Building Models with Keras | Keras Assignment Help

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It is a powerful and easy-to-use tool for building deep learning models. In this article, we will explore some of the key concepts of building models with Keras.

1. Sequential Models

The Sequential model is a linear stack of layers, where you can simply add a layer one by one. This model is straightforward and easy to understand, making it ideal for beginners. Here is an example of building a Sequential model with Keras:

from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dense(units=10, activation='softmax'))

2. Functional Models

The Functional model is a more flexible and advanced way of building models in Keras. This model allows for multiple inputs, multiple outputs, shared layers, and more complex architectures. Here is an example of building a Functional model with Keras:

from keras.layers import Input, Dense
from keras.models import Model

input_tensor = Input(shape=(100,))
x = Dense(units=64, activation='relu')(input_tensor)
output_tensor = Dense(units=10, activation='softmax')(x)

model = Model(inputs=input_tensor, outputs=output_tensor)

3. Custom Layers

Keras allows you to create custom layers to add functionality to your models. A custom layer can be defined by subclassing the Layer class and implementing the call method. Here is an example of building a custom layer in Keras:

from keras.layers import Layer

class MyLayer(Layer):
    def __init__(self, units=32, **kwargs):
        self.units = units
        super(MyLayer, self).__init__(**kwargs)

    def build(self, input_shape):
        self.kernel = self.add_weight(name='kernel', 
                                      shape=(input_shape[-1], self.units),
                                      initializer='uniform',
                                      trainable=True)
        super(MyLayer, self).build(input_shape)

    def call(self, inputs):
        return tf.matmul(inputs, self.kernel)

    def compute_output_shape(self, input_shape):
        return (input_shape[0], self.units)

4. Custom Loss Functions

Keras also allows you to define custom loss functions. A custom loss function can be defined by creating a function that takes the true values and predicted values as inputs, and returns the loss. Here is an example of building a custom loss function in Keras:

import keras.backend as K

def custom_loss(y_true, y_pred):
    squared_difference = K.square(y_true - y_pred)
    return K.mean(squared_difference, axis=-1)
 

5. Custom Metrics

Keras also allows you to define custom metrics. A custom metric can be defined by creating a function that takes the true values and predicted values as inputs, and returns the metric. Here is an example of building a custom metric in Keras:

import keras.backend as K

def custom_metric(y_true, y_pred):
    squared_difference = K.square(y_true - y_pred)
    return K.mean(squared_difference, axis=-1)

In conclusion, Keras is a powerful and flexible tool for building deep learning models. Whether you are a beginner or an advanced user, Keras provides a wide range of features and capabilities to meet your needs. With the ability to build Sequential models, Functional models, custom layers, custom loss functions, and custom metrics, you can create complex and powerful models that can tackle a wide range of problems.