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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