def test_save_load_weights():
import tempfile
tempdir = tempfile.gettempdir()
tempfile = os.path.join(tempdir, 'w.json')
# tempfile = os.path.join('', 'w.json')
dbn1 = DBN([5], random_state=1234)
X, y = get_iris()
dbn1.fit(X, y)
pred1 = dbn1.predict(X)
prob1 = dbn1.predict_proba(X)
dbn1.save(tempfile)
dbn2 = DBN([5])
dbn2.load(tempfile)
pred2 = dbn2.predict(X)
prob2 = dbn2.predict_proba(X)
eq_(dbn1.coef_, dbn2.coef_)
for i, layer in enumerate(dbn1.layers):
eq_(dbn1.layers[i].W, dbn2.layers[i].W)
eq_(pred1, pred2)
eq_(prob1, prob2)
def test_save_load_weights():
import tempfile
tempdir = tempfile.gettempdir()
tempfile = os.path.join(tempdir, 'w.json')
# tempfile = os.path.join('', 'w.json')
dbn1 = DBN([5], random_state=1234)
X, y = get_iris()
dbn1.fit(X, y)
pred1 = dbn1.predict(X)
prob1 = dbn1.predict_proba(X)
dbn1.save(tempfile)
dbn2 = DBN([5])
dbn2.load(tempfile)
pred2 = dbn2.predict(X)
prob2 = dbn2.predict_proba(X)
eq_(dbn1.coef_, dbn2.coef_)
for i, layer in enumerate(dbn1.layers):
eq_(dbn1.layers[i].W, dbn2.layers[i].W)
eq_(pred1, pred2)
eq_(prob1, prob2)
def test_sklearn_api():
''' sklearn API: not functionality
'''
dbn = DBN([5])
X, y = get_iris()
dbn.fit(X, y)
dbn.predict_proba(X)
dbn.predict(X)
def test_sklearn_api():
''' sklearn API: not functionality
'''
dbn = DBN([5])
X, y = get_iris()
dbn.fit(X, y)
dbn.predict_proba(X)
dbn.predict(X)
def test_reproducible():
X, y = get_iris()
dbn1 = DBN([5], random_state=123)
dbn1.fit(X, y)
pred1 = dbn1.predict(X)
prob1 = dbn1.predict_proba(X)
dbn2 = DBN([5], random_state=123)
dbn2.fit(X, y)
pred2 = dbn2.predict(X)
prob2 = dbn2.predict_proba(X)
eq_(dbn1.coef_, dbn2.coef_)
eq_(pred1, pred2)
eq_(prob1, prob2)
def test_reproducible():
X, y = get_iris()
dbn1 = DBN([5], random_state=123)
dbn1.fit(X, y)
pred1 = dbn1.predict(X)
prob1 = dbn1.predict_proba(X)
dbn2 = DBN([5], random_state=123)
dbn2.fit(X, y)
pred2 = dbn2.predict(X)
prob2 = dbn2.predict_proba(X)
eq_(dbn1.coef_, dbn2.coef_)
eq_(pred1, pred2)
eq_(prob1, prob2)
