Updated March 15, 2023
Introduction to Keras Model
Keras models are special neural network-oriented models that organize different layers and filter out essential information. The Keras model has two variants: Keras Sequential Model and Keras Functional API, which makes both the variants customizable and flexible according to scenario and changes. Moreover, it makes the functional APIs give a set of inputs and outputs with a single file, giving the graph model’s look and feel accordingly. It is a library with high-level language considered for deep learning on top of TensorFlow and Theano. It is written in Python language.
What is Keras Model?
Keras model is used for designing and working with neural network types that are used for building many other similar formats of architecture possessing training and feeding complex models with structures. It comprises many graphs that support the representation of a model in some other ways, with many other configurable systems and patterns for feeding values as part of training. Moreover, it provides modularity, which helps make flexible and well-suited models for customization and support. Two approaches based on this help develop sequential and functional models.
How to Use Keras model?
- As the Keras model is a python-based library, it must be used for flexibility and customized model design, especially for prediction.
- Keras model has its way of detecting trends with behavior for modeling and prediction.
- Keras model uses a model.predict() class and reconstructed_model.predict(), which have their own significance.
- Predict () class within a model can be used for creating and fitting trained data using prediction.
- Another class, i.e., reconstructed_model.predict() within a model, is used to save and load the model for reconstruction. A reconstructed model compiles and retains the state into optimization using either historical or new data.
- Certain components will also get incorporated or are already part of the Keras model for customization, which is as follows:
- Optimizer: It is used for compiling a Keras model by passing certain arguments containing the Optimizer loss function to minimize and handle any unpredictable loss.
- Loss of set and metrics: A model is compiled and is used in a way where it is used for including losses and metrics that will get taught at the time of training or modeling.
- Weights: Certain input parameters must be fed to the model for required output within a Keras model.
Create Keras Model
Ways to create a model using Sequential API and Functional API
1. Using Sequential API
The idea is to create a sequential flow within layers that possess some order and help make certain flows from top to bottom, giving individual output. It helps in creating an ANN model just by calling a Sequential API() using the Keras model package, which is represented below:
from keras.models import sequential
model_any = sequential()
- The next step is to add a layer for which a layer needs to be created, followed by passing that layer using add() function within it
From keras.models import sequential
model_any=sequential()
inpt_layer=Dense(20, input_shp=(6,)) model.add(inpt_layer)
model_any.add( inpt_layer)
- Next, Is to access the model: which means to provide the relevant information about the layers defined or added as part of the model.
layers=model.layers
Model. input: will provide all relevant input then similarly model. the output will give relevant information about the same.
- Serializing the model is another important step for serializing the model into an object like JSON and then loading it like
Config=model.getconfig() -> Returns the model in form of object.
- Then, the Summarization of the model happens, followed by Training and prediction of the model, which include components like compile, evaluate, fit, and predict.
2. Using Functional API
Functional API is an alternative to Sequential API, where the approach is almost identical. Still, it does support and gives flexibility in terms of a certain complex model where an instance is created first, followed by connecting the layers with an input or output.
- Let’s create a model by importing an input layer.
from keras.layers import Input
- Creating an input layer where we can define dimensional input shape for a model is as follows:
data=Input(shape=(5,6)
- Add a dense layer for the input
layer=Dense(5) (data)
print(layer)
- Define your model accordingly:
- Create a model with both input and output layers using functional API:
model=Model(inpt=data, otput=layer)
Keras Model Types
Keras model represents and gels well with Deep learning; it gives the following ways to generate model types:
1. Sequential Type Model
- As its name suggests, the sequential type model mostly supports and creates sequential type API, which tries to arrange the layers in a specific sequence and order.
- Most deep learning and neural network have layers provisioned in a sequence for transferring data and flow from one layer to another sequence data.
2. Functional API model
- This model is used to create and support some complex and flexible models.
- This also helps make Directed acyclic graphs (DAGs) where the architecture comprises many layers that need to be filtered from top to bottom.
- It also helps define and design branches within the architecture with some inception blocks, functions, etc.
- Highlight a few famous examples supporting the Functional API model Squeeze Net, Xception, ResNet, GoogleNet, and Inception.
3. Model Subclassing
- Model subclassing is a way to create a custom model comprising most of the functions and classes that are the root and internal models to the full custom forward pass model.
- It does help in assisting and supporting Functional or sequential types of models for manipulation and testing.
Examples of Keras Model
Below are the different examples of the Keras Model:
Example #1
This program demonstrates the use of the Keras model in prediction, incorporating the model. predict() method in a class by training a certain set of training data as shown in the output.
import tensorflow as tf
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
input_vls = keras.Input(shape=(200,), name="numbrs")
x_0 = layers.Dense(22, activation="rel_num", name="dns_0")(input_vls)
x_0 = layers.Dense(84, activation="rel_num", name="dns_2")(x_0)
output_vls = layers.Dense(12, activation="softmax_types", name="predict_values")(x_0)
model_ex = keras.Model(input_vls=inputs, output_vls=outputs)
(x_train_0, y_train_0), (x_test_0, y_test_0) = keras.datasets.mnist.load_data()
x_train_0 = x_train_0.reshape(62000, 782).astype("float64") / 255
x_test_0 = x_test_0.reshape(12000, 784).astype("float64") / 255
y_train_0 = y_train_0.astype("float64")
y_test = y_test.astype("float64")
x_val_0 = x_train_0[-10020:]
y_val_0 = y_train_0[-10010:]
x_train_0 = x_train_0[:-10000]
y_train_0 = y_train_0[:-10060]
model.compile(
optimizer=keras.optimizers.RMSprop(),
loss=keras.losses.SparseCategoricalCrossentropy(),
metrics=[keras.metrics.SparseCategoricalAccuracy()],
)
print("Fit_the_model_for_training")
history = model.fit(
x_train_0,
y_train_0,
batch_size=64,
epochs=2,
validation_data=(x_val_0, y_val_0),
)
history.history
print("Evaluate model for testing_data")
res_1 = model.evaluate(x_test_0, y_test_0, batch_size=120)
print("test_the_loss, test_accurate:", res_1)
print("Generate for_prediction..")
prediction = model.predict(x_test[:1])
print("prediction shape:", prediction.shape)
Example #2
This program represents the creation of a model using Sequential API ().
from keras.models import Sequential
from keras.layers import Dense
model=Sequential()
model.add(Dense(32,input_shpe=5,))
mode.add(Dense(16))
Example #3
This program represents the creation of a model with multiple layers using functional API()
from keras.models import Model
from keras.layers import Input, Dense
input=Input(shape=(20,))
layer_=Dense(20)(input_)
model=Model(inputsval=input_,outputsval=layer_)
model=Model(inputsval=[input_1,input_2],outputsval=[layer_1,layer_2,layer_3])
Conclusion
Keras model is used for a lot of model analysis related to deep learning and gels well with all types of the neural network, which requires an hour as most of the task carried out contains an association with AI and ANN. Tensorflow, when incorporated with Keras, makes wonder and performs quite well in analysis phases of different types of models.
Recommended Articles
This is a guide to Keras Model. Here we discuss the definition, how to use and create Keras Model, and examples and code implementation. You may also have a look at the following articles to learn more –
