5 Best Ways to Plot Your Keras Model Using Python

πŸ’‘ Problem Formulation: When working with neural networks in Keras, visualizing the model’s architecture can greatly enhance understanding and debugging. However, users might not be aware of how to achieve this. This article provides solutions, demonstrating how to take a Keras model as input and produce a visual representation as output, improving insight into layers, shapes, and connectivity.

Method 1: Using Keras’ plot_model() Utility

One of the most straightforward methods to visualize a Keras model is by using the built-in plot_model() function. This utility can generate a plot of the model, providing a graph of the layers. It requires the installation of the ‘pydot’ and ‘graphviz’ libraries. Function specification includes the model to be plotted and the file path for saving the image.

Here’s an example:

from keras.utils import plot_model
from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=20))
model.add(Dense(units=10, activation='softmax'))
plot_model(model, to_file='model.png')

An image file named ‘model.png’ showing the model’s architecture will be generated and saved locally.

This code snippet sets up a simple Sequential Keras model and then uses plot_model() to save a visual representation to ‘model.png’. The plot_model() function takes the model as input and exports a graphical layout of the neural network.

Method 2: Utilizing pydot and GraphViz Directly

If you require more control over your graph’s appearance or wish to manipulate it before plotting, direct use of pydot along with GraphViz is a viable option. This method involves creating a graph object, populating nodes and edges with model specifications, and rendering it into an image or any GraphViz-supported format.

Here’s an example:

import pydot
from keras.utils.vis_utils import model_to_dot

dot = model_to_dot(model)
graph = pydot.graph_from_dot_data(dot.to_string())
graph[0].write_png('advanced_model.png')

As with the previous example, an image file ‘advanced_model.png’ will be generated.

This code snippet first converts the Keras model into dot format with the model_to_dot() utility. Then it creates a PyDot graph from this dot data which is used to write a PNG image file. This method gives you the possibility to post-process the dot data or apply additional customization to the plot with PyDot and GraphViz functionality.

Method 3: Visualizing Model with matplotlib

A more Pythonic way that gives the user the full power of customization is to use matplotlib. This method requires transforming the model into a format understandable by matplotlib, which might entail writing a custom plotting function that iterates over layers and connections.

Here’s an example:

import matplotlib.pyplot as plt

def plot_keras_model(model, show_shapes=True, show_layer_names=True):
    # This function has to be implemented to visualize a Keras model using matplotlib
    pass

plot_keras_model(model)
plt.show()

A window displaying the model’s plot should appear upon execution.

The code snippet is a placeholder where the actual plotting logic using matplotlib would be implemented. It’s meant to illustrate the approach of writing custom functions to plot Keras models through matplotlib for more customized needs.

Method 4: Dynamic Visualization with TensorBoard

TensorBoard provides a dynamic and interactive way to visualize models. Once hooked into Keras via callbacks, you can use TensorBoard’s Graphs dashboard to visualize the model. This is also great for tracking model training and other metrics over time.

Here’s an example:

from keras.callbacks import TensorBoard

# Set up the TensorBoard callback
tensorboard_callback = TensorBoard(log_dir='./logs', histogram_freq=1)

# Train the model and include the callback
model.fit(x_train, y_train, epochs=5, callbacks=[tensorboard_callback])

# Now you can start TensorBoard and view the Graphs dashboard

The TensorBoard can be accessed by running tensorboard --logdir=./logs in the terminal and opening the provided URL in a web browser.

This code snippet demonstrates setting up TensorBoard as a callback within the Keras model training process. This enables the visualization of the model within the TensorBoard’s Graphs dashboard after initiating training.

Bonus One-Liner Method 5: Quick Summary with model.summary()

While not a graphical plot, the model.summary() function offers a quick and insightful tabular summary of the model’s architecture. It shows the layers, output shapes, and number of parameters.

Here’s an example:

print(model.summary())

This will print the model’s summary to the console.

This snippet just presents the method by directly invoking the model.summary() function which prints a comprehensive summary of the model to the console. It is a quick and simple way to get an overview of the model architecture.

Summary/Discussion

  • Method 1: Using plot_model(). Straightforward. Requires external libraries. Limited customization.
  • Method 2: Utilizing pydot and GraphViz Directly. More control. Requires familiarity with GraphViz syntax. Can be cumbersome.
  • Method 3: Visualizing Model with matplotlib. Great customization. Programming overhead to convert the model. Steeper learning curve.
  • Method 4: Dynamic Visualization with TensorBoard. Interactive. Not solely for visualization but also monitoring. Requires TensorFlow backend.
  • Method 5: Quick Summary with model.summary(). Very easy. Textual representation only. Limited detailing compared to a visual plot.