This is my test codes for comparing pytorch and tensorflow

Below codes is a pytorch code of fintuning flower example

in my machine gtx980ti , the batch size of pytorch, 8 is available, but 16 is not

```
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torchvision import datasets, transforms
from torch.autograd import Variable
import matplotlib.pyplot as plt
import numpy as np
is_cuda = torch.cuda.is_available() # if cuda is avaible, True
traindir = './flower_photos'
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
batch_size = 16
train_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(traindir,
transforms.Compose([
transforms.RandomSizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,])),
batch_size=batch_size,
shuffle=True,
num_workers=4)
cls_num = len(datasets.folder.find_classes(traindir)[0])
test_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(traindir,
transforms.Compose([
transforms.RandomSizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,])),
batch_size=batch_size,
shuffle=True,
num_workers=1)
model = torchvision.models.resnet152(pretrained = True)
### don't update model parameters
for param in model.parameters() :
param.requires_grad = False
#modify last fully connected layter
model.fc = nn.Linear(model.fc.in_features, cls_num)
fc_parameters = [
{'params': model.fc.parameters()},
]
optimizer = torch.optim.Adam(fc_parameters, lr=1e-4, weight_decay=1e-4)
loss_fn = nn.CrossEntropyLoss()
if is_cuda : model.cuda(), loss_fn.cuda()
# trainning
model.train()
train_loss = []
train_accu = []
i = 0
for epoch in range(1):
for image, target in train_loader:
image, target = Variable(image.float()), Variable(target)
if is_cuda : image, target = image.cuda(), target.cuda()
output = model(image)
loss = loss_fn(output, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
pred = output.data.max(1)[1]
accuracy = pred.eq(target.data).sum()/batch_size
train_loss.append(loss.data[0])
train_accu.append(accuracy)
if i % 300 == 0:
print(i, loss.data[0])
i += 1
```

below is tensorflow-slm code,

tensorflow implementation

```
images, _, labels = load_batch(dataset, batch_size=256, height=image_size, width=image_size)
```

in same network model, same machine(gtx980ti),

the batch size of tensorflow, 256 is available, 512 is not