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VOOZH | about |
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VOOZH | about |
Keras is an open-source high-level deep learning API, developed by Google and aimed at easy development of building and training neural networks. It's written in Python and has a simple user interface for both beginners and experienced people in building deep learning models with less code.
Table of Content
Keras is a deep learning high-level library developed in Python which facilitates easy implementation of neural network building and training. A common backbone for powerful computational facilities such as TensorFlow, Theano, or Microsoft Cognitive Toolkit (CNTK); hence are used widely listed by both novice and experienced developers in this sector of artificial intelligence.
The Keras framework allows the designing and customizing of neural networks easier as it encompasses simple to access pre-built components, layers, optimizers, activation functions and loss functions. A wide range of applications from image recognition and natural language processing to time-series forecasting is supported and facilitated through its module structure allowing working with several backends. It is one of the most powerful and versatile tools available for deep learning research and development.
You can install Keras using eitherpip or conda. Here are the steps for both:
Check Python Version: Make sure you have Python 3.6 or later installed.
Upgrade pip: It is always a good idea to update pip to the latest version.
Install Keras: Use the following command to install Keras.
Create a Conda Environment: This will help in managing dependencies and avoid conflicts.
Activate the Environment:
Install Keras:
Create a Virtual Environment: It is recommended to use virtual environments to avoid package conflicts.
2.1 for Linux/MAC
2.2 for Windows
Install Dependencies: Keras makes use of several libraries, such as NumPy and TensorFlow. They install themselves by default with pip. In case you intend to install them manually.
Confirm the Installation: You have just installed Keras. You can now verify that it installed correctly by opening up a Python shell and importing Keras.
Configuration of Backend: Keras might need configuration for backend which depends on the optional choice but is usually the setting for TensorFlow.
Keras allows creating neural networks through its Sequential API in a very intuitive and easy manner. The guide below explains the process of developing a neural network adding the layers such as Dense, Conv2D and LSTM.
The Sequential API provides the possibility to create a model by stacking layers linearly. This is specially useful for constructing simple feedforward networks where each layer has one input and one output.
You can add various types of layers to your model using the add() method. Here's how you could add different types of layers:
4.1 Dense Layer: A fully connected layer is typically used in feedforward networks.
4.2 Conv2D Layer: Convolutional layer for image data processing.
4.3 LSTM Layer: Recurrent layer for sequence prediction purposes.
4.4 Flatten Layer: Flattens the input to a multi-dimensional tensor into a single dimension.
4.5 Dropout Layer: A regularization technique that randomly sets a fraction of input units to 0 during training to prevent overfitting.
Compiling a model in Keras is the final preparation for training the model. It's during this stage that you would specify the optimizer, loss function and the metrics used for evaluating the model's performance.
An optimizer is an algorithm that changes the weights of the network according to the gradients computed during backpropagation. Some of the commonly used built-in optimizers available in Keras include:
The loss function measures how well the predictions of the model match the true target values. Keras has several loss functions depending on the type of problem:
Metrics measure the performance of your model when training and testing. The following are some of the most commonly used metrics:
Training a model in Keras is done with the 'model.fit()' method, which is necessary for weight optimization based on the training data.
The main function to train a Keras model is `model.fit()`. It accepts the training data and labels and iteratively adjusts the model weights based on the loss calculated from predictions.
9.1 Batch Size: The number of samples to be processed before the model weights are updated. Lower batch sizes increase the number of updates but tend to increase the training time since there are more iterations. Common values are 16, 32, or 64.
9.2 Epochs: An epoch refers to the total number of passes over all the samples in the training dataset. A higher number of epochs results in a higher amount of learning from the data but can lead to overfitting if too high without proper regularization or monitoring.
Keras allows the model to be evaluated after it has been trained so as to know how well it generalizes to previously unseen data. In Keras, this can be done by calling the method 'model.evaluate()', which gives some insight into the accuracy and loss of the model on a subset of the data.
The function 'model.evaluate()' computes the losses and any other metrics specified for a model given input data. It's usually applied after training to assess the model's performance on a certain validation or test data set.
Evaluation metrics provide some quantitative measure of how good the model's performance is on the data test. Common metrics include the following:
11.1 Loss: A measure of how well model predictions match true labels. Examples include: Mean Squared Error for regression tasks, Categorical Crossentropy for multi-class classification.
11.2 Accuracy: The proportion of correct predictions to all predictions on the test set. Mainly applied in classification tasks.
11.3 Precision and Recall: Applicable for imbalanced datasets where there is a need to understand the true positive rate and false positive rate.
Using trained Keras models to make predictions is very easy and its operation is mainly based on the applied methodology 'model.predict()' and its application. This function can generate outputs for the new or unseen data based on the parameters learned by the model.
The function 'model.predict()' is used to get overall predictions from the model. It takes input data and gives the output predicted by the model depending on the type of task being performed for example, classification or regression.
The output of 'model.predict()' will depend on the nature of your model:
13.1 In the case of classification: The output is generally attributes that represent probabilities for each class if one uses a model for classification. For example, in a multi-class classification problem the output and information returned could be 2D arrays in which each row corresponds to an input sample and each column corresponds to a class. These can be picked up as class labels which could be carried out with the function np.argmax().
13.2 In case of regression: If one works with a regression task using his or her model the outputs would be the continuous values for each of their predictions. The responses can be interpreted one-on-one as predictions the model has given.
Keras includes easy methods for saving and loading models which are crucial for saving the architecture, weights and training configuration of the trained model. This allows the user to resume training or make predictions without having to train the model from scratch.
The `model.save()` method saves the entire model architecture, weights, and training configuration. Saving of models can be done in several formats with the default being the Keras v3 format with the .keras extension.
To load a saved model the `tf.keras.models.load_model()` function needs to be used. It reads the model from the specified file and makes it ready for inference or further training.
Callbacks are the most powerful feature provided by Keras to customize and modify the way a model behaves during training, evaluation or inference. They allow one to perform actions at different stages of the training process like beginning or ending an epoch before or after the processing of a batch, etc.
Early Stopping is a technique where training is stopped once the model stops improving on a validation dataset to prevent its degrees of freedom from wandering too far away. It allows you to implement early stopping using the EarlyStopping callback.
Model Checkpoints allow you to save a model at predetermined intervals during training. This can help in preserving the best version of your model based on validation performance.
TensorBoard is an interesting visualization tool that gains insight into your training process. You can log metrics and visualize them through TensorBoard.
Data pre-processing is the most important step while setting up data for model training. In constant use with augmenting image datasets and normalizing input data, Keras provides us with several tools and layers to make the task easier.
Normalization is the operation of scaling input features to have a mean of 0 and a standard deviation of 1 in order to speed converging neural networks. Keras includes a built-in normalization layer that can be added to your model in a seamless way.
Data augmentation is a strategy to artificially enlarge the training dataset-size with modified versions of the images available in the dataset. This facilitates model generalization and avoids overfitting. Keras offers several built-in layers for image augmentation meant to be applied quickly in your model.
One important method of assessing the performance of a trained Keras model is `model.evaluate()`: it meets the requirements needed to compute loss and other specified metrics for the trained model based on a given dataset usually validation or testing ones.
Keras employs another function known as 'model.predict()' to predict new data. This function provides output predictions for input samples thus no loss or other metric is calculated. In other words, it differs from evaluation in the following manner
Hyperparameter tuning is central to enhancing the overall performance of machine learning models. The Keras Tuner library does this easily in Keras.
23.1 Random Search: Random Search works by making random sampling of hyperparameter combinations over a predefined search space. It is easy to implement and works quite well, especially in cases where the search space is large.
23.2 Hyperband: Hyperband is an efficient algorithm combining random search and adaptive resource allocation. It trains a number of models with different hyperparameters for some epochs and discards all but the top-performing models to continue their training.
23.3 Bayesian Optimization:This uses probabilistic models to find optimal hyperparameters by modeling the model's performance as a function of the hyperparameters. It is more effective than random search and usually converges faster toward optimal values.
23.4 Sklearn Tuner
This binds in with Scikit-learn's parameter tuning abilities. Useful for users already acquainted with the Scikit-learn API.
An imperative aspect of Keras is saving and loading models for future inference, deployments and resuming training. While saving models in Keras, they could either be saved entirely or only their weights, with each styling alternate handled based on various concerns.
24.1 Saving the Entire Model
Calling 'model.save(filepath)' saves the complete model which contains:
A saved model can be loaded by typing 'keras.models.load_model(filepath)'. This way, the same architecture, weights and the training configuration of the model are reinstantiated.
Save only the model weights by calling 'model.save_weights(filepath)' and not saving the architecture or state of the optimizer. This is useful for when fine-tuning is to be done or weights are to be loaded into a model with similar architecture.
When loading weights into your model, first define your model architecture, then type' model.load_weights(filepath)'.
The importance of efficient debugging and performance profiling is crucial in optimizing model training in Keras. Here are key aspects of profiling training and debugging common issues.
Profiling monitors and optimizes the performance of model training and analyses resource consumption and execution time. Keras provides several tools for profiling:
28.1 TensorBoard Profiler: TensorBoard visually renders the performance of Keras models during training. TensorBoard callbacks integrate with the model to capture various metrics regarding the training process. To use TensorBoard for profiling, include the following code in your training script.
28.2 Cloud Profiler: If you are using Google Cloud's Vertex AI, you can run Cloud Profiler to monitor model training performance. This tool helps understand resource consumption and optimizes operations during training.
28.3 Keras Tuner: The Keras Tuner tool does hyperparameter tuning, while also helping you view how the performance is affected during training by the different hyperparameter settings.
In case of unexpected behavior or performance problems during model training, debugging is crucial. Here are some common debugging strategies:
29.1 Check Data Pipeline: The data must be correctly preprocessed and fed into the model. The common associated issues would be incorrect shapes or data types. Assert or print statements can be used effectively to verify the dimensions of your input data and labels.
29.2 Monitor Training Metrics: With callbacks, you can track metrics like loss and accuracy during training. If these do not improve over epochs, problems with model architecture or the learning rate may need to be addressed.
29.3 Learning Rate Adjustment: If the model fails to converge further the learning rate or downsize it. A high learning rate chasing after lowest error could diverge the model because the updates are too large for convergence while too low a learning rate would cause the slowest manner of convergence.
29.4 Overfitting and Underfitting: Look for overfitting (high training accuracy and low validation accuracy) or underfitting (low accuracy both ways). Techniques like dropout layers, regularization, or increasing model complexity will help with that.
29.5 Debugging Tools: Use TF Debugger individual curve debugging from IDEs walk through code and inspect variables during runtime.
