Applied Machine Learning – things you need to know

Một số lưu ý khi áp dụng Machine Learning để giải quyết các vấn đề cụ thể:

Always use train_test_split or similar

GridSearchCV (built-in cross validation)

HDF need to be shrunk after write/update –>

ptrepack –chunkshape=auto –propindexes –complevel=9 –complib=blosc data_in.h5 data_out.h5

Use Keras optimizer instead of tensorflow itself (so that it can be saved later as part of the model)

model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.001), instead of:
model.compile(optimizer=tf.train.AdamOptimizer()

For binary classification: keras.layers.Dense(1, activation=tf.nn.sigmoid)

Except accuracy metric, other metrics like f1, recall, roc_auc when used then labels should be binarized:

from sklearn.preprocessing import LabelBinarizer
lb = LabelBinarizer()
train_labels = lb.fit_transform(train_labels)
test_labels = lb.fit_transform(test_labels)

Let GridSearchCV decided the train-validation data
in Keras: validation_split=0, validation_data=None

 

GridSearchCV evaluation:

#Training
clf = KerasClassifier(build_fn=get_model)
param_grid = dict(batch_size=batch_size, epochs=epochs)
validator = GridSearchCV(estimator=clf, param_grid=param_grid, scoring=scoring, refit=refit_scorer, cv=cv)
grid_result = validator.fit(train_images, train_labels, validation_split=0, validation_data=None)

#Evaluation
best_estimator = grid_result.best_estimator_
final_model = “final_model_” + datetime.datetime.now().strftime(“%Y-%m-%d-%H-%M”) + refit_scorer + “.h5”
best_estimator.model.save(final_model)
print(“Accuracy Score (test_data): “, best_estimator.score(test_images, test_labels))

cv_result = “cv_result_” + datetime.datetime.now().strftime(“%Y-%m-%d-%H-%M”) + “.dict”

 

requirements.txt <Python 3.5.2>

absl-py==0.4.1
astor==0.7.1
backcall==0.1.0
bleach==2.1.4
certifi==2018.8.24
chardet==3.0.4
Click==7.0
cycler==0.10.0
decorator==4.3.0
entrypoints==0.2.3
Flask==1.0.2
Flask-SQLAlchemy==2.3.2
gast==0.2.0
grpcio==1.14.2
h5py==2.8.0
html5lib==1.0.1
idna==2.7
ipykernel==4.9.0
ipython==6.5.0
ipython-genutils==0.2.0
ipywidgets==7.4.1
itsdangerous==0.24
jedi==0.12.1
Jinja2==2.10
joblib==0.13.0
jsonschema==2.6.0
jupyter==1.0.0
jupyter-client==5.2.3
jupyter-console==5.2.0
jupyter-core==4.4.0
kiwisolver==1.0.1
Markdown==2.6.11
MarkupSafe==1.0
matplotlib==2.2.3
mistune==0.8.3
nbconvert==5.3.1
nbformat==4.4.0
notebook==5.6.0
numexpr==2.6.8
numpy==1.14.5
pandas==0.23.4
pandocfilters==1.4.2
parso==0.3.1
pexpect==4.6.0
pickleshare==0.7.4
prometheus-client==0.3.1
prompt-toolkit==1.0.15
protobuf==3.6.1
ptyprocess==0.6.0
Pygments==2.2.0
pymongo==3.7.2
pyparsing==2.2.0
python-dateutil==2.7.3
pytz==2018.5
pyzmq==17.1.2
qtconsole==4.4.1
requests==2.19.1
scikit-learn==0.19.2
scipy==1.1.0
Send2Trash==1.5.0
simplegeneric==0.8.1
six==1.11.0
SQLAlchemy==1.2.12
tables==3.4.4
tensorboard==1.10.0
tensorflow==1.10.1
termcolor==1.1.0
terminado==0.8.1
testpath==0.3.1
tornado==5.1
tqdm==4.26.0
traitlets==4.3.2
urllib3==1.23
wcwidth==0.1.7
webencodings==0.5.1
Werkzeug==0.14.1
widgetsnbextension==3.4.1

 

To be updated

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