I have finished a PyTorch MLP model for the MNIST dataset, but got two different results: 0.90+ accuracy when using MNIST dataset from PyTorch, but ~0.10 accuracy when using MNIST dataset from Keras. Below is my code with dependency: PyTorch 0.3.0.post4, keras 2.1.3, tensorflow backend 1.4.1 gpu version.
# -*-coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import torch as pt
import torchvision as ptv
from keras.datasets import mnist
from torch.nn import functional as F
from torch.utils.data import Dataset, DataLoader
train_set = ptv.datasets.MNIST("./data/mnist/train", train=True, transform=ptv.transforms.ToTensor(), download=True)
test_set = ptv.datasets.MNIST("./data/mnist/test", train=False, transform=ptv.transforms.ToTensor(), download=True)
train_dataset = DataLoader(train_set, batch_size=100, shuffle=True)
test_dataset = DataLoader(test_set, batch_size=10000, shuffle=True)
class MLP(pt.nn.Module):
def __init__(self):
super(MLP, self).__init__()
self.fc1 = pt.nn.Linear(784, 512)
self.fc2 = pt.nn.Linear(512, 128)
self.fc3 = pt.nn.Linear(128, 10)
self.use_gpu = True
def forward(self, din):
din = din.view(-1, 28 * 28)
dout = F.relu(self.fc1(din))
dout = F.relu(self.fc2(dout))
# return F.softmax(self.fc3(dout))
return self.fc3(dout)
model = MLP().cuda()
print(model)
# loss func and optim
optimizer = pt.optim.SGD(model.parameters(), lr=1)
criterion = pt.nn.CrossEntropyLoss().cuda()
def evaluate_acc(pred, label):
pred = pred.cpu().data.numpy()
label = label.cpu().data.numpy()
test_np = (np.argmax(pred, 1) == label)
test_np = np.float32(test_np)
return np.mean(test_np)
def evaluate_loader(loader):
print("evaluating ...")
accurarcy_list = []
for i, (inputs, labels) in enumerate(loader):
inputs = pt.autograd.Variable(inputs).cuda()
labels = pt.autograd.Variable(labels).cuda()
outputs = model(inputs)
accurarcy_list.append(evaluate_acc(outputs, labels))
print(sum(accurarcy_list) / len(accurarcy_list))
def training(d, epochs):
for x in range(epochs):
for i, data in enumerate(d):
optimizer.zero_grad()
(inputs, labels) = data
inputs = pt.autograd.Variable(inputs).cuda()
labels = pt.autograd.Variable(labels).cuda()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
if i % 200 == 0:
print(i, ":", evaluate_acc(outputs, labels))
training(train_dataset, 4)
evaluate_loader(test_dataset)
print("###########################################################")
def load_mnist():
(x, y), (x_test, y_test) = mnist.load_data()
x = x.reshape((-1, 1, 28, 28)).astype(np.float32)
x_test = x_test.reshape((-1, 1, 28, 28)).astype(np.float32)
y = y.astype(np.int64)
y_test = y_test.astype(np.int64)
print("x.shape", x.shape, "y.shape", y.shape,
"\nx_test.shape", x_test.shape, "y_test.shape", y_test.shape,
)
return x, y, x_test, y_test
class TMPDataset(Dataset):
def __init__(self, a, b):
self.x = a
self.y = b
def __getitem__(self, item):
return self.x[item], self.y[item]
def __len__(self):
return len(self.y)
x_train, y_train, x_test, y_test = load_mnist()
test_loader = DataLoader(TMPDataset(x_test, y_test), num_workers=1, batch_size=10000)
train_loader = DataLoader(TMPDataset(x_train, y_train), shuffle=True, batch_size=100)
evaluate_loader(test_loader)
evaluate_loader(train_loader)
model = MLP().cuda()
print(model)
optimizer = pt.optim.SGD(model.parameters(), lr=1)
criterion = pt.nn.CrossEntropyLoss().cuda()
training(train_loader, 4)
evaluate_loader(test_loader)
evaluate_loader(train_loader)
I had check several samples from Keras MNIST dataset and found no error. I am wondering what is wrong with the datasets?
