How to save and reload a deep learning model in Pytorch?
MACHINE LEARNING RECIPES DATA CLEANING PYTHON DATA MUNGING PANDAS CHEATSHEET     ALL TAGS

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.

6

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. torch.save: 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

Machine Learning or Predictive Models in IoT - Energy Prediction Use Case
In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.

Forecast Inventory demand using historical sales data in R
In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.

Build a Collaborative Filtering Recommender System in Python
Use the Amazon Reviews/Ratings dataset of 2 Million records to build a recommender system using memory-based collaborative filtering in Python.

Perform Time series modelling using Facebook Prophet
In this project, we are going to talk about Time Series Forecasting to predict the electricity requirement for a particular house using Prophet.

Loan Eligibility Prediction using Gradient Boosting Classifier
This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.

Predict Credit Default | Give Me Some Credit Kaggle
In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.

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.

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.

Natural language processing Chatbot application using NLTK for text classification
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.

Data Science Project - Instacart Market Basket Analysis
Data Science Project - Build a recommendation engine which will predict the products to be purchased by an Instacart consumer again.