Unsupervised learning of mnist with variational auto encoder, clustering and evaluating the final stage
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
#Import required libraries
from keras.datasets import mnist
import numpy as np
import pandas as pd
import sklearn
#Display plot results in notebook when using Jupyter notebook
import matplotlib.pyplot as plt
%matplotlib inline
from keras.layers import Lambda, Input, Dense
from keras.models import Model
from keras.losses import mse
from keras import backend as K
import gc
Using TensorFlow backend.
feature_dims = range(2, 12)
#Read data with Keras function. Shuffle the data and split it into learning and training data
(x_train, y_train), (x_test, y_test) = mnist.load_data()
#Convert 2D data to numbers
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
#Type conversion
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
#Divide by 255 as a new variable
x_train /= 255
x_test /= 255
# one-Method for hot encoding
from keras.utils.np_utils import to_categorical
#10 classes
num_classes = 10
y_train = y_train.astype('int32')
y_test = y_test.astype('int32')
labels = y_test
# one-hot encoding
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)
def fitting(feature_dim, x_train, y_train, x_test, y_test):
original_dim = x_train.shape[1]
input_shape = (original_dim, )
latent_dim = feature_dim
# Reparametrization Trick
def sampling(args):
z_mean, z_logvar = args
batch = K.shape(z_mean)[0]
dim = K.int_shape(z_mean)[1]
epsilon = K.random_normal(shape=(batch, dim), seed = 5) # ε
return z_mean + K.exp(0.5 * z_logvar) * epsilon
#VAE model construction
inputs = Input(shape=input_shape)
x1 = Dense(256, activation='relu')(inputs)
x2 = Dense(64, activation='relu')(x1)
z_mean = Dense(latent_dim)(x2)
z_logvar = Dense(latent_dim)(x2)
z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_logvar])
encoder = Model(inputs, [z_mean, z_logvar, z], name='encoder')
encoder.summary()
latent_inputs = Input(shape=(latent_dim,))
x3 = Dense(64, activation='relu')(latent_inputs)
x4 = Dense(256, activation='relu')(x3)
outputs = Dense(original_dim, activation='sigmoid')(x4)
decoder = Model(latent_inputs, outputs, name='decoder')
decoder.summary()
z_output = encoder(inputs)[2]
outputs = [decoder(z_output),z_output]
vae = Model(inputs, outputs, name='variational_autoencoder')
#Loss function
# Kullback-Leibler Loss
kl_loss = 1 + z_logvar - K.square(z_mean) - K.exp(z_logvar)
kl_loss = K.sum(kl_loss, axis=-1)
kl_loss *= -0.5
# Reconstruction Loss
reconstruction_loss = mse(inputs, outputs[0])
reconstruction_loss *= original_dim
vae_loss = K.mean(reconstruction_loss + kl_loss)
vae.add_loss(vae_loss)
vae.compile(optimizer='adam')
vae.summary()
history = vae.fit(x_train,
epochs=50,
batch_size=256,
validation_data=(x_test, None))
result = vae.predict(x_test)
K.clear_session() #← This is
gc.collect()
from IPython.display import clear_output
clear_output()
return (history, vae, result)
#model = fitting(10, x_train, y_train, x_test, y_test)
models = [None] * len(feature_dims)
histories = [None] * len(feature_dims)
dec_imgs = [None] * len(feature_dims)
results = [None] * len(feature_dims)
for i in range(len(feature_dims)):
(histories[i], models[i], dec_imgs[i]) = fitting(feature_dims[i], x_train, y_train, x_test, y_test)
for i in range(len(feature_dims)):
print(feature_dims[i])
result = dec_imgs[i]
decoded_imgs = result[0]
#Display of test image and converted image
n = 10
plt.figure(figsize=(10, 2))
for j in range(n):
#Display test image
ax = plt.subplot(2, n, j+1)
plt.imshow(x_test[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
#View converted image
ax = plt.subplot(2, n, j+1+n)
plt.imshow(decoded_imgs[i][0][j].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
results[i] = result[1]
for i in range(len(feature_dims)):
results[i] = dec_imgs[i][1]
#model.save('model/mnist-10')
#model = keras.models.load_model('model/mnist-10')
#for i in range(len(feature_dims)):
# models[i].pop() #Remove the softmax layer in the final stage and use the feature layer as the final stage.
# models[i].summary()
#result = model.predict(x_test)
#results = [None] * len(feature_dims)
#for i in range(len(feature_dims)):
# keras.backend.clear_session()
# results[i] = models[i].predict(x_test)
def tsne(result):
#t-Dimensionality reduction with SNE
from sklearn.manifold import TSNE
tsne = TSNE(n_components=2, random_state = 0, perplexity = 30, n_iter = 1000)
return tsne.fit_transform(result)
#tsne = tsne(result)
tsnes = [None] * len(feature_dims)
for i in range(len(feature_dims)):
tsnes[i] = tsne(results[i])
#df = pd.DataFrame(tsne, columns = ['x', 'y'])
#df['label'] = labels
def km(n_clusters, result):
# k-Cluster by means
from sklearn.cluster import KMeans
return KMeans(n_clusters).fit_predict(result)
#km = km(10, result)
#df['km'] = km
kms = [None] * len(feature_dims)
for i in range(len(feature_dims)):
kms[i] = km(10, results[i])
def DBSCAN(n_clusters, result):
from sklearn.cluster import DBSCAN
db = DBSCAN(eps=0.2, min_samples=n_clusters).fit(result)
return db.labels_
#dbscan = DBSCAN(20, result)
#df['DBSCAN'] = dbscan
def hierarchy(result):
from scipy.cluster.hierarchy import linkage, dendrogram
result1 = linkage(result,
metric = 'braycurtis',
#metric = 'canberra',
#metric = 'chebyshev',
#metric = 'cityblock',
#metric = 'correlation',
#metric = 'cosine',
#metric = 'euclidean',
#metric = 'hamming',
#metric = 'jaccard',
#method= 'single')
method = 'average')
#method= 'complete')
#method='weighted')
return result1
#hierarchy = hierarchy(result)
#display(hierarchy)
def label_to_colors(label):
color_dict = dict([(color[0], color[1]['color']) for color in zip(np.unique(label), plt.rcParams['axes.prop_cycle'])])
colors = np.empty(label.shape, np.object)
for k, v in color_dict.items():
colors[label==k] = v
return colors
#def cluster_visualization(x, y, label, cluster, method, n_clusters):
def cluster_visualization(x, y, label, cluster):
plt.figure(figsize = (30, 15))
plt.subplot(1,2,1)
plt.scatter(x, y, c=label_to_colors(label))
# for i in range(10):
# tmp_df = df[df['label'] == i]
# plt.scatter(tmp_df['x'], tmp_df['y'], label=i)
# plt.legend(loc='upper left', bbox_to_anchor=(1,1))
plt.subplot(1,2,2)
plt.scatter(x, y, c=label_to_colors(cluster))
# for i in range(n_clusters):
# tmp_df = df[df[method] == i]
# plt.scatter(tmp_df['x'], tmp_df['y'], label=i)
# plt.legend(loc='upper left', bbox_to_anchor=(1,1))
for i in range(len(feature_dims)):
cluster_visualization(tsnes[i][:,0], tsnes[i][:,1], labels, kms[i])
# https://qiita.com/mamika311/items/75c24f6892f85593f7e7
from sklearn.metrics.cluster import adjusted_rand_score
for i in range(len(feature_dims)):
print("dim:" + str(feature_dims[i]) + " RMI: " + str(adjusted_rand_score(labels, kms[i])))
dim:2 RMI: 0.36620498031529986
dim:3 RMI: 0.41914836520424376
dim:4 RMI: 0.49394921137719777
dim:5 RMI: 0.5245649990462847
dim:6 RMI: 0.47705674510916446
dim:7 RMI: 0.41013993209378174
dim:8 RMI: 0.3698302406743967
dim:9 RMI: 0.32840225806718926
dim:10 RMI: 0.4466812382927318
dim:11 RMI: 0.4090677997413063
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.normalized_mutual_info_score.html
# https://qiita.com/kotap15/items/38289edfe822005e1e44
from sklearn.metrics import normalized_mutual_info_score
#display(normalized_mutual_info_score(labels, df['km']))
for i in range(len(feature_dims)):
print("dim:" + str(feature_dims[i]) + " NMI: " + str(normalized_mutual_info_score(labels, kms[i])))
dim:2 NMI: 0.5199419992579754
dim:3 NMI: 0.56100353575167
dim:4 NMI: 0.605060303081276
dim:5 NMI: 0.6020900415664949
dim:6 NMI: 0.5631744057166579
dim:7 NMI: 0.5014462787979749
dim:8 NMI: 0.46110014862882315
dim:9 NMI: 0.42836636346088663
dim:10 NMI: 0.5187118150024308
dim:11 NMI: 0.48519256224162205
def shilhouette(clusters, x_test):
from sklearn.metrics import silhouette_samples
from matplotlib import cm
plt.figure(figsize = (10, 10))
cluster_labels=np.unique(clusters)
n_clusters=cluster_labels.shape[0]
silhouette_vals=silhouette_samples(x_test,clusters,metric='euclidean')
y_ax_lower,y_ax_upper=0,0
yticks=[]
for i,c in enumerate(cluster_labels):
c_silhouette_vals=silhouette_vals[clusters==c]
print(len(c_silhouette_vals))
c_silhouette_vals.sort()
y_ax_upper +=len(c_silhouette_vals)
color=cm.jet(float(i)/n_clusters)
plt.barh(range(y_ax_lower,y_ax_upper),
c_silhouette_vals,
height=1.0,
edgecolor='none',
color=color
)
yticks.append((y_ax_lower+y_ax_upper)/2.)
y_ax_lower += len(c_silhouette_vals)
#If the silhouette coefficient is 1, clustering is good.
#Also, when the width of the silhouette is equal on average in terms of the number of clusters, it indicates that the entire data can be divided equally.
#This division width=A possible setting method is to optimize k so that the widths of the silhouette bars are equal and the silhouette coefficient approaches 1..
#Draw a line at the average position
silhouette_avg=np.mean(silhouette_vals)
plt.axvline(silhouette_avg,color="red",linestyle="--")
plt.ylabel("Cluster")
plt.xlabel("Silhouette coefficient")
for i in range(len(feature_dims)):
shilhouette(kms[i], x_test)
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