Module dimdrop.models.dec
Source code
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping
from keras.models import Model
from sklearn.cluster import KMeans
import math
import numpy as np
from .autoencoder import Autoencoder
from ..layers import ClusteringLayer
from ..util import DECSequence
class DEC(Autoencoder):
"""
Deep Embedded Clustering model
References
----------
* Junyuan Xie, Ross B. Girshick, and Ali Farhadi. Unsupervised deep
embedding for clustering analysis. *CoRR*, abs/1511.06335, 2015.
"""
def __init__(
self,
in_dim,
out_dim,
k,
layer_sizes=[500, 500, 2000],
epochs=1000,
lr=0.1,
scale=True,
log=False,
batch_size=256,
patience=3,
tol=0.01,
decay=True,
verbose=0):
super().__init__(
in_dim,
out_dim,
layer_sizes=layer_sizes,
lr=lr,
scale=scale,
log=log,
batch_size=batch_size,
patience=patience,
epochs=epochs,
regularizer=None,
pretrain_method='stacked',
decay=decay,
verbose=verbose
)
self.k = k
self.tol = tol
def fit(self, data):
super().fit(data)
data = self.data_transform(data)
clustering_layer = ClusteringLayer(
self.k, name='clustering')(self.encoder.output)
if self.verbose:
print('Initializing cluster centers')
kmeans = KMeans(n_clusters=self.k, n_init=20)
y_pred = kmeans.fit_predict(self.encoder.predict(data))
self.clustering_model = Model(inputs=self.encoder.input,
outputs=clustering_layer)
self.clustering_model.get_layer(name='clustering').set_weights(
[kmeans.cluster_centers_])
self.clustering_model.compile(
optimizer=Adam(self.lr, decay=self.lr / self.epochs),
loss='kld'
)
sequence = DECSequence(data, self.clustering_model, self.batch_size)
early_stopping = EarlyStopping(monitor='loss', patience=self.patience)
if self.verbose:
print('Clustering optimization')
self.clustering_model.fit_generator(
sequence,
math.ceil(data.shape[0] / self.batch_size),
epochs=self.epochs,
callbacks=[early_stopping],
verbose=self.verbose
)
def soft_cluster_assignments(self, data):
"""Get the soft cluster assignments of the clustering layer of the DEC
network for the input data.
Parameters
----------
data : array
The input data
Returns
-------
array of cluster assignments
"""
return np.apply_along_axis(
np.argmax,
1,
self.clustering_model.predict(data)
)Classes
class DEC (in_dim, out_dim, k, layer_sizes=[500, 500, 2000], epochs=1000, lr=0.1, scale=True, log=False, batch_size=256, patience=3, tol=0.01, decay=True, verbose=0)-
Deep Embedded Clustering model
References
- Junyuan Xie, Ross B. Girshick, and Ali Farhadi. Unsupervised deep embedding for clustering analysis. CoRR, abs/1511.06335, 2015.
Source code
class DEC(Autoencoder): """ Deep Embedded Clustering model References ---------- * Junyuan Xie, Ross B. Girshick, and Ali Farhadi. Unsupervised deep embedding for clustering analysis. *CoRR*, abs/1511.06335, 2015. """ def __init__( self, in_dim, out_dim, k, layer_sizes=[500, 500, 2000], epochs=1000, lr=0.1, scale=True, log=False, batch_size=256, patience=3, tol=0.01, decay=True, verbose=0): super().__init__( in_dim, out_dim, layer_sizes=layer_sizes, lr=lr, scale=scale, log=log, batch_size=batch_size, patience=patience, epochs=epochs, regularizer=None, pretrain_method='stacked', decay=decay, verbose=verbose ) self.k = k self.tol = tol def fit(self, data): super().fit(data) data = self.data_transform(data) clustering_layer = ClusteringLayer( self.k, name='clustering')(self.encoder.output) if self.verbose: print('Initializing cluster centers') kmeans = KMeans(n_clusters=self.k, n_init=20) y_pred = kmeans.fit_predict(self.encoder.predict(data)) self.clustering_model = Model(inputs=self.encoder.input, outputs=clustering_layer) self.clustering_model.get_layer(name='clustering').set_weights( [kmeans.cluster_centers_]) self.clustering_model.compile( optimizer=Adam(self.lr, decay=self.lr / self.epochs), loss='kld' ) sequence = DECSequence(data, self.clustering_model, self.batch_size) early_stopping = EarlyStopping(monitor='loss', patience=self.patience) if self.verbose: print('Clustering optimization') self.clustering_model.fit_generator( sequence, math.ceil(data.shape[0] / self.batch_size), epochs=self.epochs, callbacks=[early_stopping], verbose=self.verbose ) def soft_cluster_assignments(self, data): """Get the soft cluster assignments of the clustering layer of the DEC network for the input data. Parameters ---------- data : array The input data Returns ------- array of cluster assignments """ return np.apply_along_axis( np.argmax, 1, self.clustering_model.predict(data) )Ancestors
Methods
def soft_cluster_assignments(self, data)-
Get the soft cluster assignments of the clustering layer of the DEC network for the input data.
Parameters
data:array- The input data
Returns
arrayofclusterassignments
Source code
def soft_cluster_assignments(self, data): """Get the soft cluster assignments of the clustering layer of the DEC network for the input data. Parameters ---------- data : array The input data Returns ------- array of cluster assignments """ return np.apply_along_axis( np.argmax, 1, self.clustering_model.predict(data) )
Inherited members