def test_sample(self): sys.stdout.write( 'GaussianRectVarianceRBM -> Performing sample test ...') numx.random.seed(420) input_dim = 8 hidden_dim = 10 batchsize = 3 rbm = Model.GaussianRectVarianceRBM(input_dim, hidden_dim) data = numx.random.rand(batchsize, input_dim) h = rbm.probability_h_given_v(data) assert numx.all(h >= 0.0) assert numx.all(h.shape == (batchsize, hidden_dim)) h = rbm.sample_h(h) assert numx.all(h >= 0.0) assert numx.all(h <= rbm.max_act) assert numx.all(h.shape == (batchsize, hidden_dim)) v = rbm.probability_v_given_h(h) assert numx.all(v.shape == (batchsize, input_dim)) v = rbm.sample_v(v) assert numx.all(v.shape == (batchsize, input_dim)) sys.stdout.flush() print(' successfully passed!') sys.stdout.flush() pass
def test_get_parameters(self): print('GaussianRectVarianceRBM -> Performing get_parameters test ...') numx.random.seed(42) input_dim = 2 hidden_dim = 2 rbm = Model.GaussianRectVarianceRBM(input_dim, hidden_dim) assert len(rbm.get_parameters()) == 4 print('successfully passed!') sys.stdout.flush()
def test_calculate_gradients(self): sys.stdout.write( 'GaussianRectVarianceRBM -> Performing calculate_gradients test ...' ) numx.random.seed(42) input_dim = 2 hidden_dim = 2 rbm = Model.GaussianRectVarianceRBM(input_dim, hidden_dim) assert len( rbm.calculate_gradients(numx.array([[0.98, -0.56], [-0.3, 0.8]]), numx.array([[0, 1], [1, 1]]))) == 4 print(' successfully passed!') sys.stdout.flush()
def test__calculate_sigma_gradient(self): sys.stdout.write( 'GaussianRectVarianceRBM -> Performing calculate_sigma_gradient test ...' ) numx.random.seed(42) input_dim = 2 hidden_dim = 2 rbm = Model.GaussianRectVarianceRBM(input_dim, hidden_dim) deltaSigma = rbm._calculate_sigma_gradient( numx.array([[0.98, -0.56], [-0.3, 0.8]]), numx.array([[0, 1], [1, 1]])) target = numx.array([[-0.63545491, -0.07162506]]) assert numx.all(numx.abs(target - deltaSigma) < self.epsilon) print(' successfully passed!') sys.stdout.flush()
def test___init__(self): sys.stdout.write('GaussianRectVarianceRBM -> Performing init test ...') numx.random.seed(42) input_dim = 8 hidden_dim = 10 batchsize = 3 rbm = Model.GaussianRectVarianceRBM(input_dim, hidden_dim) assert numx.all(rbm.w.shape == (input_dim, hidden_dim)) assert numx.all(rbm.bv.shape == (1, input_dim)) assert numx.all(rbm.bh.shape == (1, hidden_dim)) assert numx.all(rbm.input_dim == input_dim) assert numx.all(rbm.output_dim == hidden_dim) print(' successfully passed!') sys.stdout.flush() pass