I am trying to use the keras frontend with tensorflow backend for a simple autoencoder as a multidimensional scaling technique to plot multidimensional data into 2 dimensions. Many times when I run it (not sure how to set random seed for keras btw) one of the dimensions is collapsed to yield a 1 dimensional embedding (the plot should help explain). Why is this happening? How can I make sure the dimensions are preserved and utilized by the autoencoder? I realize this is the most simple and basic form of an autoencoder that I have implemented but I would like to build on this to make better autoencoders for this task.
from sklearn.datasets import load_iris
from sklearn import model_selection
import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Load data
X = load_iris().data
Y = pd.get_dummies(load_iris().target).as_matrix()
X_tr, X_te, Y_tr, Y_te = model_selection.train_test_split(X,Y, test_size=0.3, stratify=Y.argmax(axis=1))
dims = X_tr.shape[1]
n_classes = Y_tr.shape[1]
# Autoencoder
encoding_dim = 2
# this is our input placeholder
input_data = tf.keras.Input(shape=(4,))
# "encoded" is the encoded representation of the input
encoded = tf.keras.layers.Dense(encoding_dim,
activation='relu',
)(input_data)
# "decoded" is the lossy reconstruction of the input
decoded = tf.keras.layers.Dense(4, activation='sigmoid')(encoded)
# this model maps an input to its reconstruction
autoencoder = tf.keras.models.Model(input_data, decoded)
# this model maps an input to its encoded representation
encoder = tf.keras.models.Model(input_data, encoded)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
network_training = autoencoder.fit(X_tr, X_tr,
epochs=100,
batch_size=5,
shuffle=True,
verbose=False,
validation_data=(X_te, X_te))
# Plot data
embeddings = encoder.predict(X_te)
plt.scatter(embeddings[:,0], embeddings[:,1], c=Y_te.argmax(axis=1), edgecolor="black", linewidth=1)
Run algorithm once
Run algorithm again
