CNN_train.py

# -*- coding: utf-8 -*-

import Global_Params
from keras.callbacks import EarlyStopping
import filter
import numpy as np
import tensorflow as tf
import cv2
import os
from tensorflow.keras.utils import to_categorical
from keras.preprocessing.image import img_to_array
from keras.callbacks import EarlyStopping
# from imutils import paths
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense
import tkinter
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt

from keras import backend as K

validation_split_rate = Global_Params.M_validation_split_rate


def TrainCnnModel(data, label, size, x_test, y_test):
    """
    训练CNN模型
    :param data:  图像矩阵, rbg
    :param label: 图像对应的标签, one-hot
    :param size:  图片尺寸
    :return: 返回训练后的模型
    """
    epoch_number = Global_Params.M_epoch_number
    model = Sequential()
    model.add(Conv2D(filters=32, kernel_size=(3, 3), padding='same',
                     input_shape=(size, size, 3), activation='relu'))
    model.add(Dropout(0.3))
    model.add(Conv2D(filters=32, kernel_size=(3, 3), padding='same',
                     input_shape=(size, size, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.3))
    model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu',
                     input_shape=(size, size, 3), padding='same'))
    model.add(Conv2D(filters=64, kernel_size=(3, 3), padding='same',
                     input_shape=(size, size, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(filters=128, kernel_size=(3, 3), activation='relu',
                     input_shape=(size, size, 3), padding='same'))
    model.add(Dropout(0.3))
    model.add(Conv2D(filters=128, kernel_size=(3, 3), padding='same',
                     input_shape=(size, size, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dropout(0.3))
    model.add(Dense(2500, activation='relu'))
    model.add(Dropout(0.3))
    model.add(Dense(1500, activation='relu'))
    model.add(Dropout(0.3))
    model.add(Dense(15, activation='softmax'))
    model.compile(loss="categorical_crossentropy", optimizer="Adam", metrics=["accuracy"])

    early_stopping = EarlyStopping(monitor='val_accuracy', min_delta=0.00001, patience=5, mode='max')
    print("default learning rate =", model.optimizer.learning_rate)
    K.set_value(model.optimizer.learning_rate, 0.0001)
    print("current learning rate =", model.optimizer.learning_rate)
    history = model.fit(data, label, batch_size=36, epochs=epoch_number, callbacks=[early_stopping], verbose=1,
                        validation_split=validation_split_rate)

    # 绘制训练 & 验证的准确率值
    plt.plot(history.history['accuracy'])
    plt.plot(history.history['val_accuracy'])
    plt.title('Model accuracy')
    plt.ylabel('Accuracy')
    plt.xlabel('Epoch')
    plt.legend(['Train', 'Test'], loc='upper left')
    plt.show()

    # 绘制训练 & 验证的损失值
    plt.plot(history.history['loss'])
    plt.plot(history.history['val_loss'])
    plt.title('Model loss')
    plt.ylabel('Loss')
    plt.xlabel('Epoch')
    plt.legend(['Train', 'Test'], loc='upper left')
    plt.show()

    # x_test = []
    # y_test = []
    #
    # data_size = len(label)
    #
    # for data_index in range(data_size):
    #     if (data_index + 1) % 10 == 0:
    #         x_test.append(data[data_index])
    #         y_test.append(label[data_index])

    score = model.evaluate(x_test, y_test)
    print('acc', score[1])

    # saving the model
    save_dir = Global_Params.M_model_save_path
    model_name = "model"+str(epoch_number) + "_" + str(score[1]) + "z.h5"
    model_path = os.path.join(save_dir, model_name)

    model.save(model_path)

    if not os.path.exists(save_dir):
        os.makedirs(save_dir)

    print('Saved trained model at %s ' % model_path)
    print("Train model done!")
    print("Model saved.")