#Import PyTorch
import torch
from torch import nn
# Import torchvision
import torchvision
from torchvision import datasets
from torchvision.transforms import ToTensor
# Import matplotlib for visualization
import matplotlib.pyplot as plt
# check versions
print(f"PyTorch version: {torch.__version__}\n torchvision version :{torch.__version__}")
PyTorch version: 2.1.0+cpu torchvision version :2.1.0+cpu
# setup training data
train_data=datasets.MNIST(
root="data",
train= True, # we get train set only
download=True, #download if it does not exist
transform= ToTensor(),
target_transform=None)
# setup test data
test_data=datasets.MNIST(
root="data",
train=False, # we get test dataset
download=True,
transform=ToTensor()
)
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to data\MNIST\raw\train-images-idx3-ubyte.gz
100%|████████████████████████████████████████████████████████████████████| 9912422/9912422 [00:19<00:00, 512511.80it/s]
Extracting data\MNIST\raw\train-images-idx3-ubyte.gz to data\MNIST\raw Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to data\MNIST\raw\train-labels-idx1-ubyte.gz
100%|██████████████████████████████████████████████████████████████████████| 28881/28881 [00:00<00:00, 14457058.58it/s]
Extracting data\MNIST\raw\train-labels-idx1-ubyte.gz to data\MNIST\raw Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to data\MNIST\raw\t10k-images-idx3-ubyte.gz
100%|████████████████████████████████████████████████████████████████████| 1648877/1648877 [00:02<00:00, 556752.75it/s]
Extracting data\MNIST\raw\t10k-images-idx3-ubyte.gz to data\MNIST\raw Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to data\MNIST\raw\t10k-labels-idx1-ubyte.gz
100%|█████████████████████████████████████████████████████████████████████████| 4542/4542 [00:00<00:00, 4772176.55it/s]
Extracting data\MNIST\raw\t10k-labels-idx1-ubyte.gz to data\MNIST\raw
we are going to build a computer vision model to learn patterns in trainset and use the patterns to predict the testdata
# to check lengths of data
len(train_data), len(test_data)
(60000, 10000)
# see first training sample
image, label= train_data[0]
image, label
(tensor([[[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0118, 0.0706, 0.0706, 0.0706,
0.4941, 0.5333, 0.6863, 0.1020, 0.6510, 1.0000, 0.9686, 0.4980,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.1176, 0.1412, 0.3686, 0.6039, 0.6667, 0.9922, 0.9922, 0.9922,
0.9922, 0.9922, 0.8824, 0.6745, 0.9922, 0.9490, 0.7647, 0.2510,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1922,
0.9333, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922,
0.9922, 0.9843, 0.3647, 0.3216, 0.3216, 0.2196, 0.1529, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0706,
0.8588, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.7765, 0.7137,
0.9686, 0.9451, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.3137, 0.6118, 0.4196, 0.9922, 0.9922, 0.8039, 0.0431, 0.0000,
0.1686, 0.6039, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0549, 0.0039, 0.6039, 0.9922, 0.3529, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.5451, 0.9922, 0.7451, 0.0078, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0431, 0.7451, 0.9922, 0.2745, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.1373, 0.9451, 0.8824, 0.6275,
0.4235, 0.0039, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.3176, 0.9412, 0.9922,
0.9922, 0.4667, 0.0980, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1765, 0.7294,
0.9922, 0.9922, 0.5882, 0.1059, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0627,
0.3647, 0.9882, 0.9922, 0.7333, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.9765, 0.9922, 0.9765, 0.2510, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1804, 0.5098,
0.7176, 0.9922, 0.9922, 0.8118, 0.0078, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.1529, 0.5804, 0.8980, 0.9922,
0.9922, 0.9922, 0.9804, 0.7137, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0941, 0.4471, 0.8667, 0.9922, 0.9922, 0.9922,
0.9922, 0.7882, 0.3059, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0902, 0.2588, 0.8353, 0.9922, 0.9922, 0.9922, 0.9922, 0.7765,
0.3176, 0.0078, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0706, 0.6706,
0.8588, 0.9922, 0.9922, 0.9922, 0.9922, 0.7647, 0.3137, 0.0353,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.2157, 0.6745, 0.8863, 0.9922,
0.9922, 0.9922, 0.9922, 0.9569, 0.5216, 0.0431, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.5333, 0.9922, 0.9922, 0.9922,
0.8314, 0.5294, 0.5176, 0.0627, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000]]]),
5)
# to check size of images
image.shape
torch.Size([1, 28, 28])
so it the images are grayscale with [color_channels=1, height=28, width=28]
# to see each class
class_names= train_data.classes
print(f"The class names are:{class_names}\n The first and third class names are {class_names[0]} and {class_names[2]}\n The total number of classes are {len(class_names)}")
The class names are:['0 - zero', '1 - one', '2 - two', '3 - three', '4 - four', '5 - five', '6 - six', '7 - seven', '8 - eight', '9 - nine'] The first and third class names are 0 - zero and 2 - two The total number of classes are 10
train_data.class_to_idx
{'0 - zero': 0,
'1 - one': 1,
'2 - two': 2,
'3 - three': 3,
'4 - four': 4,
'5 - five': 5,
'6 - six': 6,
'7 - seven': 7,
'8 - eight': 8,
'9 - nine': 9}
# to check how many data we have in train and testsets
len(train_data.data), len(train_data.targets), len(test_data.data), len(test_data.targets)
(60000, 60000, 10000, 10000)
import matplotlib.pyplot as plt
image, label=train_data[7]
fig=plt.figure(figsize=(5,5))
print(f"image shape:{image.shape}")
plt.imshow(image.squeeze(),cmap="gray") # to squeeze to make data in form of matplotlib
plt.title(class_names[label])
image shape:torch.Size([1, 28, 28])
Text(0.5, 1.0, '3 - three')
# Plot more random images
torch.manual_seed(42)
fig = plt.figure(figsize=(9, 9))
rows, cols = 4, 4
for i in range(1, rows * cols + 1):
random_idx = torch.randint(0, len(train_data), size=[1]).item() # see above for explanation
img, label = train_data[random_idx]
fig.add_subplot(rows, cols, i)
plt.imshow(img.squeeze(), cmap="gray")
plt.title(class_names[label])
plt.axis(False);
from torch.utils.data import DataLoader
BATCH_SIZE=32
train_dataloader = DataLoader(train_data,
batch_size=BATCH_SIZE,
shuffle= True)
test_dataloader=DataLoader(test_data,
batch_size=BATCH_SIZE,
shuffle=False)
print(f"Dataloaders:{train_dataloader, test_dataloader}")
print(f"length of train dataloader:{len(train_dataloader)} batches of {BATCH_SIZE}")
print(f"length of test dataloader:{len(test_dataloader)} batches of {BATCH_SIZE}")
Dataloaders:(<torch.utils.data.dataloader.DataLoader object at 0x000002010E781520>, <torch.utils.data.dataloader.DataLoader object at 0x000002010E7D1FA0>) length of train dataloader:1875 batches of 32 length of test dataloader:313 batches of 32
train_features_batch, train_labels_batch = next(iter(train_dataloader))
test_features_batch, test_labels_batch= next(iter(test_dataloader))
print(train_features_batch.shape, train_labels_batch.shape)
print(test_features_batch.shape, test_labels_batch.shape)
torch.Size([32, 1, 28, 28]) torch.Size([32]) torch.Size([32, 1, 28, 28]) torch.Size([32])
vizualaize a sample
torch.manual_seed(42)
random_idx= torch.randint(0, len(train_features_batch), size=[1]).item()
img, label= train_features_batch [random_idx], train_labels_batch[random_idx]
plt.imshow(img.squeeze(),cmap="gray") # squeeze to remove extra single dimension
plt.title(class_names[label])
plt.axis("off")
print(f"image size:{img.shape}")
print(f"label: {label}, label size: {label.shape}")
image size:torch.Size([1, 28, 28]) label: 9, label size: torch.Size([])
from timeit import default_timer as timer
def print_train_time(start: float, end: float, device: torch.device=None):
total_time= end-start
print(f"Train time on {device}: {total_time:.3f} seconds")
return total_time
torch.cuda.is_available()
False
device= "cuda" if torch.cuda.is_available() else "cpu"
device
'cpu'
from torch.nn.modules.conv import Conv2d
class MNISTModel(nn.Module):
""" model architecture that replicates the TinyVGG model from CNN explainer website"""
def __init__(self, input_shape: int, hidden_units: int, output_shape: int):
super().__init__()
self.conv_block_1 = nn.Sequential(
#building first block (as our data are 2d, we use nn.Conv2d and nn.MaxPool2d)
nn.Conv2d(in_channels=input_shape, # number of channels in visual data
out_channels= hidden_units,
kernel_size=3, # this is size of the filter; this could be a tupel (3,3)
stride=1, # default; Tiny VGG uses a stride of 1 for its convolutional layers
padding=1), # options = "valid" (no padding) or "same" (output has same shape as input) or int for specific number
nn.ReLU(),
nn.Conv2d(in_channels=hidden_units,
out_channels=hidden_units,
kernel_size=3,
stride=1,
padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, # size of the window to take a max over
stride=2) # default stride value is same as kernel_size
)
#building 2nd block
self.conv_block_2 = nn.Sequential(
nn.Conv2d(in_channels= hidden_units,
out_channels=hidden_units,
kernel_size=3,
stride=1,
padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=hidden_units,
out_channels=hidden_units,
kernel_size=3,
stride=1,
padding=1),
nn.MaxPool2d(kernel_size=2, stride=2),
)
# next, flatten data and send them to a linear layer, as model1 (classifier)
self.classifier = nn.Sequential(
nn.Flatten(),
nn.Linear(in_features=hidden_units*7*7,
out_features=output_shape) # number of classes =len(class_names)=10
)
def forward(self,x:torch.Tensor):
x=self.conv_block_1(x) # x goes to block1
#print(f"outputshape of conv_block_1:{x.shape}")
x=self.conv_block_2(x) # goes to block2
#print(f"outputshape of conv_block_2:{x.shape}")
x=self.classifier(x)
#print(f"outputshape of classifier:{x.shape}")
return x
# instantiate the model
torch.manual_seed(42)
modelMNIST = MNISTModel(input_shape=1, # number of chanles in our images. check: image.shape ===1
hidden_units=10,
output_shape=len(class_names)).to(device)
modelMNIST
MNISTModel(
(conv_block_1): Sequential(
(0): Conv2d(1, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU()
(2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU()
(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(conv_block_2): Sequential(
(0): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU()
(2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(classifier): Sequential(
(0): Flatten(start_dim=1, end_dim=-1)
(1): Linear(in_features=490, out_features=10, bias=True)
)
)
modelMNIST(image.unsqueeze(0).to(device))
tensor([[ 0.0407, -0.0243, 0.0283, 0.0011, -0.0440, 0.0060, 0.0481, -0.0673,
0.0462, -0.0372]], grad_fn=<AddmmBackward0>)
# check with a random tensor of same size as image to find the in_features in cassifier
rand_image_tensor =torch.randn(size=(1,28,28))
rand_image_tensor.shape
modelMNIST(rand_image_tensor.unsqueeze(0).to(device))
tensor([[ 0.0276, -0.0354, 0.0448, -0.0097, -0.0473, 0.0209, 0.0434, -0.0504,
0.0213, -0.0634]], grad_fn=<AddmmBackward0>)
# Calculate accuracy (a classification metric)
def accuracy_fn(y_true, y_pred):
correct = torch.eq(y_true, y_pred).sum().item() # torch.eq() calculates where two tensors are equal
# how many of y_true, y_pred are equal
accuracy_fn = (correct / len(y_pred)) * 100 # difinitation of accuracy
return accuracy_fn
# setup loss and optimizer
loss_fn= nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(params=modelMNIST.parameters(), lr=0.1)
# to create a function
def train_step(model: torch.nn.Module,
data_loader: torch.utils.data.DataLoader,
loss_fn:torch.nn.Module,
optimizer: torch.optim.Optimizer,
accuracy_fn,
device: torch.device= device):
# training
train_loss, train_acc= 0, 0
model.train()
for batch, (X,y) in enumerate (data_loader):
X,y= X.to(device), y.to(device) # send data to GPU
y_pred=model(X) # Forward pass
loss= loss_fn(y_pred,y)
acc= accuracy_fn(y_true=y, y_pred=y_pred.argmax(dim=1))
# raw output of model (y_pred) are logits. But accuracy_fn expects y_true and y_pred in the same format
# if y_pred is logit, we take argmax to find the logit with highest index (prediction label)
# we could also take softmax to take prediction probabilities, but indeed no need, as we only need prediction label and argmax does it.
train_loss += loss
train_acc += acc
optimizer.zero_grad() # optimizer zero grad
loss.backward() # loss backwaard
optimizer.step() #optimizer step
train_loss/= len(data_loader)
train_acc /= len(data_loader)
#print(f"Train Loss:{train_loss:.5f} | Train accuracy:{train_acc:.2f}")
print(f"Train loss: {train_loss:.5f} | Train accuracy: {train_acc:.2f}%")
def test_step(model: torch.nn.Module,
data_loader:torch.utils.data.DataLoader,
loss_fn: torch.nn.Module,
accuracy_fn,
device:torch.device= device):
# Testing
test_loss, test_acc= 0,0
# put model to eval mode
model.eval()
with torch.inference_mode():
for X,y in data_loader:
X,y= X.to(device), y.to(device) #data to GPU
test_pred=model(X) # Forward pass
test_loss += loss_fn(test_pred, y)
test_acc += accuracy_fn(y_true=y, y_pred= test_pred.argmax(dim=1))
test_loss /= len(data_loader)
test_acc/= len(data_loader)
print(f"\nTrain Loss:{test_loss:.5f} | Test Acc: {test_acc:2f}")
torch.manual_seed(42)
from tqdm.auto import tqdm
from timeit import default_timer as timer
train_time_start_on_gpu = timer()
epochs=3
for epoch in tqdm (range(epochs)):
print(f"Epoch: {epoch}\n--------")
# call train/test functions
train_step(model=modelMNIST,
data_loader=train_dataloader,
loss_fn=loss_fn,
optimizer=optimizer,
accuracy_fn= accuracy_fn)
test_step(model=modelMNIST,
data_loader= test_dataloader,
loss_fn= loss_fn,
accuracy_fn= accuracy_fn)
train_time_end_on_gpu= timer()
total_train_time_model= print_train_time(start= train_time_start_on_gpu,
end=train_time_end_on_gpu,
device=device)
0%| | 0/3 [00:00<?, ?it/s]
Epoch: 0 -------- Train loss: 0.23388 | Train accuracy: 92.35% Train Loss:0.06550 | Test Acc: 97.843450 Train time on cpu: 31.838 seconds Epoch: 1 -------- Train loss: 0.07069 | Train accuracy: 97.81% Train Loss:0.04599 | Test Acc: 98.542332 Train time on cpu: 63.418 seconds Epoch: 2 -------- Train loss: 0.05488 | Train accuracy: 98.34% Train Loss:0.04627 | Test Acc: 98.442492 Train time on cpu: 93.972 seconds
torch.manual_seed(42)
def eval_model (model: torch.nn.Module, # type of the model
data_loader: torch.utils.data.DataLoader, # the type of dataloader
loss_fn: torch.nn.Module,
accuracy_fn,
device: torch.device = device): # device-agnostic code for eval_model function
"""Evaluates a given model on a given dataset.
Args:
model (torch.nn.Module): A PyTorch model capable of making predictions on data_loader.
data_loader (torch.utils.data.DataLoader): The target dataset to predict on.
loss_fn (torch.nn.Module): The loss function of model.
accuracy_fn: An accuracy function to compare the models predictions to the truth labels.
device (str, optional): Target device to compute on. Defaults to device.
Returns:
(dict): Results of model making predictions on data_loader.
"""
loss, acc = 0,0
model.eval()
with torch.inference_mode():
for X,y in data_loader:
#Send data to the target device
X, y = X.to(device), y.to(device)
y_pred= model(X)
loss += loss_fn(y_pred, y)
acc += accuracy_fn(y_true=y, y_pred = y_pred.argmax(dim=1))
loss/= len(data_loader)
acc /= len(data_loader)
return { "model_name": model.__class__.__name__,
"model_loss": loss.item(),
"model_acc": acc}
import numpy as np
model_MNIST_results = eval_model(
model= modelMNIST,
data_loader=test_dataloader,
loss_fn=loss_fn,
accuracy_fn=accuracy_fn,
device=device
)
model_MNIST_results
{'model_name': 'MNISTModel',
'model_loss': 0.04627160727977753,
'model_acc': 98.44249201277955}
img, label= test_data[0]
img.shape
torch.Size([1, 28, 28])
def make_predictions (model: torch.nn.Module,
data: list, # data will be a list
device: torch.device=device):
pred_probs= [] # create an empty lsit for prediction probabilities
model.eval() # turn model to evaluation mode
with torch.inference_mode():
for sample in data: # for each sample in data
# prepare sample: add a batch dimension and pass it to device
sample=torch.unsqueeze(sample, dim=0).to(device) # it takes a single image. we unsqueeze because we need to add batch size dimension as 0 index. After adding extra dimension we send sample to device
# forward pass (model output raw logits)
pred_logit=model(sample)
# get prediciton probability (logit->prediction probability)
pred_prob=torch.softmax(pred_logit.squeeze(), dim=0) # squeeze to get rid of extra dimension
# Get pred_prob off GPU for further calculations
pred_probs.append(pred_prob.cpu()) # matplotlip work with data on cpu
# Stack the pred_probs to turn list into a tensor
return torch.stack(pred_probs)
import random
random.seed(42)
test_samples = []
test_labels = []
for sample, label in random.sample(list(test_data), k=9):
test_samples.append(sample)
test_labels.append(label)
# View the first test sample shape and label
print(f"Test sample image shape: {test_samples[2].shape}\nTest sample label: {test_labels[2]} ({class_names[test_labels[2]]})")
Test sample image shape: torch.Size([1, 28, 28]) Test sample label: 2 (2 - two)
# Make predictions on test samples with model 2
pred_probs= make_predictions(model=modelMNIST,
data=test_samples)
# View first two prediction probabilities list
pred_probs[:2]
# convert prediction probabilities to lables:
pred_classes= pred_probs.argmax(dim=1) # as before in notebook2
pred_classes
tensor([2, 1, 2, 4, 6, 6, 4, 9, 1])
test_labels
[2, 1, 2, 4, 6, 6, 4, 9, 1]
# plot predicitons
plt.figure(figsize=(9,9))
nrows=3 # number or rows
ncols=3 # number of comobs
for i, sample in enumerate (test_samples):
# create subplot
plt.subplot(nrows, ncols, i+1)
# plot the target image
plt.imshow(sample.squeeze(), cmap="gray")
# find the prediciton (in text form, e.g., "sandal")
pred_label=class_names[pred_classes[i]]
# get the truth label (in text form)
truth_label=class_names[test_labels[i]]
# Create the title text of the plot
title_text = f"Pred: {pred_label} | Truth: {truth_label}"
# Check for equality and change title colour accordingly
if pred_label == truth_label:
plt.title(title_text, fontsize=10, c="g") # green text if correct
else:
plt.title(title_text, fontsize=10, c="r") # red text if wrong
plt.axis(False);
from google.colab import files
# Prompt the user to upload an image file
uploaded = files.upload()
from google.colab import files
from PIL import Image
from io import BytesIO # Add this import
# Prompt the user to upload an image file
uploaded = files.upload()
# Access the first uploaded image (assuming only one image is uploaded)
uploaded_images = list(uploaded.values())
uploaded_image = Image.open(BytesIO(uploaded_images[0]))
# Preprocess the uploaded image as needed
uploaded_image = uploaded_image.convert("L")
uploaded_image = uploaded_image.resize((28, 28))
uploaded_image = np.array(uploaded_image) / 255.0
# Create a PyTorch tensor from the image
uploaded_image_tensor = torch.tensor(uploaded_image, dtype=torch.float32)
# For MNIST, reshape the image to match the model's expected input shape (1, 28, 28)
uploaded_image_tensor = uploaded_image_tensor.unsqueeze(0).unsqueeze(0).to(device)
# Make predictions using your model
with torch.no_grad():
prediction = modelMNIST(uploaded_image_tensor)
predicted_label = prediction.argmax().item()
print(f"Predicted Label: {predicted_label}")
#modelMNIST(image.unsqueeze(0).to(device))