ANN Visualizer

A great visualization python library used to work with Keras. It uses python's graphviz library to create a presentable graph of the neural network you are building.

Version 2.0 is Out!

Version 2.0 of the ann_visualizer is now released! The community demanded a CNN visualizer, so we updated our module. You can check out an example of a CNN visualization below!

Happy visualizing!

Installation

From Github

Download the ann_visualizer folder from the github repository.
Place the ann_visualizer folder in the same directory as your main python script.

From pip

Use the following command:

pip install ann_visualizer

Usage

from ann_visualizer.visualize import ann_viz;
#Build your model here
ann_viz(model)

Documentation

ann_viz(model, view=True, filename="network.gv", title="MyNeural Network")

model - The Keras Sequential model
view - If True, it opens the graph preview after executed
filename - Where to save the graph. (.gv file format)
title - A title for the graph

Example ANN

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

network = Sequential();
        #Hidden Layer#1
network.add(Dense(units=6,
                  activation='relu',
                  kernel_initializer='uniform',
                  input_dim=11));

        #Hidden Layer#2
network.add(Dense(units=6,
                  activation='relu',
                  kernel_initializer='uniform'));

        #Exit Layer
network.add(Dense(units=1,
                  activation='sigmoid',
                  kernel_initializer='uniform'));

from ann_visualizer.visualize import ann_viz;

ann_viz(network, title="");

This will output:

Example CNN

import keras;
from keras.models import Sequential;
from keras.layers import Dense;
from ann_visualizer.visualize import ann_viz
model = build_cnn_model()
ann_viz(model, title="")

def build_cnn_model():
  model = keras.models.Sequential()

  model.add(
      Conv2D(
          32, (3, 3),
          padding="same",
          input_shape=(32, 32, 3),
          activation="relu"))
  model.add(Dropout(0.2))

  model.add(
      Conv2D(
          32, (3, 3),
          padding="same",
          input_shape=(32, 32, 3),
          activation="relu"))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  model.add(Dropout(0.2))

  model.add(
      Conv2D(
          64, (3, 3),
          padding="same",
          input_shape=(32, 32, 3),
          activation="relu"))
  model.add(Dropout(0.2))

  model.add(
      Conv2D(
          64, (3, 3),
          padding="same",
          input_shape=(32, 32, 3),
          activation="relu"))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  model.add(Dropout(0.2))

  model.add(Flatten())
  model.add(Dense(512, activation="relu"))
  model.add(Dropout(0.2))

  model.add(Dense(10, activation="softmax"))

  return model