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TensorFlow 2 Tutorial: Get Started in Deep Learning With tf.keras


You can easily create learning curves for your deep learning models. First, you must update your call to the fit function to include reference to a validation dataset. This is a portion of the training set not used to fit the model, and is instead used to evaluate the performance of the model during training.

With Keras' Functional API, Your Imagination is the Limit


Most people are familiar with building sequential models, in which layers follow each other one by one. For instance, in a convolutional neural network, we may decide to pass images through a convolutional layer, a max pooling layer, a flattening layer, then a dense layer. These standard constructions of networks are known as'linear topologies'. However, many high-performing networks are not linear topologies, for example the Inception module, core to the top Inception model. In the module, an input from one layer is passed into four separate layers, which are concatenated back into one output layer.

5 Step Life-Cycle for Neural Network Models in Keras - Machine Learning Mastery


Deep learning neural networks are very easy to create and evaluate in Python with Keras, but you must follow a strict model life-cycle. In this post you will discover the step-by-step life-cycle for creating, training and evaluating deep learning neural networks in Keras and how to make predictions with a trained model. Deep Learning Neural Network Life-Cycle in Keras Photo by Martin Stitchener, some rights reserved. Below is an overview of the 5 steps in the neural network model life-cycle in Keras that we are going to look at. Deep Learning gets state-of-the-art results and Python hosts the most powerful tools.

How to Develop a Bidirectional LSTM For Sequence Classification in Python with Keras - Machine Learning Mastery


Bidirectional LSTMs are an extension of traditional LSTMs that can improve model performance on sequence classification problems. In problems where all timesteps of the input sequence are available, Bidirectional LSTMs train two instead of one LSTMs on the input sequence. The first on the input sequence as-is and the second on a reversed copy of the input sequence. This can provide additional context to the network and result in faster and even fuller learning on the problem. In this tutorial, you will discover how to develop Bidirectional LSTMs for sequence classification in Python with the Keras deep learning library.