# 把训练数据转换为动态图所需的Variable类型
img = fluid.dygraph.base.to_variable(dy_x_data)
label = fluid.dygraph.base.to_variable(y_data)
# 不需要训练label
label.stop_gradient = True
# 获取网络输出
predict = cnn(img)
# 获取准确率函数和损失函数
accuracy = fluid.layers.accuracy(input=predict, label=label)
loss = fluid.layers.cross_entropy(predict, label)
avg_loss = fluid.layers.mean(loss)
# 计算梯度
avg_loss.backward()
momentum.minimize(avg_loss)
# 将参数梯度清零以保证下一轮训练的正确性
cnn.clear_gradients()
# 打印一次信息
if batch_id % 100 == 0:
print(
"Epoch:%d, Batch:%d, Loss:%f, Accuracy:%f" % (epoch, batch_id, avg_loss.numpy(), accuracy.numpy()))
# 开始执行测试
cnn.eval()
test_cost, test_acc = test_train(test_reader, cnn, BATCH_SIZE)
# 准备重新恢复训练
cnn.train()
print("Test:%d, Loss:%f, Accuracy:%f" % (epoch, test_cost, test_acc))
if not os.path.exists('models'):
os.makedirs('models')
# 保存模型
fluid.dygraph.save_dygraph(state_dict=cnn.state_dict(), model_path="models/cnn")
