def test_dmlp_predict():
X = np.random.standard_normal((10, 2))
X, = theano_floatx(X)
mlp = DropoutMlp(2, [10], 1, ['tanh'], 'identity', 'squared', max_iter=10,
p_dropout_inpt=.2, p_dropout_hiddens=[0.5])
mlp.predict(X)
def test_dmlp_iter_fit():
X = np.random.standard_normal((10, 2))
Z = np.random.standard_normal((10, 1))
X, Z = theano_floatx(X, Z)
mlp = DropoutMlp(2, [10], 1, ['tanh'], 'identity', 'squared', max_iter=10,
p_dropout_inpt=.2, p_dropout_hiddens=[0.5])
for i, info in enumerate(mlp.iter_fit(X, Z)):
if i >= 10:
break
