def softmax_2_class_problems(): return [ (_Spec(pg.SoftmaxRegression, (10, 2), {}), datasets.random( 10, 1000, random_seed=123, sep=2.0), 100), (_Spec(pg.SoftmaxRegression, (100, 2), {}), datasets.random( 100, 1000, random_seed=123), 50), (_Spec(pg.SoftmaxRegression, (200, 2), {}), datasets.random( 200, 1000, random_seed=123, sep=1.5), 20), (_Spec(pg.SoftmaxRegression, (256, 2), {}), datasets.random( 256, 1000, random_seed=123, sep=1.5), 100), ]
def softmax_2_class_problems(): return [ (_Spec(pg.SoftmaxRegression, (10, 2), {}), datasets.random(10, 1000, random_seed=123, sep=2.0), 100), (_Spec(pg.SoftmaxRegression, (100, 2), {}), datasets.random(100, 1000, random_seed=123), 50), (_Spec(pg.SoftmaxRegression, (200, 2), {}), datasets.random(200, 1000, random_seed=123, sep=1.5), 20), (_Spec(pg.SoftmaxRegression, (256, 2), {}), datasets.random(256, 1000, random_seed=123, sep=1.5), 100), ]
def test_problems(): """Test problems for visualizations.""" # Unlike the training problem sets, these test problems are made up of # length-5 tuples. The final items in the tuple are the name of the problem # and the initialization random_seed for testing consistency. tp = [ (_Spec(pg.Quadratic, (20, ), {"random_seed": 1234}), None, None, "quad_problem", 5678), (_Spec(pg.Quadratic, (20, ), { "noise_stdev": 1.0, "random_seed": 1234 }), None, None, "quad_problem_noise", 5678), (_Spec(pg.Rosenbrock, (), {"random_seed": 1234}), None, None, "rosenbrock", 5678), (_Spec(pg.Rosenbrock, (), { "random_seed": 1234, "noise_stdev": 1.0 }), None, None, "rosenbrock_noise", 5678), (_Spec(pg.SoftmaxRegression, (10, 2), {}), datasets.random(10, 10000, random_seed=1234), 100, "softmax", 5678), (_Spec(pg.SoftmaxRegression, (10, 2), {"noise_stdev": 1.0}), datasets.random(10, 10000, random_seed=1234), 100, "softmax_noise", 5678), (_Spec(pg.FullyConnected, (10, 2), {}), datasets.random(10, 10000, random_seed=1234), 100, "mlp_small", _test_problem_mlp_scaled_init_small()), (_Spec(pg.FullyConnected, (20, 10), {}), datasets.random(20, 10000, n_classes=10, random_seed=1234), 100, "mlp_large", _test_problem_mlp_scaled_init_large()), (_Spec(pg.FullyConnected, (784, 10), { "hidden_sizes": (64, ), "activation": tf.nn.sigmoid }), datasets.mnist(), 64, "mlp_mnist_sigmoid", _test_problem_mlp_scaled_init_mnist()), (_Spec(pg.FullyConnected, (784, 10), { "hidden_sizes": (64, ), "activation": tf.nn.relu }), datasets.mnist(), 64, "mlp_mnist_relu", _test_problem_mlp_scaled_init_mnist()), (_Spec(pg.ConvNet, ((1, 28, 28), 10, [(3, 3, 8), (5, 5, 8)]), {"activation": tf.nn.sigmoid}), datasets.mnist(), 64, "convnet_mnist_sigmoid", None), (_Spec(pg.ConvNet, ((1, 28, 28), 10, [(3, 3, 8), (5, 5, 8)]), {"activation": tf.nn.relu}), datasets.mnist(), 64, "convnet_mnist_relu", None), ] return tp
def test_problems(): """Test problems for visualizations.""" # Unlike the training problem sets, these test problems are made up of # length-5 tuples. The final items in the tuple are the name of the problem # and the initialization random_seed for testing consistency. tp = [ (_Spec(pg.Quadratic, (20,), {"random_seed": 1234}), None, None, "quad_problem", 5678), (_Spec(pg.Quadratic, (20,), {"noise_stdev": 1.0, "random_seed": 1234}), None, None, "quad_problem_noise", 5678), (_Spec(pg.Rosenbrock, (), {"random_seed": 1234}), None, None, "rosenbrock", 5678), (_Spec(pg.Rosenbrock, (), {"random_seed": 1234, "noise_stdev": 1.0}), None, None, "rosenbrock_noise", 5678), (_Spec(pg.SoftmaxRegression, (10, 2), {}), datasets.random( 10, 10000, random_seed=1234), 100, "softmax", 5678), (_Spec(pg.SoftmaxRegression, (10, 2), {"noise_stdev": 1.0}), datasets.random(10, 10000, random_seed=1234), 100, "softmax_noise", 5678), (_Spec(pg.FullyConnected, (10, 2), {}), datasets.random( 10, 10000, random_seed=1234), 100, "mlp_small", _test_problem_mlp_scaled_init_small()), (_Spec(pg.FullyConnected, (20, 10), {}), datasets.random( 20, 10000, n_classes=10, random_seed=1234), 100, "mlp_large", _test_problem_mlp_scaled_init_large()), (_Spec(pg.FullyConnected, (784, 10), {"hidden_sizes": (64,), "activation": tf.nn.sigmoid}), datasets.mnist(), 64, "mlp_mnist_sigmoid", _test_problem_mlp_scaled_init_mnist()), (_Spec(pg.FullyConnected, (784, 10), {"hidden_sizes": (64,), "activation": tf.nn.relu}), datasets.mnist(), 64, "mlp_mnist_relu", _test_problem_mlp_scaled_init_mnist()), (_Spec(pg.ConvNet, ((1, 28, 28), 10, [(3, 3, 8), (5, 5, 8)]), {"activation": tf.nn.sigmoid}), datasets.mnist(), 64, "convnet_mnist_sigmoid", None), (_Spec(pg.ConvNet, ((1, 28, 28), 10, [(3, 3, 8), (5, 5, 8)]), {"activation": tf.nn.relu}), datasets.mnist(), 64, "convnet_mnist_relu", None), ] return tp