='same'))
        self.model.add(Activation('relu'))
        self.model.add(MaxPooling2D(pool_size=(2, 2)))

        self.model.add(Flatten())
        self.model.add(Dense(2048))
        self.model.add(Activation('relu'))
        self.model.add(Dropout(0.5))
        self.model.add(Dense(1024))
        self.model.add(Activation('relu'))
        self.model.add(Dropout(0.5))
        self.model.add(Dense(num_classes))
        self.model.add(Activation('softmax'))
        self.model.summary()

    def train_model(self):
        sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
        self.model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
        # 自动扩充训练样本
        train_datagen = ImageDataGenerator(
            rescale=1. / 255,
            shear_range=0.2,
            zoom_range=0.2,
            horizontal_flip=True)
        # 归一化验证集
        val_datagen = ImageDataGenerator(
            rescale=1. / 255)
        eval_datagen = ImageDataGenerator(
            rescale=1. / 255)
        # 以文件分类名划分label
        train_generator = train_datagen.flow_from_directory(
            data_path + '/train',
            target_size=(img_size, img_size),
            color_mode='grayscale',
            batch_size=batch_siz,
            class_mode='categorical')
        val_generator = val_datagen.flow_from_directory(
            data_path + '/val',
            target_size=(img_size, img_size),
            color_mode='grayscale',
            batch_size=batch_siz,
            class_mode='categorical')
        eval_generator = eval_datagen.flow_from_directory(
            data_path + '/test',
            target_size=(img_size, img_size),
            color_mode='grayscale',
            batch_size=batch_siz,
            class_mode='categorical')
        # early_stopping = EarlyStopping(monitor='loss', patience=3)
        history_fit = self.model.fit_generator(
            train_generator,
            steps_per_epoch=800 / (batch_siz / 32),  # 28709
            nb_epoch=nb_epoch,
            validation_data=val_generator,
            validation_steps=2000,
            # callbacks=[early_stopping]
        )
        #         history_eval=self.model.evaluate_generator(
        #                 eval_generator,
        #                 steps=2000)
        history_predict = self.model.predict_generator(
            eval_generator,
            steps=2000)
        with open(model_path + '/model_fit_log', 'w') as f:
            f.write(str(history_fit.history))
        with open(model_path + '/model_predict_log', 'w') as f:
            f.write(str(history_predict))

    def save_model(self):
        model_json = self.model.to_json()
        with open(model_path + "/model_json.json", "w") as json_file:
            json_file.write(model_json)
        self.model.save_weights(model_path + '/model_weight.h5')
        self.model.save(model_path + '/model.h5')


if __name__ == '__main__':
    model = Model()
    model.build_model()
    print('model built')
    model.train_model()
    print('model trained')
    model.save_model()
    print('model saved')