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keras python

keras python - using models

This beginner level and highly comprehensive course is intended for learners who are familiar with Python programming. You will become familiar with the fundamental concepts and terminologies used in deep learning. In addition, this course will help you understand the importance of deep learning techniques. You will examine simple models like perceptron before learning more complex yet powerful deep learning models. The course will provide hands-on practical knowledge of how to code simple and complex deep learning models in NumPy, a powerful Python library and Keras, a cutting-edge library for deep learning in Python. You can test your knowledge with the quizzes that are provided at the end of every lesson and coding challenges that will help you gain a higher understanding. By the end of the course, you should have a general understanding of the basics in deep learning and you will be equipped with the right tools to learn more advanced concepts.

A Beginner's Guide to Deep Learning

## The evaluate method
Evaluation is a process for checking whether the model is the best fit for the given dataset.  
> 📝 NoteL: Ideally, we make a train and validation split of the dataset. Then, check the accuracy of the model on the validation set to examine how well the model has learned the data representation. However, if we have not made the initial split, check the training accuracy. You will learn about model validation when fine-tuning the model [here](https://www.educative.io/courses/beginners-guide-to-deep-learning/model-validation-i). 

Keras provides the `evaluate` function for model evaluation.

## Arguments for the `evaluate` method

It takes the data and the label.

It returns a list with two values. Namely, loss and the `metrics` defined in the `compile` method. If `metrics` are not defined, it returns only loss.

> 📝 Note: In case of classification, define `metrics=[accuracy]` in the `compile` method. The `evaluate` method thus returns a list with two values.  The first value will be the loss of the model on the dataset and the second will be the accuracy of the model on the dataset. 

> 📝 Note: We are only interested in reporting the accuracy. Ignore the loss value.






# The evaluate method
Evaluation is a process for checking whether the model is the best fit for the given dataset.  
> 📝 NoteL: Ideally, we make a train and validation split of the dataset. Then, check the accuracy of the model on the validation set to examine how well the model has learned the data representation. However, if we have not made the initial split, check the training accuracy. You will learn about model validation when fine-tuning the model [here](https://www.educative.io/courses/beginners-guide-to-deep-learning/model-validation-i). 

Keras provides the `evaluate` function for model evaluation.

# Arguments for the `evaluate` method

It takes the data and the label.

It returns a list with two values. Namely, loss and the `metrics` defined in the `compile` method. If `metrics` are not defined, it returns only loss.

> 📝 Note: In case of classification, define `metrics=[accuracy]` in the `compile` method. The `evaluate` method thus returns a list with two values.  The first value will be the loss of the model on the dataset and the second will be the accuracy of the model on the dataset. 

> 📝 Note: We are only interested in reporting the accuracy. Ignore the loss value.






How can you evaluate your model evaluation using Keras?

✨Before We Begin

✨Introduction to Deep Learning

✨Simple Perceptron Models in NumPY

✨Towards Deep Neural Networks in NumPY

🖥️ Project: Build a Letter Classification Model

Deep Learning in NumPy Exam

✨Building Deep Learning Models with Keras

✨Fine-tune Keras Model

🖥️ Project: Build a Digit Recognition Model

✨Conclusion

Deep Learning in Keras Exam

Model Evaluation

The evaluate method

Arguments for the `evaluate` method