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dec.py
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dec.py
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from keras.initializers import *
from keras.models import *
from keras.layers import *
from keras.optimizers import *
from sklearn.cluster import KMeans
import os
import numpy as np
import sys
import utils
class ClusterLayer(Layer):
def __init__(self, output_dim, input_dim=None, weights=None, alpha=1.0, **kwargs):
self.output_dim = output_dim
self.input_dim = input_dim
self.alpha = alpha
# k-means cluster centre locations
self.initial_weights = weights
self.input_spec = [InputSpec(ndim=2)]
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(ClusterLayer, self).__init__(**kwargs)
def build(self, input_shape):
assert len(input_shape) == 2
input_dim = input_shape[1]
self.input_spec = [InputSpec(dtype=K.floatx(),
shape=(None, input_dim))]
self.W = K.variable(self.initial_weights)
self.trainable_weights = [self.W]
def call(self, x, mask=None):
q = 1.0 / (1.0 + K.sqrt(K.sum(K.square(K.expand_dims(x, 1) - self.W), axis=2)) ** 2 / self.alpha)
q = q ** ((self.alpha + 1.0) / 2.0)
q = K.transpose(K.transpose(q) / K.sum(q, axis=1))
return q
def get_output_shape_for(self, input_shape):
assert input_shape and len(input_shape) == 2
return (input_shape[0], self.output_dim)
def compute_output_shape(self, input_shape):
assert input_shape and len(input_shape) == 2
return (input_shape[0], self.output_dim)
def get_config(self):
config = {'output_dim': self.output_dim,
'input_dim': self.input_dim}
base_config = super(ClusterLayer, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class DeepEmbeddingClustering(object):
def __init__(self, args, n_clusters, alpha=1.0, cluster_centroid=None):
'''
:param n_clusters: number of cluster(classes)
:param alpha: soft-clustering hyper parameter.
:param cluster_centroid: centroid each cluster.
'''
super(DeepEmbeddingClustering, self).__init__()
self.n_clusters = n_clusters
self.input_dim = args.embedding_size
self.alpha = alpha
self.pretrained_weights_path = os.path.join(args.save_weight_path, "ae_weights_{}.h5".format(args.dataset))
self.cluster_centroid = cluster_centroid
self.batch_size = args.dec_batch_size
self.learning_rate = args.dec_lr
# encoder layer dimensions. Decoder dimension is the opposite.
self.encoders_dims = [self.input_dim, 500, 500, 2000, 50]
self.input_layer = Input(shape=(self.input_dim,), name='input')
self.dropout_fraction = 0.2
self.layer_wise_autoencoders = []
self.encoders = []
self.decoders = []
for i in range(1, len(self.encoders_dims)):
encoder_activation = 'linear' if i == (len(self.encoders_dims) - 1) else 'selu'
encoder = Dense(self.encoders_dims[i], activation=encoder_activation,
input_shape=(self.encoders_dims[i - 1],),
kernel_initializer=RandomNormal(mean=0.0, stddev=0.01, seed=None),
bias_initializer='zeros', name='encoder_dense_%d' % i)
self.encoders.append(encoder)
decoder_index = len(self.encoders_dims) - i
decoder_activation = 'linear' if i == 1 else 'selu'
decoder = Dense(self.encoders_dims[i - 1], activation=decoder_activation,
kernel_initializer=RandomNormal(mean=0.0, stddev=0.01, seed=None),
bias_initializer='zeros',
name='decoder_dense_%d' % decoder_index)
self.decoders.append(decoder)
autoencoder = Sequential([
Dropout(self.dropout_fraction, input_shape=(self.encoders_dims[i - 1],),
name='encoder_dropout_%d' % i),
encoder,
Dropout(self.dropout_fraction, name='decoder_dropout_%d' % decoder_index),
decoder
])
autoencoder.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
self.layer_wise_autoencoders.append(autoencoder)
# build the end-to-end autoencoder for fine-tuning
# Note that at this point dropout is discarded
self.encoder = Sequential(self.encoders)
self.encoder.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
self.decoders.reverse()
self.autoencoder = Sequential(self.encoders + self.decoders)
self.autoencoder.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
if cluster_centroid is not None:
assert cluster_centroid.shape[0] == self.n_clusters
assert cluster_centroid.shape[1] == self.encoder.layers[-1].output_dim
if os.path.isfile(self.pretrained_weights_path):
self.autoencoder.load_weights(self.pretrained_weights_path)
print("Load pre-trained AE")
def p_mat(self, q):
weight = q ** 2 / q.sum(0)
return (weight.T / weight.sum(1)).T
def initialize(self, args, x_data, layerwise_pretrain_iters=50000, finetune_iters=100000):
if not os.path.isfile(self.pretrained_weights_path):
iters_per_epoch = int(len(x_data) / self.batch_size)
layerwise_epochs = max(int(layerwise_pretrain_iters / iters_per_epoch), 1)
finetune_epochs = max(int(finetune_iters / iters_per_epoch), 1)
print('layer-wise pre-train')
current_input = x_data
[train_log, lr_schedule, checkpoint, early_stopping] = utils.get_callbacks_ae(args)
# greedy-layer wise training
for i, autoencoder in enumerate(self.layer_wise_autoencoders):
if i > 0:
weights = self.encoders[i - 1].get_weights()
dense_layer = Dense(self.encoders_dims[i], input_shape=(current_input.shape[1],),
activation='selu', weights=weights,
name='encoder_dense_copy_%d' % i)
encoder_model = Sequential([dense_layer])
encoder_model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
current_input = encoder_model.predict(current_input)
autoencoder.fit(current_input, current_input,
batch_size=self.batch_size, epochs=layerwise_epochs, callbacks=[lr_schedule])
self.autoencoder.layers[i].set_weights(autoencoder.layers[1].get_weights())
self.autoencoder.layers[len(self.autoencoder.layers) - i - 1].set_weights(
autoencoder.layers[-1].get_weights())
print('Fine-tuning auto-encoder')
# update encoder and decoder weights:
self.autoencoder.fit(x_data, x_data,
batch_size=self.batch_size,
epochs=finetune_epochs,
callbacks=[train_log, lr_schedule, checkpoint, early_stopping])
else:
print('Loading pre-trained weights for auto-encoder.')
self.autoencoder.load_weights(self.pretrained_weights_path)
# update encoder, decoder
for i in range(len(self.encoder.layers)):
self.encoder.layers[i].set_weights(self.autoencoder.layers[i].get_weights())
# initialize cluster centres using k-means
print('Initializing cluster centres with k-means.')
if self.cluster_centroid is None:
kmeans = KMeans(n_clusters=self.n_clusters, n_init=20)
self.y_pred = kmeans.fit_predict(self.encoder.predict(x_data))
self.cluster_centroid = kmeans.cluster_centers_
# initial centroid using K-mean
self.dec_model = Sequential([self.encoder,
ClusterLayer(self.n_clusters, weights=self.cluster_centroid, name='dec')])
self.dec_model.compile(loss='kullback_leibler_divergence', optimizer=Adam(lr=0.0001))
return
def cluster(self, args, x_data, y_data=None, test="train", tol=0.01, iter_max=1e6, **kwargs):
save_path = os.path.join(args.save_weight_path, "dec_weights_{}.h5".format(args.dataset))
if os.path.isfile(save_path):
self.dec_model.load_weights(save_path)
print('Restored Model weight')
if test=="test":
y_pred = self.dec_model.predict(x_data, verbose=0).argmax(1)
acc = utils.cluster_acc(y_data, y_pred)
print('Accuracy ' + str(np.round(acc, 5)))
return
update_interval = x_data.shape[0] / self.batch_size
print('Update interval', update_interval)
train = True
iteration, index = 0, 0
current_acc = 0
self.accuracy = 0
while train:
sys.stdout.write('\r')
# cut off iteration
if iter_max < iteration:
print('Reached maximum iteration limit. Stopping training.')
return self.y_pred
# update (or initialize) probability distributions and propagate weight changes
# from DEC model to encoder.
if iteration % update_interval == 0:
self.q = self.dec_model.predict(x_data, verbose=0)
self.p = self.p_mat(self.q)
y_pred = self.q.argmax(1)
delta_label = (np.sum((y_pred == self.y_pred)).astype(np.float32) / y_pred.shape[0])
if y_data is not None:
current_acc = utils.cluster_acc(y_data, y_pred)
print('Iteration ' + str(iteration) + ', Accuracy ' + str(np.round(current_acc, 5)))
else:
print(str(np.round(delta_label * 100, 5)) + '% change in label assignment')
if delta_label < tol:
print('Reached tolerance threshold.')
train = False
continue
else:
self.y_pred = y_pred
# weight changes if current
if self.accuracy < current_acc:
for i in range(len(self.encoder.layers)):
self.encoder.layers[i].set_weights(self.dec_model.layers[0].layers[i].get_weights())
self.cluster_centroid = self.dec_model.layers[-1].get_weights()[0]
# save checkpoint
self.dec_model.save(save_path)
self.accuracy = current_acc
print("update weight and save checkpoint")
# train on batch
sys.stdout.write('Iteration %d, ' % iteration)
if (index + 1) * self.batch_size > x_data.shape[0]:
loss = self.dec_model.train_on_batch(x_data[index * self.batch_size::],
self.p[index * self.batch_size::])
index = 0
sys.stdout.write('Loss %f' % loss)
else:
loss = self.dec_model.train_on_batch(x_data[index * self.batch_size:(index + 1) * self.batch_size],
self.p[index * self.batch_size:(index + 1) * self.batch_size])
sys.stdout.write('Loss %f' % loss)
index += 1
iteration += 1
sys.stdout.flush()
return