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#设置前缀为Ctrl + a
unbind ^b
set -g prefix 'C-a'
#将r 设置为加载配置文件，并显示信息
bind r source-file ~/.tmux.conf \; display-message "Config reloaded"

#up
bind-key k select-pane -U
#down
bind-key j select-pane -D

jiqiujia

"Pathogen
execute pathogen#infect()
syntax on
filetype plugin indent on

"NERDtree
" open NERDTree with ctrl+n
map <F2> :NERDTreeToggle<CR>
" close vim if the only window left open is a NERDTree
autocmd bufenter * if(winnr("$")==1 && exists("b:NERDTree") && b:NERDTree.isTabTree()) | q | endif

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# ### python_ncut_lib.py
# Copyright (C) 2010 R. Cameron Craddock ([email protected])
#
# This script is a part of the pyClusterROI python toolbox for the spatially constrained clustering of fMRI
# data. It provides the library functions for performing normalized cut clustering according to:
#
# Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis 
# and Machine Intelligence, 22(8), 888-905. doi: 10.1109/34.868688.
#
# Yu, S. X., & Shi, J. (2003). Multiclass spectral clustering. Proceedings Ninth IEEE International Conference

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#adjust learning rate policy by callbacks
def scheduler(epoch):
    if epoch == 5:
        model.lr.set_value(.02)
    return model.lr.get_value()

change_lr = LearningRateScheduler(scheduler)

model.fit(x_embed, y, nb_epoch=1, batch_size = batch_size, show_accuracy=True,
       callbacks=[chage_lr])

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import torch
import torch.nn as nn
import torch.nn.parallel

class DCGAN_D(nn.Container):
    def __init__(self, isize, nz, nc, ndf, ngpu, n_extra_layers=0):
        super(DCGAN_D, self).__init__()
        self.ngpu = ngpu
        assert isize % 16 == 0, "isize has to be a multiple of 16"

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import numpy as np
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

def elastic_transform(image, alpha, sigma, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_.


    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in

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#plot tiled images
fig = plt.figure(figsize=(8,8))
#adjust the white space around the figure and each subplot
plt.subplots_adjust(wspace=0.01, hspace=0.01, left=0, right=1, bottom=0, top=1)
for i in range(63):
    ax = plt.subplot(8,8,i+1)
    plt.imshow(imgs[i])
    ax.axis('off') #no frame
    #ax.get_xaxis().set_visible(False)
    #ax.get_yaxis().set_visible(False)

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import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision
import torchvision.transforms as transforms
import numpy as np

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### Adapted from TF repo

import tensorflow as tf
from tensorflow import gradients
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops


def hessian_vector_product(ys, xs, v):