深度学习入门之Pytorch带书签和源码

from datetime import datetime

import torch
import torch.nn.functional as F
from torch import nn
from torch.autograd import Variable


def get_acc（output label）:
    total = output.shape[0]
    _ pred_label = output.max（1）
    num_correct = （pred_label == label）.sum（）.data[0]
    return num_correct / total


def train（net train_data valid_data num_epochs optimizer criterion）:
    if torch.cuda.is_available（）:
        net = net.cuda（）
    prev_time = datetime.now（）
    for epoch in range（num_epochs）:
        train_loss = 0
        train_acc = 0
        net = net.train（）
        for im label in train_data:
            if torch.cuda.is_available（）:
                im = Variable（im.cuda（））  # （bs 3 h w）
                label = Variable（label.cuda（））  # （bs h w）
            else:
                im = Variable（im）
                label = Variable（label）
            # forward
            output = net（im）
            loss = criterion（output label）
            # backward
            optimizer.zero_grad（）
            loss.backward（）
            optimizer.step（）

            train_loss += loss.data[0]
            train_acc += get_acc（output label）

        cur_time = datetime.now（）
        h remainder = divmod（（cur_time - prev_time）.seconds 3600）
        m s = divmod（remainder 60）
        time_str = “Time %02d:%02d:%02d“ % （h m s）
        if valid_data is not None:
            valid_loss = 0
            valid_acc = 0
            net = net.eval（）
            for im label in valid_data:
                if torch.cuda.is_available（）:
                    im = Variable（im.cuda（） volatile=True）
                    label = Variable（label.cuda（） volatile=True）
                else:
                    im = Variable（im volatile=True）
                    label = Variable（label volatile=True）
                output = net（im）
                loss = criterion（output label）
                valid_loss += loss.data[0]
                valid_acc += get_acc（output label）
            epoch_str = （
                “Epoch %d. Train Loss: %f Train Acc: %f Valid Loss: %f Valid Acc: %f “
                % （epoch train_loss / len（train_data）
                   train_acc / len（train_data） valid_loss / len（valid_data）
                   valid_acc / len（valid_data）））
        else:
            epoch_str = （“Epoch %d. Train Loss: %f Train Acc: %f “ %
                         （epoch train_loss / len（train_data）
                          train_acc / len（train_data）））
        prev_time = cur_time
        print（epoch_str + time_str）


def conv3x3（in_channel out_channel stride=1）:
    return nn.Conv2d（
        in_channel out_channel 3 stride=stride padding=1 bias=False）


class residual_block（nn.Module）:
    def __init__（self in_channel out_channel same_shape=True）:
        super（residual_block self）.__init__（）
        self.same_shape = same_shape
        stride 

 属性            大小     日期    时间   名称
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     目录           0  2018-04-29 22:39  code-of-learn-deep-learning-with-pytorch
     目录           0  2018-04-29 22:38  code-of-learn-deep-learning-with-pytorch.git
     文件          25  2018-04-29 22:38  code-of-learn-deep-learning-with-pytorch.gitignore
     文件         337  2018-04-29 22:38  code-of-learn-deep-learning-with-pytorch.gitconfig
     文件          73  2018-04-29 22:37  code-of-learn-deep-learning-with-pytorch.gitdescription
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     目录           0  2018-04-29 22:38  code-of-learn-deep-learning-with-pytorch.githooks
     文件         478  2018-04-29 22:37  code-of-learn-deep-learning-with-pytorch.githooksapplypatch-msg.sample
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     文件         214  2018-04-29 22:38  code-of-learn-deep-learning-with-pytorch.gitlogs
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