How to save and reload a deep learning model in Pytorch?

How to save and reload a deep learning model in Pytorch?

How to save and reload a deep learning model in Pytorch?

This Pytorch recipe provides you a solution for saving and loading Pytorch models - entire models or just the parameters.

This recipe provides options to save and reload an entire model or just the parameters of the model. While reloading this recipe copies the parameter from 1 net to another net. There are 3 main functions involved in saving and loading a model in pytorch.

1. This saves a serialized object to disk. It uses python's pickle utility for serialization. Models, tensors and dictionaries can be saved using this function.
2. torch.load: torch.load: Uses pickle's unpickling facilities to deserialize pickled object files to memory. This function also facilitates the device to load the data into.
3. torch.nn.Module.load_state_dict: Loads a model's parameter dictionary using a deserialized state_dict. The learnable parameters (i.e. weights and biases) of an torch.nn.Module model are contained in the model's parameters (accessed with model.parameters()). A state_dict is simply a Python dictionary object that maps each layer to its parameter tensor.

What is PyTorch ?
Pytorch is a Python-based scientific computing package that uses the power of graphics processing units and can replace the numpy library. It is also a very popular deep learning research platform built for flexibility and speed. You can use other Python packages such as NumPy, SciPy to extend PyTorch functionalities.

What is Deep Learning Model ?
Deep learning is a subset of machine learning. Deep learning uses neural networks to make predictions. A neural network takes inputs, which are then processed using hidden layers using weights that are adjusted during training. The model then outputs a prediction.

Relevant Projects

Credit Card Fraud Detection as a Classification Problem
In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.

Build an Image Classifier for Plant Species Identification
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.

Build a Music Recommendation Algorithm using KKBox's Dataset
Music Recommendation Project using Machine Learning - Use the KKBox dataset to predict the chances of a user listening to a song again after their very first noticeable listening event.

Machine Learning project for Retail Price Optimization
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

Ola Bike Rides Request Demand Forecast
Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.

Churn Prediction in Telecom using Machine Learning in R
Estimating churners before they discontinue using a product or service is extremely important. In this ML project, you will develop a churn prediction model in telecom to predict customers who are most likely subject to churn.

Expedia Hotel Recommendations Data Science Project
In this data science project, you will contextualize customer data and predict the likelihood a customer will stay at 100 different hotel groups.

Predict Macro Economic Trends using Kaggle Financial Dataset
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

Abstractive Text Summarization using Transformers-BART Model
Deep Learning Project to implement an Abstractive Text Summarizer using Google's Transformers-BART Model to generate news article headlines.

Customer Churn Prediction Analysis using Ensemble Techniques
In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.