In the previous blog, I have explained concept behind autoencoders and its applications. In this blog we will learn one of the interesting practical application of autoencoders.
Autoencoders are the neural network that are trained to reconstruct their original input. But only reconstructing original input will be useless. The main purpose is to learn interesting features using autoencoders. In this blog we will see how autoencoders can be used to learn interesting features to visualize high dimensional data.
Let say if you are having a 10 dimensional vector, then it will be difficult to visualize it. Then you need to convert it into 2-D or 3-D representation for visualization purpose. There are some famous algorithms like principal component analysis that are used for dimensionality reduction. But if you implement an autoencoder that only uses linear activation function with mean squared error as its loss function, then it will end up performing principal component analysis.
Here we will visualize a 3 dimensional data into 2 dimensional using a simple autoencoder implemented in keras.
Autoencoder model architecture for generating 2-d representation will be as follows:
- Input layer with 3 nodes.
- 1 hidden dense layer with 2 nodes and linear activation.
- 1 output dense layer with 3 nodes and linear activation.
- Loss function is mse and optimizer is adam.
The following code will generate a compressed representation of input data.
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from keras.layers import Dense, Input, Activation from keras.models import Model import numpy as np encoder_inputs = Input(shape = (3,)) hidden_layer = Dense(2, activation = 'linear')(encoder_inputs) output = Dense(3, activation = 'linear')(hidden_layer) encoder = Model(encoder_inputs,hidden_layer) model = Model(encoder_inputs, output) batch_size = 64 n_epoch = 100 model.compile(optimizer='adam', loss='mse') model.fit(data,data, epochs=n_epoch, batch_size=batch_size, shuffle=True) |
Here is the generated 2-D representation of input 3-D data.
In the similar way you can visualize high dimensional data into 2-Dimensional or 3-Dimensional vectors.
Hope you enjoy reading.
If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.