def test_step():
print 'Testing...'
all_z = []
batch_size = 23
for i in range(int(len(test_order)/batch_size)):
input_image = get_data.get_jpg_test(test_jpg,test_order[batch_size*i:batch_size*(i+1)])/128.0
input_label = get_data.get_label(test_label,test_order[batch_size*i:batch_size*(i+1)])
feed_dict={}
feed_dict[hg.input_image]=input_image
feed_dict[hg.input_label]=input_label
feed_dict[hg.keep_prob]=1.0
z_miu= sess.run(hg.output,feed_dict)
all_z.append(z_miu)
all_z = np.array(all_z)
all_z = np.reshape(all_z,(-1,17))
np.save('all_output.npy',all_z)
def test_step():
print 'Testing...'
# all_ce_loss = 0
# all_output = []
# all_label = []
all_feature = []
batch_size = 23
for i in range(int(len(train_order)/batch_size)):
input_image = get_data.get_jpg_test(train_jpg,train_order[batch_size*i:batch_size*(i+1)])/128.0
input_label = get_data.get_label(train_label,train_order[batch_size*i:batch_size*(i+1)])
feed_dict={}
feed_dict[hg.input_image]=input_image
feed_dict[hg.input_label]=input_label
feed_dict[hg.keep_prob]=1.0
feature = sess.run(hg.feature,feed_dict)
# all_ce_loss += ce_loss
# for i in output:
# all_output.append(i)
# for i in input_label:
# all_label.append(i)
all_feature.append(feature)
# all_output = np.array(all_output)
# all_label = np.array(all_label)
#average_precision = average_precision_score(all_label,all_output)
all_feature = np.array(all_feature)
all_feature = np.reshape(all_feature,(-1,256))
np.save('train_feature.npy',all_feature)
def validation_step(current_step):
print 'Validating...'
all_ce_loss = 0
all_l2_loss = 0
all_total_loss = 0
all_output = []
all_label = []
for i in range(int(len(validation_order) / 18)):
input_image = get_data.get_jpg_test(
jpg_list, validation_order[18 * i:18 * (i + 1)]) / 128.0
input_label = get_data.get_label(
data, validation_order[18 * i:18 * (i + 1)])
input_nlcd = get_data.get_nlcd(
data, validation_order[18 * i:18 * (i + 1)])
feed_dict = {}
feed_dict[hg.input_image] = input_image
feed_dict[hg.input_nlcd] = input_nlcd
feed_dict[hg.input_label] = input_label
feed_dict[hg.keep_prob] = 1.0
ce_loss, l2_loss, total_loss, output = sess.run(
[hg.ce_loss, hg.l2_loss, hg.total_loss, hg.output], feed_dict)
all_ce_loss += ce_loss
all_l2_loss += l2_loss
all_total_loss += total_loss
for i in output:
all_output.append(i)
for i in input_label:
all_label.append(i)
all_output = np.array(all_output)
all_label = np.array(all_label)
auc = roc_auc_score(all_label, all_output)
ce_loss = all_ce_loss / 283.0
l2_loss = all_l2_loss / 283.0
total_loss = all_total_loss / 283.0
all_output = np.reshape(all_output, (-1))
all_label = np.reshape(all_label, (-1))
ap = average_precision_score(all_label, all_output)
time_str = datetime.datetime.now().isoformat()
tempstr = "{}: auc {:g}, ap {:g}, ce_loss {:g}, l2_loss {:g}, total_loss {:g}".format(
time_str, auc, ap, ce_loss, l2_loss, total_loss)
print(tempstr)
summary_writer.add_summary(MakeSummary('validation/auc', auc),
current_step)
summary_writer.add_summary(MakeSummary('validation/ap', ap),
current_step)
summary_writer.add_summary(MakeSummary('validation/ce_loss', ce_loss),
current_step)
return ce_loss
def test_step():
print 'Testing...'
all_recon_loss = 0
all_kl_loss = 0
all_vae_loss = 0
all_l2_loss = 0
all_total_loss = 0
all_output = []
all_label = []
for i in range(int(len(test_order)/18)):
input_image = get_data.get_jpg_test(jpg_list,test_order[18*i:18*(i+1)])/128.0
input_nlcd = get_data.get_nlcd(data,test_order[18*i:18*(i+1)])
input_label = get_data.get_label(data,test_order[18*i:18*(i+1)])
feed_dict={}
feed_dict[hg.input_nlcd]=input_nlcd
feed_dict[hg.input_image]=input_image
feed_dict[hg.input_label]=input_label
feed_dict[hg.keep_prob]=1.0
recon_loss,kl_loss,vae_loss,l2_loss,total_loss,output= sess.run([hg.recon_loss,hg.kl_loss,hg.vae_loss,hg.l2_loss,hg.total_loss,hg.output],feed_dict)
all_recon_loss += recon_loss
all_kl_loss += kl_loss
all_vae_loss += vae_loss
all_l2_loss += l2_loss
all_total_loss += total_loss
for i in output:
all_output.append(i)
for i in input_label:
all_label.append(i)
all_output = np.array(all_output)
all_label = np.array(all_label)
auc = roc_auc_score(all_label,all_output)
recon_loss = all_recon_loss/283.0
kl_loss = all_kl_loss/283.0
vae_loss = all_vae_loss/283.0
l2_loss = all_l2_loss/283.0
total_loss = all_total_loss/283.0
all_output=np.reshape(all_output,(-1))
all_label=np.reshape(all_label,(-1))
ap = average_precision_score(all_label,all_output)
time_str = datetime.datetime.now().isoformat()
tempstr = "{}: auc {:g}, ap {:g}, recon_loss {:g}, kl_loss {:g}, vae_loss {:g}, l2_loss {:g}, total_loss {:g}".format(time_str, auc, ap, recon_loss, kl_loss, vae_loss, l2_loss, total_loss)
print(tempstr)
auc = roc_auc_score(all_label,all_output)
print "new_auc {:g}".format(auc)
def test_step():
print 'Testing...'
all_recon_loss = 0
all_kl_loss = 0
all_vae_loss = 0
all_l2_loss = 0
all_total_loss = 0
all_output = []
all_label = []
for i in range(int(len(test_order)/18)):
input_image = get_data.get_jpg_test(jpg_list,test_order[18*i:18*(i+1)])
input_nlcd = get_data.get_nlcd(data,test_order[18*i:18*(i+1)])
input_label = get_data.get_label(data,test_order[18*i:18*(i+1)])
feed_dict={}
feed_dict[hg.input_nlcd]=input_nlcd
feed_dict[hg.input_image]=input_image
feed_dict[hg.input_label]=input_label
feed_dict[hg.keep_prob]=1.0
recon_loss,kl_loss,vae_loss,l2_loss,total_loss,output= sess.run([hg.recon_loss,hg.kl_loss,hg.vae_loss,hg.l2_loss,hg.total_loss,hg.output],feed_dict)
all_recon_loss += recon_loss
all_kl_loss += kl_loss
all_vae_loss += vae_loss
all_l2_loss += l2_loss
all_total_loss += total_loss
for i in output:
all_output.append(i)
for i in input_label:
all_label.append(i)
all_output = np.array(all_output)
all_label = np.array(all_label)
auc = roc_auc_score(all_label,all_output)
recon_loss = all_recon_loss/283.0
kl_loss = all_kl_loss/283.0
vae_loss = all_vae_loss/283.0
l2_loss = all_l2_loss/283.0
total_loss = all_total_loss/283.0
all_output=np.reshape(all_output,(-1))
all_label=np.reshape(all_label,(-1))
ap = average_precision_score(all_label,all_output)
time_str = datetime.datetime.now().isoformat()
tempstr = "{}: auc {:g}, ap {:g}, recon_loss {:g}, kl_loss {:g}, vae_loss {:g}, l2_loss {:g}, total_loss {:g}".format(time_str, auc, ap, recon_loss, kl_loss, vae_loss, l2_loss, total_loss)
print(tempstr)
auc = roc_auc_score(all_label,all_output)
print "new_auc {:g}".format(auc)
def test_step():
print 'Testing...'
all_ce_loss = 0
all_output = []
all_label = []
batch_size = 18
for i in range(int(len(test_order) / batch_size)):
input_image = get_data.get_jpg_test(
jpg_list, test_order[batch_size * i:batch_size *
(i + 1)]) / 128.0
input_label = get_data.get_label(
data, test_order[batch_size * i:batch_size * (i + 1)])
input_nlcd = get_data.get_nlcd(
data, test_order[batch_size * i:batch_size * (i + 1)])
feed_dict = {}
feed_dict[hg.input_image] = input_image
feed_dict[hg.input_label] = input_label
feed_dict[hg.input_nlcd] = input_nlcd
feed_dict[hg.keep_prob] = 1.0
ce_loss, output = sess.run([hg.ce_loss, hg.output], feed_dict)
all_ce_loss += ce_loss
for i in output:
all_output.append(i)
for i in input_label:
all_label.append(i)
all_output = np.array(all_output)
all_label = np.array(all_label)
#average_precision = average_precision_score(all_label,all_output)
loglike = all_ce_loss / (int(len(test_order) / batch_size))
np.save('output.npy', all_output)
np.save('label.npy', all_label)
auc = roc_auc_score(all_label, all_output)
#loglike = log_likelihood(all_label,all_output)
time_str = datetime.datetime.now().isoformat()
tempstr = "{}: auc {:g}, log_likelihood {:g}".format(
time_str, auc, loglike)
print(tempstr)
all_output = np.reshape(all_output, (-1))
all_label = np.reshape(all_label, (-1))
ap = average_precision_score(all_label, all_output)
auc_2 = roc_auc_score(all_label, all_output)
print 'ap:' + str(ap)
print 'auc_2:' + str(auc_2)
def validation_step(current_step):
print 'Validating...'
all_ce_loss = 0
all_l2_loss = 0
all_total_loss = 0
all_output = []
all_label = []
valid_batch = 23
for i in range(int(len(validation_order)/valid_batch)):
input_image = get_data.get_jpg_test(validation_jpg,validation_order[valid_batch*i:valid_batch*(i+1)])/128.0
input_label = get_data.get_label(validation_label,validation_order[valid_batch*i:valid_batch*(i+1)])
feed_dict={}
feed_dict[hg.input_image]=input_image
feed_dict[hg.input_label]=input_label
feed_dict[hg.keep_prob]=1.0
ce_loss,l2_loss,total_loss,output= sess.run([hg.ce_loss,hg.l2_loss,hg.total_loss,hg.output],feed_dict)
all_ce_loss += ce_loss
all_l2_loss += l2_loss
all_total_loss += total_loss
for i in output:
all_output.append(i)
for i in input_label:
all_label.append(i)
all_output = np.array(all_output)
all_label = np.array(all_label)
#auc = roc_auc_score(all_label,all_output)
ce_loss = all_ce_loss/(4048/valid_batch)
l2_loss = all_l2_loss/(4048/valid_batch)
total_loss = all_total_loss/(4048/valid_batch)
all_output=np.reshape(all_output,(-1))
all_label=np.reshape(all_label,(-1))
ap = average_precision_score(all_label,all_output)
auc = roc_auc_score(all_label,all_output)
time_str = datetime.datetime.now().isoformat()
tempstr = "{}: auc {:g}, ap {:g}, ce_loss {:g}, l2_loss {:g}, total_loss {:g}".format(time_str, auc, ap, ce_loss, l2_loss, total_loss)
print(tempstr)
summary_writer.add_summary(MakeSummary('validation/auc',auc),current_step)
summary_writer.add_summary(MakeSummary('validation/ap',ap),current_step)
summary_writer.add_summary(MakeSummary('validation/ce_loss',ce_loss),current_step)
return ce_loss
def test_step():
print 'Testing...'
all_recon_loss = 0
all_output = []
all_label = []
batch_size = FLAGS.batch_size
for i in range(int(len(test_order) / batch_size)):
input_nlcd = get_data.get_nlcd(
data, test_order[batch_size * i:batch_size * (i + 1)])
input_image = get_data.get_jpg_test(
jpg_list, test_order[batch_size * i:batch_size *
(i + 1)]) / 128.0
input_label = get_data.get_label(
data, test_order[batch_size * i:batch_size * (i + 1)])
feed_dict = {}
feed_dict[hg.input_nlcd] = input_nlcd
feed_dict[hg.input_image] = input_image
feed_dict[hg.input_label] = input_label
feed_dict[hg.keep_prob] = 1.0
output = sess.run(hg.output, feed_dict)
for i in output:
all_output.append(i)
for i in input_label:
all_label.append(i)
#recon_loss = all_recon_loss/(5094.0/batch_size)
all_output = np.array(all_output)
all_label = np.array(all_label)
# auc = roc_auc_score(all_label,all_output)
# all_output=np.reshape(all_output,(-1))
# all_label=np.reshape(all_label,(-1))
# ap = average_precision_score(all_label,all_output)
# time_str = datetime.datetime.now().isoformat()
# new_auc = roc_auc_score(all_label,all_output)
# tempstr = "{}: auc {:g}, ap {:g}, recon_loss {:g}, new_auc {:g}".format(time_str, auc, ap, recon_loss, new_auc)
# print(tempstr)
all_output = np.reshape(all_output, (-1, 100))
all_label = np.reshape(all_label, (-1, 100))
return all_output, all_label
def test_step():
print 'Testing...'
all_ce_loss = 0
all_output = []
all_label = []
batch_size = 18
for i in range(int(len(test_order)/batch_size)):
input_image = get_data.get_jpg_test(jpg_list,test_order[batch_size*i:batch_size*(i+1)])/128.0
input_label = get_data.get_label(data,test_order[batch_size*i:batch_size*(i+1)])
input_nlcd = get_data.get_nlcd(data,test_order[batch_size*i:batch_size*(i+1)])
feed_dict={}
feed_dict[hg.input_image]=input_image
feed_dict[hg.input_label]=input_label
feed_dict[hg.input_nlcd]=input_nlcd
feed_dict[hg.keep_prob]=1.0
ce_loss,output= sess.run([hg.ce_loss,hg.output],feed_dict)
all_ce_loss += ce_loss
for i in output:
all_output.append(i)
for i in input_label:
all_label.append(i)
all_output = np.array(all_output)
all_label = np.array(all_label)
#average_precision = average_precision_score(all_label,all_output)
loglike = all_ce_loss/(int(len(test_order)/batch_size))
np.save('output.npy',all_output)
np.save('label.npy',all_label)
auc = roc_auc_score(all_label,all_output)
#loglike = log_likelihood(all_label,all_output)
time_str = datetime.datetime.now().isoformat()
tempstr = "{}: auc {:g}, log_likelihood {:g}".format(time_str, auc,loglike)
print(tempstr)
all_output=np.reshape(all_output,(-1))
all_label=np.reshape(all_label,(-1))
ap = average_precision_score(all_label,all_output)
auc_2 = roc_auc_score(all_label,all_output)
print 'ap:'+str(ap)
print 'auc_2:'+str(auc_2)
def test_step():
print 'testing...'
# all_ce_loss = 0
# all_l2_loss = 0
# all_total_loss = 0
# all_output = []
# all_label = []
all_feature = []
batch_size = 17 * 3
for i in range(int(len(test_order) / batch_size)):
input_image = get_data.get_jpg_test(
jpg_list, test_order[batch_size * i:batch_size * (i + 1)])
input_label = get_data.get_label(
data, test_order[batch_size * i:batch_size * (i + 1)])
feed_dict = {}
feed_dict[hg.input_image] = input_image
feature = sess.run(hg.feature, feed_dict)
for i in feature:
all_feature.append(i)
# for i in input_label:
# all_label.append(i)
# all_output = np.array(all_output)
# all_label = np.array(all_label)
# #average_precision = average_precision_score(all_label,all_output)
# np.save('output.npy',all_output)
# np.save('label.npy',all_label)
# auc = roc_auc_score(all_label,all_output)
# loglike = log_likelihood(all_label,all_output)
# time_str = datetime.datetime.now().isoformat()
# tempstr = "{}: auc {:g}, log_likelihood {:g}".format(time_str, auc, loglike)
# print(tempstr)
# all_output=np.reshape(all_output,(-1))
# all_label=np.reshape(all_label,(-1))
# ap = average_precision_score(all_label,all_output)
# auc_2 = roc_auc_score(all_label,all_output)
# print 'ap:'+str(ap)
# print 'auc_2:'+str(auc_2)
np.save('resnet50_feature.npy', all_feature)
def test_step():
print 'Testing...'
all_recon_loss = 0
all_output = []
all_label = []
batch_size = FLAGS.batch_size
for i in range(int(len(test_order)/batch_size)):
input_nlcd = get_data.get_nlcd(data,test_order[batch_size*i:batch_size*(i+1)])
input_image = get_data.get_jpg_test(jpg_list,test_order[batch_size*i:batch_size*(i+1)])
input_label = get_data.get_label(data,test_order[batch_size*i:batch_size*(i+1)])
feed_dict={}
feed_dict[hg.input_nlcd]=input_nlcd
feed_dict[hg.input_image]=input_image
feed_dict[hg.input_label]=input_label
feed_dict[hg.keep_prob]=1.0
output= sess.run(hg.output,feed_dict)
for i in output:
all_output.append(i)
for i in input_label:
all_label.append(i)
#recon_loss = all_recon_loss/(5094.0/batch_size)
all_output = np.array(all_output)
all_label = np.array(all_label)
# auc = roc_auc_score(all_label,all_output)
# all_output=np.reshape(all_output,(-1))
# all_label=np.reshape(all_label,(-1))
# ap = average_precision_score(all_label,all_output)
# time_str = datetime.datetime.now().isoformat()
# new_auc = roc_auc_score(all_label,all_output)
# tempstr = "{}: auc {:g}, ap {:g}, recon_loss {:g}, new_auc {:g}".format(time_str, auc, ap, recon_loss, new_auc)
# print(tempstr)
all_output=np.reshape(all_output,(-1,100))
all_label=np.reshape(all_label,(-1,100))
return all_output,all_label
def test_step():
print 'testing...'
all_ce_loss = 0
all_l2_loss = 0
all_total_loss = 0
all_output = []
all_label = []
for i in range(int(len(test_order) / 18)):
input_image = get_data.get_jpg_test(
jpg_list, test_order[18 * i:18 * (i + 1)])
input_label = get_data.get_label(data,
test_order[18 * i:18 * (i + 1)])
feed_dict = {}
feed_dict[hg.input_image] = input_image
output = sess.run(hg.output, feed_dict)
for i in output:
all_output.append(i)
for i in input_label:
all_label.append(i)
all_output = np.array(all_output)
all_label = np.array(all_label)
#average_precision = average_precision_score(all_label,all_output)
np.save('output.npy', all_output)
np.save('label.npy', all_label)
auc = roc_auc_score(all_label, all_output)
loglike = log_likelihood(all_label, all_output)
time_str = datetime.datetime.now().isoformat()
tempstr = "{}: auc {:g}, log_likelihood {:g}".format(
time_str, auc, loglike)
print(tempstr)
all_output = np.reshape(all_output, (-1))
all_label = np.reshape(all_label, (-1))
ap = average_precision_score(all_label, all_output)
auc_2 = roc_auc_score(all_label, all_output)
print 'ap:' + str(ap)
print 'auc_2:' + str(auc_2)
def test_step():
print 'testing...'
# all_ce_loss = 0
# all_l2_loss = 0
# all_total_loss = 0
# all_output = []
# all_label = []
all_feature = []
batch_size=17*3
for i in range(int(len(test_order)/batch_size)):
input_image = get_data.get_jpg_test(jpg_list,test_order[batch_size*i:batch_size*(i+1)])
input_label = get_data.get_label(data,test_order[batch_size*i:batch_size*(i+1)])
feed_dict={}
feed_dict[hg.input_image]=input_image
feature = sess.run(hg.feature,feed_dict)
for i in feature:
all_feature.append(i)
# for i in input_label:
# all_label.append(i)
# all_output = np.array(all_output)
# all_label = np.array(all_label)
# #average_precision = average_precision_score(all_label,all_output)
# np.save('output.npy',all_output)
# np.save('label.npy',all_label)
# auc = roc_auc_score(all_label,all_output)
# loglike = log_likelihood(all_label,all_output)
# time_str = datetime.datetime.now().isoformat()
# tempstr = "{}: auc {:g}, log_likelihood {:g}".format(time_str, auc, loglike)
# print(tempstr)
# all_output=np.reshape(all_output,(-1))
# all_label=np.reshape(all_label,(-1))
# ap = average_precision_score(all_label,all_output)
# auc_2 = roc_auc_score(all_label,all_output)
# print 'ap:'+str(ap)
# print 'auc_2:'+str(auc_2)
np.save('resnet50_feature.npy',all_feature)
def test_step():
print 'testing...'
all_ce_loss = 0
all_l2_loss = 0
all_total_loss = 0
all_output = []
all_label = []
for i in range(int(len(test_order)/18)):
input_image = get_data.get_jpg_test(jpg_list,test_order[18*i:18*(i+1)])
input_label = get_data.get_label(data,test_order[18*i:18*(i+1)])
feed_dict={}
feed_dict[hg.input_image]=input_image
output= sess.run(hg.output,feed_dict)
for i in output:
all_output.append(i)
for i in input_label:
all_label.append(i)
all_output = np.array(all_output)
all_label = np.array(all_label)
#average_precision = average_precision_score(all_label,all_output)
np.save('output.npy',all_output)
np.save('label.npy',all_label)
auc = roc_auc_score(all_label,all_output)
loglike = log_likelihood(all_label,all_output)
time_str = datetime.datetime.now().isoformat()
tempstr = "{}: auc {:g}, log_likelihood {:g}".format(time_str, auc, loglike)
print(tempstr)
all_output=np.reshape(all_output,(-1))
all_label=np.reshape(all_label,(-1))
ap = average_precision_score(all_label,all_output)
auc_2 = roc_auc_score(all_label,all_output)
print 'ap:'+str(ap)
print 'auc_2:'+str(auc_2)
def test_step():
print 'Testing...'
# names = ['forest','human','ocean']
all_sample_z = []
batch_size = 17
# for i in range(len(orders)):
# input_image = get_data.get_jpg_test(jpg_list,orders[i])/128.0
# input_nlcd = get_data.get_nlcd(data,orders[i])
# input_label = get_data.get_label(data,orders[i])
for i in range(int(len(orders) / batch_size)):
input_image = get_data.get_jpg_test(
jpg_list, orders[batch_size * i:batch_size * (i + 1)]) / 128.0
input_nlcd = get_data.get_nlcd(
data, orders[batch_size * i:batch_size * (i + 1)])
input_label = get_data.get_label(
data, orders[batch_size * i:batch_size * (i + 1)])
feed_dict = {}
feed_dict[hg.input_nlcd] = input_nlcd
feed_dict[hg.input_image] = input_image
feed_dict[hg.input_label] = input_label
feed_dict[hg.keep_prob] = 1.0
sample_z = sess.run(hg.sample_z, feed_dict)
all_sample_z.append(sample_z)
all_sample_z = np.array(all_sample_z)
all_sample_z = np.reshape(all_sample_z, (-1, 100))
np.save('sample_z.npy', all_sample_z)
def test_step():
print 'Testing...'
# names = ['forest','human','ocean']
all_sample_z=[]
batch_size = 17
# for i in range(len(orders)):
# input_image = get_data.get_jpg_test(jpg_list,orders[i])/128.0
# input_nlcd = get_data.get_nlcd(data,orders[i])
# input_label = get_data.get_label(data,orders[i])
for i in range(int(len(orders)/batch_size)):
input_image = get_data.get_jpg_test(jpg_list,orders[batch_size*i:batch_size*(i+1)])
input_nlcd = get_data.get_nlcd(data,orders[batch_size*i:batch_size*(i+1)])
input_label = get_data.get_label(data,orders[batch_size*i:batch_size*(i+1)])
feed_dict={}
feed_dict[hg.input_nlcd]=input_nlcd
feed_dict[hg.input_image]=input_image
feed_dict[hg.input_label]=input_label
feed_dict[hg.keep_prob]=1.0
#sample_z= sess.run(hg.sample_z,feed_dict)
sample_z= sess.run(hg.condition_miu,feed_dict)
all_sample_z.append(sample_z)
all_sample_z = np.array(all_sample_z)
all_sample_z = np.reshape(all_sample_z,(-1,100))
np.save('condition_miu.npy',all_sample_z)
def test_step():
print 'Testing...'
# all_ce_loss = 0
# all_output = []
# all_label = []
all_feature = []
batch_size = 23
for i in range(int(len(train_order) / batch_size)):
input_image = get_data.get_jpg_test(
train_jpg, train_order[batch_size * i:batch_size *
(i + 1)]) / 128.0
input_label = get_data.get_label(
train_label, train_order[batch_size * i:batch_size * (i + 1)])
feed_dict = {}
feed_dict[hg.input_image] = input_image
feed_dict[hg.input_label] = input_label
feed_dict[hg.keep_prob] = 1.0
feature = sess.run(hg.feature, feed_dict)
# all_ce_loss += ce_loss
# for i in output:
# all_output.append(i)
# for i in input_label:
# all_label.append(i)
all_feature.append(feature)
# all_output = np.array(all_output)
# all_label = np.array(all_label)
#average_precision = average_precision_score(all_label,all_output)
all_feature = np.array(all_feature)
all_feature = np.reshape(all_feature, (-1, 256))
np.save('train_feature.npy', all_feature)
def test_step():
print 'Testing...'
all_recon_loss = 0
all_output = []
all_label = []
batch_size = FLAGS.batch_size
for i in range(int(len(test_order) / batch_size)):
input_image = get_data.get_jpg_test(
test_jpg, test_order[batch_size * i:batch_size *
(i + 1)]) / 128.0
input_label = get_data.get_label(
test_label, test_order[batch_size * i:batch_size * (i + 1)])
feed_dict = {}
feed_dict[hg.input_image] = input_image
feed_dict[hg.input_label] = input_label
feed_dict[hg.keep_prob] = 1.0
output = sess.run(hg.output, feed_dict)
for i in output:
all_output.append(i)
for i in input_label:
all_label.append(i)
all_output = np.array(all_output)
all_label = np.array(all_label)
all_output = np.reshape(all_output, (-1, 17))
all_label = np.reshape(all_label, (-1, 17))
return all_output, all_label
def test_step():
print 'Testing...'
all_recon_loss = 0
all_output = []
all_label = []
batch_size = FLAGS.batch_size
for i in range(int(len(test_order)/batch_size)):
input_image = get_data.get_jpg_test(test_jpg,test_order[batch_size*i:batch_size*(i+1)])/128.0
input_label = get_data.get_label(test_label,test_order[batch_size*i:batch_size*(i+1)])
feed_dict={}
feed_dict[hg.input_image]=input_image
feed_dict[hg.input_label]=input_label
feed_dict[hg.keep_prob]=1.0
output= sess.run(hg.output,feed_dict)
for i in output:
all_output.append(i)
for i in input_label:
all_label.append(i)
all_output = np.array(all_output)
all_label = np.array(all_label)
all_output=np.reshape(all_output,(-1,17))
all_label=np.reshape(all_label,(-1,17))
return all_output,all_label
def validation_step():
print 'Validating...'
all_recon_loss = 0
all_kl_loss = 0
all_vae_loss = 0
all_l2_loss = 0
all_total_loss = 0
all_output = []
all_label = []
for i in range(int(len(validation_order)/18)):
input_image = get_data.get_jpg_test(jpg_list,validation_order[18*i:18*(i+1)])
input_nlcd = get_data.get_nlcd(data,validation_order[18*i:18*(i+1)])
input_label = get_data.get_label(data,validation_order[18*i:18*(i+1)])
feed_dict={}
feed_dict[hg.input_nlcd]=input_nlcd
feed_dict[hg.input_label]=input_label
feed_dict[hg.input_image]=input_image
feed_dict[hg.keep_prob]=1.0
recon_loss,kl_loss,vae_loss,l2_loss,total_loss,output= sess.run([hg.recon_loss,hg.kl_loss,hg.vae_loss,hg.l2_loss,hg.total_loss,hg.output],feed_dict)
all_recon_loss += recon_loss
all_kl_loss += kl_loss
all_vae_loss += vae_loss
all_l2_loss += l2_loss
all_total_loss += total_loss
for i in output:
all_output.append(i)
for i in input_label:
all_label.append(i)
all_output = np.array(all_output)
all_label = np.array(all_label)
#average_precision = average_precision_score(all_label,all_output)
auc = roc_auc_score(all_label,all_output)
recon_loss = all_recon_loss/283.0
kl_loss = all_kl_loss/283.0
vae_loss = all_vae_loss/283.0
l2_loss = all_l2_loss/283.0
total_loss = all_total_loss/283.0
all_output=np.reshape(all_output,(-1))
all_label=np.reshape(all_label,(-1))
ap = average_precision_score(all_label,all_output)
time_str = datetime.datetime.now().isoformat()
tempstr = "{}: auc {:g}, ap {:g}, recon_loss {:g}, kl_loss {:g}, vae_loss {:g}, l2_loss {:g}, total_loss {:g}".format(time_str, auc, ap, recon_loss, kl_loss, vae_loss, l2_loss, total_loss)
print(tempstr)
summary_writer.add_summary(MakeSummary('validation/auc',auc),current_step)
summary_writer.add_summary(MakeSummary('validation/ap',ap),current_step)
summary_writer.add_summary(MakeSummary('validation/recon_loss',recon_loss),current_step)
summary_writer.add_summary(MakeSummary('validation/kl_loss',kl_loss),current_step)
summary_writer.add_summary(MakeSummary('validation/vae_loss',vae_loss),current_step)
return vae_loss
def main(_):
print 'reading npy...'
data = np.load('../1st.npy')
jpg_list = np.load('128bin.npy')
train_order = np.load('../train.npy')
validation_order = np.load('../validation.npy')
one_epoch_iter = len(train_order)/FLAGS.batch_size
print 'reading finished'
sess = tf.Session()
print 'building network...'
hg = vae.vae(is_training=True)
global_step = tf.Variable(0,name='global_step',trainable=False)
learning_rate = tf.train.exponential_decay(FLAGS.learning_rate,global_step,0.5*FLAGS.max_epoch*len(train_order)/FLAGS.batch_size,1.0,staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
optimizer = tf.train.AdamOptimizer(learning_rate)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(hg.total_loss,global_step=global_step)
merged_summary = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(FLAGS.summary_dir,sess.graph)
sess.run(tf.initialize_all_variables())
saver = tf.train.Saver(max_to_keep=None)
print 'building finished'
#def train_step(input_nlcd,input_label,smooth_ce_loss,smooth_l2_loss,smooth_total_loss):
def train_step(input_label,input_nlcd,input_image):
feed_dict={}
feed_dict[hg.input_nlcd]=input_nlcd
feed_dict[hg.input_label]=input_label
feed_dict[hg.input_image]=input_image
feed_dict[hg.keep_prob]=0.8
temp,step,recon_loss,kl_loss,vae_loss,l2_loss,total_loss,summary,output = sess.run([train_op,global_step,hg.recon_loss,hg.kl_loss,hg.vae_loss,hg.l2_loss,hg.total_loss,merged_summary,hg.output],feed_dict)
time_str = datetime.datetime.now().isoformat()
summary_writer.add_summary(summary,step)
return output, recon_loss, kl_loss, vae_loss, l2_loss, total_loss
def validation_step():
print 'Validating...'
all_recon_loss = 0
all_kl_loss = 0
all_vae_loss = 0
all_l2_loss = 0
all_total_loss = 0
all_output = []
all_label = []
for i in range(int(len(validation_order)/18)):
input_image = get_data.get_jpg_test(jpg_list,validation_order[18*i:18*(i+1)])
input_nlcd = get_data.get_nlcd(data,validation_order[18*i:18*(i+1)])
input_label = get_data.get_label(data,validation_order[18*i:18*(i+1)])
feed_dict={}
feed_dict[hg.input_nlcd]=input_nlcd
feed_dict[hg.input_label]=input_label
feed_dict[hg.input_image]=input_image
feed_dict[hg.keep_prob]=1.0
recon_loss,kl_loss,vae_loss,l2_loss,total_loss,output= sess.run([hg.recon_loss,hg.kl_loss,hg.vae_loss,hg.l2_loss,hg.total_loss,hg.output],feed_dict)
all_recon_loss += recon_loss
all_kl_loss += kl_loss
all_vae_loss += vae_loss
all_l2_loss += l2_loss
all_total_loss += total_loss
for i in output:
all_output.append(i)
for i in input_label:
all_label.append(i)
all_output = np.array(all_output)
all_label = np.array(all_label)
#average_precision = average_precision_score(all_label,all_output)
auc = roc_auc_score(all_label,all_output)
recon_loss = all_recon_loss/283.0
kl_loss = all_kl_loss/283.0
vae_loss = all_vae_loss/283.0
l2_loss = all_l2_loss/283.0
total_loss = all_total_loss/283.0
all_output=np.reshape(all_output,(-1))
all_label=np.reshape(all_label,(-1))
ap = average_precision_score(all_label,all_output)
time_str = datetime.datetime.now().isoformat()
tempstr = "{}: auc {:g}, ap {:g}, recon_loss {:g}, kl_loss {:g}, vae_loss {:g}, l2_loss {:g}, total_loss {:g}".format(time_str, auc, ap, recon_loss, kl_loss, vae_loss, l2_loss, total_loss)
print(tempstr)
summary_writer.add_summary(MakeSummary('validation/auc',auc),current_step)
summary_writer.add_summary(MakeSummary('validation/ap',ap),current_step)
summary_writer.add_summary(MakeSummary('validation/recon_loss',recon_loss),current_step)
summary_writer.add_summary(MakeSummary('validation/kl_loss',kl_loss),current_step)
summary_writer.add_summary(MakeSummary('validation/vae_loss',vae_loss),current_step)
return vae_loss
best_vae_loss = 10000
best_iter = 0
smooth_recon_loss=0.0
smooth_kl_loss=0.0
smooth_vae_loss=0.0
smooth_l2_loss=0.0
smooth_total_loss=0.0
temp_label=[]
temp_output=[]
for one_epoch in range(FLAGS.max_epoch):
print('epoch '+str(one_epoch+1)+' starts!')
np.random.shuffle(train_order)
for i in range(int(len(train_order)/float(FLAGS.batch_size))):
start = i*FLAGS.batch_size
end = (i+1)*FLAGS.batch_size
input_image = get_data.get_jpg_test(jpg_list,train_order[start:end])
input_nlcd = get_data.get_nlcd(data,train_order[start:end])
input_label = get_data.get_label(data,train_order[start:end])
output, recon_loss, kl_loss, vae_loss, l2_loss, total_loss = train_step(input_label,input_nlcd,input_image)
smooth_recon_loss+=recon_loss
smooth_kl_loss+=kl_loss
smooth_vae_loss+=vae_loss
smooth_l2_loss+=l2_loss
smooth_total_loss+=total_loss
temp_label.append(input_label)
temp_output.append(output)
current_step = tf.train.global_step(sess,global_step)
if current_step%10==0:
recon_loss=smooth_recon_loss/10.0
kl_loss=smooth_kl_loss/10.0
vae_loss=smooth_vae_loss/10.0
l2_loss=smooth_l2_loss/10.0
total_loss=smooth_total_loss/10.0
temp_output = np.reshape(np.array(temp_output),(-1))
temp_label = np.reshape(np.array(temp_label),(-1))
ap = average_precision_score(temp_label,temp_output)
temp_output = np.reshape(temp_output,(-1,100))
temp_label = np.reshape(temp_label,(-1,100))
try:
auc = roc_auc_score(temp_label,temp_output)
except ValueError:
print 'ytrue error for auc'
else:
time_str = datetime.datetime.now().isoformat()
tempstr = "{}: step {}, auc {:g}, ap {:g}, recon_loss {:g}, kl_loss {:g}, vae_loss {:g}, l2_loss {:g}, total_loss {:g}".format(time_str, current_step, auc, ap, recon_loss, kl_loss, vae_loss, l2_loss, total_loss)
print(tempstr)
summary_writer.add_summary(MakeSummary('train/auc',auc),current_step)
summary_writer.add_summary(MakeSummary('train/ap',ap),current_step)
temp_output=[]
temp_label=[]
smooth_recon_loss = 0
smooth_kl_loss = 0
smooth_vae_loss = 0
smooth_l2_loss = 0
smooth_total_loss = 0
if current_step%int(one_epoch_iter*FLAGS.save_epoch)==0:
vae_loss = validation_step()
if vae_loss<best_vae_loss:
print 'currently the vae_loss is over the previous best one!!!'
best_vae_loss=vae_loss
best_iter = current_step
print 'saving model'
path = saver.save(sess,FLAGS.model_dir+'fc_model',global_step=current_step)
print 'have saved model to '+path
print 'training has been finished !'
print 'the best vae_loss on validation is '+str(best_vae_loss)
print 'the best checkpoint is '+str(best_iter)
def main(_):
print 'reading npy...'
data = np.load('../1st.npy')
jpg_list = np.load('128bin.npy')
train_order = np.load('../train.npy')
validation_order = np.load('../validation.npy')
one_epoch_iter = len(train_order) / FLAGS.batch_size
print 'reading finished'
sess = tf.Session()
print 'building network...'
hg = vae.vae(is_training=True)
global_step = tf.Variable(0, name='global_step', trainable=False)
learning_rate = tf.train.exponential_decay(
FLAGS.learning_rate,
global_step,
0.5 * FLAGS.max_epoch * len(train_order) / FLAGS.batch_size,
1.0,
staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
optimizer = tf.train.AdamOptimizer(learning_rate)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(hg.total_loss, global_step=global_step)
merged_summary = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(FLAGS.summary_dir, sess.graph)
sess.run(tf.initialize_all_variables())
saver = tf.train.Saver(max_to_keep=None)
print 'building finished'
#def train_step(input_nlcd,input_label,smooth_ce_loss,smooth_l2_loss,smooth_total_loss):
def train_step(input_label, input_nlcd, input_image):
feed_dict = {}
feed_dict[hg.input_nlcd] = input_nlcd
feed_dict[hg.input_label] = input_label
feed_dict[hg.input_image] = input_image
feed_dict[hg.keep_prob] = 0.8
temp, step, recon_loss, kl_loss, vae_loss, l2_loss, total_loss, summary, output = sess.run(
[
train_op, global_step, hg.recon_loss, hg.kl_loss, hg.vae_loss,
hg.l2_loss, hg.total_loss, merged_summary, hg.output
], feed_dict)
time_str = datetime.datetime.now().isoformat()
summary_writer.add_summary(summary, step)
return output, recon_loss, kl_loss, vae_loss, l2_loss, total_loss
def validation_step():
print 'Validating...'
all_recon_loss = 0
all_kl_loss = 0
all_vae_loss = 0
all_l2_loss = 0
all_total_loss = 0
all_output = []
all_label = []
for i in range(int(len(validation_order) / 18)):
input_image = get_data.get_jpg_test(
jpg_list, validation_order[18 * i:18 * (i + 1)])
input_nlcd = get_data.get_nlcd(
data, validation_order[18 * i:18 * (i + 1)])
input_label = get_data.get_label(
data, validation_order[18 * i:18 * (i + 1)])
feed_dict = {}
feed_dict[hg.input_nlcd] = input_nlcd
feed_dict[hg.input_label] = input_label
feed_dict[hg.input_image] = input_image
feed_dict[hg.keep_prob] = 1.0
recon_loss, kl_loss, vae_loss, l2_loss, total_loss, output = sess.run(
[
hg.recon_loss, hg.kl_loss, hg.vae_loss, hg.l2_loss,
hg.total_loss, hg.output
], feed_dict)
all_recon_loss += recon_loss
all_kl_loss += kl_loss
all_vae_loss += vae_loss
all_l2_loss += l2_loss
all_total_loss += total_loss
for i in output:
all_output.append(i)
for i in input_label:
all_label.append(i)
all_output = np.array(all_output)
all_label = np.array(all_label)
#average_precision = average_precision_score(all_label,all_output)
auc = roc_auc_score(all_label, all_output)
recon_loss = all_recon_loss / 283.0
kl_loss = all_kl_loss / 283.0
vae_loss = all_vae_loss / 283.0
l2_loss = all_l2_loss / 283.0
total_loss = all_total_loss / 283.0
all_output = np.reshape(all_output, (-1))
all_label = np.reshape(all_label, (-1))
ap = average_precision_score(all_label, all_output)
time_str = datetime.datetime.now().isoformat()
tempstr = "{}: auc {:g}, ap {:g}, recon_loss {:g}, kl_loss {:g}, vae_loss {:g}, l2_loss {:g}, total_loss {:g}".format(
time_str, auc, ap, recon_loss, kl_loss, vae_loss, l2_loss,
total_loss)
print(tempstr)
summary_writer.add_summary(MakeSummary('validation/auc', auc),
current_step)
summary_writer.add_summary(MakeSummary('validation/ap', ap),
current_step)
summary_writer.add_summary(
MakeSummary('validation/recon_loss', recon_loss), current_step)
summary_writer.add_summary(MakeSummary('validation/kl_loss', kl_loss),
current_step)
summary_writer.add_summary(
MakeSummary('validation/vae_loss', vae_loss), current_step)
return vae_loss
best_vae_loss = 10000
best_iter = 0
smooth_recon_loss = 0.0
smooth_kl_loss = 0.0
smooth_vae_loss = 0.0
smooth_l2_loss = 0.0
smooth_total_loss = 0.0
temp_label = []
temp_output = []
for one_epoch in range(FLAGS.max_epoch):
print('epoch ' + str(one_epoch + 1) + ' starts!')
np.random.shuffle(train_order)
for i in range(int(len(train_order) / float(FLAGS.batch_size))):
start = i * FLAGS.batch_size
end = (i + 1) * FLAGS.batch_size
input_image = get_data.get_jpg_test(jpg_list,
train_order[start:end])
input_nlcd = get_data.get_nlcd(data, train_order[start:end])
input_label = get_data.get_label(data, train_order[start:end])
output, recon_loss, kl_loss, vae_loss, l2_loss, total_loss = train_step(
input_label, input_nlcd, input_image)
smooth_recon_loss += recon_loss
smooth_kl_loss += kl_loss
smooth_vae_loss += vae_loss
smooth_l2_loss += l2_loss
smooth_total_loss += total_loss
temp_label.append(input_label)
temp_output.append(output)
current_step = tf.train.global_step(sess, global_step)
if current_step % 10 == 0:
recon_loss = smooth_recon_loss / 10.0
kl_loss = smooth_kl_loss / 10.0
vae_loss = smooth_vae_loss / 10.0
l2_loss = smooth_l2_loss / 10.0
total_loss = smooth_total_loss / 10.0
temp_output = np.reshape(np.array(temp_output), (-1))
temp_label = np.reshape(np.array(temp_label), (-1))
ap = average_precision_score(temp_label, temp_output)
temp_output = np.reshape(temp_output, (-1, 100))
temp_label = np.reshape(temp_label, (-1, 100))
try:
auc = roc_auc_score(temp_label, temp_output)
except ValueError:
print 'ytrue error for auc'
else:
time_str = datetime.datetime.now().isoformat()
tempstr = "{}: step {}, auc {:g}, ap {:g}, recon_loss {:g}, kl_loss {:g}, vae_loss {:g}, l2_loss {:g}, total_loss {:g}".format(
time_str, current_step, auc, ap, recon_loss, kl_loss,
vae_loss, l2_loss, total_loss)
print(tempstr)
summary_writer.add_summary(MakeSummary('train/auc', auc),
current_step)
summary_writer.add_summary(MakeSummary('train/ap', ap),
current_step)
temp_output = []
temp_label = []
smooth_recon_loss = 0
smooth_kl_loss = 0
smooth_vae_loss = 0
smooth_l2_loss = 0
smooth_total_loss = 0
if current_step % int(one_epoch_iter * FLAGS.save_epoch) == 0:
vae_loss = validation_step()
if vae_loss < best_vae_loss:
print 'currently the vae_loss is over the previous best one!!!'
best_vae_loss = vae_loss
best_iter = current_step
print 'saving model'
path = saver.save(sess,
FLAGS.model_dir + 'fc_model',
global_step=current_step)
print 'have saved model to ' + path
print 'training has been finished !'
print 'the best vae_loss on validation is ' + str(best_vae_loss)
print 'the best checkpoint is ' + str(best_iter)
def validation_step():
print 'Validating...'
all_recon_loss = 0
all_kl_loss = 0
all_vae_loss = 0
all_l2_loss = 0
all_total_loss = 0
all_output = []
all_label = []
valid_batch = 23
for i in range(int(len(validation_order) / valid_batch)):
input_image = get_data.get_jpg_test(
validation_jpg,
validation_order[valid_batch * i:valid_batch * (i + 1)])
input_label = get_data.get_label(
validation_label,
validation_order[valid_batch * i:valid_batch * (i + 1)])
feed_dict = {}
feed_dict[hg.input_label] = input_label
feed_dict[hg.input_image] = input_image
feed_dict[hg.keep_prob] = 1.0
recon_loss, kl_loss, vae_loss, l2_loss, total_loss, output = sess.run(
[
hg.recon_loss, hg.kl_loss, hg.vae_loss, hg.l2_loss,
hg.total_loss, hg.output
], feed_dict)
all_recon_loss += recon_loss
all_kl_loss += kl_loss
all_vae_loss += vae_loss
all_l2_loss += l2_loss
all_total_loss += total_loss
for i in output:
all_output.append(i)
for i in input_label:
all_label.append(i)
all_output = np.array(all_output)
all_label = np.array(all_label)
#average_precision = average_precision_score(all_label,all_output)
recon_loss = all_recon_loss / (4048 / valid_batch)
kl_loss = all_kl_loss / (4048 / valid_batch)
vae_loss = all_vae_loss / (4048 / valid_batch)
l2_loss = all_l2_loss / (4048 / valid_batch)
total_loss = all_total_loss / (4048 / valid_batch)
all_output = np.reshape(all_output, (-1))
all_label = np.reshape(all_label, (-1))
ap = average_precision_score(all_label, all_output)
auc = roc_auc_score(all_label, all_output)
time_str = datetime.datetime.now().isoformat()
tempstr = "{}: auc {:g}, ap {:g}, recon_loss {:g}, kl_loss {:g}, vae_loss {:g}, l2_loss {:g}, total_loss {:g}".format(
time_str, auc, ap, recon_loss, kl_loss, vae_loss, l2_loss,
total_loss)
print(tempstr)
summary_writer.add_summary(MakeSummary('validation/auc', auc),
current_step)
summary_writer.add_summary(MakeSummary('validation/ap', ap),
current_step)
summary_writer.add_summary(
MakeSummary('validation/recon_loss', recon_loss), current_step)
summary_writer.add_summary(MakeSummary('validation/kl_loss', kl_loss),
current_step)
summary_writer.add_summary(
MakeSummary('validation/vae_loss', vae_loss), current_step)
return vae_loss
