BIGBALLON

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BIGBALLON

#!/usr/bin/env python
# -*- coding:utf-8 -*-

import os
import sys
import time
import random
import numpy as np

def debug_out(debug_str):

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#!/bin/bash
#
# script to extract ImageNet dataset
# ILSVRC2012_img_train.tar (about 138 GB)
# ILSVRC2012_img_val.tar (about 6.3 GB)
# make sure ILSVRC2012_img_train.tar & ILSVRC2012_img_val.tar in your current directory
#
#  https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md
# 
#  train/

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# 1
python3 train.py -b 128 -e 200 -d cifar10 -lr_m tanh_epoch -net lenet -depth 5 -width 1 -tanh_begin -4 -tanh_end 4 -log ./cifar10_tb4_te4_lenet_1
python3 train.py -b 128 -e 200 -d cifar10 -lr_m tanh_epoch -net lenet -depth 5 -width 1 -tanh_begin -3.5 -tanh_end 2.5 -log ./cifar10_tb35_te25_lenet_1
python3 train.py -b 128 -e 200 -d cifar10 -lr_m tanh_epoch -net lenet -depth 5 -width 1 -tanh_begin -4 -tanh_end 2.5 -log ./cifar10_tb4_te25_lenet_1
python3 train.py -b 128 -e 200 -d cifar10 -lr_m tanh_epoch -net lenet -depth 5 -width 1 -tanh_begin -2.5 -tanh_end 2.5 -log ./cifar10_tb25_te25_lenet_1
#
python3 train.py -b 128 -e 200 -d fashion_mnist -lr_m tanh_epoch -net lenet -depth 5 -width 1 -tanh_begin -4 -tanh_end 4 -log ./fashion_mnist_tb4_te4_lenet_1
python3 train.py -b 128 -e 200 -d fashion_mnist -lr_m tanh_epoch -net lenet -depth 5 -width 1 -tanh_begin -3.5 -tanh_end 2.5 -log ./fashion_mnist_tb35_te25_lenet_1
python3 train.py -b 128 -e 200 -d fashion_mnist -lr_m tanh_epoch -net lenet -depth 5 -width 1 -ta

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# *-* coding: UTF-8 *-*
__author__ = 'BG'
import urllib2
import os
import re

class ZOLPIC:
    def __init__(self):
        if not os.path.exists('./PIC'):
            os.mkdir(r'./PIC')

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import keras
import numpy as np
import math
from keras.datasets import fashion_mnist
from keras.preprocessing.image import ImageDataGenerator
from keras.layers.normalization import BatchNormalization
from keras.layers import Conv2D, Dense, Input, add, Activation, Flatten, AveragePooling2D
from keras.callbacks import LearningRateScheduler, TensorBoard
from keras.regularizers import l2
from keras import optimizers

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"""
Lasagne implementation of SGDR on WRNs from "SGDR: Stochastic Gradient Descent with Restarts" (http://arxiv.org/abs/XXXX)
This code is based on Lasagne Recipes available at
https://github.com/Lasagne/Recipes/blob/master/papers/deep_residual_learning/Deep_Residual_Learning_CIFAR-10.py
and on WRNs implementation by Florian Muellerklein available at
https://gist.github.com/FlorianMuellerklein/3d9ba175038a3f2e7de3794fa303f1ee

"""

from __future__ import print_function

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[global]
floatX = float32
device=cuda
optimizer=fast_run

[blas]
ldflags = -L/usr/local/lib -lopenblas

[nvcc]
fastmath = True

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• [Tensorflow中使用指定的GPU及GPU显存][1]

//终端执行程序时设置使用的GPU
CUDA_VISIBLE_DEVICES=1 python my_script.py

//python代码中设置使用的GPU
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "2"

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import keras
import numpy as np
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.layers.normalization import BatchNormalization
from keras.layers import Conv2D, Dense, Input, add, Activation, GlobalAveragePooling2D
from keras.callbacks import LearningRateScheduler, TensorBoard, ModelCheckpoint
from keras.models import Model
from keras import optimizers, regularizers
from keras import backend as K