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 mnist_conv_problems():
return [
# (_Spec(pg.ConvNet, ((1, 28, 28), 10, [(3, 3, 8), (5, 5, 8)]),
# {"activation": tf.nn.sigmoid}), datasets.mnist(train=True), 64),
(_Spec(pg.ConvNet, ((1, 28, 28), 10, [(3, 3, 8), (5, 5, 8)]),
{"activation": tf.nn.relu}), datasets.mnist(train=True), 64)
]
def test_mnist_conv_orig_problems():
return [(_Spec(pg.ConvNetOrig, ((1, 28, 28), 10, [(3, 3, 8), (5, 5, 8)]),
{"activation": tf.nn.relu}), datasets.mnist(train=False),
64)]
def test_mnist_conv_problems():
return [(_Spec(pg.ConvNet,
((1, 28, 28), 10, [(3, 3, 16, 1), (5, 5, 32, 1)]), {
"activation": tf.nn.relu,
"affine_size": 512
}), datasets.mnist(train=False), 128)]
def test_mnist_mlp_deeper_problems():
tp = [(_Spec(pg.FullyConnected, (784, 10), {
"hidden_sizes": (20, 20),
"activation": tf.nn.sigmoid
}), datasets.mnist(train=False), 128)]
return tp