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from keras.layers import Input, Dense
from keras.models import Model
from keras import optimizers
from keras.callbacks import LearningRateScheduler
from keras.callbacks import EarlyStopping
import glob
from sklearn.preprocessing import MinMaxScaler
import keras
import scipy.io as sio
import numpy as np
import sys
import os
import random
from sklearn import preprocessing
from random import randint
#from cop_kmeans import cop_kmeans, l2_distance
import math
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
#config = tf.ConfigProto()
#config.gpu_options.per_process_gpu_memory_fraction = 0.5
#set_session(tf.Session(config=config))
def deepSSAEMulti(n_dim, n_hidden1, n_hidden2, n_classes):
input_layer = Input(shape=(n_dim,))
encoded = Dense(n_hidden1, activation='relu')(input_layer)
encoded = Dense(n_hidden2, activation='relu', name="low_dim_features")(encoded)
decoded = Dense(n_hidden1, activation='relu')(encoded)
decoded = Dense(n_dim, activation='sigmoid')(decoded)
classifier = Dense(n_classes, activation='softmax')(encoded)
rmsPropOpt = optimizers.RMSprop(lr=0.0005)
rmsPropOpt1 = optimizers.RMSprop(lr=0.0005)
autoencoder = Model(inputs=[input_layer], outputs=[decoded])
autoencoder.compile(optimizer=rmsPropOpt, loss=['mse'])
ssautoencoder = Model(inputs=[input_layer], outputs=[decoded, classifier])
ssautoencoder.compile(optimizer=rmsPropOpt1, loss=['mse','categorical_crossentropy'], loss_weights=[1., 1.])
return [autoencoder, ssautoencoder]
def feature_extraction(model, data, layer_name):
feat_extr = Model(inputs= model.input, outputs= model.get_layer(layer_name).output)
return feat_extr.predict(data)
def learn_SingleReprSS(X_tot, idx_train, Y):
n_classes = len(np.unique(Y))
idx_train = idx_train.astype("int")
X_train = X_tot[idx_train]
Y_train = Y[idx_train]
encoded_Y_train = keras.utils.to_categorical(Y_train, n_classes)
n_row, n_col = X_tot.shape
perc_50 = math.ceil( n_col -1)
perc_10 = math.ceil( n_col * 0.5)
perc_5 = math.ceil( (n_col * 0.5) - 1)
perc_1 = math.ceil( n_col * 0.25)
n_hidden1 = randint(perc_10, perc_50)
n_hidden2 = randint(perc_1, perc_5)
ae, ssae = deepSSAEMulti(n_col, n_hidden1, n_hidden2, n_classes)
for i in range(200):
print "epoch: %d" % i
ae.fit(X_tot, X_tot, epochs=1, batch_size=64, shuffle=True, verbose=1)
ssae.fit(X_train, [X_train, encoded_Y_train], epochs=1, batch_size=8, shuffle=True, verbose=1)
new_train_feat = feature_extraction(ae, X_tot, "low_dim_features")
return new_train_feat
def learn_representationSS(X_tot, idx_train, Y, ens_size):
intermediate_reprs = np.array([])
for l in range(ens_size):
embeddings = learn_SingleReprSS(X_tot, idx_train, Y)
if intermediate_reprs.size == 0:
intermediate_reprs = embeddings
else:
intermediate_reprs = np.column_stack((intermediate_reprs, embeddings))
return intermediate_reprs
def normData(data):
X = np.array(data)
scaler = MinMaxScaler()
scaler.fit(X)
return scaler.transform(X)
if __name__ == "__main__":
#Directory Name on which data are stored
directory = sys.argv[1]
#File in the directory/labels folder with label information
#The file has as many row as the number of labeled example
#Each row has two information: the position of the labeled example w.r.t. the data file data.npy, the associated label
fileName = sys.argv[2]
dataset_name = directory+"/data.npy"
dataset_cl_name = directory+"/class.npy"
dataset = np.load(dataset_name)
dataset = normData(dataset)
dataset_cl = np.load(dataset_cl_name)
#Size of the ensemble
ens_size = 30
dirEmb = "embeddings"
dir_path = directory+"/"+dirEmb
if not os.path.exists(dir_path):
os.makedirs(dir_path)
#for fileName in files:
fName = fileName.split("/")[-1]
run_id, nsamples = fName.split(".")[0].split("_")
outFileName = dir_path+"/"+run_id+"_"+nsamples+".npy"
if os.path.exists(outFileName):
print "ALREADY EXIST %s" % outFileName
exit()
print "CREATE EMBEDDINGS for the file %s" % fileName
sys.stdout.flush()
idx_cl = np.load(fileName)
idx_train = idx_cl[:,0]
new_feat_ssae = learn_representationSS(dataset, idx_train, dataset_cl, ens_size)
outFileName = dir_path+"/"+run_id+"_"+nsamples+".npy"
np.save(outFileName, new_feat_ssae)