Python Coding challenge - Day 788| What is the output of the following Python Code?

 

Code Explanation:

1. Importing Required Modules

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense

What This Does:

Sequential →

This class allows you to create a linear stack of layers — meaning each layer’s output goes directly into the next layer.

It’s best for simple models where the flow is straight from input → hidden → output (no branching or multiple inputs/outputs).

Dense →

This is a fully connected neural network layer.

Each neuron in a Dense layer connects to every neuron in the previous layer.

It’s the most common type of layer used in feedforward networks.

2. Creating the Sequential Model

model = Sequential([

    Dense(8, input_dim=4, activation='relu'),

    Dense(1, activation='sigmoid')

])

What This Does:

Here, you are defining a Sequential neural network consisting of two layers.

(a) First Layer — Input + Hidden Layer

Dense(8, input_dim=4, activation='relu')

Explanation:

Dense(8) →

Creates a Dense layer with 8 neurons (units).

Each neuron learns its own set of weights and bias values.

input_dim=4 →

The model expects each input sample to have 4 features.

For example, if you’re using the Iris dataset, each sample might have 4 measurements.

activation='relu' →

Uses the ReLU (Rectified Linear Unit) activation function:

f(x)=max(0,x)

This introduces non-linearity, allowing the network to learn more complex patterns.

It also helps avoid the vanishing gradient problem during training.

Purpose:

This is your input + hidden layer that takes 4 inputs and produces 8 outputs (after applying ReLU).

(b) Second Layer — Output Layer

Dense(1, activation='sigmoid')

Explanation:

Dense(1) →

Creates a layer with 1 neuron — meaning the network will output a single value.

activation='sigmoid' →

​

This converts the output into a value between 0 and 1.

Purpose:

This is your output layer, typically used for binary classification tasks (e.g., yes/no, 0/1).

3. Checking the Number of Layers

print(len(model.layers))

What This Does:

model.layers → Returns a list of all layers in your model.

len(model.layers) → Returns how many layers are in that list.

Since you added:

One Dense layer (with 8 units)

Another Dense layer (with 1 unit)

The output will be:

2

Final Output:

2

500 Days Python Coding Challenges with Explanation