(续）在深度计算框架MindSpore中如何对不持续的计算进行处理——对数据集进行一定epoch数量的训练后，进行其他工作处理，再返回来接着进行一定epoch数量的训练——单步计算
内容接前文：
https://www.cnblogs.com/devilmaycry812839668/p/14988686.html
这里我们考虑的数据集是自建数据集，那么效果又会如何呢？？？
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(10):
a = x*i
b = y*i
print(a, b)
yield (a, b) dataset = ds.GeneratorDataset(source=generator_multidimensional, column_names=["input", "output"]) model.train(1, dataset, callbacks=LossMonitor(1), dataset_sink_mode=True) print('*' * 100) x, y = np.array([[0.2]], dtype=np.float32), np.array([[0.2]], dtype=np.float32)
model.train(1, dataset, callbacks=LossMonitor(1), 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)
运行结果：
WARNING: 'ControlDepend' is deprecated from version 1.1 and will be removed in a future version, use 'Depend' instead.
[WARNING] ME(22644:139765219266688,MainProcess):2021-07-09-03:51:42.606.193 [mindspore/ops/operations/array_ops.py:2302] WARN_DEPRECATED: The usage of Pack is deprecated. Please use Stack.
[[0.]] [[0.]]
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[[0.4]] [[0.4]]
[[0.5]] [[0.5]]
[[0.6]] [[0.6]]
[[0.7]] [[0.7]]
[[0.8]] [[0.8]]
[[0.90000004]] [[0.90000004]]
[[0.]] [[0.]]
[[0.1]] [[0.1]]
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[[0.1]] [[0.1]]
[[0.2]] [[0.2]]
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[[0.8]] [[0.8]]
[[0.90000004]] [[0.90000004]]
epoch: 1 step: 10, loss is 14095.578
****************************************************************************************************
[[0.]] [[0.]]
[[0.2]] [[0.2]]
[[0.4]] [ERROR] ANALYZER(22644,python):2021-07-09-03:51:44.281.599 [mindspore/ccsrc/pipeline/jit/static_analysis/evaluator.cc:74] Eval] Function construct_wrapper, The number of parameters of this function is 20, but the number of provided arguments is 22. NodeInfo:
Traceback (most recent call last):
File "/tmp/pycharm_project_753/second_nnnew_line_regression.py", line 85, in <module>
[[0.4]] main()
File "/tmp/pycharm_project_753/second_nnnew_line_regression.py", line 63, in main [[0.6]] [[0.6]]model.train(1, dataset, callbacks=LossMonitor(1), dataset_sink_mode=False) File "/usr/local/python-3.7.5/lib/python3.7/site-packages/mindspore/train/model.py", line 592, in train
[[0.8]] [[0.8]]
[[1.]] sink_size=sink_size)[[1.]] File "/usr/local/python-3.7.5/lib/python3.7/site-packages/mindspore/train/model.py", line 385, in _train
[[1.2]] [[1.2]]
[[1.4]] [[1.4]]self._train_process(epoch, train_dataset, list_callback, cb_params) File "/usr/local/python-3.7.5/lib/python3.7/site-packages/mindspore/train/model.py", line 513, in _train_process
[[1.6]] [[1.6]]
[[1.8000001]] [[1.8000001]]outputs = self._train_network(*next_element) File "/usr/local/python-3.7.5/lib/python3.7/site-packages/mindspore/nn/cell.py", line 322, in __call__
out = self.compile_and_run(*inputs)
File "/usr/local/python-3.7.5/lib/python3.7/site-packages/mindspore/nn/cell.py", line 578, in compile_and_run
self.compile(*inputs)
File "/usr/local/python-3.7.5/lib/python3.7/site-packages/mindspore/nn/cell.py", line 565, in compile
_executor.compile(self, *inputs, phase=self.phase, auto_parallel_mode=self._auto_parallel_mode)
File "/usr/local/python-3.7.5/lib/python3.7/site-packages/mindspore/common/api.py", line 505, in compile
result = self._executor.compile(obj, args_list, phase, use_vm)
TypeError: mindspore/ccsrc/pipeline/jit/static_analysis/evaluator.cc:74 Eval] Function construct_wrapper, The number of parameters of this function is 20, but the number of provided arguments is 22. NodeInfo: # 进程已结束，退出代码为 1
经过多次实验，发现：
设置为：
model.train(1, dataset, callbacks=LossMonitor(1), dataset_sink_mode=True)
print('*' * 100)
x, y = np.array([[0.2]], dtype=np.float32), np.array([[0.2]], dtype=np.float32)
model.train(1, dataset, callbacks=LossMonitor(1), dataset_sink_mode=False)
则会报错。
而：
model.train(1, dataset, callbacks=LossMonitor(1), dataset_sink_mode=True)
print('*' * 100)
x, y = np.array([[0.2]], dtype=np.float32), np.array([[0.2]], dtype=np.float32)
model.train(1, dataset, callbacks=LossMonitor(1), dataset_sink_mode=True)
model.train(1, dataset, callbacks=LossMonitor(1), dataset_sink_mode=False)
print('*' * 100)
x, y = np.array([[0.2]], dtype=np.float32), np.array([[0.2]], dtype=np.float32)
model.train(1, dataset, callbacks=LossMonitor(1), dataset_sink_mode=False)
model.train(1, dataset, callbacks=LossMonitor(1), dataset_sink_mode=False)
print('*' * 100)
x, y = np.array([[0.2]], dtype=np.float32), np.array([[0.2]], dtype=np.float32)
model.train(1, dataset, callbacks=LossMonitor(1), dataset_sink_mode=True)
则都不会报错。
可以看到如果两次调用同一个mode来训练持续数据，那么dataset_sink_mode 的设置还是很重要的，
这里面推荐的dataset_sink_mode的设置是如果同一个model多次训练，那么所有的训练时dataset_sink_mode都设置为False ,
这样经验上来说能够更大可能性保证正常运行。
====================================================================
那么我们对多次的 model.train 进行训练和一次的model.train 进行训练，那么在运算效率上会有多大区别呢？？？
1.
一次model.tain 进行20000epochs的训练， 代码如下：
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(100):
a = x*i
b = y*i
#print(a, b)
yield (a, b) dataset = ds.GeneratorDataset(source=generator_multidimensional, column_names=["input", "output"]) model.train(20000, dataset, callbacks=LossMonitor(100), dataset_sink_mode=False) """
print('*' * 100) x, y = np.array([[0.2]], dtype=np.float32), np.array([[0.2]], dtype=np.float32)
model.train(1, dataset, callbacks=LossMonitor(1), 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)
训练时间：
1464.53s
1460.07s
1452.64s
2.
2次model.tain 分别进行10000epochs的训练， 代码如下：
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(100):
a = x*i
b = y*i
#print(a, b)
yield (a, b) dataset = ds.GeneratorDataset(source=generator_multidimensional, column_names=["input", "output"]) model.train(10000, dataset, callbacks=LossMonitor(100), dataset_sink_mode=False) print('*' * 100) x, y = np.array([[0.1]], dtype=np.float32), np.array([[0.1]], dtype=np.float32)
model.train(10000, dataset, callbacks=LossMonitor(100), 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)
训练时间：
1460.29s
1454.51s
1457.07s
3.
10次model.tain 分别进行2000epochs的训练， 代码如下：
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(100):
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(10):
print(i, '\t', '*' * 100)
model.train(2000, dataset, callbacks=LossMonitor(100), 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)
训练时间：
1457.52s
4.
100次model.tain 分别进行200epochs的训练， 代码如下：
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(100):
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(100):
print(i, '\t', '*' * 100)
model.train(200, dataset, callbacks=LossMonitor(100), 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)
训练时间：
1457.56s
5.
1000次model.tain 分别进行20epochs的训练， 代码如下：
6.
10000次model.tain 分别进行2epochs的训练， 代码如下：
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(100):
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(10000):
print(i, '\t', '*' * 100)
model.train(2, dataset, callbacks=[LossMonitor(100)], 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)
训练时间：
1464.64s
7.
20000次model.tain 分别进行1epochs的训练， 代码如下：
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(100):
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):
#print(i, '\t', '*' * 100)
model.train(1, dataset, callbacks=[LossMonitor(100)], 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)
训练时间：
1475.06s
1469.48s
1475.70s
1471.46s
=========================================================
从测试的数据上来看使用多次的model.train确实要比一次调用model.train要耗费时间，不过考虑到这样能够更多的支持其他功能，这种性能损耗也是完全可以接受的。
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
(续）在深度计算框架MindSpore中如何对不持续的计算进行处理——对数据集进行一定epoch数量的训练后，进行其他工作处理，再返回来接着进行一定epoch数量的训练——单步计算的更多相关文章
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