Keras Neural Network

 

Introduction to Keras Neural Network

Keras neural network is a model and we can define the same by using sequential API. The sequential API is nothing but a framework that was used for creating the models of instances in the sequential class. The keras neural network model contains input variables, two neurons hidden layer, and the output layer with output as binary. We can create the additional layer and add the same to the model.

Key Takeaways

• The keras neural network functional API is used to create the models by using various inputs and outputs. It makes us possible to share those layers.
• The functional API is a data structure that was saving a single file that was used to rebuild the model.

What is Keras Neural Network?

The recurrent neural network is a class of neural networks that were used for sequence modelings such as natural language and time series. The RNN layer is used for the loop to iterate over the timestamp of the sequence while maintaining the internal state which was encoding information regarding the timestamp. We are also using the functional API for creating the models by using input and output.

This model will enable us to construct a complex neural network by adding and removing layers. The neural network model can be sequential, implying that the layers are at the top of input and output. If suppose the model is operational then we can say that it can be changed. We can also include the training module into API, by using the methods which were used to generate the model. This network model provides methods for training and testing.

How to Use Keras Neural Network?

To use it we need to install the tensorflow in our system.

1. In the first step we are installing the tensorflow module in our system by using the pip command as follows.

Code:

python -m pip install tensorflow

Output:

2. We can verify the installation by importing the tensorflow module, we can also check the version of tensorflow as follows.

Code:

import tensorflow as tf

Output:

3. After installing the tensorflow now in this step we are importing the required dependency which was required in using it as follows.

Code:

import numpy as np
import pandas as pd
import tensorflow as tf

Output:

4. After importing the module now in this step we are importing the csv file by using read_csv function as follows.

Code:

csv_read = pd.read_csv('KNN.csv')
csv_read.head()

Output:

5. After importing the dataset, in this step we are processing the data from csv file as follows.

Code:

x_op = csv_read.iloc [:, 3:-1].values
y_op = csv_read.iloc [:, -1].values
print(x_op)
print(y_op)

Output:

6. After processing the data, now we are encoding the column by using a label encoder as follows.

Code:

from sklearn.preprocessing import LabelEncoder
le_encode = LabelEncoder()
x_op[:, 2] = le_encode.fit_transform (x_op[:, 2])
print (x_op)

Output:

7. After encoding the dataset now in this step we are splitting our dataset into train and test.

Code:

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split()

Output:

8. After splitting the dataset in test and train now in this step we are building the neural network.

Code:

neural = tf.keras.models.Sequential()
neural.add(tf.keras.layers.Dense)
neural.add(tf.keras.layers.Dense)
neural.add(tf.keras.layers.Dense)

Output:

9. After building the neural network now we are creating the neural network model.

Code:

from tensorflow.keras.utils import plot_model
plot_model(neural,
to_file = "neural.png",
show_shapes=True,
show_layer_names=True,
)

Output:

Keras Neural Network Layers

Given below are the different layers as follows:

1. Dense Layer

This layer is widely used in keras for creating the deeply connected layer into the neural network. Below is the example of a neural network as follows.

Code:

from keras.models import Sequential
from keras.layers import Activation, Dense
mod = Sequential()
lay = Dense(12, input_shape = (6,))
mod.add(lay)
lay.input_shape
lay.output_shape

Output:

2. Flatten Layer

This layer is used in input flattening, the below example shows flatten layer.

Code:

from keras.models import Sequential
from keras.layers import Activation, Dense, Flatten
mod = Sequential()
lay = Dense(12, input_shape = (6,))
mod.add (lay)
lay2 = Flatten()
lay.input_shape
lay.output_shape

Output:

3. Dropout Layer

This layer is used to reduce the over fitting of the neural network.

Code:

import keras
keras.layers.Dropout()

Output:

4. Reshape Layer

This layer will contain the responsibility of changing the shape from the input. The below example show reshape layer.

Code:

from keras.models import Sequential
from keras.layers import Activation, Dense, Reshape
mod = Sequential()
lay = Dense(12, input_shape = (6,))
mod.add (lay)
lay2 = Reshape((12, 6))
lay.input_shape
lay.output_shape

Output:

5. Permute Layers

This layer uses the pattern to alter the input shape, the below example shows permute layer as follows.

Code:

from keras.models import Sequential
from keras.layers import Activation, Dense, Permute
mod = Sequential()
lay = Dense(12, input_shape = (6,))
mod.add (lay)
lay2 = Permute((2, 1))
lay.input_shape
lay.output_shape

Output:

6. RepeatVector Layer

This layer will be repeating the input from a fixed number, the below example shows the repeat vector layer as follows.

Code:

from keras.models import Sequential
from keras.layers import Activation, Dense, RepeatVector
mod = Sequential()
lay = Dense(12, input_shape = (6,))
mod.add (lay)
lay2 = RepeatVector (12)
lay.input_shape
lay.output_shape

Output:

7. Pooling Layer

This layer is used in applying the pooling operations onto the data which was temporal.

Code:

keras.layers.MaxPooling1D (
   …..
   )

Output:

8. Locally Connected Layer

This layer possesses the same functionality as conv1D. The below example shows the locally connected layer as follows.

Code:

from keras.models import Sequential
from keras.layers import Activation, Dense,LocallyConnected1D
mod = Sequential()
mod.add(LocallyConnected1D()
mod.add(LocallyConnected1D(6, 3))

Output:

Import Keras Neural Network

We need to import the below libraries at the time of using it.

Code:

import numpy as np
import pandas as pd
import tensorflow as tf

Output:

For using the keras neural network layer we need to import the below modules in our code as follows.

Code:

from keras.models import Sequential
from keras.layers import Activation, Dense,LocallyConnected1D
from keras.layers import Activation, RepeatVector
from keras.layers import Activation, Permute
from keras.layers import Activation, Reshape
from keras.layers import Activation, Flatten
from keras.layers import Activation, Dropout

Output:

FAQ

Given below are the FAQs mentioned:

Q1. What is the use of neural network in keras?

Answer: The neural network in keras is allowing to develop the model by using layer by layer fashion. It will work well by using simple layer stacks.

Q2. What is the use of keras functional API in neural network?

Answer: The keras functional API is used to create the models by using various types of inputs and output. We can use this to construct the graphs.

Q3. What is keras neural network callback?

Answer: The neural network callback is a way that was used to tack the model training by using a variety of activities.

Conclusion

The RNN layer uses for loop to iterate over the timestamp os sequence while maintaining the internal state which was encoding information regarding the timestamp. The sequential API is nothing but a framework that was used for creating the models of instances in the sequential class.

Recommended Articles

This is a guide to Keras Neural Network. Here we discuss the introduction, and how to use the keras neural network. and FAQ. You may also have a look at the following articles to learn more –

1. Keras Tuner
2. What is Keras GPU?
3. Keras Embedding
4. Keras predict