When working on machine learning problems, sometimes you want to construct your own custom loss function(s). This article will introduce abstract Keras backend for that purpose. Keras loss functions From Keras loss documentation, there are several built-in loss functions, e.g. mean_absolute_percentage_error, cosine_proximity, kullback_leibler_divergence etc. When compiling a Keras model, we often...
In part 1, we introduced a simple RNN for time-series data. To continue, this article applies a deep version of RNN on a real dataset to predict monthly milk production. The data Monthly milk production: pounds per cow. Jan 1962 – Dec 1975. You can download the data using this link. Download: CSV file The data contains the production of 168 months (14 years). We will use an RNN to predict...
If you are human and curious about your future, then the recurrent neural network (RNN) is definitely a tool to consider. Part 1 will demonstrate some simple RNNs using TensorFlow 2.0 and Keras functional API. What is RNN An RNN is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence (time series). This...
In TensorFlow website, there is a good example of word embedding implementation with Keras. Nevertheless, we are curious to see how it looks like when implementing word2vec with PURE TensorFlow 2.0. What is CBOW In the previous article, we introduced Word2vec (w2v) with Gensim library. Word2vec consists of two-layer neural networks that are trained to reconstruct linguistic contexts of words. The...
In part 1, we introduced fruit classification with pure python implementation. In this part, we will use the Keras library instead. What is Keras Keras is an open-sourceneural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. Designed to enable fast experimentation...