jiqiujia

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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#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])

jiqiujia

# ### python_ncut_lib.py
# Copyright (C) 2010 R. Cameron Craddock (cameron.craddock@gmail.com)
#
# 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

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

jiqiujia

#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

from math import ceil
import numpy as np
from sklearn.cluster import KMeans

import theano
import theano.tensor as T

def compute_reps(extract_fn, X, chunk_size):
    """Compute representations for input in chunks."""
    chunks = int(ceil(float(X.shape[0]) / chunk_size))

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-- Two dashes start a one-line comment.

--[[
     Adding two ['s and ]'s makes it a
     multi-line comment.
--]]

----------------------------------------------------
-- 1. Variables and flow control.
----------------------------------------------------

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def create_conf_matrix(expected, predicted, n_classes):
    m = np.zeros((n_classes, n_classes))
    for pred, exp in zip(predicted, expected):
        m[pred][exp] += 1
    return m

def calc_accuracy(conf_matrix):
    t = sum(sum(l) for l in conf_matrix)
    return sum(conf_matrix[i][i] for i in range(len(conf_matrix))) / t

jiqiujia

class BatchNormLayer(lasagne.layers.Layer):
    def __init__(self, incoming, b=lasagne.init.Constant(0.), g=lasagne.init.Constant(1.), 
                 W=lasagne.init.Normal(0.05), nonlinearity=relu, **kwargs):
        super(BatchNormLayer, self).__init__(incoming, **kwargs)
        self.nonlinearity = nonlinearity
        k = self.input_shape[1]
        if b is not None:
            self.b = self.add_param(b, (k,), name="b", regularizable=False)
        if g is not None:
            self.g = self.add_param(g, (k,), name="g")

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def architecture_string(layer):
    model_arch = ''

    for i, layer in enumerate(lasagne.layers.get_all_layers(layer)):
        name = string.ljust(layer.__class__.__name__, 28)
        model_arch += "  %2i  %s %s  " % (i, name,
                                          lasagne.layers.get_output_shape(layer))

        if hasattr(layer, 'filter_size'):
            model_arch += str(layer.filter_size[0])