def test_e_dropout_rbm_total_energy(): new_e_dropout_rbm = e_dropout_rbm.EDropoutRBM() h = torch.ones(1, 128) v = torch.ones(1, 128) energy = new_e_dropout_rbm.total_energy(h, v) assert energy != 0
def test_e_dropout_rbm_M_setter(): new_e_dropout_rbm = e_dropout_rbm.EDropoutRBM() try: new_e_dropout_rbm.M = 1 except: new_e_dropout_rbm.M = torch.Tensor() assert isinstance(new_e_dropout_rbm.M, torch.Tensor)
def test_e_dropout_rbm_hidden_sampling(): new_e_dropout_rbm = e_dropout_rbm.EDropoutRBM() new_e_dropout_rbm.M = torch.ones((1, 128)) v = torch.ones(1, 128) probs, states = new_e_dropout_rbm.hidden_sampling(v) assert probs.size(1) == 128 assert states.size(1) == 128
def test_e_dropout_rbm_energy_dropout(): new_e_dropout_rbm = e_dropout_rbm.EDropoutRBM() new_e_dropout_rbm.M = torch.ones((1, 128)) e = torch.ones(1, 128) p_prob = torch.ones(1, 128) n_prob = torch.ones(1, 128) new_e_dropout_rbm.energy_dropout(e, p_prob, n_prob) pass
def test_e_dropout_rbm_reconstruct(): test = torchvision.datasets.KMNIST( root='./data', train=False, download=True, transform=torchvision.transforms.ToTensor()) new_e_dropout_rbm = e_dropout_rbm.EDropoutRBM(n_visible=784, n_hidden=128, steps=1, learning_rate=0.1, momentum=0, decay=0, temperature=1, use_gpu=False) e, v = new_e_dropout_rbm.reconstruct(test) assert e >= 0 assert v.size(1) == 784
def test_e_dropout_rbm_fit(): train = torchvision.datasets.KMNIST( root='./data', train=True, download=True, transform=torchvision.transforms.ToTensor()) new_e_dropout_rbm = e_dropout_rbm.EDropoutRBM(n_visible=784, n_hidden=128, steps=1, learning_rate=0.1, momentum=0, decay=0, temperature=1, use_gpu=False) e, pl = new_e_dropout_rbm.fit(train, batch_size=128, epochs=1) assert e >= 0 assert pl <= 0
def test_e_dropout_rbm_M(): new_e_dropout_rbm = e_dropout_rbm.EDropoutRBM() assert isinstance(new_e_dropout_rbm.M, torch.Tensor)