Model summary and visualization in TensorFlow

Introduction

Model summary helps you see the layers and parameters of your model. Visualization shows the model structure as a picture.

You want to check if your model layers are connected correctly.

You want to know how many parameters your model has.

You want to share a clear picture of your model with others.

You want to debug or understand the model architecture better.

Syntax

TensorFlow

model.summary()

from tensorflow.keras.utils import plot_model
plot_model(model, to_file='model.png', show_shapes=True)

model.summary() prints a text table of layers and parameters.

plot_model saves a picture file of the model structure.

Examples

Prints the model layers and parameter counts in the console.

TensorFlow

model.summary()

Saves a simple image of the model to a file named model.png.

TensorFlow

plot_model(model, to_file='model.png')

Saves an image showing the shape of inputs and outputs for each layer.

TensorFlow

plot_model(model, to_file='model.png', show_shapes=True)

Sample Model

This code creates a small neural network with one hidden layer and one output layer. It prints the summary and saves a picture of the model showing layer shapes.

TensorFlow

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.utils import plot_model

# Build a simple model
model = Sequential([
    Dense(10, activation='relu', input_shape=(5,)),
    Dense(1, activation='sigmoid')
])

# Show model summary
model.summary()

# Save model visualization
plot_model(model, to_file='model.png', show_shapes=True)

print('Model visualization saved as model.png')

Important Notes

The model.summary() output helps you understand model size and complexity.

The visualization image requires pydot and graphviz installed to work.

Use visualization to explain your model to others or check layer connections.

Summary

Model summary shows layers and parameter counts in text form.

Model visualization creates a picture of the model structure.

Both help you understand and share your model design easily.