CodeBhavya

AI & MACHINE LEARNING PROGRAM • LEVEL 17 — NEURAL NETWORKS

Run a Dense Layer with Python

Learn run a dense layer with python with a short, executable Python example.

IntermediateDense layerForward pass

PROBLEM UNDERSTANDING

Input and expected output

Sample input
No input required
Sample output
[2.6, 0.2]

COMPLETE PYTHON PROGRAM

Complete Python implementation

ai-ml-17-dense-layer.py
Open in compiler
inputs=[1,2]
weights=[[0.5,1.0],[-1.0,0.5]]
bias=[0.1,0.2]
outputs=[sum(x*w for x,w in zip(inputs,row))+b for row,b in zip(weights,bias)]
print(outputs)

GUIDED CODE TOUR • NOT LIVE EXECUTION

Study the program line by line

Use the real compiler button above to run and debug with different inputs.

CURRENT STEP

Select Start to walk through the important lines.

SELECTED LINE

No line selected

EXPECTED OUTPUT FOR THE SAMPLE

[2.6, 0.2]
0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

  1. Initialize the sample values used to run a dense layer.
  2. Process the data step by step using Dense layer and Forward pass.
  3. Display the result for run a dense layer and compare it with the documented sample output.

This example of run a dense layer processes the sample values in a controlled iteration. It demonstrates Dense layer and Forward pass and prints a deterministic result that can be checked against the sample output.

EFFICIENCY

Time and space complexity

Time complexity

O(i · o)

Auxiliary space

O(o)

DEBUGGING CHECKLIST

Common mistakes

Check this

For run a dense layer, keep the data shape and value types consistent with Dense layer.

Check this

Keep every dependent statement inside the correct indented Python block.

Check this

Verify the final Dense layer and Forward pass result against the sample before trying new data.

Try it yourself

Practice: Run the program with the sample input, predict its output, and then test one boundary case of your own.