Welcome back.
We learned about combining the model, how to optimize the model, how to measure performance, and
what metrics to track during training.
This is all inside a function called build model.
Now let's call this function here.
So create the model by using model equals to build model function.
Also we're going to print the model summary.
But at first let me make a new message here.
Model architecture okay.
Now let's run all the cells run all because my runtime is disconnected.
Or you can simply run this cell.
And here we go.
Let's scroll down.
And you see the model is sequential calls the function.
We use the sequential function before.
And this model summary shows a layer information key metrics.
And it's very useful.
So let me define the layer type.
We have dance dance one dropout and dance two.
Those are the types of layers that dance conversion to the LSTM and etc..
Output shape of each layer.
Number of parameters weights plus biases in each layer.
So the first layer here.
The second layer third layer and the fourth layer.
Total parameters.
4865 total number of trainable parameters.
Trainable parameters.
Parameters that will be updated during training.
Non-trainable parameters.
Fixed parameters from frozen layers.
We don't have frozen layers, only we have the trainable parameters that will be updated during training.
This is a very useful for debugging, parameter counting, shape verification and memory estimation.
So I want from you to remember this shape because we're going to use it later on in our TensorFlow model.
And we're going to configure the Android input and output according to those shapes.
So it's very crucial.
It's very important to understand the output and input shapes okay.
So this pattern is essential for understanding and verifying your neural network before training begins.