提交 eca140df 编写于 作者: Y yangyaqin1@huawei.com

feedforward

上级 4d8ef28a
......@@ -209,8 +209,8 @@ def get_data():
```python
train_x, train_y, test_x, test_y = get_data()
train_x = train_x.reshape(-1, 1, 28, 28)
test_x = test_x.reshape(-1, 1, 28, 28)
train_x = train_x.reshape(-1, 1, cfg.image_height, cfg.image_width)
test_x = test_x.reshape(-1, 1, cfg.image_height, cfg.image_width)
train_x = train_x / 255.0
test_x = test_x / 255.0
train_x = train_x.astype('Float32')
......@@ -269,14 +269,12 @@ class Forward_fashion(nn.Cell):
self.fc1 = nn.Dense(cfg.channel * cfg.image_height * cfg.image_width, 128)
self.relu = nn.ReLU()
self.fc2 = nn.Dense(128, self.num_class)
self.softmax = nn.Softmax()
def construct(self, x):
x = self.flatten(x)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.softmax(x)
return x
```
......@@ -305,65 +303,65 @@ model.train(cfg.epoch_size, ds_train, callbacks=[ckpoint_cb, loss_cb], dataset_s
```
============== Starting Training ==============
epoch: 1 step 1000, loss is 1.6292920112609863
Epoch time: 8827.199, per step time: 8.827, avg loss: 1.629
epoch: 1 step 1000, loss is 0.567767322063446
Epoch time: 11428.370, per step time: 11.428, avg loss: 0.568
************************************************************
epoch: 2 step 1000, loss is 1.6026073694229126
Epoch time: 1601.618, per step time: 1.602, avg loss: 1.603
epoch: 2 step 1000, loss is 0.3970850706100464
Epoch time: 2018.074, per step time: 2.018, avg loss: 0.397
************************************************************
epoch: 3 step 1000, loss is 1.6622530221939087
Epoch time: 1629.635, per step time: 1.630, avg loss: 1.662
epoch: 3 step 1000, loss is 0.31815576553344727
Epoch time: 1971.219, per step time: 1.971, avg loss: 0.318
************************************************************
epoch: 4 step 1000, loss is 1.6305657625198364
Epoch time: 1471.701, per step time: 1.472, avg loss: 1.631
epoch: 4 step 1000, loss is 0.3128049373626709
Epoch time: 1974.937, per step time: 1.975, avg loss: 0.313
************************************************************
epoch: 5 step 1000, loss is 1.5535054206848145
Epoch time: 1770.755, per step time: 1.771, avg loss: 1.554
epoch: 5 step 1000, loss is 0.3095005750656128
Epoch time: 2029.930, per step time: 2.030, avg loss: 0.310
************************************************************
epoch: 6 step 1000, loss is 1.5950586795806885
Epoch time: 1985.995, per step time: 1.986, avg loss: 1.595
epoch: 6 step 1000, loss is 0.25628671050071716
Epoch time: 1934.886, per step time: 1.935, avg loss: 0.256
************************************************************
epoch: 7 step 1000, loss is 1.6165529489517212
Epoch time: 1928.856, per step time: 1.929, avg loss: 1.617
epoch: 7 step 1000, loss is 0.24347715079784393
Epoch time: 1897.307, per step time: 1.897, avg loss: 0.243
************************************************************
epoch: 8 step 1000, loss is 1.5757038593292236
Epoch time: 1814.812, per step time: 1.815, avg loss: 1.576
epoch: 8 step 1000, loss is 0.28936269879341125
Epoch time: 1921.264, per step time: 1.921, avg loss: 0.289
************************************************************
epoch: 9 step 1000, loss is 1.5905802249908447
Epoch time: 1558.252, per step time: 1.558, avg loss: 1.591
epoch: 9 step 1000, loss is 0.4469510316848755
Epoch time: 1875.093, per step time: 1.875, avg loss: 0.447
************************************************************
epoch: 10 step 1000, loss is 1.5414245128631592
Epoch time: 1456.539, per step time: 1.457, avg loss: 1.541
epoch: 10 step 1000, loss is 0.2915213108062744
Epoch time: 1876.605, per step time: 1.877, avg loss: 0.292
************************************************************
epoch: 11 step 1000, loss is 1.5789177417755127
Epoch time: 1591.151, per step time: 1.591, avg loss: 1.579
epoch: 11 step 1000, loss is 0.24928903579711914
Epoch time: 1910.094, per step time: 1.910, avg loss: 0.249
************************************************************
epoch: 12 step 1000, loss is 1.5879883766174316
Epoch time: 2011.590, per step time: 2.012, avg loss: 1.588
epoch: 12 step 1000, loss is 0.12853321433067322
Epoch time: 1974.167, per step time: 1.974, avg loss: 0.129
************************************************************
epoch: 13 step 1000, loss is 1.5823071002960205
Epoch time: 1663.497, per step time: 1.663, avg loss: 1.582
epoch: 13 step 1000, loss is 0.14836660027503967
Epoch time: 1841.105, per step time: 1.841, avg loss: 0.148
************************************************************
epoch: 14 step 1000, loss is 1.6213573217391968
Epoch time: 1914.475, per step time: 1.914, avg loss: 1.621
epoch: 14 step 1000, loss is 0.26581835746765137
Epoch time: 1694.728, per step time: 1.695, avg loss: 0.266
************************************************************
epoch: 15 step 1000, loss is 1.6282684803009033
Epoch time: 1921.290, per step time: 1.921, avg loss: 1.628
epoch: 15 step 1000, loss is 0.2012856900691986
Epoch time: 1937.829, per step time: 1.938, avg loss: 0.201
************************************************************
epoch: 16 step 1000, loss is 1.5011317729949951
Epoch time: 1899.634, per step time: 1.900, avg loss: 1.501
epoch: 16 step 1000, loss is 0.14978612959384918
Epoch time: 1793.748, per step time: 1.794, avg loss: 0.150
************************************************************
epoch: 17 step 1000, loss is 1.566664457321167
Epoch time: 1495.834, per step time: 1.496, avg loss: 1.567
epoch: 17 step 1000, loss is 0.3085048198699951
Epoch time: 1667.389, per step time: 1.667, avg loss: 0.309
************************************************************
epoch: 18 step 1000, loss is 1.552886724472046
Epoch time: 1449.513, per step time: 1.450, avg loss: 1.553
epoch: 18 step 1000, loss is 0.17254383862018585
Epoch time: 1558.955, per step time: 1.559, avg loss: 0.173
************************************************************
epoch: 19 step 1000, loss is 1.6042685508728027
Epoch time: 1721.564, per step time: 1.722, avg loss: 1.604
epoch: 19 step 1000, loss is 0.10585948824882507
Epoch time: 1567.354, per step time: 1.567, avg loss: 0.106
************************************************************
epoch: 20 step 1000, loss is 1.5174891948699951
Epoch time: 1633.871, per step time: 1.634, avg loss: 1.517
epoch: 20 step 1000, loss is 0.27113234996795654
Epoch time: 1589.239, per step time: 1.589, avg loss: 0.271
************************************************************
#### 评估测试
......@@ -374,7 +372,7 @@ metric = model.eval(ds_test)
print(metric)
```
{'acc': 0.8715863453815261}
{'acc': 0.8862449799196788}
#### 预测
......@@ -384,22 +382,27 @@ test_ = ds_test.create_dict_iterator().get_next()
test = Tensor(test_['x'], mindspore.float32)
predictions = model.predict(test)
predictions = predictions.asnumpy()
for i in range(10):
for i in range(15):
p_np = predictions[i, :]
p_list = p_np.tolist()
print('第' + str(i) + '个sample预测结果:', p_list.index(max(p_list)), ' 真实结果:', test_['y'][i])
```
第0个sample预测结果: 3 真实结果: 3
第1个sample预测结果: 2 真实结果: 2
第2个sample预测结果: 3 真实结果: 3
第3个sample预测结果: 9 真实结果: 9
第0个sample预测结果: 1 真实结果: 1
第1个sample预测结果: 0 真实结果: 0
第2个sample预测结果: 2 真实结果: 2
第3个sample预测结果: 2 真实结果: 2
第4个sample预测结果: 8 真实结果: 8
第5个sample预测结果: 4 真实结果: 4
第6个sample预测结果: 4 真实结果: 4
第7个sample预测结果: 8 真实结果: 8
第8个sample预测结果: 3 真实结果: 3
第9个sample预测结果: 1 真实结果: 1
第7个sample预测结果: 1 真实结果: 1
第8个sample预测结果: 6 真实结果: 2
第9个sample预测结果: 8 真实结果: 8
第10个sample预测结果: 5 真实结果: 5
第11个sample预测结果: 8 真实结果: 0
第12个sample预测结果: 5 真实结果: 5
第13个sample预测结果: 6 真实结果: 6
第14个sample预测结果: 9 真实结果: 9
#### 对预测结果可视化
......@@ -471,29 +474,18 @@ args, unknown = parser.parse_known_args()
MindSpore暂时没有提供直接访问OBS数据的接口,需要通过MoXing提供的API与OBS交互。将OBS中存储的数据拷贝至执行容器:
- 方式一,拷贝自己账户下OBS桶内的数据集。
```python
import moxing
moxing.file.copy_parallel(src_url=args.data_url, dst_url='Fashion-MNIST/')
```
- 方式二,拷贝他人账户下OBS桶内的数据集,前提是他人账户下的OBS桶已设为公共读/公共读写,且需要他人账户的访问密钥、私有访问密钥、OBS桶-概览-基本信息-Endpoint。
```python
import moxing
# set moxing/obs auth info, ak:Access Key Id, sk:Secret Access Key, server:endpoint of obs bucket
moxing.file.set_auth(ak='VCT2GKI3GJOZBQYJG5WM', sk='t1y8M4Z6bHLSAEGK2bCeRYMjo2S2u0QBqToYbxzB',
server="obs.cn-north-4.myhuaweicloud.com")
# copy dataset from obs bucket to container/cache
moxing.file.copy_parallel(src_url="s3://share-course/dataset/fashion-mnist/", dst_url='Fashion-MNIST/')
```
拷贝自己账户下OBS桶内的数据集。
```python
import moxing
moxing.file.copy_parallel(src_url=args.data_url, dst_url='Fashion-MNIST/')
```
如需将训练输出(如模型Checkpoint)从执行容器拷贝至OBS,请参考:
```python
import moxing as mox
mox.file.copy_parallel(src_url='model_fashion', dst_url=args.train_url)
import moxing
moxing.file.copy_parallel(src_url='model_fashion', dst_url=args.train_url)
```
### 创建训练作业
......
feedforward/images/output_2.png

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feedforward/images/output_2.png

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feedforward/images/output_2.png
feedforward/images/output_2.png
feedforward/images/output_2.png
feedforward/images/output_2.png
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......@@ -18,6 +18,37 @@ from mindspore import Tensor
context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--data_url', required=True, default=None, help='Location of data.')
parser.add_argument('--train_url', required=True, default=None, help='Location of training outputs.')
args, unknown = parser.parse_known_args()
import moxing
# copy dataset from your own OBS bucket.
moxing.file.copy_parallel(src_url=args.data_url, dst_url='Fashion-MNIST')
cfg = edict({
'train_size': 60000, # 训练集大小
'test_size': 10000, # 测试集大小
'channel': 1, # 图片通道数
'image_height': 28, # 图片高度
'image_width': 28, # 图片宽度
'batch_size': 60,
'num_classes': 10, # 分类类别
'lr': 0.001, # 学习率
'epoch_size': 20, # 训练次数
'data_dir_train': os.path.join('Fashion-MNIST', 'train'),
'data_dir_test': os.path.join('Fashion-MNIST', 'test'),
'save_checkpoint_steps': 1, # 多少步保存一次模型
'keep_checkpoint_max': 3, # 最多保存多少个模型
'output_directory': './model_fashion', # 保存模型路径
'output_prefix': "checkpoint_fashion_forward" # 保存模型文件名字
})
def read_image(file_name):
'''
:param file_name: 文件路径
......@@ -94,57 +125,18 @@ class Forward_fashion(nn.Cell):
self.fc1 = nn.Dense(cfg.channel * cfg.image_height * cfg.image_width, 128)
self.relu = nn.ReLU()
self.fc2 = nn.Dense(128, self.num_class)
self.softmax = nn.Softmax()
def construct(self, x):
x = self.flatten(x)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.softmax(x)
return x
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--data_url', required=True, default=None, help='Location of data.')
parser.add_argument('--train_url', required=True, default=None, help='Location of training outputs.')
args, unknown = parser.parse_known_args()
import moxing
# WAY1: copy dataset from your own OBS bucket.
# moxing.file.copy_parallel(src_url=args.data_url, dst_url='Fashion-MNIST')
# WAY2: copy dataset from other's OBS bucket, which has been set public read or public read&write.
# set moxing/obs auth info, ak:Access Key Id, sk:Secret Access Key, server:endpoint of obs bucket
moxing.file.set_auth(ak='VCT2GKI3GJOZBQYJG5WM', sk='t1y8M4Z6bHLSAEGK2bCeRYMjo2S2u0QBqToYbxzB',
server="obs.cn-north-4.myhuaweicloud.com")
# copy dataset from obs bucket to container/cache
moxing.file.copy_parallel(src_url="s3://share-course/dataset/fashion-mnist/", dst_url='Fashion-MNIST/')
cfg = edict({
'train_size': 60000, # 训练集大小
'test_size': 10000, # 测试集大小
'channel': 1, # 图片通道数
'image_height': 28, # 图片高度
'image_width': 28, # 图片宽度
'batch_size': 60,
'num_classes': 10, # 分类类别
'lr': 0.001, # 学习率
'epoch_size': 20, # 训练次数
'data_dir_train': os.path.join('Fashion-MNIST', 'train'),
'data_dir_test': os.path.join('Fashion-MNIST', 'test'),
'save_checkpoint_steps': 1, # 多少步保存一次模型
'keep_checkpoint_max': 3, # 最多保存多少个模型
'output_directory': './model_fashion', # 保存模型路径
'output_prefix': "checkpoint_fashion_forward" # 保存模型文件名字
})
train_x, train_y, test_x, test_y = get_data()
train_x = train_x.reshape(-1, 1, 28, 28)
test_x = test_x.reshape(-1, 1, 28, 28)
train_x = train_x.reshape(-1, 1, cfg.image_height, cfg.image_width)
test_x = test_x.reshape(-1, 1, cfg.image_height, cfg.image_width)
train_x = train_x / 255.0
test_x = test_x / 255.0
train_x = train_x.astype('Float32')
......@@ -190,9 +182,9 @@ test_ = ds_test.create_dict_iterator().get_next()
test = Tensor(test_['x'], mindspore.float32)
predictions = model.predict(test)
predictions = predictions.asnumpy()
for i in range(10):
for i in range(15):
p_np = predictions[i, :]
p_list = p_np.tolist()
print('第' + str(i) + '个sample预测结果:', p_list.index(max(p_list)), ' 真实结果:', test_['y'][i])
mox.file.copy_parallel(src_url='model_fashion', dst_url=args.train_url)
moxing.file.copy_parallel(src_url='model_fashion', dst_url=args.train_url)
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