Train an Artificial Neural Network with TensorFlow's Keras API

Key Takeaways

hey I'm Mandy from deep lizard in this episode we'll see how to train an artificial neural network using the Charis api integrated with tensorflow [Music] in previous episodes we went through steps to generate data and also build an artificial neural network so now we'll bring these two together to actually train the network on the data that we created and processed all right so picking up where we were last time in our jupiter notebook make sure that you still have all of your imports included and already ran so that we can continue where we were before so first we have after building our model we are going to call this model dot compiled function and this just prepares the model for training so it gets everything in order that's needed before we can actually train the model so first we are specifying to the compile function what optimizer that we want to use and we are choosing to use the very common optimizer atom with a learning rate of 0.0001 and next we specify the type of loss that we need to use which is in our case we're going to use sparse categorical cross symbol we're going to use sparse categorical cross entropy and then lastly we specify what metrics we want to see so this is just for the model performance what we want to be able to judge our model by and we are specifying this list which just includes accuracy which is a very common way to be able to evaluate model performance so if we run this cell all right so the model has been compiled and is ready for training and training occurs whenever we call this fit function now recall earlier in the course we actually looked at the documentation for this fit function so that we knew how to process our input data so to fit the first parameter that we're specifying is X here which is our input data which is currently stored in this scale to Train samples variable then Y which is our target data or our labels our labels are currently stored in the Train Bulls variable so we are specifying that here next we specify our batch size that we want to use for training so this is how many samples are included in one batch to be passed and processed by the network at one time so we're setting this to 10 and the number of epochs that we want to run we're setting this to 30 so that means that the model is going to process or train on all of the data in the data set 30 times before completing the total training process next we're specifying this shuffle shuffle parameter which we are setting to true now by default this is already set to true but I was just bringing it to your attention to show to make you aware of the fact that the data is being shuffled by default when we pass it to the network which is a good thing because we want any order that is inside of the data set to be kind of erased before we pass the data to the model so that the models not necessarily learning anything about the order of the data set so this is true by default so we don't necessarily have to specify that I was just letting you know and actually we'll see something about that in the next episode about why this is important regarding validation data but we'll see that coming up the last parameter that we specify here is verbose which is just an option to allow us to see output from whenever we run this fit function so we can either set it to 0 1 or 2 2 is the most verbose level in terms of output messages so we are setting that here so that we can get the highest level of output so now let's run the cell so that training can begin all right so training has just stopped and we have run for 30 epochs and if we look at the progress of the model so we're starting out on our first epoch our loss value is currently 0.68 and our accuracy is 50% so no better than chance but pretty quickly looking at the accuracy we can tell that it is steadily increasing all the way until we get to our last epoch which we are yielding 94% accuracy and our losses also steadily be crease from the 0.65 range to now being at point two seven so as you can see the this model trained very quickly with each epoch taking only under one second to run and within 30 Hawks we are already at a 94% accuracy rate now although this is a very simple model and we were training it on very simple data we can see that without much effort at all we were able to yield pretty great results in a relatively quick manner of time as well in subsequent episodes we'll demonstrate how to work with more complex models as well as more complex data but for now hopefully this example serve the purpose of encouraging you on how easy it is to get started with Kerris by the way we are currently in Vietnam filming this episode if you didn't know we also have a vlog channel where we document our travels and share a little bit more about ourselves so check that out at people's our vlog on YouTube also be sure to check out the corresponding blog for this episode along with other resources available on beep laser comm and check out the people's r-type mine where you can gain exclusive access to perks and rewards thanks for contributing to collective intelligence I'll see you next time [Music] [Music]