注意:本文内容适用于 Tmux 2.3 及以上的版本,但是绝大部分的特性低版本也都适用,鼠标支持、VI 模式、插件管理在低版本可能会与本文不兼容。
启动新会话:
tmux [new -s 会话名 -n 窗口名]
恢复会话:
import torch | |
import torchvision | |
class VGGPerceptualLoss(torch.nn.Module): | |
def __init__(self, resize=True): | |
super(VGGPerceptualLoss, self).__init__() | |
blocks = [] | |
blocks.append(torchvision.models.vgg16(pretrained=True).features[:4].eval()) | |
blocks.append(torchvision.models.vgg16(pretrained=True).features[4:9].eval()) | |
blocks.append(torchvision.models.vgg16(pretrained=True).features[9:16].eval()) |
from sklearn.datasets import load_iris | |
iris = load_iris() | |
# Model (can also use single decision tree) | |
from sklearn.ensemble import RandomForestClassifier | |
model = RandomForestClassifier(n_estimators=10) | |
# Train | |
model.fit(iris.data, iris.target) | |
# Extract single tree |
import torch | |
import torch.nn as nn | |
import numpy as np | |
import torch.optim as optim | |
from torch.autograd import Variable | |
# (1, 0) => target labels 0+2 | |
# (0, 1) => target labels 1 | |
# (1, 1) => target labels 3 | |
train = [] |
""" 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 |
""" | |
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
BSD License | |
""" | |
import numpy as np | |
# data I/O | |
data = open('input.txt', 'r').read() # should be simple plain text file | |
chars = list(set(data)) | |
data_size, vocab_size = len(data), len(chars) |