both as a Military Head-of-State and as civilian President, Obasanjo left no one in doubt of his political will to uphold the sanctity of our unity as one indivisible Nigeria.
Senator Mark enjoined Obasanjo to continue to make his reservoir of knowledge and wealth of experience available to the younger generation to tap from.
Information and resources on local produce.
The Ecological Farmers' Association of Ontario, practising ecological, sustainable agriculture. Organic farming techniques. Members list, and tips.Driver charged after Benton collision
FOLEY — A Sauk Rapids man was charged Monday in Benton County with DWI and three counts of criminal vehicular operation after a collision Friday evening injured a passenger in his vehicle and another driver.
Benjamin Paul Theisen, 29, is accused of having a blood-alcohol level more than three times the legal limit when he ran a stop sign and was hit by another vehicle. The collision happened Friday afternoon at Mayhew Lake Road and 15th Street Northeast.
Theisen made his first court appearance on the charges Monday. Benton County District Court Judge Robert Raupp set bail for Theisen at $50,000 without conditions or $15,000 with conditions.
Theisen's vehicle was headed west on 15th Street Northeast about 4:55 p.m. when it failed to stop at the intersection with Mayhew Lake Road. The vehicle crossed in front of a Dodge Ram pickup and was hit in the intersection. The pickup was driven by 50-year-old Barbara Lori Torres of Rice.
Theisen ran from the scene of the collision and tried to evade capture by running through businesses and a marshy area, according to the criminal complaint charging Theisen. When deputies arrested Theisen, he told them "you got me" and "I'm drunk," according to the court complaint.
A preliminary breath test showed that his blood-alcohol level was.266. The legal limit for driving in Minnesota is.08.
Sauk Rapids man jailed after 2 hurt in hit-and-run
Torres was treated for back and neck pain and bruises to her legs.
Brady Eugene Haugen, 28, of Sauk Rapids, was trapped inside the vehicle Theisen was driving. He was extricated by the Sauk Rapids Fire Department and also taken to St. Cloud Hospital.
Benton County deputies and Sauk Rapids police searched the area for Theisen. He was found in a parking lot at 390 Industrial Boulevard and arrested.
Follow David Unze on Twitter @sctimesunze or call him at 255-8740.…reveals what’s important to your heart.
Is our small groups system really creating disciples?
Is our small groups system a great picture of Gospel community?
What does it look like for our whole church to be moving in the same direction?
Am I leading in such a way that others are growing to love God and people more?
Is my ministry online truly helpful? What needs to change so that it does more good?
Am I leading my family well? How can I do a better job?
Is my son growing towards being a man of God?
These are the things keeping me up at night.
If you’re leading an organization…what is it that is causing you to sweat, go to work early, pray more, fast more, sleep less, and work harder?
i’ve just been thru a season as a pastor where i would actually suggest the opposite. i realized that I was striving. and then it was as if God’s word jumped off the page: “cease striving and know that I am God, I will be exalted among the nations”. wow. what a relief. what a weight off! i have been trying to live into that peace, but my nature wants to keep on trying to work it out.
What a fantastic set of questions to be wrestling with! I don’t know the answers, but I’m pretty sure that with those questions, the right heart, and (as rtucker suggests) total reliance on God, I’m pretty sure God will bless you in your pondering. That said… get some sleep, and know that the fruit/results/growth is absolutely in God’s hands.import torch
import torch.nn as nn
from torch.nn import functional as F

from utils import helpers as utl
from torchkit import pytorch_utils as ptu


class RNNEncoder(nn.Module):
    def __init__(
        self,
        # network size
        layers_before_gru=(),
        hidden_size=64,
        layers_after_gru=(),
        task_embedding_size=32,
        # actions, states, rewards
        action_size=2,
        action_embed_size=10,
        state_size=2,
        state_embed_size=10,
        reward_size=1,
        reward_embed_size=5,
        #
        distribution="gaussian",
    ):
        super(RNNEncoder, self).__init__()

        self.task_embedding_size = task_embedding_size
        self.hidden_size = hidden_size

        if distribution == "gaussian":
            self.reparameterise = self._sample_gaussian
        else:
            raise NotImplementedError

        # embed action, state, reward
        self.state_encoder = utl.FeatureExtractor(state_size, state_embed_size, F.relu)
        self.action_encoder = utl.FeatureExtractor(
            action_size, action_embed_size, F.relu
        )
        self.reward_encoder = utl.FeatureExtractor(
            reward_size, reward_embed_size, F.relu
        )

        # fully connected layers before the recurrent cell
        curr_input_size = action_embed_size + state_embed_size + reward_embed_size
        self.fc_before_gru = nn.ModuleList([])
        for i in range(len(layers_before_gru)):
            self.fc_before_gru.append(nn.Linear(curr_input_size, layers_before_gru[i]))
            curr_input_size = layers_before_gru[i]

        # recurrent unit
        self.gru = nn.GRU(
            input_size=curr_input_size,
            hidden_size=hidden_size,
            num_layers=1,
        )

        for name, param in self.gru.named_parameters():
            if "bias" in name:
                nn.init.constant_(param, 0)
            elif "weight" in name:
                nn.init.orthogonal_(param)

        # fully connected layers after the recurrent cell
        curr_input_size = hidden_size
        self.fc_after_gru = nn.ModuleList([])
        for i in range(len(layers_after_gru)):
            self.fc_after_gru.append(nn.Linear(curr_input_size, layers_after_gru[i]))
            curr_input_size = layers_after_gru[i]

        # output layer
        self.fc_mu = nn.Linear(curr_input_size, task_embedding_size)
        self.fc_logvar = nn.Linear(curr_input_size, task_embedding_size)

    def _sample_gaussian(self, mu, logvar, num=None):
        if num is None:
            std = torch.exp(0.5 * logvar)
            eps = torch.randn_like(std)
            return eps.mul(std).add_(mu)
        else:
            if logvar.shape[0] > 1:
                mu = mu.unsqueeze(0)
                logvar = logvar.unsqueeze(0)
            if logvar.dim() > 2:  # if 3 dims, first must be 1
                assert logvar.shape[0] == 1, "error in dimensions!"
                std = torch.exp(0.5 * logvar).repeat(num, 1, 1)
                eps = torch.randn_like(std)
                mu = mu.repeat(num, 1, 1)
            else:
                std = torch.exp(0.5 * logvar).repeat(num, 1)
                eps = torch.randn_like(std)
                mu = mu.repeat(num, 1)
            return eps.mul(std).add_(mu)

    def reset_hidden(self, hidden_state, reset_task):
        if hidden_state.dim()!= reset_task.dim():
            if reset_task.dim() == 2:
                reset_task = reset_task.unsqueeze(0)
            elif reset_task.dim() == 1:
                reset_task = reset_task.unsqueeze(0).unsqueeze(2)
        hidden_state = hidden_state * (1 - reset_task)
        return hidden_state

    def prior(self, batch_size, sample=True):

        # TODO: somehow incorporate the initial state

        # we start out with a hidden