一、构建模型
from __future__ import absolute_import, division, print_function, unicode_literals import tensorflow as tf from tensorflow.keras.layers import Dense, Flatten, Conv2D from tensorflow.keras import Model from tensorflow import saved_model gpus = tf.config.experimental.list_physical_devices('GPU')##获取可用GPU for gpu in (gpus): tf.config.experimental.set_memory_growth(gpu, True)##设置显存使用方式 (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 ##数据预处理归一化 x_train = x_train[..., tf.newaxis] ##增加一个通道维数 x_test = x_test[..., tf.newaxis] train_set = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)##切分数据集为BatchDataset，混淆数据集 test_set = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32) class MyModel(Model):##cnn网络模型定义 def __init__(self): super(MyModel, self).__init__() self.conv1 = Conv2D(32, 3, activation='relu') self.flatten = Flatten() self.d1 = Dense(128, activation='relu') self.d2 = Dense(10, activation='softmax') @tf.function def call(self, x): x = self.conv1(x) x = self.flatten(x) x = self.d1(x) return self.d2(x) # mynetwork = tf.keras.models.Sequential([ ##一般模型 # tf.keras.layers.Flatten(input_shape=(28, 28)), # tf.keras.layers.Dense(128, activation='relu'), # tf.keras.layers.Dropout(0.2), # tf.keras.layers.Dense(10, activation='softmax') # ]) mynetwork = MyModel() loss_object = tf.keras.losses.SparseCategoricalCrossentropy()##损失函数定义 optimizer = tf.keras.optimizers.Adam()##优化器定义 train_loss = tf.keras.metrics.Mean(name='train_loss')##损失值 train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')##准确率 test_loss = tf.keras.metrics.Mean(name='test_loss') test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy') @tf.function ##训练 def train_step(images, labels): with tf.GradientTape() as tape: predictions = mynetwork(images) loss = loss_object(labels, predictions) gradients = tape.gradient(loss, mynetwork.trainable_variables) optimizer.apply_gradients(zip(gradients, mynetwork.trainable_variables)) train_loss(loss) train_accuracy(labels, predictions) @tf.function ##测试 def test_step(images, labels): predictions = mynetwork(images) t_loss = loss_object(labels, predictions) test_loss(t_loss) test_accuracy(labels, predictions) for epoch in range(5): # 在下一个epoch开始时，重置评估指标 train_loss.reset_states() train_accuracy.reset_states() test_loss.reset_states() test_accuracy.reset_states() for images, labels in train_set: train_step(images, labels) for test_images, test_labels in test_set: test_step(test_images, test_labels) template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}' print(template.format(epoch+1, train_loss.result(), train_accuracy.result()*100, test_loss.result(), test_accuracy.result()*100)) tf.saved_model.save(mynetwork, 'saved_model')##保存模型，表明文件夹即可
二、预测结果
可以看到，5个epoch后准确率已经非常高，通过非卷积网络训练模型的准确率低于卷积网络，读者可以自行试验
参考：
https://tensorflow.google.cn/tutorials/quickstart/advanced?hl=zh_cn