Hello everyone I am working on CIFAR10 dataset using pytorch. I have developed a model which works absolutely fine but the main problem occurrs while runing the following code:
import time
start_time=time.time()
epochs=5
train_losses=[]
test_losses=[]
train_correct=[]
test_correct=[]
for i in range(epochs):
tsn_corr=0
tst_corr=0
for b, (X_train,y_train) in enumerate(train_loader):
b+=1
y_pred=model(X_train)
loss=criterion(y_pred,y_train)
#Tally the number of correct predictions
predicted= torch.max(y_pred.data, 1)[1]
batch_corr=(predicted==y_train).sum()
tsn_corr += batch_corr
#optimize paramters
optimizer.zero_grad()
loss.backward()
optimizer.step()
#print interim results
if b%600 == 0:
print(f"epochs: {i}, batch: {b}, loss: {loss.item():10.8f}")
loss=loss.detach().numpy()
train_losses.append(loss)
train_correct.append(tsn_corr)
#Running the test_batches
with torch.no_grad():
for b, (X_test,y_test) in enumerate(test_loader):
b+=1
y_val=model(X_test)
#TALLY THE NUMBER OF CORRECT PREDICTIONS
predicted=torch.max(y_val.data, 1)[1]
batch_corr= (predicted==y_test).sum()
tst_corr += batch_corr
loss=criterion(y_val,y_test)
loss=loss.detach().numpy()
test_losses.append(loss)
test_correct.append(tst_corr)
the following error occurrs while running the following code:
NotImplementedError Traceback (most recent call last)
<ipython-input-43-48e21e83e9f7> in <module>
15 b+=1
16
---> 17 y_pred=model(X_train)
18 loss=criterion(y_pred,y_train)
19
~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in _forward_unimplemented(self, *input)
199 registered hooks while the latter silently ignores them.
200 """
--> 201 raise NotImplementedError
202
203
NotImplementedError:
Can someone tell me what can I do to fix this code. Apart from this previous all codes work fine and the model which I made using ConvolutionalNeural-Networks also runs successfully meaning that there is no problem with the model. I guess this detail might help. It might be noted that this code works just fine on MNIST dataset. I dont know what is the problem with CIFAR datasets
Your model class needs to implement a forward method. See the PyTorch Example on Subclassing to see an example.
Related
I want to use cross-validation instead of the normal validation set approach just as a means to get a better estimate of the test error rate. I am using spark-MLLib Dataframe based API. However if I run the following code -
cv = tuning.CrossValidator(estimator=randomForestRegressor, evaluator=evaluator, numFolds=5)
cv_model = cv.fit(vsdf)
I get the error -
KeyError Traceback (most recent call last)
<ipython-input-44-d4e7a9d3602e> in <module>
----> 1 cv_model = cv.fit(vsdf)
C:\Spark\spark-3.1.2-bin-hadoop3.2\python\pyspark\ml\base.py in fit(self, dataset, params)
159 return self.copy(params)._fit(dataset)
160 else:
--> 161 return self._fit(dataset)
162 else:
163 raise ValueError("Params must be either a param map or a list/tuple of param maps, "
C:\Spark\spark-3.1.2-bin-hadoop3.2\python\pyspark\ml\tuning.py in _fit(self, dataset)
667 def _fit(self, dataset):
668 est = self.getOrDefault(self.estimator)
--> 669 epm = self.getOrDefault(self.estimatorParamMaps)
670 numModels = len(epm)
671 eva = self.getOrDefault(self.evaluator)
C:\Spark\spark-3.1.2-bin-hadoop3.2\python\pyspark\ml\param\__init__.py in getOrDefault(self, param)
344 return self._paramMap[param]
345 else:
--> 346 return self._defaultParamMap[param]
347
348 def extractParamMap(self, extra=None):
KeyError: Param(parent='CrossValidator_a9121a59fda3', name='estimatorParamMaps', doc='estimator param maps')
I guess this is because I have not provided any parameter-map to search over. Is there no way to do cross-validation in spark-ml without a parameter grid?
Yes, you can do it. You need to pass an empty parameter grid though.
something like this should work, it will behave like a normal K-fold cross validator.
params_grid = ParamGridBuilder().build()
I got this error when trying out the sample code (https://minizinc-python.readthedocs.io/en/latest/getting_started.html) of the minizinc web.
from minizinc import Instance, Model, Solver
# Load n-Queens model from file
nqueens = Model("./nqueens.mzn")
# Find the MiniZinc solver configuration for Gecode
gecode = Solver.lookup("gecode")
# Create an Instance of the n-Queens model for Gecode
instance = Instance(gecode, nqueens)
# Assign 4 to n
instance["n"] = 4
result = instance.solve()
# Output the array q
print(result["q"])
The error I got was:
AssertionError Traceback (most recent call last)
<ipython-input-1-a64f1a5182f8> in <module>
2
3 # Load n-Queens model from file
----> 4 nqueens = Model("./nqueens.mzn")
5 # Find the MiniZinc solver configuration for Gecode
6 gecode = Solver.lookup("gecode")
C:\ProgramData\Anaconda3\lib\site-packages\minizinc\model.py in __init__(self, files)
85 self._lock = threading.Lock()
86 if isinstance(files, Path) or isinstance(files, str):
---> 87 self.add_file(files)
88 elif files is not None:
89 for file in files:
C:\ProgramData\Anaconda3\lib\site-packages\minizinc\model.py in add_file(self, file, parse_data)
159 if not isinstance(file, Path):
160 file = Path(file)
--> 161 assert file.exists()
162 if not parse_data:
163 with self._lock:
AssertionError:
I've downloaded both minizinc and python. I tried using jupyternotebook and spyder, but they both had the same issue.
If anyone has faced the same issue and fixed the problem I'll appreciate any feedback regarding this problem.
I am trying to get workers to output some information from their ipython kernel and execute various commands in the ipython session. I tried the examples in the documentation and the ipyparallel example works, but not the second example (with ipython magics). I cannot get the workers to execute any commands. For example, I am stuck on the following issue:
from dask.distributed import Client
client = Client()
info = client.start_ipython_workers()
list_workers = info.keys()
%remote info[list_workers[0]]
The last line returns an error:
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-19-9118451af441> in <module>
----> 1 get_ipython().run_line_magic('remote', "info['tcp://127.0.0.1:50497'] worker.active")
~/miniconda/envs/dask/lib/python3.7/site-packages/IPython/core/interactiveshell.py in run_line_magic(self, magic_name, line, _stack_depth)
2334 kwargs['local_ns'] = self.get_local_scope(stack_depth)
2335 with self.builtin_trap:
-> 2336 result = fn(*args, **kwargs)
2337 return result
2338
~/miniconda/envs/dask/lib/python3.7/site-packages/distributed/_ipython_utils.py in remote_magic(line, cell)
115 info_name = split_line[0]
116 if info_name not in ip.user_ns:
--> 117 raise NameError(info_name)
118 connection_info = dict(ip.user_ns[info_name])
119
NameError: info['tcp://127.0.0.1:50497']
I would appreciate any examples of how to get any information from the ipython kernel running on workers.
Posting here just for keeping track, I raised an issue for this on GitHub: https://github.com/dask/distributed/issues/4522
As I understand connected_components() method in NetworkX should generate components in a given undirected graph (There are strongly_connected_components() and weakly_connected_components() for directed graph). I have generated an undirected graph G and while trying to implement networkx.connected_components(G), I am getting the error NetworkXNotImplemented: not implemented for directed type.
Note: G was the interaction network of users of the Pretty Good Privacy (PGP) algorithm (http://deim.urv.cat/~aarenas/data/welcome.htm). The network is a single giant component.
I have implemented the method for many other undirected networks.
import networkx as nx
G=nx.read_pajek("PGPgiantcompo.net")
C=max(nx.connected_components(G), key=len)
Expected result:
C contains the giant component which is G itself.
Actual result:
NetworkXNotImplemented Traceback (most recent call last)
<ipython-input-1-27a0ad1fa789> in <module>()
---> 27 C=max(nx.connected_components(G), key=len)
28 Giant_frozen=G.subgraph(C)
29 nx.draw(Giant_frozen, with_labels=True, font_weight='bold')
<decorator-gen-295> in connected_components(G)
F:\Anaconda3\lib\site-packages\networkx\utils\decorators.py in _not_implemented_for(not_implement_for_func, *args, **kwargs)
78 if match:
79 msg = 'not implemented for %s type' % ' '.join(graph_types)
---> 80 raise nx.NetworkXNotImplemented(msg)
81 else:
82 return not_implement_for_func(*args, **kwargs)
NetworkXNotImplemented: not implemented for directed type
I'm working with RNN and using Pytorch & Torchtext. I've got a problem with building vocab in my RNN. My code is as follows:
TEXT = Field(tokenize=tokenizer, lower=True)
LABEL = LabelField(dtype=torch.float)
trainds = TabularDataset(
path='drive/{}'.format(TRAIN_PATH), format='tsv',
fields=[
('label_start', LABEL),
('label_end', None),
('title', None),
('symbol', None),
('text_content', TEXT),
])
testds = TabularDataset(
path='drive/{}'.format(TEST_PATH), format='tsv',
fields=[
('text_content', TEXT),
])
TEXT.build_vocab(trainds, testds)
When I want to build vocab, I'm getting this annoying error:
AttributeError: 'Example' object has no attribute 'text_content'
I'm sure, that there is no missing text_content attr. I made try-catch in order to display this specific case:
try:
print(len(trainds[i]))
except:
print(trainds[i].text_content)
Surprisingly, I don't get any error and this specific print command shows:
['znana', 'okresie', 'masarni', 'walc', 'y', 'myśl', 'programie', 'sprawy', ...]
So it indicates, that there is text_content attr. When I perform this on a smaller dataset, it works like a charm. This problem occurs when I want to work with proper data. I ran out of ideas. Maybe someone had a similar case and can explain it.
My full traceback:
AttributeError Traceback (most recent call last)
<ipython-input-16-cf31866a07e7> in <module>()
155
156 if __name__ == "__main__":
--> 157 main()
158
<ipython-input-16-cf31866a07e7> in main()
117 break
118
--> 119 TEXT.build_vocab(trainds, testds)
120 print('zbudowano dla text')
121 LABEL.build_vocab(trainds)
/usr/local/lib/python3.6/dist-packages/torchtext/data/field.py in build_vocab(self, *args, **kwargs)
260 sources.append(arg)
261 for data in sources:
--> 262 for x in data:
263 if not self.sequential:
264 x = [x]
/usr/local/lib/python3.6/dist-packages/torchtext/data/dataset.py in __getattr__(self, attr)
152 if attr in self.fields:
153 for x in self.examples:
--> 154 yield getattr(x, attr)
155
156 #classmethod
AttributeError: 'Example' object has no attribute 'text_content'
This problem arises when the fields are not passed in the same order as they are in the csv/tsv file. Order must be same. Also check if no extra or less fields are mentioned than there are in the csv/tsv file..
I had the same problem.
The reason was that some rows in my input csv dataset were empty.