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Merge branch 'master' of https://github.com/aladdinpersson/Machine-Learning-Collection

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Aladdin Persson
2021-03-26 14:43:25 +01:00
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51
ML/Pytorch/Basics/pytorch_rnn_gru_lstm.py
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@@ -8,15 +8,14 @@ Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
# Imports # Imports
import torch import torch
import torchvision import torchvision # torch package for vision related things
import torch.nn as nn # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions import torch.nn.functional as F # Parameterless functions, like (some) activation functions
import torch.optim as optim # For all Optimization algorithms, SGD, Adam, etc. import torchvision.datasets as datasets # Standard datasets
import torch.nn.functional as F # All functions that don't have any parameters import torchvision.transforms as transforms # Transformations we can perform on our dataset for augmentation
from torch.utils.data import ( from torch import optim # For optimizers like SGD, Adam, etc.
DataLoader, from torch import nn # All neural network modules
) # Gives easier dataset managment and creates mini batches from torch.utils.data import DataLoader # Gives easier dataset managment by creating mini batches etc.
import torchvision.datasets as datasets # Has standard datasets we can import in a nice way from tqdm import tqdm # For a nice progress bar!
import torchvision.transforms as transforms # Transformations we can perform on our dataset
# Set device # Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -29,7 +28,7 @@ num_classes = 10
sequence_length = 28 sequence_length = 28
learning_rate = 0.005 learning_rate = 0.005
batch_size = 64 batch_size = 64
num_epochs = 2 num_epochs = 3
# Recurrent neural network (many-to-one) # Recurrent neural network (many-to-one)
class RNN(nn.Module): class RNN(nn.Module):
@@ -101,18 +100,12 @@ class RNN_LSTM(nn.Module):
# Load Data # Load Data
train_dataset = datasets.MNIST( train_dataset = datasets.MNIST(root="dataset/", train=True, transform=transforms.ToTensor(), download=True)
root="dataset/", train=True, transform=transforms.ToTensor(), download=True test_dataset = datasets.MNIST(root="dataset/", train=False, transform=transforms.ToTensor(), download=True)
)
test_dataset = datasets.MNIST(
root="dataset/", train=False, transform=transforms.ToTensor(), download=True
)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True) test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)
# Initialize network # Initialize network (try out just using simple RNN, or GRU, and then compare with LSTM)
model = RNN_LSTM(input_size, hidden_size, num_layers, num_classes).to(device) model = RNN_LSTM(input_size, hidden_size, num_layers, num_classes).to(device)
# Loss and optimizer # Loss and optimizer
@@ -121,7 +114,7 @@ optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# Train Network # Train Network
for epoch in range(num_epochs): for epoch in range(num_epochs):
for batch_idx, (data, targets) in enumerate(train_loader): for batch_idx, (data, targets) in enumerate(tqdm(train_loader)):
# Get data to cuda if possible # Get data to cuda if possible
data = data.to(device=device).squeeze(1) data = data.to(device=device).squeeze(1)
targets = targets.to(device=device) targets = targets.to(device=device)
@@ -134,16 +127,11 @@ for epoch in range(num_epochs):
optimizer.zero_grad() optimizer.zero_grad()
loss.backward() loss.backward()
# gradient descent or adam step # gradient descent update step/adam step
optimizer.step() optimizer.step()
# Check accuracy on training & test to see how good our model # Check accuracy on training & test to see how good our model
def check_accuracy(loader, model): def check_accuracy(loader, model):
if loader.dataset.train:
print("Checking accuracy on training data")
else:
print("Checking accuracy on test data")
num_correct = 0 num_correct = 0
num_samples = 0 num_samples = 0
@@ -160,13 +148,10 @@ def check_accuracy(loader, model):
num_correct += (predictions == y).sum() num_correct += (predictions == y).sum()
num_samples += predictions.size(0) num_samples += predictions.size(0)
print( # Toggle model back to train
f"Got {num_correct} / {num_samples} with \
accuracy {float(num_correct)/float(num_samples)*100:.2f}"
)
# Set model back to train
model.train() model.train()
return num_correct / num_samples
check_accuracy(train_loader, model) print(f"Accuracy on training set: {check_accuracy(train_loader, model)*100:2f}")
check_accuracy(test_loader, model) print(f"Accuracy on test set: {check_accuracy(test_loader, model)*100:.2f}")

67
ML/Pytorch/Basics/pytorch_simple_CNN.py
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@@ -1,34 +1,33 @@
""" """
Example code of a simple CNN network training on MNIST dataset. A simple walkthrough of how to code a convolutional neural network (CNN)
The code is intended to show how to create a CNN network as well using the PyTorch library. For demonstration we train it on the very
as how to initialize loss, optimizer, etc. in a simple way to get common MNIST dataset of handwritten digits. In this code we go through
training to work with function that checks accuracy as well. how to create the network as well as initialize a loss function, optimizer,
check accuracy and more.
Video explanation: https://youtu.be/wnK3uWv_WkU Programmed by Aladdin Persson
Got any questions leave a comment on youtube :) * 2020-04-08: Initial coding
* 2021-03-24: More detailed comments and small revision of the code
Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
* 2020-04-08 Initial coding
""" """
# Imports # Imports
import torch import torch
import torch.nn as nn # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions import torchvision # torch package for vision related things
import torch.optim as optim # For all Optimization algorithms, SGD, Adam, etc. import torch.nn.functional as F # Parameterless functions, like (some) activation functions
import torch.nn.functional as F # All functions that don't have any parameters import torchvision.datasets as datasets # Standard datasets
from torch.utils.data import ( import torchvision.transforms as transforms # Transformations we can perform on our dataset for augmentation
DataLoader, from torch import optim # For optimizers like SGD, Adam, etc.
) # Gives easier dataset managment and creates mini batches from torch import nn # All neural network modules
import torchvision.datasets as datasets # Has standard datasets we can import in a nice way from torch.utils.data import DataLoader # Gives easier dataset managment by creating mini batches etc.
import torchvision.transforms as transforms # Transformations we can perform on our dataset from tqdm import tqdm # For nice progress bar!
# Simple CNN # Simple CNN
class CNN(nn.Module): class CNN(nn.Module):
def __init__(self, in_channels=1, num_classes=10): def __init__(self, in_channels=1, num_classes=10):
super(CNN, self).__init__() super(CNN, self).__init__()
self.conv1 = nn.Conv2d( self.conv1 = nn.Conv2d(
in_channels=1, in_channels=in_channels,
out_channels=8, out_channels=8,
kernel_size=(3, 3), kernel_size=(3, 3),
stride=(1, 1), stride=(1, 1),
@@ -51,7 +50,6 @@ class CNN(nn.Module):
x = self.pool(x) x = self.pool(x)
x = x.reshape(x.shape[0], -1) x = x.reshape(x.shape[0], -1)
x = self.fc1(x) x = self.fc1(x)
return x return x
@@ -59,24 +57,20 @@ class CNN(nn.Module):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Hyperparameters # Hyperparameters
in_channel = 1 in_channels = 1
num_classes = 10 num_classes = 10
learning_rate = 0.001 learning_rate = 0.001
batch_size = 64 batch_size = 64
num_epochs = 5 num_epochs = 3
# Load Data # Load Data
train_dataset = datasets.MNIST( train_dataset = datasets.MNIST(root="dataset/", train=True, transform=transforms.ToTensor(), download=True)
root="dataset/", train=True, transform=transforms.ToTensor(), download=True test_dataset = datasets.MNIST(root="dataset/", train=False, transform=transforms.ToTensor(), download=True)
)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_dataset = datasets.MNIST(
root="dataset/", train=False, transform=transforms.ToTensor(), download=True
)
test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True) test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)
# Initialize network # Initialize network
model = CNN().to(device) model = CNN(in_channels=in_channels, num_classes=num_classes).to(device)
# Loss and optimizer # Loss and optimizer
criterion = nn.CrossEntropyLoss() criterion = nn.CrossEntropyLoss()
@@ -84,7 +78,7 @@ optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# Train Network # Train Network
for epoch in range(num_epochs): for epoch in range(num_epochs):
for batch_idx, (data, targets) in enumerate(train_loader): for batch_idx, (data, targets) in enumerate(tqdm(train_loader)):
# Get data to cuda if possible # Get data to cuda if possible
data = data.to(device=device) data = data.to(device=device)
targets = targets.to(device=device) targets = targets.to(device=device)
@@ -101,14 +95,7 @@ for epoch in range(num_epochs):
optimizer.step() optimizer.step()
# Check accuracy on training & test to see how good our model # Check accuracy on training & test to see how good our model
def check_accuracy(loader, model): def check_accuracy(loader, model):
if loader.dataset.train:
print("Checking accuracy on training data")
else:
print("Checking accuracy on test data")
num_correct = 0 num_correct = 0
num_samples = 0 num_samples = 0
model.eval() model.eval()
@@ -123,12 +110,10 @@ def check_accuracy(loader, model):
num_correct += (predictions == y).sum() num_correct += (predictions == y).sum()
num_samples += predictions.size(0) num_samples += predictions.size(0)
print(
f"Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}"
)
model.train() model.train()
return num_correct/num_samples
check_accuracy(train_loader, model) print(f"Accuracy on training set: {check_accuracy(train_loader, model)*100:.2f}")
check_accuracy(test_loader, model) print(f"Accuracy on test set: {check_accuracy(test_loader, model)*100:.2f}")

86
ML/Pytorch/Basics/pytorch_simple_fullynet.py
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@@ -1,60 +1,69 @@
""" """
Working code of a simple Fully Connected (FC) network training on MNIST dataset. A simple walkthrough of how to code a fully connected neural network
The code is intended to show how to create a FC network as well using the PyTorch library. For demonstration we train it on the very
as how to initialize loss, optimizer, etc. in a simple way to get common MNIST dataset of handwritten digits. In this code we go through
training to work with function that checks accuracy as well. how to create the network as well as initialize a loss function, optimizer,
check accuracy and more.
Video explanation: https://youtu.be/Jy4wM2X21u0 Programmed by Aladdin Persson
Got any questions leave a comment on youtube :) * 2020-04-08: Initial coding
* 2021-03-24: Added more detailed comments also removed part of
Programmed by Aladdin Persson <aladdin.persson at hotmail dot com> check_accuracy which would only work specifically on MNIST.
* 2020-04-08 Initial coding
""" """
# Imports # Imports
import torch import torch
import torchvision import torchvision # torch package for vision related things
import torch.nn as nn # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions import torch.nn.functional as F # Parameterless functions, like (some) activation functions
import torch.optim as optim # For all Optimization algorithms, SGD, Adam, etc. import torchvision.datasets as datasets # Standard datasets
import torch.nn.functional as F # All functions that don't have any parameters import torchvision.transforms as transforms # Transformations we can perform on our dataset for augmentation
from torch.utils.data import ( from torch import optim # For optimizers like SGD, Adam, etc.
DataLoader, from torch import nn # All neural network modules
) # Gives easier dataset managment and creates mini batches from torch.utils.data import DataLoader # Gives easier dataset managment by creating mini batches etc.
import torchvision.datasets as datasets # Has standard datasets we can import in a nice way from tqdm import tqdm # For nice progress bar!
import torchvision.transforms as transforms # Transformations we can perform on our dataset
# Create Fully Connected Network # Here we create our simple neural network. For more details here we are subclassing and
# inheriting from nn.Module, this is the most general way to create your networks and
# allows for more flexibility. I encourage you to also check out nn.Sequential which
# would be easier to use in this scenario but I wanted to show you something that
# "always" works.
class NN(nn.Module): class NN(nn.Module):
def __init__(self, input_size, num_classes): def __init__(self, input_size, num_classes):
super(NN, self).__init__() super(NN, self).__init__()
# Our first linear layer take input_size, in this case 784 nodes to 50
# and our second linear layer takes 50 to the num_classes we have, in
# this case 10.
self.fc1 = nn.Linear(input_size, 50) self.fc1 = nn.Linear(input_size, 50)
self.fc2 = nn.Linear(50, num_classes) self.fc2 = nn.Linear(50, num_classes)
def forward(self, x): def forward(self, x):
"""
x here is the mnist images and we run it through fc1, fc2 that we created above.
we also add a ReLU activation function in between and for that (since it has no parameters)
I recommend using nn.functional (F)
"""
x = F.relu(self.fc1(x)) x = F.relu(self.fc1(x))
x = self.fc2(x) x = self.fc2(x)
return x return x
# Set device # Set device cuda for GPU if it's available otherwise run on the CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Hyperparameters # Hyperparameters of our neural network which depends on the dataset, and
# also just experimenting to see what works well (learning rate for example).
input_size = 784 input_size = 784
num_classes = 10 num_classes = 10
learning_rate = 0.001 learning_rate = 0.001
batch_size = 64 batch_size = 64
num_epochs = 1 num_epochs = 3
# Load Data # Load Training and Test data
train_dataset = datasets.MNIST( train_dataset = datasets.MNIST(root="dataset/", train=True, transform=transforms.ToTensor(), download=True)
root="dataset/", train=True, transform=transforms.ToTensor(), download=True test_dataset = datasets.MNIST(root="dataset/", train=False, transform=transforms.ToTensor(), download=True)
)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_dataset = datasets.MNIST(
root="dataset/", train=False, transform=transforms.ToTensor(), download=True
)
test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True) test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)
# Initialize network # Initialize network
@@ -66,7 +75,7 @@ optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# Train Network # Train Network
for epoch in range(num_epochs): for epoch in range(num_epochs):
for batch_idx, (data, targets) in enumerate(train_loader): for batch_idx, (data, targets) in enumerate(tqdm(train_loader)):
# Get data to cuda if possible # Get data to cuda if possible
data = data.to(device=device) data = data.to(device=device)
targets = targets.to(device=device) targets = targets.to(device=device)
@@ -85,15 +94,9 @@ for epoch in range(num_epochs):
# gradient descent or adam step # gradient descent or adam step
optimizer.step() optimizer.step()
# Check accuracy on training & test to see how good our model # Check accuracy on training & test to see how good our model
def check_accuracy(loader, model): def check_accuracy(loader, model):
if loader.dataset.train:
print("Checking accuracy on training data")
else:
print("Checking accuracy on test data")
num_correct = 0 num_correct = 0
num_samples = 0 num_samples = 0
model.eval() model.eval()
@@ -109,12 +112,9 @@ def check_accuracy(loader, model):
num_correct += (predictions == y).sum() num_correct += (predictions == y).sum()
num_samples += predictions.size(0) num_samples += predictions.size(0)
print(
f"Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}"
)
model.train() model.train()
return num_correct/num_samples
check_accuracy(train_loader, model) print(f"Accuracy on training set: {check_accuracy(train_loader, model)*100:.2f}")
check_accuracy(test_loader, model) print(f"Accuracy on test set: {check_accuracy(test_loader, model)*100:.2f}")

3
ML/Pytorch/Basics/pytorch_tensorbasics.py
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@@ -11,6 +11,9 @@ But also other things such as setting the device (GPU/CPU) and converting
between different types (int, float etc) and how to convert a tensor to an between different types (int, float etc) and how to convert a tensor to an
numpy array and vice-versa. numpy array and vice-versa.
Programmed by Aladdin Persson
* 2020-06-27: Initial coding
""" """
import torch import torch

2
ML/Pytorch/CNN_architectures/pytorch_inceptionet.py
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@@ -11,7 +11,7 @@ Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
# Imports # Imports
import torch import torch
import torch.nn as nn # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions from torch import nn
class GoogLeNet(nn.Module): class GoogLeNet(nn.Module):

28
README.md
View File

@@ -38,22 +38,22 @@ If you have any specific video suggestion please make a comment on YouTube :)
### Basics ### Basics
* [![Youtube Link][logo]](https://youtu.be/x9JiIFvlUwk) &nbsp; [Tensor Basics](https://github.com/AladdinPerzon/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_tensorbasics.py) * [![Youtube Link][logo]](https://youtu.be/x9JiIFvlUwk) &nbsp; [Tensor Basics](https://github.com/AladdinPerzon/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_tensorbasics.py)
* [![Youtube Link][logo]](https://youtu.be/Jy4wM2X21u0) &nbsp; [Feedforward Neural Network](https://github.com/AladdinPerzon/Machine-Learning-Collection/blob/804c45e83b27c59defb12f0ea5117de30fe25289/ML/Pytorch/Basics/pytorch_simple_fullynet.py#L26-L35) * [![Youtube Link][logo]](https://youtu.be/Jy4wM2X21u0) &nbsp; [Feedforward Neural Network](https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_simple_fullynet.py)
* [![Youtube Link][logo]](https://youtu.be/wnK3uWv_WkU) &nbsp; [Convolutional Neural Network](https://github.com/AladdinPerzon/Machine-Learning-Collection/blob/157a5f458f272a513eb6b4a19d6613aec32dc21c/ML/Pytorch/Basics/pytorch_simple_CNN.py#L25-L41) * [![Youtube Link][logo]](https://youtu.be/wnK3uWv_WkU) &nbsp; [Convolutional Neural Network](https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_simple_CNN.py)
* [![Youtube Link][logo]](https://youtu.be/Gl2WXLIMvKA) &nbsp; [Recurrent Neural Network](https://github.com/AladdinPerzon/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_rnn_gru_lstm.py) * [![Youtube Link][logo]](https://youtu.be/Gl2WXLIMvKA) &nbsp; [Recurrent Neural Network](https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_rnn_gru_lstm.py)
* [![Youtube Link][logo]](https://youtu.be/jGst43P-TJA) &nbsp; [Bidirectional Recurrent Neural Network](https://github.com/AladdinPerzon/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_bidirectional_lstm.py) * [![Youtube Link][logo]](https://youtu.be/jGst43P-TJA) &nbsp; [Bidirectional Recurrent Neural Network](https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_bidirectional_lstm.py)
* [![Youtube Link][logo]](https://youtu.be/g6kQl_EFn84) &nbsp; [Loading and saving model](https://github.com/AladdinPerzon/Machine-Learning-Collection/blob/804c45e83b27c59defb12f0ea5117de30fe25289/ML/Pytorch/Basics/pytorch_loadsave.py#L26-L34) * [![Youtube Link][logo]](https://youtu.be/g6kQl_EFn84) &nbsp; [Loading and saving model](https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_loadsave.py)
* [![Youtube Link][logo]](https://youtu.be/ZoZHd0Zm3RY) &nbsp; [Custom Dataset (Images)](https://github.com/AladdinPerzon/Machine-Learning-Collection/blob/aba36b89b438ca8f608a186f4d61d1b60c7f24e0/ML/Pytorch/Basics/custom_dataset/custom_dataset.py#L12-L29) * [![Youtube Link][logo]](https://youtu.be/ZoZHd0Zm3RY) &nbsp; [Custom Dataset (Images)](https://github.com/aladdinpersson/Machine-Learning-Collection/tree/master/ML/Pytorch/Basics/custom_dataset)
* [![Youtube Link][logo]](https://youtu.be/9sHcLvVXsns) &nbsp; [Custom Dataset (Text)](https://github.com/AladdinPerzon/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/custom_dataset_txt/loader_customtext.py) * [![Youtube Link][logo]](https://youtu.be/9sHcLvVXsns) &nbsp; [Custom Dataset (Text)](https://github.com/aladdinpersson/Machine-Learning-Collection/tree/master/ML/Pytorch/Basics/custom_dataset_txt)
* [![Youtube Link][logo]](https://youtu.be/qaDe0qQZ5AQ) &nbsp; [Transfer Learning and finetuning](https://github.com/AladdinPerzon/Machine-Learning-Collection/blob/804c45e83b27c59defb12f0ea5117de30fe25289/ML/Pytorch/Basics/pytorch_pretrain_finetune.py#L33-L54) * [![Youtube Link][logo]](https://youtu.be/qaDe0qQZ5AQ) &nbsp; [Transfer Learning and finetuning](https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_pretrain_finetune.py)
* [![Youtube Link][logo]](https://youtu.be/Zvd276j9sZ8) &nbsp; [Data augmentation using Torchvision](https://github.com/AladdinPerzon/Machine-Learning-Collection/blob/804c45e83b27c59defb12f0ea5117de30fe25289/ML/Pytorch/Basics/pytorch_transforms.py#L56-L72) * [![Youtube Link][logo]](https://youtu.be/Zvd276j9sZ8) &nbsp; [Data augmentation using Torchvision](https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_transforms.py)
* [![Youtube Link][logo]](https://youtu.be/rAdLwKJBvPM) &nbsp; [Data augmentation using Albumentations](https://github.com/aladdinpersson/Machine-Learning-Collection/tree/master/ML/Pytorch/Basics/albumentations_tutorial) * [![Youtube Link][logo]](https://youtu.be/rAdLwKJBvPM) &nbsp; [Data augmentation using Albumentations](https://github.com/aladdinpersson/Machine-Learning-Collection/tree/master/ML/Pytorch/Basics/albumentations_tutorial)
* [![Youtube Link][logo]](https://youtu.be/RLqsxWaQdHE) &nbsp; [TensorBoard Example](https://github.com/AladdinPerzon/Machine-Learning-Collection/blob/79f2e1928906f3cccbae6c024f3f79fd05262cd1/ML/Pytorch/Basics/pytorch_tensorboard_.py#L72-L120) * [![Youtube Link][logo]](https://youtu.be/RLqsxWaQdHE) &nbsp; [TensorBoard Example](https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_tensorboard_.py)
* [![Youtube Link][logo]](https://youtu.be/y6IEcEBRZks) &nbsp; [Calculate Mean and STD of Images](https://github.com/AladdinPerzon/Machine-Learning-Collection/blob/55637e6afbb8cc8be6a63e04bbc899704f862911/ML/Pytorch/Basics/pytorch_std_mean.py#L41-L53) * [![Youtube Link][logo]](https://youtu.be/y6IEcEBRZks) &nbsp; [Calculate Mean and STD of Images](https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_std_mean.py)
* [![Youtube Link][logo]](https://youtu.be/RKHopFfbPao) &nbsp; [Simple Progress bar]() * [![Youtube Link][logo]](https://youtu.be/RKHopFfbPao) &nbsp; [Simple Progress bar](https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_progress_bar.py)
* [![Youtube Link][logo]](https://youtu.be/1SZocGaCAr8) &nbsp; [Deterministic Behavior](https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/set_deterministic_behavior/pytorch_set_seeds.py) * [![Youtube Link][logo]](https://youtu.be/1SZocGaCAr8) &nbsp; [Deterministic Behavior](https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/set_deterministic_behavior/pytorch_set_seeds.py)
* [![Youtube Link][logo]](https://youtu.be/P31hB37g4Ak) &nbsp; [Learning Rate Scheduler](https://github.com/AladdinPerzon/Machine-Learning-Collection/blob/804c45e83b27c59defb12f0ea5117de30fe25289/ML/Pytorch/Basics/pytorch_lr_ratescheduler.py#L45-L78) * [![Youtube Link][logo]](https://youtu.be/P31hB37g4Ak) &nbsp; [Learning Rate Scheduler](https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_lr_ratescheduler.py)
* [![Youtube Link][logo]](https://youtu.be/xWQ-p_o0Uik) &nbsp; [Initialization of weights](https://github.com/AladdinPerzon/Machine-Learning-Collection/blob/804c45e83b27c59defb12f0ea5117de30fe25289/ML/Pytorch/Basics/pytorch_init_weights.py#L35-L49) * [![Youtube Link][logo]](https://youtu.be/xWQ-p_o0Uik) &nbsp; [Initialization of weights](https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_init_weights.py)
### More Advanced ### More Advanced