How to plot models using keras?

How to plot models using keras?

How to plot models using keras?

This recipe helps you plot models using keras


Recipe Objective

Plot models using keras.

Plotting of models tells us the how our code proceeds, one can easily demonstrates our keras model.

There is a built-in plot_model facility within Keras. It allows us to create a visualization of your model.

Step 1- Importing Libraries

#importing Libraries import pandas as pd import numpy as np from keras.datasets import mnist from sklearn.model_selection import train_test_split from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout from tensorflow.keras import layers from keras.utils import plot_model

Step 2- Loading dataset

#Loading Dataset (X_train, y_train), (X_test, y_test) = mnist.load_data()

Step 3- Defining the model.

We will define the model and then define the layers, kernel initializer, and its input nodes shape.

#Model model = Sequential() model.add(layers.Dense(64, kernel_initializer='uniform', input_shape=(10,)))

Step 4- Defining the activation function.

Define the activation function and add layers to the model

#Adding Layers model.add(layers.Activation('relu')) model.add(Dense(512)) model.add(Dropout(0.2)) model.add(Dense(256, activation='relu')) model.add(Dropout(0.1))

Step 5- Setting up the image.

Adding the parameters for the image to setup.

# Model configuration img_width, img_height = 28, 28 batch_size = 250 no_epochs = 25 no_classes = 10 validation_split = 0.2 verbosity = 1

Step 6- Adding optimizer.

Add the optimizer according to the need.

model.compile( optimizer='adamax')

Step 7- Plot the model

#plot model plot_model(model, to_file='model.png')

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