Thread by @gusthema

This week, let's talk about model building a little.

In the TensorFlow world, the simplest way of building a model is with a Sequential model.

But what is it and how to do it?

[4 minutes]

1/9🧵

First, let's go over some basics.

The goal here is to build a Neural Network, or in other words, create a set of neurons, distributed in layers and connected by weights.

Each Layer applies some computation on the values or tensors it receives.

2/9🧵

The simplest way to create this NN is to just do a plain stack of layers where each layer has exactly one input and one output.

This is exactly what a Sequential model does

3/9🧵

After you have your model, you can access each layer and it's weights.

You might want to do that if you want to do feature extraction for example

4/9🧵

You can also visualize the model structure using the summary method

This can you give some insights of the intermediate shapes and help fix issues

💡:layer1 and layer2 (or any layer that is not input or output) is also known as a hidden layer

5/9🧵

If you want a more visual version, you can also use tf.keras.utils.plot_model(model)

This shows a more visual version of the model.

💡: Both this and the summary will work even if you build your model not using a Sequential model.

6/9🧵

You should NOT use a Sequential model when:
• Your model or any layer needs multiple inputs or outputs
• You need a non-linear model
• You need layer sharing

7/9🧵

Here is a full tutorial with the code I've used: tensorflow.org/guide/keras/se…

I highly recommend going over and play with it.
It's 15 minutes that will help you.

8/9🧵

This is not the only way to build a model.

I'll come back to this during the week with the other techniques.

Stay "tuned" 🤓

9/9🧵