def _sample_min_max_well(rng):
"""Sample a min max well problem."""
is_noise = utils.sample_bool(rng, 0.5)
return {
"dim":
utils.sample_log_int(rng, 10, 1000),
"noise_stdev":
utils.sample_log_float(rng, 0.01, 10.0) if is_noise else 0.0,
}
def _sample_quadratic_problem(rng):
"""Sample a quadratic problem."""
is_noise = utils.sample_bool(rng, 0.5)
return {
"dim":
utils.sample_log_int(rng, 10, 1000),
"noise_stdev":
utils.sample_log_float(rng, 0.01, 10.0) if is_noise else 0.0,
}
def _sample_fully_connected(rng):
"""Sample a fully connected problem."""
n_layer = rng.choice([2, 3, 4, 5])
fixed = utils.sample_bool(rng, 0.5)
cfg = {
"n_features": utils.sample_log_int(rng, 1, 16),
"n_classes": 2,
"activation": utils.sample_activation(rng),
"bs": utils.sample_log_int(rng, 1, 200),
"n_samples": utils.sample_log_int(rng, 1, 30),
}
if fixed:
cfg["hidden_sizes"] = [utils.sample_log_int(rng, 4, 32)] * n_layer
else:
cfg["hidden_sizes"] = [
utils.sample_log_int(rng, 4, 32) for _ in range(n_layer)
]
return cfg
def sample_conv_pooling_family_cfg(seed):
"""Sample a task config for a conv net with pooling model on image datasets.
These configs are nested python structures that provide enough information
to create an instance of the problem.
Args:
seed: int Random seed to generate task from.
Returns:
A nested dictionary containing a configuration.
"""
rng = np.random.RandomState(seed)
cfg = {}
layer_choices = [1, 2, 3, 4, 5]
max_layer = np.max(layer_choices)
n_layers = rng.choice(layer_choices)
# pattern for how strides are chosen. Either all 2s, repeated 1,2
# or repeated 2,1.
stride_pattern = rng.choice(["all_two", "one_two", "two_one"])
if stride_pattern == "all_two":
cfg["strides"] = ([2] * max_layer)[0:n_layers]
elif stride_pattern == "one_two":
cfg["strides"] = ([1, 2] * max_layer)[0:n_layers]
elif stride_pattern == "two_one":
cfg["strides"] = ([2, 1] * max_layer)[0:n_layers]
cfg["strides"] = list(zip(cfg["strides"], cfg["strides"]))
cfg["hidden_units"] = [
utils.sample_log_int(rng, 8, 64) for _ in range(n_layers)
]
cfg["activation"] = utils.sample_activation(rng)
cfg["w_init"] = utils.sample_initializer(rng)
cfg["padding"] = [
str(rng.choice([snt.SAME, snt.VALID])) for _ in range(n_layers)
]
cfg["pool_type"] = str(rng.choice(["mean", "max", "squared_mean"]))
cfg["use_bias"] = bool(rng.choice([True, False]))
cfg["dataset"] = utils.sample_image_dataset(rng)
cfg["center_data"] = bool(rng.choice([True, False]))
return cfg
def _sample_norm(rng):
"""Sample a norm problem."""
return {
"dim": utils.sample_log_int(rng, 3, 1000),
"norm_power": rng.uniform(0.1, 5.0),
}
