Has solution in files like the src/spyder/tensorflow_examples_udacity_1_notmnist_soved_for_****k_set.ipynb
It used to solve classification problem with LogisticRegression - see docs - and
gets next accuracy of predicting:
- 0.804 for 10000 samples
- 0.8273 for 200000 samples
- 0.8273 for 500000 samples
Trained models are in files
- resources/trained_nn/finalized_model_log_regr_200K_samples.sav
- resources/trained_nn/finalized_model_log_regr_500K_samples.sav
You can use this models by using code like this:
import numpy as np
from six.moves import cPickle as pickle
from sklearn.linear_model import LogisticRegression
...
loaded_model = pickle.load(open(filename_for_log_regr, 'rb')) #here is a loaded LogisticRegression
...
Be careful to point the data_root just because code loads and unpacks LOTS OF data
Result of prediction for 7:
Solved for MNIST dataset with LogisticRegression(solver='sag', multi_class='ovr', n_jobs=1) with score = 0.9182
Another example from docs shows a classification vectors for each of classes we have:
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Qt5 with Python3.5 on Ubuntu 16.04:
