Model Class in Java

In this section, we will be acknowledged about the model class in Java, its purpose and its uses. Also, we will learn how is this created and leveraged in java.

Model Class

To "model" the content in your application, a model class is often required. You could, for instance, create a Model class which replicates a database table or a JSON. These classes' objects could be utilized as means of sending and receiving data. In contrast to the arithmetic and scientific models, which are both more abstract models of the system, a modeling approach is a concrete illustration.

The layers can be combined into an object that has aspects like training and inference, which is particularly advantageous. Before to design or programming, it permits the establishment of a structural software or system model.

tf.keras.Model()

Let us discuss a simple example for better understanding.

Example

For instance, using this tool, you may create model Java classes on JSON. See this. Because models are just plain old java objects, a model class is typically a POJO. However, you are free to create a POJO without employing it as the model.

Arguments

It possesses various arguments like

• Input
• Output
• Name

Input

It can be described as something of an input that the model receives. It can either be a list containing objects, such as keras.Input, or an object of input.

Output

It references to the model's results.

Name

It could be a string which specifies the name of the model.

Instantiation

The two methods for instantiating the models are as follows:

• We'll use "Functional API" to assist us in the first method. We will begin with the input, then connect the layer calls to indicate with a forward pass of a model, and then create the model by employing the inputs and outputs.

import tensorflow as tf  
  
inputs = tf.keras.Input(shape=(3,))  
x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)  
outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)  
model = tf.keras.Model(inputs=inputs, outputs=outputs)  

• By customizing the Model class, we will accomplish this in the second approach. Here, we'll define the layers in _init_ before running the model's forward pass in the call.

import tensorflow as tf  
  
class MyModel(tf.keras.Model):  
  
  def __init__(self):  
    super(MyModel, self).__init__()  
    self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)  
    self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)  
  
  def call(self, inputs):  
    x = self.dense1(inputs)  
    return self.dense2(x)  
  
model = MyModel()  

In order to specify distinct behaviour in both training and inference, we can also subclass the Model and add an optional Boolean training argument to the call:

import tensorflow as tf  
  
class MyModel(tf.keras.Model):  
  
  def __init__(self):  
    super(MyModel, self).__init__()  
    self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)  
    self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)  
    self.dropout = tf.keras.layers.Dropout(0.5)  
  
  def call(self, inputs, training=False):  
    x = self.dense1(inputs)  
    if training:  
      x = self.dropout(x, training=training)  
    return self.dense2(x)  
  
model = MyModel()  

After building the model, we may configure it by adding losses and model-specific metrics. compile(). Using the model, the model could be trained. fit() and model assistance. The prediction function in the model can be used.

Simplified Approach

Model.summary(line_length=None, positions=None, print_fn=None)  

Mostly in form of a string, it could be utilized to print the network summary.

Arguments

• line_length
• positions
• print_fn

line_length

It is characterized as the total length of both the printed lines. Additionally, it can be configured to adjust for showing different terminals' window sizes.

Positions

It refers to the position of each and every log element in a line, which may be either relative or absolute. If it isn't given, the default value is set to [.33,.55,.67, 1.].

Print_fn

It can be used as a default print function that prints and is invoked after each line of a summary. It can be changed to the customized function in order to obtain the string summary.

Note: It may generate the value error if we call the summary() before building the model.

get_layer Approach

Model.get_layer(name=None, index=None)  

It facilitates the discovery of a layer using either its distinctive name or index. In the event that both the name and the index are previously provided, the value will take precedence, allowing for the use of indices that are built from the bottom up (horizontal traversal graph).

Arguments

• Name
• Index

Name

It can be described as the name of the layer as a string.

Index

It references to that of an integer which represents the index of the layer.

Output

It generates an instance of a layer.

Note: If the layer's name or index are incorrect, a value error gets displayed.