Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
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Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
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Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
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Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
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Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw

/home/yifanq/.conda/envs/pyserini/lib/python3.6/site-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ../torch/csrc/utils/tensor_numpy.cpp:180.)
  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)
Shape of X [N, C, H, W]: torch.Size([64, 1, 28, 28])
Shape of y: torch.Size([64]) torch.int64
Using cuda device
NeuralNetwork(
  (flatten): Flatten(start_dim=1, end_dim=-1)
  (linear_relu_stack): Sequential(
    (0): Linear(in_features=784, out_features=512, bias=True)
    (1): ReLU()
    (2): Linear(in_features=512, out_features=512, bias=True)
    (3): ReLU()
    (4): Linear(in_features=512, out_features=10, bias=True)
  )
)
Epoch 1
-------------------------------
loss: 2.313538  [    0/60000]
loss: 2.293854  [ 6400/60000]
loss: 2.279889  [12800/60000]
loss: 2.267428  [19200/60000]
loss: 2.259280  [25600/60000]
loss: 2.236139  [32000/60000]
loss: 2.228364  [38400/60000]
loss: 2.213714  [44800/60000]
loss: 2.203546  [51200/60000]
loss: 2.163703  [57600/60000]
Test Error:
 Accuracy: 49.9%, Avg loss: 2.165564


Epoch 2
-------------------------------
loss: 2.175565  [    0/60000]
loss: 2.159365  [ 6400/60000]
loss: 2.113833  [12800/60000]
loss: 2.121040  [19200/60000]
loss: 2.081152  [25600/60000]
loss: 2.023684  [32000/60000]
loss: 2.037789  [38400/60000]
loss: 1.979666  [44800/60000]
loss: 1.980838  [51200/60000]
loss: 1.891988  [57600/60000]
Test Error:
 Accuracy: 60.3%, Avg loss: 1.904942

Epoch 3
-------------------------------
loss: 1.936851  [    0/60000]
loss: 1.899984  [ 6400/60000]
loss: 1.801094  [12800/60000]
loss: 1.827890  [19200/60000]
loss: 1.734551  [25600/60000]
loss: 1.675667  [32000/60000]
loss: 1.689272  [38400/60000]
loss: 1.609945  [44800/60000]
loss: 1.628446  [51200/60000]
loss: 1.504872  [57600/60000]
Test Error:
 Accuracy: 62.5%, Avg loss: 1.537890

Epoch 4
-------------------------------
loss: 1.603766  [    0/60000]
loss: 1.559234  [ 6400/60000]
loss: 1.426101  [12800/60000]
loss: 1.481380  [19200/60000]
loss: 1.378243  [25600/60000]
loss: 1.363728  [32000/60000]
loss: 1.374860  [38400/60000]
loss: 1.315051  [44800/60000]
loss: 1.344338  [51200/60000]
loss: 1.232845  [57600/60000]
Test Error:
 Accuracy: 63.6%, Avg loss: 1.266344

Epoch 5
-------------------------------
loss: 1.343015  [    0/60000]
loss: 1.315170  [ 6400/60000]
loss: 1.164128  [12800/60000]
loss: 1.256149  [19200/60000]
loss: 1.142950  [25600/60000]
loss: 1.160474  [32000/60000]
loss: 1.183086  [38400/60000]
loss: 1.132169  [44800/60000]
loss: 1.165353  [51200/60000]
loss: 1.074910  [57600/60000]
Test Error:
 Accuracy: 64.7%, Avg loss: 1.099324

Done!