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How to Visualize a Deep Learning Neural Network Model in Keras

Terminal Updated on September eleven, 2019

The Keras Python deep learning library provides tools to visualize and improve empathise your neural network models.

In this tutorial, you will discover exactly how to summarize and visualize your deep learning models in Keras.

Later on completing this tutorial, you will know:

How to create a textual summary of your deep learning model.
How to create a graph plot of your deep learning model.
Best practise tips when developing deep learning models in Keras.

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How to Visualize a Deep Learning Neural Network Model in Keras
Photograph by Ed Dunens, some rights reserved.

Tutorial Overview

This tutorial is divided into iv parts; they are:

Instance Model
Summarize Model
Visualize Model
Best Practice Tips

Example Model

We tin can start off by defining a elementary multilayer Perceptron model in Keras that nosotros tin can apply equally the field of study for summarization and visualization.

The model we will define has one input variable, a subconscious layer with two neurons, and an output layer with ane binary output.

For example:

[1 input] -> [two neurons] -> [1 output]

If you are new to Keras or deep learning, see this step-by-step Keras tutorial.

The code list for this network is provided below.

from keras . models import Sequential

from keras . layers import Dumbo

model = Sequential ( )

model . add ( Dumbo ( two , input_dim = one , activation = 'relu' ) )

model . add ( Dense ( 1 , activation = 'sigmoid' ) )

Summarize Model

Keras provides a style to summarize a model.

The summary is textual and includes data nigh:

The layers and their guild in the model.
The output shape of each layer.
The number of parameters (weights) in each layer.
The total number of parameters (weights) in the model.

The summary can be created by calling the summary() function on the model that returns a cord that in turn can be printed.

Below is the updated example that prints a summary of the created model.

from keras . models import Sequential

from keras . layers import Dumbo

model = Sequential ( )

model . add ( Dumbo ( ii , input_dim = 1 , activation = 'relu' ) )

model . add together ( Dense ( 1 , activation = 'sigmoid' ) )

impress ( model . summary ( ) )

Running this example prints the following table.

_________________________________________________________________

Layer (type) Output Shape Param #

=================================================================

dense_1 (Dense) (None, 2) 4

_________________________________________________________________

dense_2 (Dense) (None, 1) three

=================================================================

Total params: 7

Trainable params: 7

Non-trainable params: 0

_________________________________________________________________

Nosotros can clearly run into the output shape and number of weights in each layer.

Visualize Model

The summary is useful for simple models, simply can be confusing for models that accept multiple inputs or outputs.

Keras also provides a part to create a plot of the network neural network graph that can make more complex models easier to sympathise.

The plot_model() role in Keras will create a plot of your network. This role takes a few useful arguments:

model: (required) The model that you wish to plot.
to_file: (required) The name of the file to which to relieve the plot.
show_shapes: (optional, defaults to False) Whether or not to bear witness the output shapes of each layer.
show_layer_names: (optional, defaults to True) Whether or not to show the proper name for each layer.

Below is the updated example that plots the created model.

Notation, the example assumes that you have the graphviz graph library and the Python interface installed.

from keras . models import Sequential

from keras . layers import Dense

from keras . utils . vis_utils import plot_model

model = Sequential ( )

model . add ( Dense ( 2 , input_dim = one , activation = 'relu' ) )

model . add together ( Dumbo ( 1 , activation = 'sigmoid' ) )

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

Running the example creates the file model_plot.png with a plot of the created model.

Plot of Neural Network Model Graph

Best Practice Tips

I generally recommend to ever create a summary and a plot of your neural network model in Keras.

I recommend this for a few reasons:

Confirm layer social club. Information technology is piece of cake to add layers in the wrong order with the sequential API or to connect them together incorrectly with the functional API. The graph plot can help you confirm that the model is connected the way you intended.
Ostend the output shape of each layer. It is mutual to accept problems when defining the shape of input data for complex networks similar convolutional and recurrent neural networks. The summary and plot can help you confirm the input shape to the network is equally you intended.
Ostend parameters. Some network configurations tin use far fewer parameters, such as the use of a TimeDistributed wrapped Dense layer in an Encoder-Decoder recurrent neural network. Reviewing the summary can help spot cases of using far more parameters than expected.

Farther Reading

This section provides more resources on the topic if you are looking to get deeper.

Model visualization Keras API
Graphviz – Graph Visualization Software
Simple Python interface for Graphviz

Summary

In this tutorial, you discovered how to summarize and visualize your deep learning models in Keras.

Specifically, you learned:

How to create a textual summary of your deep learning model.
How to create a graph plot of your deep learning model.
All-time exercise tips when developing deep learning models in Keras.

Do y'all have any questions?
Enquire your questions in the comments below and I will practise my best to respond.

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