5 Best Ways to Plot Your Keras Model in Python

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πŸ’‘ Problem Formulation: In the world of machine learning, it’s crucial to visualize the architecture of your neural network models to better understand, debug and optimize them. This article explores how to leverage Keras, a popular deep learning library in Python, to plot your model’s structure. The desired output is visual diagrams that can range from simple plots showing the model’s layers, to more complex ones that include details about the shape and connectivity of tensors.

Method 1: Using Keras’ model.summary()

Although not a graphical plot, the model.summary() method in Keras provides a quick and easy textual representation of your model’s architecture. This includes details such as layer names, output shapes, and the number of parameters. It helps to quickly get an overview of your model, especially for simple architectures.

Here’s an example:

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

model = Sequential()
model.add(Dense(10, input_shape=(10,)))
model.add(Dense(1))

model.summary()

This will output a table summarizing the architecture of the model.

This method is suited for getting a quick overview directly in the Python console. However, it will not produce a graphical plot, and can become less readable for very large models.

Method 2: Using the plot_model utility

The plot_model function in Keras provides a way to create a graphical plot of your model. It shows the layer names, the shapes of the tensors flowing between them, and can even display the connections for complex multi-input or multi-output models.

Here’s an example:

from keras.utils.vis_utils import plot_model

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

This creates an image file ‘model_plot.png’ showing a graphical representation of your model.

This technique offers a more detailed visualization compared to model.summary() and could be very useful for presentations or documentation. However, it requires having graphviz installed, and the generated plots may become crowded for very large models.

Method 3: Using TensorBoard

TensorBoard is a powerful visualization tool that comes with TensorFlow. It allows you to observe the training process, and also visualize the model graph. Keras integrates smoothly with TensorBoard, allowing you to log your model for graphical representation within the TensorBoard UI.

Here’s an example:

from keras.callbacks import TensorBoard

model.compile(optimizer='adam', loss='binary_crossentropy')
tensorboard_callback = TensorBoard(log_dir="logs")

model.fit(x_train, y_train, epochs=5, callbacks=[tensorboard_callback])

Run TensorBoard by executing tensorboard --logdir=logs and open the provided URL in a browser to see the model graph.

TensorBoard’s graph visualization is interactive and can handle complex model topologies. However, it requires running a separate application and is not the simplest solution for quick model architecture checks.

Method 4: Using matplotlib

For those who prefer a coding approach, you can use matplotlib to plot a model. While Keras does not provide a direct method, you could write a custom function that reads the model architecture and plots it using matplotlib. This method provides flexibility to customize the plot.

Here’s an example:

# Pseudo code – a concrete implementation would be needed
import matplotlib.pyplot as plt

def plot_keras_model(model):
    # Custom function to visualize the model
    pass

plot_keras_model(model)
plt.show()

This assumes you have already implemented `plot_keras_model`. When run, it will display the model using matplotlib’s rendering.

This method offers full control over the visualization but requires more effort to implement. It’s recommended for those who need customized plots and are comfortable with matplotlib.

Bonus One-Liner Method 5: Using Netron

You can export your Keras model to an ONNX file, which can then be visualized using Netron, a viewer for neural network, deep learning and machine learning models. It doesn’t directly plot inside a Python program, but it’s a powerful one-liner to quickly see your model outside of Python.

# Assume your Keras model is named 'model'
model.save('model.onnx')

Opening the ‘model.onnx’ file in Netron will display the model.

Netron offers a clean, interactive interface to view models, with the convenience of not needing to write any plotting code. The downside is that it’s an external tool and not integrated into the Python environment.

Summary/Discussion

  • Method 1: Keras’ model.summary(). Ideal for quick, textual summaries of models. Not suited for complex or large models.
  • Method 2: Keras’ plot_model. Offers graphical representation and is good for documentation purposes. Requires Graphviz and might be challenging to read for large models.
  • Method 3: TensorBoard. Best for detailed and interactive graph visualization. Needs separate environment setup.
  • Method 4: Matplotlib. Provides maximum customization and control over the plot. Requires additional coding.
  • Method 5: Netron (Bonus). Quick and easy external visualization tool, but not integrated into Python.