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import cv2 | |
from typing import Tuple | |
def resize_with_pad(image: np.array, | |
new_shape: Tuple[int, int], | |
padding_color: Tuple[int] = (255, 255, 255)) -> np.array: | |
"""Maintains aspect ratio and resizes with padding. | |
Params: | |
image: Image to be resized. |
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import torch | |
import torch.nn as nn | |
import torch.optim as optim | |
from torch.autograd import Variable | |
import torch.nn.functional as F | |
import matplotlib.pyplot as plt | |
import numpy as np |
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import torch, torch.nn as nn | |
from torch.autograd import Variable | |
text = ['BOS', 'How', 'are', 'you', 'EOS'] | |
seq_len = len(text) | |
batch_size = 1 | |
embedding_size = 1 | |
hidden_size = 1 | |
output_size = 1 |
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# -*- coding: utf-8 -*- | |
import os | |
import argparse | |
import gym | |
import numpy as np | |
from itertools import count | |
import torch | |
import torch.nn as nn |
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""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
import numpy as np | |
import cPickle as pickle | |
import gym | |
# hyperparameters | |
H = 200 # number of hidden layer neurons | |
batch_size = 10 # every how many episodes to do a param update? | |
learning_rate = 1e-4 | |
gamma = 0.99 # discount factor for reward |