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import tensorflow as tf
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import os
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from matplotlib import pyplot as plt
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from PIL import Image
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import numpy as np
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# 数据准备
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mnist = tf.keras.datasets.mnist# 导入MNIST数据集
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# 指定要投入网络的训练集和测试集
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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x_train, x_test = x_train/255.0, x_test/255.0
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def creat_model():
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model = tf.keras.models.Sequential([
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tf.keras.layers.Flatten(), # 将输入层的数据压成一维数据
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# 全连接层
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# tf.keras.layers.Densen(神经元个数,activation='激活函数')
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tf.keras.layers.Dense(128, activation='relu'), # 128个神经元，激活函数为'relu'
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tf.keras.layers.Dense(10, activation='softmax') # 10个神经元，激活函数为'softmax'
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])
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# 使用compile()配置神经网络训练方法
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# model.compile(optimizer=优化器,
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# loss=损失函数,
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# metrics=["评测指标"]
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# )
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model.compile(optimizer='adam', # 优化器为'adam'
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loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
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# 损失函数为'sparse_categorical_crossentropy'
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metrics=["sparse_categorical_accuracy"]) # y_以数值形式给出，y以独热码形式给出
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return model
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def model_fit(model,check_save_path):
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if os.path.exists(check_save_path+'.index'):
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print("load modals...")
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model.load_weights(check_save_path)
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# 使用callbacks.ModelCheckpoint()保存模型
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# tf.keras.callbacks.ModelCheckpoint(filepath=保存模型的路径,
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# save_weights_only=若设置为True则仅保存模型权重,设置为False则保存整个模型
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# save_best_only=若设置为True将只保存在验证集上性能最好的模型
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# )
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cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=check_save_path, # 保存路径
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save_weights_only=True, # 只保存模型权重
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save_best_only=True) # 只保存在验证集上性能最好的模型
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# 模型训练
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# 在fit()中执行训练
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# model.fit(训练集的输入特征,训练集的标签,
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# batch_size=,epochs=,
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# validation_split=从训练集中划分多少比例给测试集,
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# validation_freq=多少次epoch测试一次
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# )
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history = model.fit(x_train, y_train,
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batch_size=32, epochs=5, # 每一批batch大小为32,迭代次数epochs为5
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validation_split=0.15, # 从训练集中划分15%给验证集
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validation_freq=1, # 测试的间隔次数为1
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callbacks=[cp_callback]) # 回调参数
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model.summary() # 打印网络结构和参数统计
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final_loss, final_acc = model.evaluate(x_test, y_test, verbose=2)
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print("Model accuracy: ", final_acc, ", model loss: ", final_loss)
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acc = history.history['sparse_categorical_accuracy']
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val_acc = history.history['val_sparse_categorical_accuracy']
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loss = history.history['loss']
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val_loss = history.history['val_loss']
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plt.subplot(1, 2, 1)
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plt.plot(acc, label='Training Accuracy')
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plt.plot(val_acc, label='Validation Accuracy')
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plt.title('Training and Validation Accuracy')
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plt.legend()
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plt.subplot(1, 2, 2)
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plt.plot(loss, label='Training Loss')
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plt.plot(val_loss, label='Validation Loss')
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plt.title('Training and Validation Loss')
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plt.legend()
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plt.show()
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def eva_acc(str, model):
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model.load_weights(str)
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# 准确率测试
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final_loss, final_acc = model.evaluate(x_test, y_test, verbose=2) # 输入数据和标签,输出损失和精确度,verbose = 2输出一行记录
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print("Model accuracy: ", final_acc, ", model loss: ", final_loss)
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if __name__=="__main__":
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check_save_path = "./checkpoint/mnist.ckpt"
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model = creat_model()
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model_fit(model, check_save_path)
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eva_acc(check_save_path, model)
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