sodiumplus3 score 31
2019/04/29 01:27 投稿
| chainerのtrainer.run()をJupiter notebookで実行したときにエラーが起こる |
| ###trainer.run()だけを実行したい |
| Chainerで適当な機械学習モデルを組んで、jupyter notebook上で単独セルで`trainer.run()`を実行すると、1回目は問題なく実行されますが、2回目以降そのセルの実行時に`RuntimeError: cannot run training loop multiple times`が出てしまいます。毎回Restart & Run allを実行しなければならないのでしょうか？ |
| サンプル置いときます。 |
| ``` |
| import chainer |
| import chainer.functions as F |
| import chainer.links as L |
| import numpy as np |
| import matplotlib.pyplot as plt |
| from sklearn.datasets import load_iris |
| x,t = load_iris(return_X_y=True) |
| x = x.astype('float32') |
| t = t.astype('int32') |
| from sklearn.preprocessing import StandardScaler |
| sc = StandardScaler() |
| sc.fit(x) |
| sc.transform(x) |
| from chainer.datasets import TupleDataset,split_dataset_random |
| dataset = TupleDataset(x,t) |
| n_train = int(len(dataset)*0.7) |
| n_valid = int(len(dataset)*0.2) |
| train,valid_test = split_dataset_random(dataset,n_train,seed=0) |
| valid,test = split_dataset_random(valid_test,n_valid,seed=0) |
| from chainer import iterators |
| batch_size = 5 |
| train_iter = iterators.SerialIterator(train,batch_size) |
| valid_iter = iterators.SerialIterator(valid,batch_size,shuffle=False,repeat=False) |
| class Net(chainer.Chain): |
| def __init__(self,n_mid=10,n_out=3): |
| super(Net,self).__init__() |
| with self.init_scope(): |
| self.l1 = L.Linear(None,n_mid) |
| self.l2 = L.Linear(n_mid,n_mid) |
| self.l3 = L.Linear(n_mid,n_out) |
| def forward(self,x): |
| h = F.relu(self.l1(x)) |
| h = F.relu(self.l2(h)) |
| h = self.l3(h) |
| return h |
| from chainer import optimizers,training |
| predictor = Net() |
| net = L.Classifier(predictor) |
| print(net.predictor) |
| optimizer = optimizers.SGD().setup(net) |
| updater = training.StandardUpdater(train_iter,optimizer,device=-1) |
| from chainer.training.triggers import EarlyStoppingTrigger |
| trainer = training.Trainer(updater,(25,'epoch'),out='results/iris_result') |
| from chainer.training import extensions |
| trainer.extend(extensions.LogReport(trigger=(1,'epoch'),log_name='log')) |
| trainer.extend(extensions.PrintReport(['epoch', 'iteration', 'main/loss', 'main/accuracy']))) |
| ``` |
| ``` |
| trainer.run() |
| ``` |
| これらをセルを分けて実行すると質問の状況が得られます。 |
sodiumplus3 score 31
2019/04/29 01:26 投稿
| chainerのtrainer.run()をJupiter notebookで実行したときにエラーが起こる |
| ###trainer.run()だけを実行したい |
| Chainerで適当な機械学習モデルを組んで、jupyter notebook上で単独セルで`trainer.run()`を実行すると、1回目は問題なく実行されますが、2回目以降そのセルの実行時に`RuntimeError: cannot run training loop multiple times`が出てしまいます。毎回Restart & Run allを実行しなければならないのでしょうか？ |
| サンプル置いときます。 |
| ``` |
| import chainer |
| import chainer.functions as F |
| import chainer.links as L |
| import numpy as np |
| import matplotlib.pyplot as plt |
| from sklearn.datasets import load_iris |
| x,t = load_iris(return_X_y=True) |
| x = x.astype('float32') |
| t = t.astype('int32') |
| from sklearn.preprocessing import StandardScaler |
| sc = StandardScaler() |
| sc.fit(x) |
| sc.transform(x) |
| from chainer.datasets import TupleDataset,split_dataset_random |
| dataset = TupleDataset(x,t) |
| n_train = int(len(dataset)*0.7) |
| n_valid = int(len(dataset)*0.2) |
| train,valid_test = split_dataset_random(dataset,n_train,seed=0) |
| valid,test = split_dataset_random(valid_test,n_valid,seed=0) |
| c = np.zeros(3) |
| for i in range(n_train): |
| c[int(train[i][1])] += 1 |
| print(c) |
| from chainer import iterators |
| batch_size = 5 |
| train_iter = iterators.SerialIterator(train,batch_size) |
| valid_iter = iterators.SerialIterator(valid,batch_size,shuffle=False,repeat=False) |
| class Net(chainer.Chain): |
| def __init__(self,n_mid=10,n_out=3): |
| super(Net,self).__init__() |
| with self.init_scope(): |
| self.l1 = L.Linear(None,n_mid) |
| self.l2 = L.Linear(n_mid,n_mid) |
| self.l3 = L.Linear(n_mid,n_out) |
| def forward(self,x): |
| h = F.relu(self.l1(x)) |
| h = F.relu(self.l2(h)) |
| h = self.l3(h) |
| return h |
| from chainer import optimizers,training |
| predictor = Net() |
| net = L.Classifier(predictor) |
| print(net.predictor) |
| optimizer = optimizers.SGD().setup(net) |
| updater = training.StandardUpdater(train_iter,optimizer,device=-1) |
| from chainer.training.triggers import EarlyStoppingTrigger |
| trainer = training.Trainer(updater,(25,'epoch'),out='results/iris_result') |
| from chainer.training import extensions |
| trainer.extend(extensions.LogReport(trigger=(1,'epoch'),log_name='log')) |
| trainer.extend(extensions.PrintReport(['epoch', 'iteration', 'main/loss', 'main/accuracy']))) |
| ``` |
| ``` |
| trainer.run() |
| ``` |
| これらをセルを分けて実行すると質問の状況が得られます。 |
sodiumplus3 score 31
2019/04/29 01:25 投稿
| chainerのtrainer.run()をJupiter notebookで実行したときにエラーが起こる |
| ###trainer.run()だけを実行したい |
| Chainerで適当な機械学習モデルを組んで、jupyter notebook上で単独セルで`trainer.run()`を実行すると、1回目は問題なく実行されますが、2回目以降そのセルの実行時に`RuntimeError: cannot run training loop multiple times`が出てしまいます。毎回Restart & Run allを実行しなければならないのでしょうか？ |
| サンプル置いときます。 |
| ``` |
| import chainer |
| import chainer.functions as F |
| import chainer.links as L |
| import numpy as np |
| import matplotlib.pyplot as plt |
| from sklearn.datasets import load_iris |
| x,t = load_iris(return_X_y=True) |
| x = x.astype('float32') |
| t = t.astype('int32') |
| from sklearn.preprocessing import StandardScaler |
| sc = StandardScaler() |
| sc.fit(x) |
| sc.transform(x) |
| from chainer.datasets import TupleDataset,split_dataset_random |
| dataset = TupleDataset(x,t) |
| n_train = int(len(dataset)*0.7) |
| n_valid = int(len(dataset)*0.2) |
| train,valid_test = split_dataset_random(dataset,n_train,seed=0) |
| valid,test = split_dataset_random(valid_test,n_valid,seed=0) |
| c = np.zeros(3) |
| for i in range(n_train): |
| c[int(train[i][1])] += 1 |
| print(c) |
| from chainer import iterators |
| batch_size = 5 |
| train_iter = iterators.SerialIterator(train,batch_size) |
| valid_iter = iterators.SerialIterator(valid,batch_size,shuffle=False,repeat=False) |
| class Net(chainer.Chain): |
| def __init__(self,n_mid=10,n_out=3): |
| super(Net,self).__init__() |
| with self.init_scope(): |
| self.l1 = L.Linear(None,n_mid) |
| self.l2 = L.Linear(n_mid,n_mid) |
| self.l3 = L.Linear(n_mid,n_out) |
| def forward(self,x): |
| h = F.relu(self.l1(x)) |
| h = F.relu(self.l2(h)) |
| h = self.l3(h) |
| return h |
| from chainer import optimizers,training |
| predictor = Net() |
| net = L.Classifier(predictor) |
| print(net.predictor) |
| optimizer = optimizers.SGD().setup(net) |
| updater = training.StandardUpdater(train_iter,optimizer,device=-1) |
| from chainer.training.triggers import EarlyStoppingTrigger |
| trainer = training.Trainer(updater,(25,'epoch'),out='results/iris_result') |
| from chainer.training import extensions |
| trainer.extend(extensions. |
| trainer.extend(extensions.PrintReport(['epoch', 'iteration', 'main/loss', 'main/accuracy']))) |
| ``` |
| ``` |
| trainer.run() |
| ``` |
| これらをセルを分けて実行すると質問の状況が得られます。 |
sodiumplus3 score 31
2019/04/29 01:23 投稿
| chainerのtrainer.run()をJupiter notebookで実行したときにエラーが起こる |
| ###trainer.run()だけを実行したい |
| Chainerで適当な機械学習モデルを組んで、jupyter notebook上で単独セルで`trainer.run()`を実行すると、1回目は問題なく実行されますが、2回目以降そのセルの実行時に`RuntimeError: cannot run training loop multiple times`が出てしまいます。毎回Restart & Run allを実行しなければならないのでしょうか？ |
| Chainerで適当な機械学習モデルを組んで、jupyter notebook上で単独セルで`trainer.run()`を実行すると、1回目は問題なく実行されますが、2回目以降そのセルの実行時に`RuntimeError: cannot run training loop multiple times`が出てしまいます。毎回Restart & Run allを実行しなければならないのでしょうか？ |
| サンプル置いときます。 |
| ``` |
| import chainer |
| import chainer.functions as F |
| import chainer.links as L |
| import numpy as np |
| import matplotlib.pyplot as plt |
| from sklearn.datasets import load_iris |
| x,t = load_iris(return_X_y=True) |
| x = x.astype('float32') |
| t = t.astype('int32') |
| from sklearn.preprocessing import StandardScaler |
| sc = StandardScaler() |
| sc.fit(x) |
| sc.transform(x) |
| from chainer.datasets import TupleDataset,split_dataset_random |
| dataset = TupleDataset(x,t) |
| n_train = int(len(dataset)*0.7) |
| n_valid = int(len(dataset)*0.2) |
| train,valid_test = split_dataset_random(dataset,n_train,seed=0) |
| valid,test = split_dataset_random(valid_test,n_valid,seed=0) |
| c = np.zeros(3) |
| for i in range(n_train): |
| c[int(train[i][1])] += 1 |
| print(c) |
| from chainer import iterators |
| batch_size = 5 |
| train_iter = iterators.SerialIterator(train,batch_size) |
| valid_iter = iterators.SerialIterator(valid,batch_size,shuffle=False,repeat=False) |
| class Net(chainer.Chain): |
| def __init__(self,n_mid=10,n_out=3): |
| super(Net,self).__init__() |
| with self.init_scope(): |
| self.l1 = L.Linear(None,n_mid) |
| self.l2 = L.Linear(n_mid,n_mid) |
| self.l3 = L.Linear(n_mid,n_out) |
| def forward(self,x): |
| h = F.relu(self.l1(x)) |
| h = F.relu(self.l2(h)) |
| h = self.l3(h) |
| return h |
| from chainer import optimizers,training |
| predictor = Net() |
| net = L.Classifier(predictor) |
| print(net.predictor) |
| optimizer = optimizers.SGD().setup(net) |
| updater = training.StandardUpdater(train_iter,optimizer,device=-1) |
| from chainer.training.triggers import EarlyStoppingTrigger |
| trainer = training.Trainer(updater,(25,'epoch'),out='results/iris_result') |
| from chainer.training import extensions |
| trainer.extend(extensions.LogReport(trigger=(1,'epoch'),log_name='log')) |
| ``` |
| ``` |
| trainer.run() |
| ``` |
| これらをセルを分けて実行すると質問の状況が得られます。 |
