Python Tutorial: Keras input and dense layers

Key Takeaways

hi I'm Zack Dean mayor and in this course I'll be teaching you advanced deep learning concepts using the Charis functional API you will learn how to build functional Kerris models including advanced topics such as shared layers categorical embeddings multiple inputs and multiple outputs the Charis functional API is extremely simple yet immensely powerful by the end of this class you will build a model that is capable of solving a regression and a classification problem at the same time chapter one is a refresher on building simple models where you will learn how to use the Charis functional API in Chapter two you will build a Karass model with two inputs in Chapter three you will learn how to generalize your two input model to three or more inputs and finally in Chapter four you will build models with multiple outputs that can solve multiple problems you will be using two datasets of college basketball games from American colleges the first data set is from the regular season and has the following data the IDS of the two teams that played whether the first team was home or away whether the first team won or lost the game and by how many points the first team won or lost for the tournament data set you also have the tournament seed which is a pre-tournament ranking for each team these seeds range from 1 to 16 where the best four teams get a seed of one and the worst four teams get a seed of 16 you will use the difference in the two team seeds as an input to your model here are the first five rows of both the data sets you can see that the team variables are encoded as integers and the tournament data set has one additional column the difference between the tournament seats for both teams other than the seed difference the two datasets have identical columns within a given year a team's roster stays relatively constant but between years it can change a lot as seniors graduate and freshmen start therefore for every year each school is given a unique integer ID Terrace models at their simplest are fundamentally composed of two parts an input layer and an output layer to start I'll define a very simple Kerris model which only expects a single input I'll specify this using the input function from the Charis layers module the number of columns in the input is specified using the shape parameter this tells the model how much data to expect note that the shape argument expects a tupple the input function returns a tensor if you print this tensor you'll see that it is a TF tensor object which indicates it is ready to be used by our model as input now that we've defined our input layer let's define the output layer outputs in Kerris are most commonly a single dense layer which specifies the shape of the expected output in this case we are expecting our model to predict a single value so we pass one unit to the dense layer if you print the output layer the result is not a tensorflow tensor it is a function which takes a tensor as input and produces a tensor as output the difference between layers in tensors is key to understanding the Charis functional API layers are used to construct a deep learning model in tensors are used to define the data flow through the model in this case the input layer defines a tensor which we then pass to the output layer function the final output of our model is a tensor it is time for you to build some layers