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I am using PyTorch and I want to change the learning rate after some epochs.
However, the code that is provided on most documentations, which is:
optimizer = torch.optim.Adam([
dict(params=model.parameters(), lr=learning_rate),
])
#This line specifically
optimizer.params_group[0]['lr'] = learning_rate
does not work.
Actually PyCharm hints at it:
Unresolved attribute reference 'params_group' for class 'Adam'
As a result, the error thrown is:
AttributeError: 'Adam' object has no attribute 'params_group'
How should one manually change the learning rate in PyTorch (1.6)?
Param_groups is not the feasible solution devised by the pytorch and thus you should be implementing pytorch.optim.lr_scheduler. Read more about this at other stackoverflow answer here.
import torch.optim.lr_scheduler.StepLR #step learning rate
scheduler = StepLR(optimizer, step_size=5, gamma=0.1)
In the comment above, #Alizera Omidi provided the answer.
There was a typo in the params_group, which is in fact param_groups
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Please can someone help me on why I am getting this error. I am unable to fit the model
I want to fit the modelenter image description here Here is the error code:enter image description here
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I am using LSTM for binary classification therefore my last dense layer looks like that:
model.add(Dense(1,activation='sigmoid'))
I want to get the probability for each input but
model.predict_proba(X_test)
return values bigger than 1. I understand that it because I didnt use 'softmax' in the last layer but at binary classification I have to use sigmoid yet how can I could get probabilities range [0,1]?
the values I get now is:
array([[1.1198873e-07],
[1.1001220e-07],
[7.2163729e-08],
...,
[1.1758399e-09],
[1.6062747e-10],
[1.5407189e-11]], dtype=float32)
Those values are not above one. This is scientific notation.
1.1198873e-07 = 1.1198873/10,000,000 = 0.00000011198873
So these are actually far below 1.
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I am new to machine learning. I am trying to learn feature selection from this link. Here they have a line of code which is given below
search = GridSearchCV(pipeline, grid, scoring='neg_mean_squared_error', n_jobs=-1, cv=cv)
But whenever I try to run this code I get the error
I cannot find where to import neg_mean_squared_error from. I am not sure where I should write the function myself or not. The tutorial isn't clear on this issue.
It is just a typo.
You need
neg_mean_absolute_error
You typed
neg_mean_absolure_error
using an r instead of t
Reference: https://scikit-learn.org/stable/modules/model_evaluation.html
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Removed text due to this being due to a simple typo issue (the accepted answer gives the correct solution). I will mention that if anyone is using the "Learning Spark" O`Reilly Book from 2015, the typo described is actually present in Example 3-35. Moral of the story: don't trust that textbook code to be syntactically correct. Pay attention.
Have a closer look at your syntax.
Here's the corrected version a bit more structured.
import pyspark as ps
conf = ps.SparkConf().setMaster("local").setAppName("My App")
sc = ps.SparkContext(conf = conf)
nums = sc.parallelize([1,2,3,4])
f1 = lambda acc, value: (acc[0] + value, acc[1] + 1)
f2 = lambda acc1, acc2: (acc1[0] + acc2[0], acc1[1] + acc2[1]
nums.aggregate((0, 0), f1, f2)
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I have 2 code submissions on codeforces -
https://codeforces.com/contest/762/submission/29695191
& https://codeforces.com/contest/762/submission/29695201
both have the exact same code except for the usage of $ vs. () . One with the $ exceeded the time limit on the 6th test and the other passed all the tests.
Any suggestions on why that might be the case?
They're exactly the same. There's just some variance in runtime - your passing test case is within 5% of failing on time as well.