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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
parent cb19107179 37762890f0
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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
import torch
import torchvision
import torch.nn as nn # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions
import torch.optim as optim # For all Optimization algorithms, SGD, Adam, etc.
import torch.nn.functional as F # All functions that don't have any parameters
from torch.utils.data import (
DataLoader,
) # Gives easier dataset managment and creates mini batches
import torchvision.datasets as datasets # Has standard datasets we can import in a nice way
import torchvision.transforms as transforms # Transformations we can perform on our dataset
import torchvision # torch package for vision related things
import torch.nn.functional as F # Parameterless functions, like (some) activation functions
import torchvision.datasets as datasets # Standard datasets
import torchvision.transforms as transforms # Transformations we can perform on our dataset for augmentation
from torch import optim # For optimizers like SGD, Adam, etc.
from torch import nn # All neural network modules
from torch.utils.data import DataLoader # Gives easier dataset managment by creating mini batches etc.
from tqdm import tqdm # For a nice progress bar!
# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -29,7 +28,7 @@ num_classes = 10
sequence_length = 28
learning_rate = 0.005
batch_size = 64
num_epochs = 2
num_epochs = 3
# Recurrent neural network (many-to-one)
class RNN(nn.Module):
@@ -101,18 +100,12 @@ class RNN_LSTM(nn.Module):
# Load Data
train_dataset = datasets.MNIST(
root="dataset/", train=True, transform=transforms.ToTensor(), download=True
)
test_dataset = datasets.MNIST(
root="dataset/", train=False, transform=transforms.ToTensor(), download=True
)
train_dataset = datasets.MNIST(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)
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)
# Loss and optimizer
@@ -121,7 +114,7 @@ optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# Train Network
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
data = data.to(device=device).squeeze(1)
targets = targets.to(device=device)
@@ -134,16 +127,11 @@ for epoch in range(num_epochs):
optimizer.zero_grad()
loss.backward()
# gradient descent or adam step
# gradient descent update step/adam step
optimizer.step()
# Check accuracy on training & test to see how good our 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_samples = 0
@@ -160,13 +148,10 @@ def check_accuracy(loader, model):
num_correct += (predictions == y).sum()
num_samples += predictions.size(0)
print(
f"Got {num_correct} / {num_samples} with \
accuracy {float(num_correct)/float(num_samples)*100:.2f}"
)
# Set model back to train
# Toggle model back to train
model.train()
return num_correct / num_samples
check_accuracy(train_loader, model)
check_accuracy(test_loader, model)
print(f"Accuracy on training set: {check_accuracy(train_loader, model)*100:2f}")
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.
The code is intended to show how to create a CNN network as well
as how to initialize loss, optimizer, etc. in a simple way to get
training to work with function that checks accuracy as well.
A simple walkthrough of how to code a convolutional neural network (CNN)
using the PyTorch library. For demonstration we train it on the very
common MNIST dataset of handwritten digits. In this code we go through
how to create the network as well as initialize a loss function, optimizer,
check accuracy and more.
Video explanation: https://youtu.be/wnK3uWv_WkU
Got any questions leave a comment on youtube :)
Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
* 2020-04-08 Initial coding
Programmed by Aladdin Persson
* 2020-04-08: Initial coding
* 2021-03-24: More detailed comments and small revision of the code
"""
# Imports
import torch
import torch.nn as nn # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions
import torch.optim as optim # For all Optimization algorithms, SGD, Adam, etc.
import torch.nn.functional as F # All functions that don't have any parameters
from torch.utils.data import (
DataLoader,
) # Gives easier dataset managment and creates mini batches
import torchvision.datasets as datasets # Has standard datasets we can import in a nice way
import torchvision.transforms as transforms # Transformations we can perform on our dataset
import torchvision # torch package for vision related things
import torch.nn.functional as F # Parameterless functions, like (some) activation functions
import torchvision.datasets as datasets # Standard datasets
import torchvision.transforms as transforms # Transformations we can perform on our dataset for augmentation
from torch import optim # For optimizers like SGD, Adam, etc.
from torch import nn # All neural network modules
from torch.utils.data import DataLoader # Gives easier dataset managment by creating mini batches etc.
from tqdm import tqdm # For nice progress bar!
# Simple CNN
class CNN(nn.Module):
def __init__(self, in_channels=1, num_classes=10):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(
in_channels=1,
in_channels=in_channels,
out_channels=8,
kernel_size=(3, 3),
stride=(1, 1),
@@ -51,7 +50,6 @@ class CNN(nn.Module):
x = self.pool(x)
x = x.reshape(x.shape[0], -1)
x = self.fc1(x)
return x
@@ -59,24 +57,20 @@ class CNN(nn.Module):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Hyperparameters
in_channel = 1
in_channels = 1
num_classes = 10
learning_rate = 0.001
batch_size = 64
num_epochs = 5
num_epochs = 3
# Load Data
train_dataset = datasets.MNIST(
root="dataset/", train=True, transform=transforms.ToTensor(), download=True
)
train_dataset = datasets.MNIST(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)
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)
# Initialize network
model = CNN().to(device)
model = CNN(in_channels=in_channels, num_classes=num_classes).to(device)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
@@ -84,7 +78,7 @@ optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# Train Network
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
data = data.to(device=device)
targets = targets.to(device=device)
@@ -101,14 +95,7 @@ for epoch in range(num_epochs):
optimizer.step()
# Check accuracy on training & test to see how good our 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_samples = 0
model.eval()
@@ -123,12 +110,10 @@ def check_accuracy(loader, model):
num_correct += (predictions == y).sum()
num_samples += predictions.size(0)
print(
f"Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}"
)
model.train()
return num_correct/num_samples
check_accuracy(train_loader, model)
check_accuracy(test_loader, model)
print(f"Accuracy on training set: {check_accuracy(train_loader, model)*100:.2f}")
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.
The code is intended to show how to create a FC network as well
as how to initialize loss, optimizer, etc. in a simple way to get
training to work with function that checks accuracy as well.
A simple walkthrough of how to code a fully connected neural network
using the PyTorch library. For demonstration we train it on the very
common MNIST dataset of handwritten digits. In this code we go through
how to create the network as well as initialize a loss function, optimizer,
check accuracy and more.
Video explanation: https://youtu.be/Jy4wM2X21u0
Got any questions leave a comment on youtube :)
Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
* 2020-04-08 Initial coding
Programmed by Aladdin Persson
* 2020-04-08: Initial coding
* 2021-03-24: Added more detailed comments also removed part of
check_accuracy which would only work specifically on MNIST.
"""
# Imports
import torch
import torchvision
import torch.nn as nn # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions
import torch.optim as optim # For all Optimization algorithms, SGD, Adam, etc.
import torch.nn.functional as F # All functions that don't have any parameters
from torch.utils.data import (
DataLoader,
) # Gives easier dataset managment and creates mini batches
import torchvision.datasets as datasets # Has standard datasets we can import in a nice way
import torchvision.transforms as transforms # Transformations we can perform on our dataset
import torchvision # torch package for vision related things
import torch.nn.functional as F # Parameterless functions, like (some) activation functions
import torchvision.datasets as datasets # Standard datasets
import torchvision.transforms as transforms # Transformations we can perform on our dataset for augmentation
from torch import optim # For optimizers like SGD, Adam, etc.
from torch import nn # All neural network modules
from torch.utils.data import DataLoader # Gives easier dataset managment by creating mini batches etc.
from tqdm import tqdm # For nice progress bar!
# 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):
def __init__(self, input_size, num_classes):
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.fc2 = nn.Linear(50, num_classes)
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 = self.fc2(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")
# 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
num_classes = 10
learning_rate = 0.001
batch_size = 64
num_epochs = 1
num_epochs = 3
# Load Data
train_dataset = datasets.MNIST(
root="dataset/", train=True, transform=transforms.ToTensor(), download=True
)
# Load Training and Test data
train_dataset = datasets.MNIST(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)
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)
# Initialize network
@@ -66,7 +75,7 @@ optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# Train Network
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
data = data.to(device=device)
targets = targets.to(device=device)
@@ -85,15 +94,9 @@ for epoch in range(num_epochs):
# gradient descent or adam step
optimizer.step()
# Check accuracy on training & test to see how good our 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_samples = 0
model.eval()
@@ -109,12 +112,9 @@ def check_accuracy(loader, model):
num_correct += (predictions == y).sum()
num_samples += predictions.size(0)
print(
f"Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}"
)
model.train()
return num_correct/num_samples
check_accuracy(train_loader, model)
check_accuracy(test_loader, model)
print(f"Accuracy on training set: {check_accuracy(train_loader, model)*100:.2f}")
print(f"Accuracy on test set: {check_accuracy(test_loader, model)*100:.2f}")

3
ML/Pytorch/Basics/pytorch_tensorbasics.py
View File

@@ -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
numpy array and vice-versa.
Programmed by Aladdin Persson
* 2020-06-27: Initial coding
"""
import torch

2
ML/Pytorch/CNN_architectures/pytorch_inceptionet.py
View File

@@ -11,7 +11,7 @@ Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
# Imports
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):

28
README.md
View File

@@ -38,22 +38,22 @@ If you have any specific video suggestion please make a comment on YouTube :)
### 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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/RKHopFfbPao) &nbsp; [Simple Progress bar]()
* [![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/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_std_mean.py)
* [![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/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/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/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/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_init_weights.py)
### More Advanced