├── .gitignore ├── LICENSE ├── README.md ├── feature_loader.py ├── glorot_uniform_init.py ├── group_weight.py ├── sigmoid_with_binary_cross_entropy.py ├── softmax_with_cross_entropy_loss.py ├── test_binary_cross_entropy.ipynb └── test_tf_binary_cross_entropy.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | 27 | # PyInstaller 28 | # Usually these files are written by a python script from a template 29 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 30 | *.manifest 31 | *.spec 32 | 33 | # Installer logs 34 | pip-log.txt 35 | pip-delete-this-directory.txt 36 | 37 | # Unit test / coverage reports 38 | htmlcov/ 39 | .tox/ 40 | .coverage 41 | .coverage.* 42 | .cache 43 | nosetests.xml 44 | coverage.xml 45 | *,cover 46 | .hypothesis/ 47 | 48 | # Translations 49 | *.mo 50 | *.pot 51 | 52 | # Django stuff: 53 | *.log 54 | local_settings.py 55 | 56 | # Flask stuff: 57 | instance/ 58 | .webassets-cache 59 | 60 | # Scrapy stuff: 61 | .scrapy 62 | 63 | # Sphinx documentation 64 | docs/_build/ 65 | 66 | # PyBuilder 67 | target/ 68 | 69 | # IPython Notebook 70 | .ipynb_checkpoints 71 | 72 | # pyenv 73 | .python-version 74 | 75 | # celery beat schedule file 76 | celerybeat-schedule 77 | 78 | # dotenv 79 | .env 80 | 81 | # virtualenv 82 | venv/ 83 | ENV/ 84 | 85 | # Spyder project settings 86 | .spyderproject 87 | 88 | # Rope project settings 89 | .ropeproject 90 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # pytorch-misc 2 | Some useful tools for pytorch. 3 | 4 | - feature_loader.py 5 | 6 | Realize asynchronous feature extraction with multi-threading. 7 | 8 | - glorot_uniform_init.py 9 | 10 | A better network initialization. 11 | 12 | - group_weight.py 13 | 14 | Avoid L2 regularization on biases & batch norm params. 15 | -------------------------------------------------------------------------------- /feature_loader.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.autograd import Variable 3 | from thread import start_new_thread 4 | 5 | try: 6 | import queue 7 | except ImportError: 8 | import Queue as queue 9 | 10 | mini_mini_batch_size=64 11 | 12 | class FeatureLoader(object): 13 | def __init__(self, dataloader, extractor, dev_id, max_queue_size=5): 14 | assert isinstance(dataloader, torch.utils.data.DataLoader) 15 | assert isinstance(extractor, torch.nn.Module) 16 | self.dataloader = dataloader 17 | self.extractor = extractor 18 | self.dev_id = dev_id 19 | self.max_queue_size = max_queue_size 20 | 21 | def __iter__(self): 22 | return FeatureExtractIter(self.dataloader, self.extractor, self.dev_id, self.max_queue_size) 23 | 24 | def __len__(self): 25 | return len(self.dataloader) 26 | 27 | 28 | class FeatureExtractIter(object): 29 | def __init__(self, dataloader, extractor, dev_id, max_queue_size): 30 | self.q = queue.Queue(maxsize=max_queue_size) 31 | start_new_thread(extract_feature, (dataloader, self.q, extractor, dev_id)) 32 | 33 | def __iter__(self): 34 | return self 35 | 36 | def __next__(self): 37 | obj = self.q.get() 38 | if obj is None: 39 | raise StopIteration 40 | else: 41 | return obj 42 | 43 | next = __next__ 44 | 45 | 46 | def extract_feature(dataloader, q, extractor, dev_id): 47 | """ Note that we should change collate_fn so that 48 | input2s: [mini-mini-batch1,...,mini-mini-batch4] """ 49 | try: 50 | for i, (input1, target) in enumerate(dataloader, 0): 51 | out = [] 52 | for x in input1: 53 | x = Variable(x, volatile=True) 54 | x = x.cuda(dev_id) 55 | x = extractor(x) 56 | out.append(x.cpu().data) 57 | out = torch.cat(out, 0) 58 | q.put((out, target)) 59 | except KeyboardInterrupt: 60 | dataloader._shutdown_workers() 61 | finally: 62 | q.put(None) 63 | 64 | 65 | from torch.utils.data.dataloader import default_collate 66 | def collate_fn(batch): 67 | imgs, targets=default_collate(batch) 68 | out=[ imgs[i:(i+mini_mini_batch_size)].clone() 69 | for i in xrange(0, len(imgs) , mini_mini_batch_size) ] 70 | return out, targets 71 | -------------------------------------------------------------------------------- /glorot_uniform_init.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | import math 3 | 4 | 5 | class MyNet(nn.Module): 6 | # USE THIS FOR YOUR VISION NETWORK! 7 | def _initialize_weights(self): 8 | print('initializing') 9 | for m in self.modules(): 10 | if isinstance(m, nn.Conv2d): 11 | receptive_field_size = m.kernel_size[0] * m.kernel_size[1] 12 | fansum = (m.out_channels + m.in_channels) * receptive_field_size 13 | scale = 1. / max(1., float(fansum) / 2.) 14 | stdv = math.sqrt(3. * scale) 15 | m.weight.data.uniform_(-stdv, stdv) 16 | 17 | if m.bias is not None: 18 | m.bias.data.zero_() 19 | elif isinstance(m, nn.BatchNorm2d): 20 | m.weight.data.fill_(1) 21 | m.bias.data.zero_() 22 | elif isinstance(m, nn.BatchNorm1d): 23 | m.weight.data.fill_(1) 24 | m.bias.data.zero_() 25 | elif isinstance(m, nn.Linear): 26 | fansum = m.weight.size(1) + m.weight.size(0) 27 | scale = 1. / max(1., float(fansum) / 2.) 28 | stdv = math.sqrt(3. * scale) 29 | m.weight.data.uniform_(-stdv, stdv) 30 | if m.bias is not None: 31 | m.bias.data.zero_() 32 | 33 | def __init__(self, num_classes): 34 | super(MyNet, self).__init__() 35 | self.features = nn.Sequential( 36 | ????????????????????????????????? 37 | ) 38 | self.classifier = nn.Sequential( 39 | ????????????????????????????????? 40 | ) 41 | self._initialize_weights() 42 | 43 | def forward(self, x): 44 | x = self.features(x) 45 | x = x.view(x.size(0), -1) 46 | x = self.classifier(x) 47 | return x 48 | 49 | -------------------------------------------------------------------------------- /group_weight.py: -------------------------------------------------------------------------------- 1 | from torch import nn 2 | from torch.nn.modules.conv import _ConvNd 3 | from torch.nn.modules.batchnorm import _BatchNorm 4 | 5 | # Example: 6 | # model = My100000LayerCNNPublishedIn2030(num_classes=10000000) 7 | # from weight_grouper import group_weight 8 | # params=group_weight(model) 9 | # optimizer = torch.optim.SGD(params, 0.1, 10 | # momentum=0.9, 11 | # weight_decay=0.0005,nesterov=True) 12 | 13 | 14 | def group_weight(module): 15 | group_decay = [] 16 | group_no_decay = [] 17 | 18 | for m in module.modules(): 19 | if isinstance(m, nn.Linear): 20 | group_decay.append(m.weight) 21 | if m.bias is not None: 22 | group_no_decay.append(m.bias) 23 | elif isinstance(m, _ConvNd): 24 | group_decay.append(m.weight) 25 | if m.bias is not None: 26 | group_no_decay.append(m.bias) 27 | elif isinstance(m, _BatchNorm): 28 | if m.bias is not None: 29 | group_no_decay.append(m.weight) 30 | if m.bias is not None: 31 | group_no_decay.append(m.bias) 32 | 33 | assert len(list(module.parameters())) == len(group_decay) + len(group_no_decay) 34 | groups = [dict(params=group_decay), dict(params=group_no_decay, weight_decay=.0)] 35 | return groups 36 | -------------------------------------------------------------------------------- /sigmoid_with_binary_cross_entropy.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.autograd import Function 3 | from torch.nn import functional as F 4 | from torch.nn.modules.loss import _WeightedLoss, _assert_no_grad 5 | 6 | 7 | # This class replaces the official (unstable) MultiLabelSoftMarginLoss. 8 | 9 | class SigmoidWithBinaryCrossEntropy(_WeightedLoss): 10 | def __init__(self, weight=None, size_average=True, inplace=False): 11 | if inplace: # run sigmoid in place 12 | self.function = _SigmoidWithBinaryCrossEntropyInplace 13 | else: 14 | self.function = _SigmoidWithBinaryCrossEntropy 15 | super(SigmoidWithBinaryCrossEntropy, self).__init__(weight, size_average) 16 | 17 | def forward(self, input, target): 18 | _assert_no_grad(target) 19 | return self.function( 20 | self.weight, self.size_average)(input, target) 21 | 22 | 23 | class _SigmoidWithBinaryCrossEntropyInplace(Function): 24 | def __init__(self, weight=None, size_average=True): 25 | self.weight = weight 26 | self.size_average = size_average 27 | super(_SigmoidWithBinaryCrossEntropyInplace, self).__init__() 28 | 29 | def forward(self, input, target): 30 | input.sigmoid_() 31 | self.save_for_backward(input, target) 32 | return F.binary_cross_entropy( 33 | torch.autograd.Variable(input, requires_grad=False), 34 | torch.autograd.Variable(target, requires_grad=False), 35 | weight=self.weight, size_average=self.size_average 36 | ).data 37 | 38 | def backward(self, sth): 39 | input, target = self.saved_tensors 40 | grad_input = grad_target = None 41 | 42 | if self.needs_input_grad[0]: 43 | mult = 1. / (target.size(0) if self.size_average else 1) / (target.size(1) if self.weight is None else 1) 44 | grad_input = input.sub(target).mul_(mult) 45 | if self.weight is not None: 46 | grad_input.mul_(self.weight.view(1, target.size(1)).expand_as(target)) 47 | return grad_input, grad_target 48 | 49 | 50 | class _SigmoidWithBinaryCrossEntropy(Function): 51 | def __init__(self, weight=None, size_average=True): 52 | self.weight = weight 53 | self.size_average = size_average 54 | super(_SigmoidWithBinaryCrossEntropy, self).__init__() 55 | 56 | def forward(self, input, target): 57 | self.save_for_backward(input, target) 58 | return torch.nn.MultiLabelSoftMarginLoss(weight=self.weight, 59 | size_average=self.size_average)( 60 | torch.autograd.Variable(input, requires_grad=False), 61 | torch.autograd.Variable(target, requires_grad=False), 62 | ).data 63 | 64 | def backward(self, sth): 65 | input, target = self.saved_tensors 66 | grad_input = grad_target = None 67 | 68 | if self.needs_input_grad[0]: 69 | mult = 1. / (target.size(0) if self.size_average else 1) / (target.size(1) if self.weight is None else 1) 70 | f = input.sigmoid() 71 | grad_input = f.sub_(target).mul_(mult) 72 | if self.weight is not None: 73 | grad_input.mul_(self.weight.view(1, target.size(1)).expand_as(target)) 74 | return grad_input, grad_target 75 | -------------------------------------------------------------------------------- /softmax_with_cross_entropy_loss.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.autograd import Function 3 | 4 | # NOT USEFUL! THE OFFICIAL IMPLEMENTATION IS CORRECT. 5 | 6 | # Example: 7 | # output = model(data) 8 | # loss = SoftmaxWithCrossEntropy(output, target) 9 | # loss.backward() 10 | # optimizer.step() 11 | 12 | def SoftmaxWithCrossEntropy(input, label): 13 | return _SoftmaxWithCrossEntropy()(input, label) 14 | 15 | 16 | class _SoftmaxWithCrossEntropy(Function): 17 | def forward(self, input, label): 18 | self.save_for_backward(input, label) 19 | return torch.nn.CrossEntropyLoss()( 20 | torch.autograd.Variable(input, requires_grad=False), 21 | torch.autograd.Variable(label, requires_grad=False) 22 | ).data 23 | 24 | def backward(self, sth): 25 | input, label = self.saved_tensors 26 | grad_fs = grad_label = None 27 | 28 | if self.needs_input_grad[0]: 29 | fs = torch.nn.Softmax()( 30 | torch.autograd.Variable(input, requires_grad=False) 31 | ).data 32 | 33 | # neg. one hot label 34 | y = input.new().resize_as_(input).zero_() 35 | for i, l in enumerate(label): 36 | y[i, l] = -1. 37 | 38 | fs.add_(y).mul_(1. / len(label)) 39 | grad_fs = fs 40 | if self.needs_input_grad[1]: 41 | raise NotImplementedError() 42 | 43 | return grad_fs, grad_label 44 | -------------------------------------------------------------------------------- /test_binary_cross_entropy.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stdout", 10 | "output_type": "stream", 11 | "text": [ 12 | "0.1.12_2\n" 13 | ] 14 | } 15 | ], 16 | "source": [ 17 | "import torch\n", 18 | "print(torch.__version__)" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 2, 24 | "metadata": { 25 | "collapsed": true 26 | }, 27 | "outputs": [], 28 | "source": [ 29 | "ce=torch.nn.MultiLabelSoftMarginLoss() # Sigmoid + binary cross-entropy\n\r", 30 | "sig=torch.nn.Sigmoid()" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": 3, 36 | "metadata": {}, 37 | "outputs": [], 38 | "source": [ 39 | "# set up activation & label\n\r", 40 | "a=torch.FloatTensor([[0.1, -0.1, 10, 50., 25., 12.5, 0, -12.5, -25., -50.]]) # activations\n\r", 41 | "y=torch.FloatTensor([[1., 1., 0., 0., 0., 0., 0., 0., 0., 0.]]) # labels\n\r", 42 | "a=torch.autograd.Variable(a, requires_grad=True)\n\r", 43 | "y=torch.autograd.Variable(y, requires_grad=False)" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 4, 49 | "metadata": {}, 50 | "outputs": [ 51 | { 52 | "name": "stdout", 53 | "output_type": "stream", 54 | "text": [ 55 | "Variable containing:\n\r", 56 | "\n\r", 57 | "Columns 0 to 5 \n\r", 58 | " 5.2498e-01 4.7502e-01 9.9995e-01 1.0000e+00 1.0000e+00 1.0000e+00\n\r", 59 | "\n\r", 60 | "Columns 6 to 9 \n\r", 61 | " 5.0000e-01 3.7266e-06 1.3888e-11 1.9287e-22\n\r", 62 | "[torch.FloatTensor of size 1x10]\n\r", 63 | "\n\r" 64 | ] 65 | } 66 | ], 67 | "source": [ 68 | "f=sig(a)\n\r", 69 | "print(f)" 70 | ] 71 | }, 72 | { 73 | "cell_type": "code", 74 | "execution_count": 5, 75 | "metadata": {}, 76 | "outputs": [ 77 | { 78 | "name": "stdout", 79 | "output_type": "stream", 80 | "text": [ 81 | "Variable containing:\n\r", 82 | " 7.9836\n\r", 83 | "[torch.FloatTensor of size 1]\n\r", 84 | "\n\r" 85 | ] 86 | } 87 | ], 88 | "source": [ 89 | "ce.zero_grad()\n\r", 90 | "l=ce(a,y)\n\r", 91 | "print(l)" 92 | ] 93 | }, 94 | { 95 | "cell_type": "code", 96 | "execution_count": 6, 97 | "metadata": {}, 98 | "outputs": [], 99 | "source": [ 100 | "l.backward() # obtained gradient" 101 | ] 102 | }, 103 | { 104 | "cell_type": "code", 105 | "execution_count": 7, 106 | "metadata": {}, 107 | "outputs": [ 108 | { 109 | "name": "stdout", 110 | "output_type": "stream", 111 | "text": [ 112 | "(0, -0.04750208184123039)\n\r", 113 | "(1, -0.05249791964888573)\n\r", 114 | "(2, 0.099995456635952)\n\r", 115 | "(3, 0.0)\n\r", 116 | "(4, 0.0)\n\r", 117 | "(5, 0.09999960660934448)\n\r", 118 | "(6, 0.05000000074505806)\n\r", 119 | "(7, 3.726638340140198e-07)\n\r", 120 | "(8, 1.2955112243390188e-12)\n\r", 121 | "(9, 3.720075951787754e-33)\n\r" 122 | ] 123 | } 124 | ], 125 | "source": [ 126 | "for i,g in enumerate(a.grad.data[0]): # obtained gradient\n\r", 127 | " print((i,g))" 128 | ] 129 | }, 130 | { 131 | "cell_type": "code", 132 | "execution_count": 8, 133 | "metadata": {}, 134 | "outputs": [ 135 | { 136 | "name": "stdout", 137 | "output_type": "stream", 138 | "text": [ 139 | "(0, -0.04750208184123039)\n\r", 140 | "(1, -0.05249791592359543)\n\r", 141 | "(2, 0.099995456635952)\n\r", 142 | "(3, 0.10000000149011612)\n\r", 143 | "(4, 0.10000000149011612)\n\r", 144 | "(5, 0.09999962151050568)\n\r", 145 | "(6, 0.05000000074505806)\n\r", 146 | "(7, 3.726639192791481e-07)\n\r", 147 | "(8, 1.3887943529564128e-12)\n\r", 148 | "(9, 1.9287499249081747e-23)\n\r" 149 | ] 150 | } 151 | ], 152 | "source": [ 153 | "ans=(f-y)/f.size(1) # correct answer\n\r", 154 | "for i,g in enumerate(ans.data[0]):\n\r", 155 | " print((i,g))" 156 | ] 157 | }, 158 | { 159 | "cell_type": "markdown", 160 | "metadata": {}, 161 | "source": [ 162 | "In # 0 1 2 3 4 5 6 7 8 9\n\r", 163 | "a=torch.FloatTensor([[0.1, -0.1, 10, 50., 25., 12.5, 0, -12.5, -25., -50.]]) # activations\n\r", 164 | "y=torch.FloatTensor([[ 1., 1., 0., 0., 0., 0., 0., 0., 0., 0.]]) # labels\n\r", 165 | "\n\r", 166 | "For # 0, 2, 6, the garident is correct.\n\r", 167 | "For # 1, 5, 7, the different is subtle.\n\r", 168 | "For # 8, 9, there is a noticeable difference.\n\r", 169 | "For # 3, 4, the gradient is completely wrong ( 0.0 vs 1/10 )." 170 | ] 171 | }, 172 | { 173 | "cell_type": "code", 174 | "execution_count": null, 175 | "metadata": { 176 | "collapsed": true 177 | }, 178 | "outputs": [], 179 | "source": [] 180 | } 181 | ], 182 | "metadata": { 183 | "kernelspec": { 184 | "display_name": "Python 2", 185 | "language": "python", 186 | "name": "python2" 187 | }, 188 | "language_info": { 189 | "codemirror_mode": { 190 | "name": "ipython", 191 | "version": 2 192 | }, 193 | "file_extension": ".py", 194 | "mimetype": "text/x-python", 195 | "name": "python", 196 | "nbconvert_exporter": "python", 197 | "pygments_lexer": "ipython2", 198 | "version": "2.7.5" 199 | } 200 | }, 201 | "nbformat": 4, 202 | "nbformat_minor": 2 203 | } 204 | -------------------------------------------------------------------------------- /test_tf_binary_cross_entropy.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import tensorflow as tf" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "metadata": { 18 | "collapsed": true 19 | }, 20 | "outputs": [], 21 | "source": [ 22 | "batch_size = 1" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 5, 28 | "metadata": {}, 29 | "outputs": [], 30 | "source": [ 31 | "data = tf.placeholder(tf.float32,shape=(batch_size,10))\n", 32 | "label = tf.placeholder(tf.float32,shape=(batch_size,10))\n", 33 | "loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=label,logits=data)" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": 6, 39 | "metadata": {}, 40 | "outputs": [], 41 | "source": [ 42 | "grad = tf.gradients(loss,[data])" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": 13, 48 | "metadata": { 49 | "collapsed": true 50 | }, 51 | "outputs": [], 52 | "source": [ 53 | "a=[[0.1, -0.1, 10, 50., 25., 12.5, 0, -12.5, -25., -50.]]\n", 54 | "y=[[1., 1., 0., 0., 0., 0., 0., 0., 0., 0.]]" 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": 14, 60 | "metadata": {}, 61 | "outputs": [ 62 | { 63 | "data": { 64 | "text/plain": [ 65 | "[[0.1, -0.1, 10, 50.0, 25.0, 12.5, 0, -12.5, -25.0, -50.0]]" 66 | ] 67 | }, 68 | "execution_count": 14, 69 | "metadata": {}, 70 | "output_type": "execute_result" 71 | } 72 | ], 73 | "source": [ 74 | "a" 75 | ] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "execution_count": 15, 80 | "metadata": { 81 | "collapsed": true 82 | }, 83 | "outputs": [], 84 | "source": [ 85 | "with tf.Session() as sess:\n", 86 | " l,g=sess.run((loss,grad),feed_dict={data: a,label: y})" 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": 16, 92 | "metadata": {}, 93 | "outputs": [ 94 | { 95 | "name": "stdout", 96 | "output_type": "stream", 97 | "text": [ 98 | "[[ 6.44396663e-01 7.44396687e-01 1.00000458e+01 5.00000000e+01\n", 99 | " 2.50000000e+01 1.25000038e+01 6.93147182e-01 3.72664636e-06\n", 100 | " 1.38879437e-11 1.92874989e-22]]\n" 101 | ] 102 | } 103 | ], 104 | "source": [ 105 | "print(l)" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": 17, 111 | "metadata": {}, 112 | "outputs": [ 113 | { 114 | "name": "stdout", 115 | "output_type": "stream", 116 | "text": [ 117 | "[array([[ -4.75020796e-01, -5.24979234e-01, 9.99954581e-01,\n", 118 | " 1.00000000e+00, 1.00000000e+00, 9.99996245e-01,\n", 119 | " 5.00000000e-01, 3.72663953e-06, 1.38879437e-11,\n", 120 | " 1.92874989e-22]], dtype=float32)]\n" 121 | ] 122 | } 123 | ], 124 | "source": [ 125 | "print(g)" 126 | ] 127 | } 128 | ], 129 | "metadata": { 130 | "kernelspec": { 131 | "display_name": "Python 2", 132 | "language": "python", 133 | "name": "python2" 134 | }, 135 | "language_info": { 136 | "codemirror_mode": { 137 | "name": "ipython", 138 | "version": 2 139 | }, 140 | "file_extension": ".py", 141 | "mimetype": "text/x-python", 142 | "name": "python", 143 | "nbconvert_exporter": "python", 144 | "pygments_lexer": "ipython2", 145 | "version": "2.7.5" 146 | } 147 | }, 148 | "nbformat": 4, 149 | "nbformat_minor": 2 150 | } 151 | --------------------------------------------------------------------------------