import os from tensorflow.keras.datasets import mnist import tensorflow as tf from tensorflow.python.keras import Model from tensorflow.python.keras.datasets import cifar10 from tensorflow.python.keras.layers import Flatten, Dense, Conv2D, BatchNormalization, AvgPool2D, Activation, MaxPool2D, \ Dropout (x_train, y_train), (x_test, y_test) = cifar10.load_data() x_train, x_test = x_train/255.0, x_test/255.0 checkpoint_save_path = './checkpoint/model.ckpt' # 搭建模型类, 口诀：CBAPD，C卷积B批标准化A激活P池化D全连接 class ConvModel(Model): def __init__(self): super(ConvModel, self).__init__() # filters: 卷积核个数 kernel_size：卷积核尺寸 strides：横纵向步长 padding：是否使用全零填充,same为是 activation：激活函数 self.conv1 = Conv2D(filters=6, kernel_size=(5, 5), strides=(1, 1), padding='same', activation=None) # 在激活函数前，先进行一次批标准化，使得输入值更靠近0均值 self.bn = BatchNormalization() # 激活函数 self.activation = Activation('relu') # 池化，减少输入特征值 self.pool = MaxPool2D(pool_size=(2, 2), strides=2, padding='same') # Dropout防止过拟合 self.dropout1 = Dropout(0.2) # 特征抽取完，拉直维度后通过全连接层输出 self.flatten = Flatten() self.d1 = Dense(128, activation='relu') self.dropout2 = Dropout(0.2) self.d2 = Dense(10, activation='softmax') def call(self, x): x = self.conv1(x) x = self.bn(x) x = self.activation(x) x = self.pool(x) x = self.dropout1(x) x = self.flatten(x) x = self.d1(x) x = self.dropout2(x) y = self.d2(x) return y model = ConvModel() # 模型优化 model.compile(optimizer=tf.keras.optimizers.Adam(), loss=tf.keras.losses.sparse_categorical_crossentropy, metrics=['sparse_categorical_accuracy']) # callback保存模型 model_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path, save_weights_only=True, save_best_only=True) # 曾经保存过，直接加载权重参数 if os.path.exists(checkpoint_save_path + '.index'): model.load_weights(checkpoint_save_path) # 开始训练 model.fit(x=x_train, y=y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), callbacks=[model_callback]) # 结果总览 model.summary()