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%% Cell type:markdown id: tags:
## Test Pytorch
%% Cell type:code id: tags:
``` python
import torch
N, D = 14, 10
x = torch.randn(N, D).type(torch.FloatTensor)
print(x)
from torchvision import models
alexnet = models.alexnet()
print(alexnet)
```
%% Output
tensor([[ 0.6650, 2.0888, -0.3361, -0.6212, 0.6276, -0.3091, 1.6601, -0.1404,
0.0224, -1.1360],
[-0.8311, -1.6385, 0.3011, 1.2989, 0.2328, -0.0061, 0.4039, 0.3956,
-0.2067, -0.8877],
[-0.2899, -1.7310, -0.4318, 1.3837, 3.1771, 1.7354, 0.3680, -0.0743,
-0.1610, 0.8281],
[-0.2591, -2.0913, 0.8630, -0.3533, 0.9323, -1.4520, 0.4476, -0.2588,
-0.0963, 1.8449],
[ 0.4599, 0.3258, 0.7780, 0.6943, -0.4343, -0.0536, -0.1049, 0.2867,
0.5493, -1.2934],
[-0.6990, 0.0783, -0.8745, 1.2521, 1.5363, 0.8770, -0.5319, -0.2629,
0.7732, -0.5001],
[-0.2902, -0.8901, 0.1904, 0.7456, 0.5802, 0.0443, -1.2447, 2.1954,
0.5382, 0.2219],
[ 1.0372, -0.7516, 0.7940, 0.8207, 0.6601, 0.0317, 0.1410, -1.7062,
-0.6549, 0.6287],
[ 0.6680, 1.0136, -0.0813, -1.4382, 0.4640, 1.2923, -1.0299, 0.5684,
1.6626, -1.1921],
[-1.1864, -0.6625, -1.0846, 0.7550, -0.8748, 0.2835, -1.4264, 1.0279,
-0.6046, 0.6298],
[-1.9253, -0.4960, -0.8379, 1.8726, -2.0282, 0.0869, 1.2508, 0.0390,
-1.7213, 0.3269],
[ 1.7196, -1.6188, -0.4604, -1.0196, -0.8883, 1.2987, 0.4795, 0.5581,
-1.0138, -0.2184],
[-0.6600, 0.5816, -0.6574, 0.4684, 1.2546, -0.4140, -0.2636, 0.4267,
0.1736, -1.5019],
[-0.8852, 0.6677, -1.3074, -1.2241, -1.4054, 0.0919, 1.5832, 1.4357,
-1.9016, 0.7274]])
AlexNet(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
(1): ReLU(inplace=True)
(2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): ReLU(inplace=True)
(5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): ReLU(inplace=True)
(8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): ReLU(inplace=True)
(10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace=True)
(12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(avgpool): AdaptiveAvgPool2d(output_size=(6, 6))
(classifier): Sequential(
(0): Dropout(p=0.5, inplace=False)
(1): Linear(in_features=9216, out_features=4096, bias=True)
(2): ReLU(inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Linear(in_features=4096, out_features=4096, bias=True)
(5): ReLU(inplace=True)
(6): Linear(in_features=4096, out_features=1000, bias=True)
)
)
%% Cell type:markdown id: tags:
## Exercise 1: CNN on CIFAR10
%% Cell type:markdown id: tags:
See if cuda is available ==> it is not
%% Cell type:code id: tags:
``` python
import torch
# check if CUDA is available
train_on_gpu = torch.cuda.is_available()
if not train_on_gpu:
print("CUDA is not available. Training on CPU ...")
else:
print("CUDA is available! Training on GPU ...")
```
%% Output
CUDA is not available. Training on CPU ...
%% Cell type:markdown id: tags:
Load the CIFAR10 dataset
%% Cell type:code id: tags:
``` python
import numpy as np
from torchvision import datasets, transforms
from torch.utils.data.sampler import SubsetRandomSampler
# number of subprocesses to use for data loading
num_workers = 0
# how many samples per batch to load
batch_size = 20
# percentage of training set to use as validation
valid_size = 0.2
# convert data to a normalized torch.FloatTensor
transform = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
)
# choose the training and test datasets
train_data = datasets.CIFAR10("data", train=True, download=True, transform=transform)
test_data = datasets.CIFAR10("data", train=False, download=True, transform=transform)
# obtain training indices that will be used for validation
num_train = len(train_data)
indices = list(range(num_train))
np.random.shuffle(indices)
split = int(np.floor(valid_size * num_train))
train_idx, valid_idx = indices[split:], indices[:split]
# define samplers for obtaining training and validation batches
train_sampler = SubsetRandomSampler(train_idx)
valid_sampler = SubsetRandomSampler(valid_idx)
# prepare data loaders (combine dataset and sampler)
train_loader = torch.utils.data.DataLoader(
train_data, batch_size=batch_size, sampler=train_sampler, num_workers=num_workers
)
valid_loader = torch.utils.data.DataLoader(
train_data, batch_size=batch_size, sampler=valid_sampler, num_workers=num_workers
)
test_loader = torch.utils.data.DataLoader(
test_data, batch_size=batch_size, num_workers=num_workers
)
# specify the image classes
classes = [
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck",
]
```
%% Output
Files already downloaded and verified
Files already downloaded and verified
%% Cell type:code id: tags:
``` python
```
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