def __init__(self, dims, n_clusters=4, alpha=1.0):
super(IDEC, self).__init__()
self.dims = dims
self.input_dim = dims[0]
self.n_stacks = len(self.dims) - 1
self.n_clusters = n_clusters
self.alpha = alpha
self.autoencoder = autoencoder(self.dims)
hidden = self.autoencoder.get_layer(name='encoder_%d' %
(self.n_stacks - 1)).output
self.encoder = Model(inputs=self.autoencoder.input, outputs=hidden)
# prepare IDEC model
clustering_layer = ClusteringLayer(self.n_clusters,
alpha=self.alpha,
name='clustering')(hidden)
self.model = Model(inputs=self.autoencoder.input,
outputs=[clustering_layer, self.autoencoder.output])
self.pretrained = False
self.centers = []
self.y_pred = []
def __init__(self, dims, n_clusters=10, alpha=1.0, batch_size=256):
super(IDEC, self).__init__()
self.dims = dims
self.input_dim = dims[0]
self.n_stacks = len(self.dims) - 1
self.n_clusters = n_clusters
self.alpha = alpha
self.batch_size = batch_size
self.autoencoder = autoencoder(self.dims)
def __init__(self,
dims,
gamma=0.1,
n_clusters=10,
alpha=1.0,
init='glorot_uniform'):
# super(IDEC,self).__init__(dims,
# n_clusters=10,
# alpha=1.0,
# init='glorot_uniform')
self.dims = dims
self.input_dim = dims[0]
self.n_stacks = len(self.dims) - 1
self.n_clusters = n_clusters
self.alpha = alpha
self.autoencoder, self.encoder = autoencoder(self.dims, init=init)
# prepare DEC model
clustering_layer = ClusteringLayer(self.n_clusters, name='clustering')(
self.encoder.output)
self.model = Model(inputs=self.autoencoder.input,
outputs=[clustering_layer, self.autoencoder.output])