Tensorflow

TensorFlow Implementation for Neural Networks

Overview of Neural Network Training

Week 2 focuses on “training of a neural network”
Previous week covered inference in neural networks
Goal: Learning to train neural networks with your own data

Neural Network Architecture Example

Handwritten digit recognition (0 or 1)
Network structure:
- Input layer: image data
- First hidden layer: 25 units
- Second hidden layer: 15 units
- Output layer: 1 unit

TensorFlow Implementation Steps

Define the model architecture
- Use sequential structure to connect layers
- Specify number of units and activation functions
Compile the model
- Specify the loss function (binary crossentropy in this example)
Train the model
- Use the fit function with dataset (X, Y)
- Specify number of epochs (training iterations)

Step 1: Define Model Architecture

Uses familiar structure from previous week
Sequential model connecting three layers:
- First hidden layer: 25 units with sigmoid activation
- Second hidden layer: 15 units
- Output layer: final result

Step 2: Compile Model

Key element: specifying the loss function
Example uses “binary crossentropy loss function”
- Details of this function will be covered in the next video

Step 3: Fit/Train Model

Call the fit function with:
- Model architecture (from Step 1)
- Loss function (from Step 2)
- Training dataset (X, Y)
Epochs parameter: determines how many steps of gradient descent to run
- Similar to concept from Course 1

TensorFlow provides a streamlined implementation for neural network training through these three key steps: define, compile, and fit. Understanding the underlying concepts behind each step is essential for effective debugging and optimization of neural network models.