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Commit 3bbeb840 authored by Sensing's avatar Sensing
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import tensorflow as tf
import numpy as np
import pickle
import normalization
import cv2
import math
import os
os.environ['CUDA_VISIBLE_DEVICES']='2'
class autoencoder():
def __init__(
self,
train_data = None,
batch_size = 16,
learning_rate = 0.001,
training_epochs = 20,
time_scale = 20,
param_file = False,
is_train = True
):
self.train = train_data
self.batch_size = batch_size
self.lr = learning_rate
self.learning_rate=learning_rate
self.is_train = is_train
self.training_epochs = training_epochs
self.time_scale = time_scale
self.build()
print "Neural networks build!"
self.saver = tf.train.Saver()
self.sess = tf.Session()
init = tf.global_variables_initializer()
self.sess.run(init)
if is_train is True:
if param_file is True:
self.saver.restore(self.sess, "./params/train.ckpt")
print("loading neural-network params...")
self.learn()
else:
print "learning initialization!"
self.learn()
else:
self.saver.restore(self.sess, "./params/train.ckpt")
self.show()
def build(self):
self.input = tf.placeholder(tf.float32, shape = [None, 30, self.time_scale, 4], name='csi_input')
self.tag = tf.placeholder(tf.float32, shape = [None, 120, 160, 1], name ='image_origin')
self.output= tf.placeholder(tf.float32, shape = [None, 120, 160,1], name='image_output')
with tf.variable_scope('CNN'):
alpha = 0.01
w_initializer = tf.random_normal_initializer(0.,0.1)
b_initializer = tf.constant_initializer(0.1)
self.W_e_conv1 = tf.get_variable('w1', [3, 3, 4, 8], initializer=w_initializer)
b_e_conv1 = tf.get_variable('b1', [8, ], initializer=b_initializer)
self.conv1 = tf.nn.relu(
tf.add(tf.nn.conv2d(self.input, self.W_e_conv1, strides=[1, 2, 2, 1], padding='SAME'), b_e_conv1))
print self.conv1.shape
self.W_e_conv2 = tf.get_variable('w2', [1, 1, 8, 8], initializer=w_initializer)
b_e_conv2 = tf.get_variable('b2', [8, ], initializer=b_initializer)
self.conv2 = tf.nn.relu(
tf.add(tf.nn.conv2d(self.conv1, self.W_e_conv2, strides=[1, 1, 1, 1], padding='SAME'), b_e_conv2))
print self.conv2.shape
self.W_e_conv3 = tf.get_variable('w3', [3, 3, 8, 32], initializer=w_initializer)
b_e_conv3 = tf.get_variable('b3', [32, ], initializer=b_initializer)
self.conv3 = tf.nn.relu(
tf.add(tf.nn.conv2d(self.conv2, self.W_e_conv3, strides=[1, 2, 2, 1], padding='SAME'), b_e_conv3))
print self.conv3.shape
self.W_e_conv4 = tf.get_variable('w4', [1, 1, 32, 32], initializer=w_initializer)
b_e_conv4 = tf.get_variable('b4', [32, ], initializer=b_initializer)
self.conv4 = tf.nn.relu(
tf.add(tf.nn.conv2d(self.conv3, self.W_e_conv4, strides=[1, 1, 1, 1], padding='SAME'), b_e_conv4))
print self.conv4.shape
self.W_e_conv5 = tf.get_variable('w5', [3, 3, 32, 128], initializer=w_initializer)
b_e_conv5 = tf.get_variable('b5', [128, ], initializer=b_initializer)
self.conv5 = tf.nn.relu(
tf.add(tf.nn.conv2d(self.conv4, self.W_e_conv5, strides=[1, 2, 2, 1], padding='SAME'), b_e_conv5))
print self.conv5.shape
self.W_e_conv6 = tf.get_variable('w6', [1, 1, 128, 128], initializer=w_initializer)
b_e_conv6 = tf.get_variable('b6', [128, ], initializer=b_initializer)
self.conv6 = tf.nn.relu(
tf.add(tf.nn.conv2d(self.conv5, self.W_e_conv6, strides=[1, 1, 1, 1], padding='SAME'), b_e_conv6))
print self.conv6.shape
weight_features = self.SENET(self.conv6, 1)
weight_features = tf.reshape(weight_features, [-1, 4 * 3 * 128])
self.w_code = tf.get_variable('w_code', [4 * 3 * 128, 8 * 10 * 128], initializer=w_initializer, )
self.b_code = tf.get_variable('b_code', [8 * 10 * 128, ], initializer=b_initializer, )
encoder = tf.nn.relu(tf.matmul(weight_features, self.w_code) + self.b_code)
encoder = tf.reshape(encoder, [-1, 8, 10, 128])
decoder_1 = tf.image.resize_images(encoder, size=(15, 20),
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
self.W_d_conv1 = tf.get_variable('w_d_1', [1, 1, 128, 64], initializer=w_initializer)
decoder_1 = tf.nn.conv2d(decoder_1, self.W_d_conv1, strides=[1, 1, 1, 1], padding='SAME', )
decoder_1 = tf.maximum(alpha * decoder_1, decoder_1)
print decoder_1.shape
self.W_d_conv2 = tf.get_variable('w_d_2', [1, 1, 64, 64], initializer=w_initializer)
decoder_2 = tf.nn.conv2d(decoder_1, self.W_d_conv2, strides=[1, 1, 1, 1], padding='SAME', )
decoder_2 = tf.maximum(alpha * decoder_2, decoder_2)
print decoder_2.shape
decoder_3 = tf.image.resize_images(decoder_2, size=(30, 40),
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
self.W_d_conv3 = tf.get_variable('w_d_3', [3, 3, 64, 32], initializer=w_initializer)
decoder_3 = tf.nn.conv2d(decoder_3, self.W_d_conv3, strides=[1, 1, 1, 1], padding='SAME', )
decoder_3 = tf.maximum(alpha * decoder_3, decoder_3)
print decoder_3.shape
self.W_d_conv4 = tf.get_variable('w_d_4', [3, 3, 32, 32], initializer=w_initializer)
decoder_4 = tf.nn.conv2d(decoder_3, self.W_d_conv4, strides=[1, 1, 1, 1], padding='SAME', )
decoder_4 = tf.maximum(alpha * decoder_4, decoder_4)
print decoder_4.shape
decoder_5 = tf.image.resize_images(decoder_4, size=(60, 80),
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
self.W_d_conv5 = tf.get_variable('w_d_5', [3, 3, 32, 8], initializer=w_initializer)
decoder_5 = tf.nn.conv2d(decoder_5, self.W_d_conv5, strides=[1, 1, 1, 1], padding='SAME', )
decoder_5 = tf.maximum(alpha * decoder_5, decoder_5)
print decoder_5.shape
self.W_d_conv6 = tf.get_variable('w_d_6', [3, 3, 8, 8], initializer=w_initializer)
decoder_6 = tf.nn.conv2d(decoder_5, self.W_d_conv6, strides=[1, 1, 1, 1], padding='SAME', )
decoder_6 = tf.maximum(alpha * decoder_6, decoder_6)
print decoder_6.shape
decoder_7 = tf.image.resize_images(decoder_6, size=(120, 160),
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
self.W_d_conv7 = tf.get_variable('w_d_7', [3, 3, 8, 1], initializer=w_initializer)
decoder_7 = tf.nn.conv2d(decoder_7, self.W_d_conv7, strides=[1, 1, 1, 1], padding='SAME', )
decoder_7 = tf.maximum(alpha * decoder_7, decoder_7)
print decoder_7.shape
self.output = tf.reshape(decoder_7, [-1, 120, 160, 1])
max = tf.reduce_max(self.output)
min = tf.reduce_min(self.output)
self.output = (self.output - min) / (max - min)
self.output = tf.clip_by_value(self.output, 1e-7, 0.9999999)
with tf.variable_scope('loss'):
alpha, beta, rho = 5e-6, 7.5e-6, 0.08
Wset = [self.W_e_conv1, self.W_e_conv2, self.W_e_conv3, self.w2, self.W_d_conv1,self.W_d_conv2,self.W_d_conv3]
self.loss =tf.reduce_mean(-tf.reduce_sum(self.tag*tf.log(self.output)+(1.0-self.tag)*tf.log(1.0-self.output)))
with tf.variable_scope('train'):
self.optimizer = tf.train.AdamOptimizer(self.lr).minimize(self.loss)
def batch_Convert(self, csidata, image, csi_index_list):
csidata_batch, image_batch = None, None
for index in range(len(image)):
xs = csidata[:,csi_index_list[index]-self.time_scale+1:csi_index_list[index]+1 ,:]
ys = image[index]
if (index)%4==0:
csidata_batch = np.array([xs]) if csidata_batch is None else np.append(csidata_batch, [xs], axis=0)
image_batch = np.array([ys]) if image_batch is None else np.append(image_batch, [ys], axis= 0)
return csidata_batch, image_batch
def learn(self):
stop_flag=0
for j in range(self.training_epochs):
for train_data in self.train:
print train_data[2].shape
xs = train_data[0].astype(np.float32)
xs = np.nan_to_num(xs)
batch_xs, batch_ys = self.batch_Convert(xs, train_data[1], train_data[2])
print batch_xs.shape
batch_xs = np.reshape(batch_xs, [-1, 30, self.time_scale, 4])
batch_ys = batch_ys.astype(np.float32)
batch_ys = np.nan_to_num(batch_ys)
batch_ys = np.reshape(batch_ys, [-1, 120, 160, 1])
batch_ys = np.clip(batch_ys, 1e-7, 0.9999999)
for i in range(2000):
loss = 0
_, c ,output,tag= self.sess.run([self.optimizer, self.loss,self.output,self.tag], feed_dict={self.input: batch_xs, self.tag: batch_ys})
if i==10:
pass
loss += c
if math.isnan(loss) is True:
stop_flag=1
break
if np.any(np.isnan(batch_xs)):
print "Input Nan Type Error!! "
if np.any(np.isnan(batch_ys)):
print "Tag Nan Type Error!! "
if i % 5 == 0:
print("Total Epoch:", '%d' % (j), "Pic Rpoch:",'%d' % (i), "total cost=", "{:.9f}".format(loss))
if stop_flag==1:
break
if stop_flag==1:
break
print("Optimization Finished!")
self.saver.save(self.sess, "./params/train.ckpt")
def show(self):
"""
display the performance of autoencoder
:return: a autoencoder model using unsupervised learning
"""
count = 0
for train_data in self.train:
xs = train_data[0].astype(np.float32)
xs = np.nan_to_num(xs)
batch_xs, batch_ys = self.batch_Convert(xs, train_data[1], train_data[2])
batch_xs = np.reshape(batch_xs, [-1, 30, self.time_scale, 4])
output = self.sess.run(self.output, feed_dict={self.input: batch_xs})
output = np.reshape(output, (-1, 120, 160, 1))
for i in range(len(output)):
output[i] = output[i] * 255
output1 = output[i].astype(np.uint8)
# cv2.imshow("Image", output1)
# cv2.waitKey(0)
# cv2.imwrite('generator/'+str(count)+'.jpg',output1)
#
#
batch_ys[i] = batch_ys[i] * 255
target = batch_ys[i].astype(np.uint8)
cv2.imwrite('target/' + str(count) + '.jpg', target)
count += 1
def conv2d(self, x, W):
return tf.nn.conv2d(x, W, strides=[1,2,2,1], padding='SAME')
def deconv2d(self, x,W, output_shape):
return tf.nn.conv2d_transpose(x, W, output_shape, strides=[1,2,2,1], padding = 'SAME')
def kldlv(rho, rho_hat):
invrho = tf.subtract(tf.constant(1.), rho)
invrhohat = tf.subtract(tf.constant(1.), rho_hat)
logrho = tf.add(logfunc(rho, rho_hat), logfunc(invrho, invrhohat))
return logrho
def logfunc(x, x2):
return tf.multiply(x, tf.log(tf.div(x, x2)))
def batchNormalization(data):
for each_item in range(len(data)):
data[each_item] = normalization.MINMAXNormalization(data[each_item])
def package(train_data):
csi_rx1,csi_rx2, image ,index= train_data[0], train_data[1],train_data[2],train_data[3]
tn_data = np.append(csi_rx1, csi_rx2, axis=0)
tn_data = np.transpose(tn_data, [1,2,0])
return [tn_data, image,index]
if __name__ =="__main__":
np.set_printoptions(threshold=np.inf)
train_data=[]
for i in range( ):#data range
index=i+1
if index == :#abnormal data
pass
elif index == :#test data
pass
# with open('../data_523/data_index/training_data_' + str(index) + '.pkl', 'rb') as handle:
# data_temp = pickle.load(handle)
# batchNormalization(data_temp[0])
# #batchNormalization(data_temp[1])
# data_nor = package(data_temp)
# train_data.append(data_nor)
else:
with open('../data_523/data_index_dwt/training_data_' + str(index) + '.pkl', 'rb') as handle:
data_temp = pickle.load(handle)
batchNormalization(data_temp[0])
#batchNormalization(data_temp[1])
data_nor = package(data_temp)
train_data.append(data_nor)
print len(train_data)
autoencoder(train_data=train_data)
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