def test_mlp_fit():
X = np.random.standard_normal((10, 2))
Z = np.random.standard_normal((10, 1))
X, Z = theano_floatx(X, Z)
mlp = Mlp(2, [10], 1, ['tanh'], 'identity', 'squared', max_iter=10)
mlp.fit(X, Z)
def test_mlp_fit_with_imp_weight():
X = np.random.standard_normal((10, 2))
Z = np.random.standard_normal((10, 1))
W = np.random.random((10, 1)) > 0.5
X, Z, W = theano_floatx(X, Z, W)
mlp = Mlp(2, [10], 1, ['tanh'], 'identity', 'squared', max_iter=10, imp_weight=True)
mlp.fit(X, Z, W)
def test_mlp_pickle():
X = np.random.standard_normal((10, 2))
Z = np.random.standard_normal((10, 1))
X, Z = theano_floatx(X, Z)
mlp = Mlp(2, [10], 1, ['tanh'], 'identity', 'squared', max_iter=2)
climin.initialize.randomize_normal(mlp.parameters.data, 0, 1)
mlp.fit(X, Z)
Y = mlp.predict(X)
pickled = cPickle.dumps(mlp)
mlp2 = cPickle.loads(pickled)
Y2 = mlp2.predict(X)
assert np.allclose(Y, Y2)
def test_mlp_pickle():
X = np.random.standard_normal((10, 2))
Z = np.random.standard_normal((10, 1))
X, Z = theano_floatx(X, Z)
mlp = Mlp(2, [10], 1, ['tanh'], 'identity', 'squared', max_iter=2)
climin.initialize.randomize_normal(mlp.parameters.data, 0, 1)
mlp.fit(X, Z)
Y = mlp.predict(X)
pickled = cPickle.dumps(mlp)
mlp2 = cPickle.loads(pickled)
Y2 = mlp2.predict(X)
assert np.allclose(Y, Y2)
