How to plot models using keras?
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How to plot models using keras?

How to plot models using keras?

This recipe helps you plot models using keras

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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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