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ConvDEC.py
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ConvDEC.py
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"""
Tensorflow implementation for ConvDEC and ConvDEC-DA algorithms:
- Xifeng Guo, En Zhu, Xinwang Liu, and Jianping Yin. Deep Embedded Clustering with Data Augmentation. ACML 2018.
Author:
Xifeng Guo. 2018.6.30
"""
from tensorflow.keras.layers import Conv2D, Conv2DTranspose, Dense, Flatten, Reshape, InputLayer
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from FcDEC import FcDEC, ClusteringLayer
def CAE(input_shape=(28, 28, 1), filters=[32, 64, 128, 10]):
model = Sequential()
if input_shape[0] % 8 == 0:
pad3 = 'same'
else:
pad3 = 'valid'
model.add(InputLayer(input_shape))
model.add(Conv2D(filters[0], 5, strides=2, padding='same', activation='relu', name='conv1'))
model.add(Conv2D(filters[1], 5, strides=2, padding='same', activation='relu', name='conv2'))
model.add(Conv2D(filters[2], 3, strides=2, padding=pad3, activation='relu', name='conv3'))
model.add(Flatten())
model.add(Dense(units=filters[3], name='embedding'))
model.add(Dense(units=filters[2]*int(input_shape[0]/8)*int(input_shape[0]/8), activation='relu'))
model.add(Reshape((int(input_shape[0]/8), int(input_shape[0]/8), filters[2])))
model.add(Conv2DTranspose(filters[1], 3, strides=2, padding=pad3, activation='relu', name='deconv3'))
model.add(Conv2DTranspose(filters[0], 5, strides=2, padding='same', activation='relu', name='deconv2'))
model.add(Conv2DTranspose(input_shape[2], 5, strides=2, padding='same', name='deconv1'))
encoder = Model(inputs=model.input, outputs=model.get_layer('embedding').output)
return model, encoder
class ConvDEC(FcDEC):
def __init__(self,
input_shape,
filters=[32, 64, 128, 10],
n_clusters=10):
self.n_clusters = n_clusters
self.input_shape = input_shape
self.datagen = ImageDataGenerator(width_shift_range=0.1, height_shift_range=0.1, rotation_range=10)
self.datagenx = ImageDataGenerator()
self.autoencoder, self.encoder = CAE(input_shape, filters)
# Define ConvIDEC model
clustering_layer = ClusteringLayer(self.n_clusters, name='clustering')(self.encoder.output)
self.model = Model(inputs=self.autoencoder.input,
outputs=clustering_layer)