Build A TensorFlow Neural Network

An example based tutorial on how to build Tensorflow neural network for better accuracy.

You can query a model directly and test the results returned when using different parameter values with the Cloud console, or by calling the Vertex AI API directly.

Prompt Freeform

Teach me how to use TensorFlow to create an accurate neural network with some code examples. Please include insights into how the accuracy of the model is improved by adding layers to it.


Building Accurate Neural Networks with TensorFlow

This guide will walk you through creating a neural network in TensorFlow, focusing on accuracy and the impact of adding layers.

1. Setting up the Environment:

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

2. Loading and Preprocessing Data:

# Load your dataset (e.g., MNIST)
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

# Normalize pixel values
x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0

# One-hot encode labels
y_train = keras.utils.to_categorical(y_train, num_classes=10)
y_test = keras.utils.to_categorical(y_test, num_classes=10)

3. Defining the Model:

model = keras.Sequential([
layers.Flatten(input_shape=(28, 28)), # Flatten the input image
layers.Dense(128, activation='relu'), # First hidden layer with ReLU activation
layers.Dense(10, activation='softmax') # Output layer with softmax activation

4. Compiling the Model:


5. Training the Model:, y_train, epochs=10, batch_size=32, validation_data=(x_test, y_test))

6. Evaluating the Model:

loss, accuracy = model.evaluate(x_test, y_test, verbose=0)
print('Test Loss:', loss)
print('Test Accuracy:', accuracy)

Insights on Adding Layers:

  • Increased Complexity: Adding layers increases the model's complexity, allowing it to learn more intricate patterns in the data.
  • Feature Extraction: Each layer acts as a feature extractor, transforming the input data into more abstract representations.
  • Non-Linearity: Activation functions like ReLU introduce non-linearity, enabling the model to learn complex relationships.
  • Overfitting: Adding too many layers can lead to overfitting, where the model performs well on the training data but poorly on unseen data.

Improving Accuracy:

  • Adding Layers: Experiment with different layer configurations and activation functions.
  • Regularization: Techniques like dropout and L2 regularization prevent overfitting.
  • Optimizer: Choose an appropriate optimizer like Adam or SGD.
  • Learning Rate: Adjust the learning rate to find the optimal balance between convergence speed and accuracy.
  • Data Augmentation: Generate more training data by applying transformations like rotations and flips.

Example with Additional Layers:

model = keras.Sequential([
layers.Flatten(input_shape=(28, 28)),
layers.Dense(256, activation='relu'), # First hidden layer
layers.Dense(128, activation='relu'), # Second hidden layer
layers.Dense(10, activation='softmax') # Output layer


Building accurate neural networks requires careful consideration of model architecture, training parameters, and regularization techniques. By adding layers, you increase the model's complexity and ability to learn intricate patterns, but it's crucial to avoid overfitting. Experimentation and fine-tuning are essential for achieving optimal performance.

Model: gemini-1.5-flash-001
Temperature: 1
Max output tokens: 8192
TopK: 40
TopP: 0.95