内容接前文：
https://www.cnblogs.com/devilmaycry812839668/p/14988686.html
https://www.cnblogs.com/devilmaycry812839668/p/14990021.html
前面是我们自己按照个人理解实现的单步计算，随着对这个计算框架MindSpore的深入了解我们了解到其实官方是提供了单步计算函数的。
具体函数：
from mindspore.nn import TrainOneStepCell, WithLossCell
根据官方资料：
根据官方提供的函数，给出如下代码：
import mindspore import numpy as np # 引入numpy科学计算库 import matplotlib.pyplot as plt # 引入绘图库 np.random.seed(123) # 随机数生成种子 import mindspore.nn as nn import mindspore.ops as ops from mindspore import Tensor from mindspore import ParameterTuple, Parameter from mindspore import dtype as mstype from mindspore import Model import mindspore.dataset as ds from mindspore.train.callback import ModelCheckpoint, CheckpointConfig from mindspore.train.callback import LossMonitor from mindspore.nn import TrainOneStepCell, WithLossCell class Net(nn.Cell): def __init__(self, input_dims, output_dims): super(Net, self).__init__() self.matmul = ops.MatMul() self.weight_1 = Parameter(Tensor(np.random.randn(input_dims, 128), dtype=mstype.float32), name='weight_1') self.bias_1 = Parameter(Tensor(np.zeros(128), dtype=mstype.float32), name='bias_1') self.weight_2 = Parameter(Tensor(np.random.randn(128, 64), dtype=mstype.float32), name='weight_2') self.bias_2 = Parameter(Tensor(np.zeros(64), dtype=mstype.float32), name='bias_2') self.weight_3 = Parameter(Tensor(np.random.randn(64, output_dims), dtype=mstype.float32), name='weight_3') self.bias_3 = Parameter(Tensor(np.zeros(output_dims), dtype=mstype.float32), name='bias_3') def construct(self, x): x1 = self.matmul(x, self.weight_1) + self.bias_1 x2 = self.matmul(x1, self.weight_2) + self.bias_2 x3 = self.matmul(x2, self.weight_3) + self.bias_3 return x3 def main(): net = Net(1, 1) # loss function loss = nn.MSELoss() # optimizer optim = nn.SGD(params=net.trainable_params(), learning_rate=0.000001) # make net model # model = Model(net, loss, optim, metrics={'loss': nn.Loss()}) net_with_criterion = WithLossCell(net, loss) train_network = TrainOneStepCell(net_with_criterion, optim) # 数据集 x, y = np.array([[0.1]], dtype=np.float32), np.array([[0.1]], dtype=np.float32) x = Tensor(x) y = Tensor(y) for i in range(20000*100): #print(i, ' ', '*' * 100) train_network.set_train() res = train_network(x, y) # right # False, False # False, True # True, True xxx # not right # True, False if __name__ == '__main__': """ 设置运行的背景context """ from mindspore import context # 为mindspore设置运行背景context #context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU') context.set_context(mode=context.GRAPH_MODE, device_target='GPU') import time a = time.time() main() b = time.time() print(b-a)
运行时间：
1158.24s
1154.29s
1152.69s
=====================================================
前文我们给出的单步计算 model.train 的代码修改如下：
import mindspore import numpy as np # 引入numpy科学计算库 import matplotlib.pyplot as plt # 引入绘图库 np.random.seed(123) # 随机数生成种子 import mindspore.nn as nn import mindspore.ops as ops from mindspore import Tensor from mindspore import ParameterTuple, Parameter from mindspore import dtype as mstype from mindspore import Model import mindspore.dataset as ds from mindspore.train.callback import ModelCheckpoint, CheckpointConfig from mindspore.train.callback import LossMonitor class Net(nn.Cell): def __init__(self, input_dims, output_dims): super(Net, self).__init__() self.matmul = ops.MatMul() self.weight_1 = Parameter(Tensor(np.random.randn(input_dims, 128), dtype=mstype.float32), name='weight_1') self.bias_1 = Parameter(Tensor(np.zeros(128), dtype=mstype.float32), name='bias_1') self.weight_2 = Parameter(Tensor(np.random.randn(128, 64), dtype=mstype.float32), name='weight_2') self.bias_2 = Parameter(Tensor(np.zeros(64), dtype=mstype.float32), name='bias_2') self.weight_3 = Parameter(Tensor(np.random.randn(64, output_dims), dtype=mstype.float32), name='weight_3') self.bias_3 = Parameter(Tensor(np.zeros(output_dims), dtype=mstype.float32), name='bias_3') def construct(self, x): x1 = self.matmul(x, self.weight_1) + self.bias_1 x2 = self.matmul(x1, self.weight_2) + self.bias_2 x3 = self.matmul(x2, self.weight_3) + self.bias_3 return x3 def main(): net = Net(1, 1) # loss function loss = nn.MSELoss() # optimizer optim = nn.SGD(params=net.trainable_params(), learning_rate=0.000001) # make net model model = Model(net, loss, optim, metrics={'loss': nn.Loss()}) # 数据集 x, y = np.array([[0.1]], dtype=np.float32), np.array([[0.1]], dtype=np.float32) def generator_multidimensional(): for i in range(1): a = x*i b = y*i #print(a, b) yield (a, b) dataset = ds.GeneratorDataset(source=generator_multidimensional, column_names=["input", "output"]) for i in range(20000*100): #print(i, ' ', '*' * 100) model.train(1, dataset, dataset_sink_mode=False) # right # False, False # False, True # True, True xxx # not right # True, False if __name__ == '__main__': """ 设置运行的背景context """ from mindspore import context # 为mindspore设置运行背景context #context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU') context.set_context(mode=context.GRAPH_MODE, device_target='GPU') import time a = time.time() main() b = time.time() print(b-a)
运行时间：
2173.19s
2181.61s
==================================================================
可以看到，在单步计算时，如果使用框架提供的单步训练函数会更好的提升算法运算效率，运算效率提升的幅度也很大，所有在进行单步训练或者非持续数据量训练时使用框架提供的单步训练函数是首选。
单步训练函数：
from mindspore.nn import TrainOneStepCell, WithLossCell
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本文实验环境为 MindSpore1.1 docker版本
宿主机：Ubuntu18.04系统
CPU:I7-8700
GPU:1060ti NVIDIA显卡