MNIST() can use MNIST dataset as shown below:
*Memos:
- The 1st argument is
root(Required-Type:strorpathlib.Path). *An absolute or relative path is possible. - The 2nd argument is
train(Optional-Default:False-Type:float). *If it'sTrue, train data(60,000 samples) is used while if it'sFalse, test data(60,000 samples) is used. - The 3rd argument is
transform(Optional-Default:None-Type:callable). - The 4th argument is
target_transform(Optional-Default:None-Type:callable). - The 5th argument is
download(Optional-Default:False-Type:bool): *Memos:- If it's
True, the dataset is downloaded from the internet and extracted(unzipped) toroot. - If it's
Trueand the dataset is already downloaded, it's extracted. - If it's
Trueand the dataset is already downloaded and extracted, nothing happens. - It should be
Falseif the dataset is already downloaded and extracted because it's faster. - You can manually download and extract the dataset from here to e.g.
data/MNIST/raw/.
- If it's
from torchvision.datasets import MNIST
train_data = MNIST(
root="data"
)
train_data = MNIST(
root="data",
train=True,
transform=None,
target_transform=None,
download=False
)
test_data = MNIST(
root="data",
train=False
)
len(train_data), len(test_data)
# (60000, 10000)
train_data
# Dataset MNIST
# Number of datapoints: 60000
# Root location: data
# Split: Train
train_data.root
# 'data'
train_data.train
# True
print(train_data.transform)
# None
print(train_data.target_transform)
# None
train_data.download
# <bound method MNIST.download of Dataset MNIST
# Number of datapoints: 60000
# Root location: data
# Split: Train>
train_data[0]
# (<PIL.Image.Image image mode=L size=28x28>, 5)
train_data[1]
# (<PIL.Image.Image image mode=L size=28x28>, 0)
train_data[2]
# (<PIL.Image.Image image mode=L size=28x28>, 4)
train_data[3]
# (<PIL.Image.Image image mode=L size=28x28>, 1)
train_data[4]
# (<PIL.Image.Image image mode=L size=28x28>, 9)
train_data.classes
# ['0 - zero', '1 - one', '2 - two', '3 - three', '4 - four',
# '5 - five', '6 - six', '7 - seven', '8 - eight', '9 - nine']
from torchvision.datasets import MNIST
train_data = MNIST(
root="data"
)
test_data = MNIST(
root="data",
train=False
)
import matplotlib.pyplot as plt
def show_images(data):
plt.figure(figsize=(12, 2))
col = 5
for i, (image, label) in enumerate(data, 1):
plt.subplot(1, col, i)
plt.title(label)
plt.imshow(image)
if i == col:
break
plt.show()
show_images(data=train_data)
show_images(data=test_data)
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