def __init__(self, data_set='mnist', batch_size=None, version=1):
super().__init__()
self.sequence = load_data(data_set, batch_size)
num_of_labels = self.sequence.label_shape[0]
feature_size = self.sequence.feature_shape[0]
self.model = Model(feature_size, num_of_labels)
past_history = 5
self.loss_hist = np.zeros((past_history, 1))
self.grad_hist = np.zeros((past_history, feature_size, num_of_labels))
self.wght_hist = np.zeros(self.grad_hist.shape)
if version == 0:
state_size = self.model.size
elif version == 1:
state_size = self.model.size + 1
elif version == 2:
state_size = 2 * self.model.size + 1
elif version == 3:
state_size = (self.model.size + 1) * past_history
self.observation_space = Box(low=-1e3,
high=1e3,
dtype=np.float32,
shape=(state_size, ))
self.action_space = Box(low=-1e3,
high=1e3,
dtype=np.float32,
shape=(self.model.size, ))
self.seed()
self.adjusted_loss_hist = np.zeros(self.loss_hist.shape)
self.adjusted_grad_hist = np.zeros(self.grad_hist.shape)
self.adjusted_wght_hist = np.zeros(self.wght_hist.shape)
self.version = version
def __init__(self, log_file=None):
data = load_data()
features, labels = np.hsplit(data, [-1])
self.features = features
self.labels, num_of_labels = to_onehot(labels)
self.feature_size = features.shape[-1]
self.model = Model(self.feature_size, num_of_labels)
self.H = 3
self.obj_list = np.zeros(self.H)
self.grad_list = np.zeros((self.H, self.feature_size, num_of_labels))
self.W_list = np.zeros_like(self.grad_list)
#self.N = self.obj_list.size + self.grad_list.size + self.w.size
self.N = 2 * self.model.size + 1
self.observation_space = spaces.Box(low=-1e8,
high=1e8,
shape=(self.N, ),
dtype=np.float32)
self.action_space = spaces.Box(low=-1e8,
high=1e8,
shape=(1, ),
dtype=np.float32)
self.steps = 1
self.seed()
self.rewards = []
self.running_rewards = []
self.running_acc = []
self.num_of_labels = num_of_labels
self.global_steps = 0
self.seed_no = 0
def __init__(self, data_set='mnist', batch_size=None, n_of_steps=None):
super().__init__()
self.sequence = load_data(data_set, batch_size)
num_of_labels = self.sequence.label_shape[0]
feature_size = self.sequence.feature_shape[0]
self.model = Model(feature_size, num_of_labels)
past_history = 3
self.loss_hist = np.zeros((past_history, 1))
self.grad_hist = np.zeros((past_history, feature_size,
num_of_labels))
self.wght_hist = np.zeros(self.grad_hist.shape)
state_size = 2*self.model.size + 1
self.observation_space = Box(low=-1e3, high=1e3, dtype=np.float32,
shape=(state_size,))
self.action_space = Box(low=-1e3, high=1e3, dtype=np.float32,
shape=(self.model.size,))
self.seed()
def __init__(self,
data_set='iris',
batch_size=None,
version=1,
max_batches=400,
max_history=5,
observation_version=0,
action_version=0,
reward_version=0):
super().__init__()
self.model = get_problem(data_set=load_data(data_set, batch_size))
model_size = (self.model.size, )
self.history = History(5,
losses=(),
gradients=model_size,
weights=model_size)
result = utils_env.get_obs_version(model_size, max_history, version)
obs_space, self.adjusted_history = result
if action_version == 0:
action_low = -4.
action_high = 4.
elif action_version == 1:
action_low = -1e8
action_high = 1e8
else:
raise RuntimeError()
self.observation_space = Dict({
MultiOptimize.AGENT_FMT.format(i): obs_space
for i in range(self.model.size)
})
self.action_space = Dict({
MultiOptimize.AGENT_FMT.format(i): Box(low=action_low,
high=action_high,
dtype=np.float32,
shape=(1, ))
for i in range(self.model.size)
})
self.seed()
self.max_history = max_history
self.max_batches = max_batches
self.version = VersionType(version, observation_version,
action_version, reward_version)