Compare the example of Pytorch and Keras on Cifar10 data - tensorflow

Question

I use CIFAR10 dataset to learn how to code using Keras and PyTorch.
The environment is Python 3.6.7, Torch 1.0.0, Keras 2.2.4, Tensorflow 1.14.0.
I use the same batch size, number of epochs, learning rate and optimizer.
I use DenseNet121 as the model.
After training, Keras get 69% accuracy in test data.
PyTorch just get 54% in test data.
I know the results are different, but why is the result so bad in PyTorch?
Here is the Keras code:
import os, keras
from keras.datasets import cifar10
from keras.applications.densenet import DenseNet121
batch_size = 32
num_classes = 10
epochs = 20
# The data, split between train and test sets:
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
# model
model = DenseNet121(include_top=True, weights=None, input_shape=(32,32,3), classes=10)
# initiate RMSprop optimizer
opt = keras.optimizers.SGD(lr=0.001, momentum=0.9)
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
shuffle=True)
# Score trained model.
scores = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])
Here is the Pytorch code:
import torch
import torchvision
import torchvision.transforms as transforms
from torch import flatten
import torch.optim as optim
from torchvision import transforms, models
from torch.nn import Linear, Softmax, Module, Sequential, CrossEntropyLoss
import numpy as np
from tqdm import tqdm
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
transform = transforms.Compose([transforms.ToTensor()])
trainset = torchvision.datasets.CIFAR10(root='./DataSet', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True, num_workers=0)
testset = torchvision.datasets.CIFAR10(root='./DataSet', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=0)
import torch.nn as nn
import torch.nn.functional as F
class Net(Module):
def __init__(self):
super(Net, self).__init__()
self.funFeatExtra = Sequential(*[i for i in list(models.densenet121().children())[:-1]])
self.funFlatten = flatten
self.funOutputLayer = Linear(1024, 10)
self.funSoftmax = Softmax(dim=1)
def forward(self, x):
x = self.funFeatExtra(x)
x = self.funFlatten(x, 1)
x = self.funOutputLayer(x)
x = self.funSoftmax(x)
return x
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(20): # loop over the dataset multiple times
running_loss = 0.0
for i, data in tqdm(enumerate(trainloader, 0)):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net.cuda()(inputs.cuda())
loss = criterion(outputs, labels.cuda())
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
# if i % 2000 == 1999: # print every 2000 mini-batches
# print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000))
# running_loss = 0.0
print('Finished Training')
########################################################################
# The results seem pretty good.
#
# Let us look at how the network performs on the whole dataset.
correct = 0
total = 0
with torch.no_grad():
for data in tqdm(testloader):
images, labels = data
outputs = net.cpu()(images.cpu())
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))

Answer 1

You are not supposed to softmax the model output before you pass it to CrossEntropyLoss. Per the documentation:
This criterion combines nn.LogSoftmax() and nn.NLLLoss() in one single class.
...
The input is expected to contain raw, unnormalized scores for each class.
You can softmax them separately (outside of forward()) when calculating accuracy.