def test_predict_from_model_array(self):
params = nn_params.copy()
params.update({'nclasses': n_classes})
optimizer = GenericSolver(**solver_param)
datasets.dump_svmlight_file(xtr, ytr, tr_f)
datasets.dump_svmlight_file(xte, yte, te_f)
# Train model
clf = MLP(**params)
clf.fit(xtr_arr, validation_data=[xte_arr], solver=optimizer)
y_pred_tr = clf.predict(xtr_arr)
y_pred_te = clf.predict(xte_arr)
# Load from tained model
params = nn_params.copy()
params.update({
'finetuning': True,
'template': None,
'nclasses': n_classes
})
clf = MLP(sname=clf.sname,
repository=clf.model['repository'],
**params)
assert np.array_equal(y_pred_tr, clf.predict(xtr_arr))
assert np.array_equal(y_pred_te, clf.predict(xte_arr))
os_utils._remove_files([tr_f, te_f])
# create and save train.svm and test.svm
tr_f = os.path.abspath('x_train.svm')
te_f = os.path.abspath('x_test.svm')
datasets.dump_svmlight_file(xtr, ytr, tr_f)
datasets.dump_svmlight_file(xte, yte, te_f)
# Define models and class weights
clf = MLP(**params)
train_data, test_data = SVMConnector(path=tr_f), SVMConnector(path=te_f)
logs = clf.fit(train_data,
validation_data=[test_data],
solver=solver,
class_weights=class_weights,
batch_size=128)
params.update({"resume": True})
clf = MLP(**params)
logs = clf.fit(train_data,
validation_data=[test_data],
solver=solver,
class_weights=class_weights,
batch_size=128)
yte_pred = clf.predict(test_data)
report = metrics.classification_report(yte, yte_pred)
print(report)
# remove saved svm files
os_utils._remove_files([tr_f, te_f])
base_lr=0.1,
snapshot=100)
clf = MLP(sname=sname, repository=model_repo, **params)
clf.fit(xtr_svm, validation_data=[xte_svm, xtr_svm], solver=optimizer)
del clf
# load pre trained model
params = {
'host': host,
'port': port,
'nclasses': nclasses,
'finetuning': True,
'template': None
}
clf = MLP(sname=sname, repository=model_repo, **params)
ytr_pred, yte_pred = clf.predict(xtr_svm), clf.predict(xte_svm)
report = metrics.classification_report(yte, yte_pred)
print(report)
# Predict using another file
params = {
'host': host,
'port': port,
'nclasses': nclasses,
'weights': os.path.join(model_repo, 'model_iter_400.caffemodel'),
'template': None
}
clf = MLP(sname=sname, repository=model_repo, **params)
ytr_pred, yte_pred = clf.predict(xtr_svm), clf.predict(xte_svm)
report = metrics.classification_report(yte, yte_pred)
print(report)
