How to graph tf.keras model in Tensorflow- 2 . 0 ?

In TensorFlow 2, several methods can be used to visualize the structure of tf.keras models. This is highly useful for understanding, debugging, and optimizing models. Common methods include using the tf.keras.utils.plot_model function to generate a graphical representation of the model, or using the model.summary() method to display a textual summary of the model. Below, I will detail how to use plot_model to visualize the model structure.

1. Installing Necessary Libraries

Before using tf.keras.utils.plot_model, ensure that TensorFlow 2, pydot, and graphviz are installed, as these are required for generating the graphical representation. Installation commands are as follows:

bash
pip install tensorflow
pip install pydot
pip install graphviz

Additionally, ensure that the system path includes the Graphviz executable. For Windows systems, this may require manual addition.

2. Building a Simple Model

First, we need to build a simple tf.keras model:

python
import tensorflow as tf

model = tf.keras.models.Sequential([
    tf.keras.layers.Input(shape=(28, 28, 1), name='Input-Layer'),
    tf.keras.layers.Conv2D(32, kernel_size=(3, 3), activation='relu', name='Conv-Layer-1'),
    tf.keras.layers.MaxPooling2D(pool_size=(2, 2), name='MaxPool-Layer-1'),
    tf.keras.layers.Flatten(name='Flatten-Layer'),
    tf.keras.layers.Dense(128, activation='relu', name='Dense-Layer-1'),
    tf.keras.layers.Dropout(0.5, name='Dropout-Layer'),
    tf.keras.layers.Dense(10, activation='softmax', name='Output-Layer')
])

3. Visualizing the Model Structure

Use tf.keras.utils.plot_model to visualize the model structure:

python
from tensorflow.keras.utils import plot_model

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

This command generates a file named model.png containing the graphical representation of the model. The show_shapes=True parameter indicates that input and output dimensions are displayed in the diagram; the show_layer_names=True parameter indicates that layer names are shown.

4. Viewing the Textual Summary of the Model

Additionally, you can use the model.summary() method to obtain detailed information about each layer of the model, including layer names, output shapes, and parameter counts:

python
model.summary()

Example

Consider developing a convolutional neural network for handwritten digit recognition. Using the above methods, you can visually inspect the structure and connections of each layer, which aids in understanding how the model transforms input images into output class predictions.

The above are the basic steps and methods for visualizing tf.keras models in TensorFlow 2. These visual and textual tools can help you better understand, present, and optimize your models.