def train(data, path):
os.system("mkdir -p " + path)
norm = data['norm']
pvc = data['pvc']
feat = np.concatenate([norm, pvc], axis=0)
#feat = norm
x = tf.placeholder(tf.float32, [None, input_dim], name='x')
z = tf.placeholder(tf.float32, [None, z_dim], name='z')
model = DAE()
loss = model.loss(x)
_z = model.encode(x)
x_h = model.decode(z)
tf.add_to_collection('encode', _z)
tf.add_to_collection('decode', x_h)
tf.add_to_collection('prd', loss['pmse'])
tf.add_to_collection('cc', loss['corr'])
w_loss = 60 * loss['pmse'] - 40 * loss['corr']
optimize = tf.train.AdamOptimizer(learning_rate=5e-5,
beta1=0.9,
beta2=0.99).minimize(w_loss)
#optimize = tf.train.RMSPropOptimizer(learning_rate=1e-5).minimize(loss)
merged = tf.summary.merge_all()
with tf.Session() as sess:
start = time.time()
init = tf.global_variables_initializer()
sess.run(init)
saver = tf.train.Saver()
writer = tf.summary.FileWriter(path + 'logs', sess.graph)
for i in range(iter_num):
np.random.shuffle(feat)
for j in range(0, len(feat), batch_num):
_ = sess.run([optimize], feed_dict={x: feat[j:j + batch_num]})
if i % 100 == 0:
xb = feat[j:]
_, err, result = sess.run([optimize, loss, merged],
feed_dict={x: xb})
writer.add_summary(result, len(feat) * i + j)
print('Epoch [%4d] Time [%5.4f] PRD: [%.4f] CC: [%.4f]' %
(i + 1, time.time() - start, err['pmse'], err['corr']))
if i % 1000 == 0:
saver.save(sess, path + 'test_best_model')
def train(data, Test_data, path):
os.system("mkdir -p " + path)
#print(path)
noi = data['noi']
#feat = _preprocessing(noi)
feat = noi
# Train Clean Target
clean = data['clean']
#print(clean.shape)
#print(int(clean.shape[0]))
clean = numpy.matlib.repmat(clean, 5, 1)
#print(feat.shape, clean.shape)
#target = _preprocessing(clean)
target = clean
#print(feat.shape)
# validation data
Test_noi = Test_data['noi']
#Test_feat = _preprocessing(Test_noi)
Test_feat = Test_noi
# valid Clean Target
Test_clean = Test_data['clean']
clean_Test = numpy.matlib.repmat(Test_clean, 5, 1)
#Test_target= _preprocessing(clean_Test)
Test_target = clean_Test
#print(Test_feat.shape, Test_target.shape)
x = tf.placeholder(tf.float32, [None, input_dim], name='x')
z = tf.placeholder(tf.float32, [None, z_dim], name='z')
y = tf.placeholder(tf.float32, [None, input_dim], name='y')
model = DAE()
loss = model.loss(x, y)
training_summary = tf.summary.scalar("training_loss", loss['mse'])
validation_summary = tf.summary.scalar("validation_loss", loss['mse'])
_z = model.encode(x)
x_h = model.decode(z)
tf.add_to_collection('encode', _z)
tf.add_to_collection('decode', x_h)
tf.add_to_collection('err_mse', loss['mse'])
#reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
#reg_constant = 0.01
#l2_loss = tf.losses.get_regularization_loss()
#w_loss = loss['mse'] + l2_loss # + reg_constant * sum(reg_losses)
w_loss = loss['mse']
#lossL2 = tf.add_n([ tf.nn.l2_loss(v) for v in tf.trainable_variables if 'bias' not in v.name ]) * 0.001
#w_loss = loss['mse'] + lossL2
print(
np.sum([
np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()
]))
optimize = tf.train.AdamOptimizer(learning_rate=5e-5,
beta1=0.9,
beta2=0.99).minimize(w_loss)
#optimize = tf.train.RMSPropOptimizer(learning_rate=1e-5).minimize(loss)
#merged = tf.summary.merge_all()
with tf.Session() as sess:
start = time.time()
init = tf.global_variables_initializer()
sess.run(init)
saver = tf.train.Saver()
writer = tf.summary.FileWriter(path + 'logs', sess.graph)
patience = 10
best_loss = 10000.
for i in range(epoch):
Num = np.arange(feat.shape[0])
np.random.shuffle(Num)
feat = feat[Num, :]
target = target[Num, :]
#length = int(feat.shape[0]/2)
feat_1 = feat[0:-int(feat.shape[0] / 2), :]
feat_2 = feat[-int(feat.shape[0] / 2):, :]
com = np.array([feat_1, feat_2])
target_1 = target[0:-int(target.shape[0] / 2), :]
target_2 = target[-int(target.shape[0] / 2):, :]
com_target = np.array([target_1, target_2])
#print(feat_1.shape, feat_2.shape, com.shape)
#feat_1 = np.reshape(feat[0:-int(feat.shape[0]/2),:], (int(feat.shape[0]/2),1024,-1))
#feat_2 = np.reshape(feat[-int(feat.shape[0]/2):,:], (int(feat.shape[0]/2),1024,-1))
for k in range(2):
feat_noi = np.reshape(com[k, :, :], (-1, 1024))
#print(feat.shape, com[k,:,:].shape)
#feat_noi = com[k,:,:]
target_clean = com_target[k, :, :]
#print(feat_noi.shape)
for j in range(0, len(feat_noi), batch_num):
sess.run(
[optimize],
feed_dict={
x: feat_noi[j:j + batch_num],
y: target_clean[j:j + batch_num]
})
if i % 10 == 0:
# To log training accuracy.
err, train_summ = sess.run([loss, training_summary],
feed_dict={
x: feat_noi,
y: target_clean
})
writer.add_summary(train_summ, i)
# To log validation accuracy.
Test_err, valid_summ = sess.run([loss, validation_summary],
feed_dict={
x: Test_feat,
y: Test_target
})
writer.add_summary(valid_summ, i)
if Test_err['mse'] < best_loss:
best_loss = Test_err['mse']
patience = 10
saver.save(sess, path + 'test_best_model')
else:
patience -= 1
print(
'Epoch [%4d] Time [%5.4f] MSE [%.6f] Val_MSE [%.6f]' %
(i + 1, time.time() - start, err['mse'],
Test_err['mse']))
if patience == 0:
print('Early Stopping')
break