Updated March 14, 2023
Definition of Keras Sequential
Keras is an API that gets well with Neural network models related to artificial intelligence and machine learning so is the keras sequential which deals with ordering or sequencing of layers within a model. It basically makes the layers associated with neural networks work with Keras API or Keras library for seamless functionality. Keras sequential is one of the modeling ways or say model which takes only one input as feed and expects one output as its name suggests. This type of model is quite capable to handle simple and layer-based problems.
What is Keras sequential?
As its name suggests it is one of the models that is used to investigate varied types of neural networks where the model gets in one input as feedback and expects an output as desired. The Keras API and library is incorporated with a sequential model to judge the entire simple model not the complex kind of model. It passes on the data and flows in sequential order from top to bottom approach till the data reaches at end of the model.
Keras sequential class
Keras sequential class is one of the important class as part of the entire Keras sequential model. This class helps in creating a cluster where a cluster is formed with layers of information or data that flows with top to bottom approach having a lot of layers incorporated with tf.Keras. a model where most of its features are trained with algorithms that provide a lot of sequence to the model.
Syntax representing the creation.
tf.keras.Sequential (layers=No_lyr, name=No_lyr)
tf.Keras.Sequential: Here it is tried to call the sequential class where arguments passed are having no layer and name as of now.
Layers=No_lyr, name=No_lyr where both the arguments given values will behave according to class.
Sequential class contains many API and methods such as:
– Add() Method
– Pop() Method
Sequential class also contains many of the core Keras where the input is fed, and an output is expected with trained and inferred results as per requirement.
The top to the bottom approach of data flow helps in making the layers more enhanced and informative that will be required at the time of manipulation and filtration.
How to use Keras sequential?
To use this model there are certain pre-requisites and steps that need to be followed appropriately:
- A proper setup is required initially.
- Then this setup will incorporate Keras library or API which will have exactly one layer of input with one layer of output.
- Then select a proper method like add() or remove() where the attributes will be based on the requirement.
# A proper setup initially will consider the following imports:
Here the TensorFlow imports the required Keras layers that will be further used for importing Keras layers from TensorFlow.
# Usage of Keras Sequential
Here the model is used for training any neural network where a stack of layers Is embedded with keras where each layer has one input with Keras extended with tensor and similarly one output tensor.
Example: This code snippet represents how to use the sequential model for creating three layers post which sequential model is used for testing the same.
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from keras.models import Sequential
from keras.layers import Dense, Activation
model = Sequential([
layr_1 = layers.Dense(4, activation="relu", name="layr_1")
layr_2 = layers.Dense(5, activation="relu", name="layr_2")
layr_3 = layers.Dense(6, name="layr_3")
x_0 = tf.ons((4, 5))
y_1 = layr_3(layr_2(layr_1(x_0)))
])
# Sequential add() method
This method is used for adding layers on top of an already created stack of layers as shown in the previous example.
Syntax representing the add() method
Sequential.add(layer_1, layer_2, layer_3)
Arguments: layer_1, layer_2, and layer_3 are the arguments passed to the sequential. add () method where all these are layers that can be stacked on top of already existing layers.
If proper layers are not present, then it might throw some errors like:
- TypeError: If layer present Is not part of an instance of the existing layer.
- ValueError: If layer present is not known with the fed input shape.
Example: Code snippet showing add() method to add layers within the existing layers of the sequential model.
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from keras.models import Sequential
from keras.layers import Dense, Activation
model = Sequential([
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(6, inpt_shape=(18,)))
model.add(tf.keras.layers.Dense(2))
len(model.weights)
])
# Sequential pop() method
Sequential. pop() method is used for removing the last layer of the model which might give TypeError if there are no layers in the model.
Example: This code snippet is used for removing the layers if not needed by adding the corresponding pop() method as shown in the output.
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from keras.models import Sequential
from keras.layers import Dense, Activation
model = Sequential([
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(6, inpt_shape=(18,)))
model.add(tf.keras.layers.Dense(2))
len(model.weights)
model.pop()
print(len(model.layers))
])
# Once the entire model is ready then the model summary can be called and viewed simultaneously as shown below:
model.summary()
Keras sequential Neural Network
In short sequential Neural Networks gels well with Keras library which is a powerful and easy-to-use library developed for the analysis of deep learning models. This Keras sequential model in turn consists of TensorFlow and Theano for training these deep learning models. Although there is not a lot of coding involved still it comprises certain steps that need to be carried out accordingly:
– Load Data: Initially all the data is loaded.
– Defining proper Keras model like sequential Keras model.
– Then compilation of the sequential Keras model is performed that is associated with the neural network.
– Once the compilation is completed then it Is judged whether the values get fitted appropriately over the neural network.
– Followed by fitting the best values a step of evaluation is performed with the Keras model.
– Then all the network and layers present as part of the sequential model needs to be bound all together.
– Certain predictions are needed to be carried out as part of the algorithm which makes of predict() method as part of it.
Conclusion
Keras sequential model is suitable for analysis and comparison of simple neural network-oriented models which comprises layers and their associated data using top to bottom flow. It makes use of a single set of input as to value and a single set of output as per flow. The information or data retrieved is filtered and final data for manipulation.
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