Finite

Continuing from the previous post(172_Time Series Forecasting with LSTMs). def train_model( model, train_data, train_labels, test_data=None, test_labels=None): loss_fn = torch.nn.MSELoss(reduction='sum') optimiser = torch.optim.Adam(model.parameters(), lr=1e-3) num_epochs = 60 train_hist = np.zeros(num_epochs) test_hist = np.zeros(num_epochs) for t in range(num_epochs): model.r..

Time series data typically captures a series of data points recorded at consistent intervals. When dealing with such time series data, Long Short-Term Memory (LSTM) models have become a highly useful and widely adopted approach. LSTM, a type of recurrent neural network (RNN), is effective for processing data sequences. In this blog post, we will explore how to use LSTM to predict future coronavi..

Continuing from the previous post(170_Image Classification using Torchvision(3)). def show_confusion_matrix(confusion_matrix, class_names): cm = confusion_matrix.copy() cell_counts = cm.flatten() cm_row_norm = cm / cm.sum(axis=1)[:, np.newaxis] row_percentages = ["{0:.2f}".format(value) for value in cm_row_norm.flatten()] cell_labels = [f"{cnt}\n{per}" for cnt, per in zip(cell_cou..

Continuing from the previous post(169_Image Classification using Torchvision(2)). %%timebase_model, history = train_model(base_model, data_loaders, dataset_sizes, device)  To make the results easier to interpret, add a visualization function. def plot_training_history(history): fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 6)) ax1.plot(history['train_loss'], label='train loss') ax1.plot(h..

Continuing from the previous post(168_Image Classification using Torchvision). image_datasets = { d: ImageFolder(f'{DATA_DIR}/{d}', transforms[d]) for d in DATASETS}data_loaders = { d: DataLoader(image_datasets[d], batch_size=4, shuffle=True, num_workers=4) for d in DATASETS} Here, to facilitate easier training, PyTorch datasets and data loaders are created for each image dataset folder. Dat..

I found an example of image classification using transfer learning, and I plan to follow the code to study it. The reference book is mentioned at the end of this post. The explanations are detailed, making it a good resource for studying. First, the required libraries are as follows. import torch, torchvisionfrom pathlib import Pathimport numpy as npimport cv2import pandas as pdfrom tqdm import ..

Continuing from the last posting, I will write the code and provide explanations. def calculate_accuracy(y_true, y_pred): predicted = y_pred.ge(.5).view(-1) return (y_true == predicted).sum().float() / len(y_true)def round_tensor(t, decimal_places = 3): return round(t.item(), decimal_places) The calculate_accuracy function calculates and returns the model's prediction accuracy by determ..

Today, I read a book that is good for studying by following along, and it contains detailed code, so I plan to follow and analyze the code. So far, I have dealt with classification problems a lot, but now I am going to implement a simple regression model using Pytorch. The required libraries are as follows. import torchimport numpy as npimport pandas as pdimport seaborn as snsfrom pylab import r..

If a model is too complex relative to the given training dataset, it tends to overfit the training data and may not generalize well to unseen data. To reduce the degree of overfitting, collecting more training examples can be beneficial. However, in many real-world scenarios, collecting more data is often very challenging. By plotting the model's training and validation accuracy as a function of..

Various methods exist for general cross-validation techniques, such as hold-out cross-validation and k-fold cross-validation. These techniques help to reliably estimate the generalization performance of a model, that is, how well the model performs on unseen data.  A classical and popular approach to estimate the generalization performance of machine learning models is the hold-out method. The i..