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import tensorflow.keras as keras
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import numpy as np
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import os
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import tensorflow as tf
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import matplotlib.pyplot as plt
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mnist = keras.datasets.mnist
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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# 数据归一化
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x_train = x_train/255.0
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x_test = x_test/255.0
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# 为了卷积层输入格式，增加一个维度
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x_train = tf.expand_dims(x_train, -1)
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x_test = tf.expand_dims(x_test, -1)
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# print("train shape:", x_train.shape)
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# print("test shape:", x_test.shape)
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# 使用此类进行图形增强
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datagen = keras.preprocessing.image.ImageDataGenerator(
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rotation_range=20, # 整数。随机旋转的度数范围。
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width_shift_range=0.20, # 浮点数，图片宽度的某个比例，数据提升时图片随机水平偏移的幅度。
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shear_range=15, # 浮点数，剪切强度（逆时针方向的剪切变换角度）。是用来进行剪切变换的程度。
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zoom_range=0.10, # 浮点数或形如[lower,upper]的列表，随机缩放的幅度，若为浮点数，则相当于[lower,upper] = [1 - zoom_range, 1+zoom_range]。用来进行随机的放大。
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validation_split=0.15, # 浮点型。保留用于验证集的图像比例（严格在0,1之间）
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horizontal_flip=False # 布尔值,随机水平翻转。
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)
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train_datagen = datagen.flow(
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x_train,
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y_train,
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batch_size=256,
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subset="training"
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)
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validation_genetor = datagen.flow(
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x_train,
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y_train,
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batch_size=64,
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subset="validation"
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)
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def creat_model():
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"""
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此函数用于构建模型。
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调用keras.Sequential()建立模型的框架。
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使用compile()配置神经网络训练方法
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:return:
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"""
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model = keras.Sequential([
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keras.layers.Reshape((28, 28, 1)),
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# 卷积层定义
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keras.layers.Conv2D(filters=32, kernel_size=(5, 5), activation="relu", padding="same",
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input_shape=(28, 28, 1)),
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# 池化层定义
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keras.layers.MaxPool2D((2, 2)),
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# 卷积层、池化层
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keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation="relu", padding="same"),
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keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation="relu", padding="same"),
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keras.layers.MaxPool2D((2, 2)),
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# 卷积层、池化层
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keras.layers.Conv2D(filters=128, kernel_size=(3, 3), activation="relu", padding="same"),
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keras.layers.Conv2D(filters=128, kernel_size=(3, 3), activation="relu", padding="same"),
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keras.layers.MaxPool2D((2, 2)),
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# 分类器：多个全连接层
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keras.layers.Flatten(),
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keras.layers.Dense(512, activation="sigmoid"),
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keras.layers.Dropout(0.25),
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keras.layers.Dense(512, activation="sigmoid"),
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keras.layers.Dropout(0.25),
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keras.layers.Dense(256, activation="sigmoid"),
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keras.layers.Dropout(0.1),
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keras.layers.Dense(10, activation="sigmoid")
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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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model.compile(optimizer='adam',
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loss=keras.losses.SparseCategoricalCrossentropy(from_logits=False),
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metrics=["sparse_categorical_accuracy"])
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return model
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def model_fit(model, check_save_path):
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# 判断是否有保存的模型，有就加载之
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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,
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save_weights_only=True,
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save_best_only=True)
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# 用于动态调整学习率，参数如下：
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# monitor：监测的值，可以是accuracy，val_loss,val_accuracy
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# factor：缩放学习率的值，学习率将以lr = lr*factor的形式被减少
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# patience：当patience个epoch过去而模型性能不提升时，学习率减少的动作会被触发
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# min_lr：学习率最小值，能缩小到的下限
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reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',
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factor=0.1,
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patience=2,
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min_lr=0.000001,
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verbose=1)
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# 早停机制
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# monitor：要监测的数量
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# min_delta：在被监测的数据中被认为是提升的最小变化，即绝对变化小于min_delta，将被视为没有提升。
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# patience：没有进步的训练轮数，在这之后训练就会被停止。
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earlystop_callback = keras.callbacks.EarlyStopping(
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monitor='val_sparse_categorical_accuracy',
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min_delta=0.0001,
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patience=2)
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history = model.fit(train_datagen, epochs=10, validation_data=validation_genetor, callbacks=[reduce_lr, cp_callback, earlystop_callback], verbose=1)
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model.summary()
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# 用于可视化
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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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# 此函数通过测试集检测准确率
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def model_valtest(model, check_save_path):
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model.load_weights(check_save_path)
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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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if __name__ == "__main__":
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check_save_path = "./checkpoint/mnist_cnn3.ckpt"
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model = creat_model()
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#model_fit(model, check_save_path)
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model_valtest(model, check_save_path) |
