# Autoencoder for Dimensionality Reduction

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We often use ICA or PCA to extract features from the high-dimensional data. The autoencoder is another interesting algorithm to achieve the same purpose in the context of Deep Learning.

with the purpose of learning a function to approximate the input data itself such that F(X) = X, an autoencoder consists of two parts, namely encoder and decoder. While the encoder aims to compress the original input data into a low-dimensional representation, the decoder tries to reconstruct the original input data based on the low-dimension representation generated by the encoder. As a result, the autoencoder has been widely used to remove the data noise as well to reduce the data dimension.

First of all, we will show the basic structure of an autoencoder with 1-layer encoder and 1-layer decoder, as below. In the example, we will compress the input data with 10 columns into a compressed on with 3 columns.

from pandas import read_csv, DataFrame from numpy.random import seed from sklearn.preprocessing import minmax_scale from sklearn.model_selection import train_test_split from keras.layers import Input, Dense from keras.models import Model df = read_csv("credit_count.txt") Y = df[df.CARDHLDR == 1].DEFAULTS X = df[df.CARDHLDR == 1].ix[:, 2:12] # SCALE EACH FEATURE INTO [0, 1] RANGE sX = minmax_scale(X, axis = 0) ncol = sX.shape[1] X_train, X_test, Y_train, Y_test = train_test_split(sX, Y, train_size = 0.5, random_state = seed(2017)) ### AN EXAMPLE OF SIMPLE AUTOENCODER ### # InputLayer (None, 10) # Dense (None, 5) # Dense (None, 10) input_dim = Input(shape = (ncol, )) # DEFINE THE DIMENSION OF ENCODER ASSUMED 3 encoding_dim = 3 # DEFINE THE ENCODER LAYER encoded = Dense(encoding_dim, activation = 'relu')(input_dim) # DEFINE THE DECODER LAYER decoded = Dense(ncol, activation = 'sigmoid')(encoded) # COMBINE ENCODER AND DECODER INTO AN AUTOENCODER MODEL autoencoder = Model(input = input_dim, output = decoded) # CONFIGURE AND TRAIN THE AUTOENCODER autoencoder.compile(optimizer = 'adadelta', loss = 'binary_crossentropy') autoencoder.fit(X_train, X_train, nb_epoch = 50, batch_size = 100, shuffle = True, validation_data = (X_test, X_test)) # THE ENCODER TO EXTRACT THE REDUCED DIMENSION FROM THE ABOVE AUTOENCODER encoder = Model(input = input_dim, output = encoded) encoded_input = Input(shape = (encoding_dim, )) encoded_out = encoder.predict(X_test) encoded_out[0:2] #array([[ 0. , 1.26510417, 1.62803197], # [ 2.32508397, 0.99735016, 2.06461048]], dtype=float32)

In the next example, we will relax the constraint of layers and employ a stack of layers to achievement the same purpose as above.

### AN EXAMPLE OF DEEP AUTOENCODER WITH MULTIPLE LAYERS # InputLayer (None, 10) # Dense (None, 20) # Dense (None, 10) # Dense (None, 5) # Dense (None, 3) # Dense (None, 5) # Dense (None, 10) # Dense (None, 20) # Dense (None, 10) input_dim = Input(shape = (ncol, )) # DEFINE THE DIMENSION OF ENCODER ASSUMED 3 encoding_dim = 3 # DEFINE THE ENCODER LAYERS encoded1 = Dense(20, activation = 'relu')(input_dim) encoded2 = Dense(10, activation = 'relu')(encoded1) encoded3 = Dense(5, activation = 'relu')(encoded2) encoded4 = Dense(encoding_dim, activation = 'relu')(encoded3) # DEFINE THE DECODER LAYERS decoded1 = Dense(5, activation = 'relu')(encoded4) decoded2 = Dense(10, activation = 'relu')(decoded1) decoded3 = Dense(20, activation = 'relu')(decoded2) decoded4 = Dense(ncol, activation = 'sigmoid')(decoded3) # COMBINE ENCODER AND DECODER INTO AN AUTOENCODER MODEL autoencoder = Model(input = input_dim, output = decoded4) # CONFIGURE AND TRAIN THE AUTOENCODER autoencoder.compile(optimizer = 'adadelta', loss = 'binary_crossentropy') autoencoder.fit(X_train, X_train, nb_epoch = 100, batch_size = 100, shuffle = True, validation_data = (X_test, X_test)) # THE ENCODER TO EXTRACT THE REDUCED DIMENSION FROM THE ABOVE AUTOENCODER encoder = Model(input = input_dim, output = encoded4) encoded_input = Input(shape = (encoding_dim, )) encoded_out = encoder.predict(X_test) encoded_out[0:2] #array([[ 3.74947715, 0. , 3.22947764], # [ 3.93903661, 0.17448257, 1.86618853]], dtype=float32)

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This article is originally published at https://statcompute.wordpress.com

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