def __init__(self,
rateTrain=0.0,
lr=1e-3,
index=0,
size=0,
dataMode='test'):
# parameter ----
if dataMode == 'large':
self.xDim = 20
self.yDim = 20
elif dataMode == 'middle':
self.xDim = 15
self.yDim = 15
elif dataMode == 'small':
self.xDim = 10
self.yDim = 10
self.tDim = 71
self.index = index
self.paramDim = 2
# ----
# Dataset ----
self.myData = burgers2Ddata.Data(pdeMode='burgers2d',
dataMode=dataMode)
self.alltestX, self.alltestY, self.testT, self.testX, self.testY, self.testU, self.testV, self.testNU, self.idx, self.idy = self.myData.traintest(
size=size)
# ----
# Placeholder ----
# u
self.inobsu = tf.compat.v1.placeholder(
tf.float32, shape=[None, self.xDim, self.yDim, 1])
self.inobsv = tf.compat.v1.placeholder(
tf.float32, shape=[None, self.xDim, self.yDim, 1])
self.inobs = tf.concat([self.inobsu, self.inobsv], -1)
self.outobsu = tf.compat.v1.placeholder(
tf.float64, shape=[None, self.xDim, self.yDim, self.tDim])
self.outobsv = tf.compat.v1.placeholder(
tf.float64, shape=[None, self.xDim, self.yDim, self.tDim])
self.initu = tf.compat.v1.placeholder(
tf.float64,
shape=[None, self.alltestX.shape[0], self.alltestY.shape[0], 1])
self.initv = tf.compat.v1.placeholder(
tf.float64,
shape=[None, self.alltestX.shape[0], self.alltestY.shape[0], 1])
# param nu
self.y = tf.compat.v1.placeholder(tf.float32,
shape=[None, self.paramDim])
# ----
#pdb.set_trace()
# Restore neural network ----
# pred nu [ndata,]
self.predparam = self.lambdaNN(self.inobs)
# ----
# for predparam
self.placeparam = tf.compat.v1.placeholder(tf.float64,
shape=[None, self.paramDim])
# optimizer ----
config = tf.compat.v1.ConfigProto(gpu_options=tf.compat.v1.GPUOptions(
per_process_gpu_memory_fraction=0.1, allow_growth=True))
self.saver = tf.compat.v1.train.Saver()
self.sess = tf.compat.v1.Session(config=config)
# ----
#pdb.set_trace()
# Restore model ----
ckptpath = os.path.join('model', f'{dataMode}burgers2d_d{size}')
ckpt = tf.train.get_checkpoint_state(ckptpath)
lastmodel = ckpt.model_checkpoint_path
self.saver.restore(self.sess, lastmodel)
print('>>> Restore train model')
# ----
# float32 -> float64
self.predparam = tf.cast(self.predparam, tf.float64)
# for predparam
self.placeparam = tf.compat.v1.placeholder(tf.float64,
shape=[None, self.paramDim])
# PDE ----
# output: u,v
self.preduv = pdeburgers2D.burgers2d(self.placeparam[:, None, 0],
self.placeparam[:, None, 1],
self.initu, self.initv)
self.preduv_first = pdeburgers2D.burgers2d(self.predparam[:, None, 0],
self.predparam[:, None, 1],
self.initu, self.initv)
# ----
#pdb.set_trace()
# space data -> [none, self.xDim, t] ----
self.indx = tf.compat.v1.placeholder(tf.int32, shape=[self.xDim, 1])
self.indy = tf.compat.v1.placeholder(tf.int32, shape=[self.yDim, 1])
# [x,data,y,t]
trans_predu = tf.transpose(self.preduv[0], perm=[1, 0, 2, 3])
trans_predu_first = tf.transpose(self.preduv_first[0],
perm=[1, 0, 2, 3])
trans_predv = tf.transpose(self.preduv[1], perm=[1, 0, 2, 3])
trans_predv_first = tf.transpose(self.preduv_first[1],
perm=[1, 0, 2, 3])
# [100(x),data,y,t]
gather_predu = tf.gather_nd(trans_predu, self.indx)
gather_predu_first = tf.gather_nd(trans_predu_first, self.indx)
gather_predv = tf.gather_nd(trans_predv, self.indx)
gather_predv_first = tf.gather_nd(trans_predv_first, self.indx)
# for v
trans_predu_ = tf.transpose(gather_predu, perm=[2, 1, 0, 3])
trans_predu_first_ = tf.transpose(gather_predu_first,
perm=[2, 1, 0, 3])
trans_predv_ = tf.transpose(gather_predv, perm=[2, 1, 0, 3])
trans_predv_first_ = tf.transpose(gather_predv_first,
perm=[2, 1, 0, 3])
gather_predu_ = tf.gather_nd(trans_predu_, self.indy)
gather_predu_first_ = tf.gather_nd(trans_predu_first_, self.indy)
gather_predv_ = tf.gather_nd(trans_predv_, self.indy)
gather_predv_first_ = tf.gather_nd(trans_predv_first_, self.indy)
# [data,self.xDim,t]
trans_u = tf.transpose(gather_predu_, perm=[1, 0, 2, 3])
trans_u_first = tf.transpose(gather_predu_first_, perm=[1, 0, 2, 3])
trans_v = tf.transpose(gather_predv_, perm=[1, 0, 2, 3])
trans_v_first = tf.transpose(gather_predv_first_, perm=[1, 0, 2, 3])
space_predu = tf.transpose(trans_u, perm=[0, 2, 1, 3])
space_predu_first = tf.transpose(trans_u_first, perm=[0, 2, 1, 3])
space_predv = tf.transpose(trans_v, perm=[0, 2, 1, 3])
space_predv_first = tf.transpose(trans_v_first, perm=[0, 2, 1, 3])
# ----
#pdb.set_trace()
# loss param ----
self.loss1 = tf.reduce_mean(
tf.square(
tf.cast(self.y[:, 0], tf.float64) - self.placeparam[:, 0]))
self.loss2 = tf.reduce_mean(
tf.square(
tf.cast(self.y[:, 1], tf.float64) - self.placeparam[:, 1]))
self.loss1_first = tf.reduce_mean(
tf.square(
tf.cast(self.y[:, 0], tf.float64) - self.predparam[:, 0]))
self.loss2_first = tf.reduce_mean(
tf.square(
tf.cast(self.y[:, 1], tf.float64) - self.predparam[:, 1]))
# ----
# loss uv ----
self.lossu = tf.reduce_mean(tf.square(self.outobsu - space_predu))
self.lossu_first = tf.reduce_mean(
tf.square(self.outobsu - space_predu_first))
self.lossv = tf.reduce_mean(tf.square(self.outobsv - space_predv))
self.lossv_first = tf.reduce_mean(
tf.square(self.outobsv - space_predv_first))
# loss uv ----
self.lossuv = self.lossu + self.lossv
self.lossuv_first = self.lossu_first + self.lossv_first
# ----
# gradient ----
self.alpha = tf.compat.v1.placeholder(tf.float64, shape=[1])
self.grad = tf.gradients(self.lossuv, self.placeparam)[0]
# only lambda2
self.grad_lambda2 = tf.gradients(self.lossuv, self.placeparam)[0][:, 1]
self.grad_first = tf.gradients(
self.lossuv_first,
self.predparam)[0] # for first time(use predict parameter)
#pdb.set_trace()
self.nextparam1 = self.placeparam[:, 0] - (tf.reduce_sum(self.grad) *
self.alpha)
self.nextparam2 = self.placeparam[:, 1] - (tf.reduce_sum(self.grad) *
self.alpha)
self.nextparam_lambda2 = self.placeparam[:, 1] - (self.grad_lambda2 *
self.alpha)
self.nextparam1_first = self.predparam[:, 0] - (
tf.reduce_sum(self.grad_first) * self.alpha) # for first time
self.nextparam2_first = self.predparam[:, 1] - (
tf.reduce_sum(self.grad_first) * self.alpha) # for first time
def __init__(self,
rateTrain=0.0,
lr=1e-3,
nBatch=100,
trialID=0,
size=0,
dataMode='test'):
# parameter ----
if dataMode == 'large':
self.xDim = 20
self.yDim = 20
elif dataMode == 'middle':
self.xDim = 15
self.yDim = 15
elif dataMode == 'small':
self.xDim = 10
self.yDim = 10
self.tDim = 71
self.nBatch = nBatch
self.trialID = trialID
self.lambdaDim = 2
self.size = size
# ----
# for plot
self.myPlot = burgers2Dplot.Plot(dataMode=dataMode, trialID=trialID)
# Dataset ----
self.myData = burgers2Ddata.Data(pdeMode='burgers2d',
dataMode=dataMode)
# all x,y[51,1] t[201,1] & x,y,t[xdim or ydim,1] u,v[data,xdim,ydim,tdim] nu[data,]
self.alltestX, self.alltestY, self.testT, self.testX, self.testY, self.testU, self.testV, self.testNU, self.idx, self.idy = self.myData.traintest(
size=size)
#pdb.set_trace()
# ----
# Placeholder ----
# input u, v (one time) [xdim, ydim, t1]
self.inobsu = tf.compat.v1.placeholder(
tf.float32, shape=[None, self.xDim, self.yDim, self.tDim])
self.inobsv = tf.compat.v1.placeholder(
tf.float32, shape=[None, self.xDim, self.yDim, self.tDim])
self.inobs = tf.concat([self.inobsu, self.inobsv], -1)
# output param b
self.y = tf.compat.v1.placeholder(tf.float32,
shape=[None, self.lambdaDim])
# ----
# neural network ----
self.predy = self.lambdaNN(self.inobs, rate=rateTrain)
self.predy_test = self.lambdaNN(self.inobs, reuse=True)
# ----
# loss ----
# param loss
self.loss1 = tf.reduce_mean(tf.square(self.y[:, 0] - self.predy[:, 0]))
self.loss2 = tf.reduce_mean(tf.square(self.y[:, 1] - self.predy[:, 1]))
self.loss1_test = tf.reduce_mean(
tf.square(self.y[:, 0] - self.predy_test[:, 0]))
self.loss2_test = tf.reduce_mean(
tf.square(self.y[:, 1] - self.predy_test[:, 1]))
# ----
self.loss = self.loss1 + self.loss2
# Optimizer ----
self.opt = tf.compat.v1.train.AdamOptimizer(lr).minimize(self.loss)
# ----
# ----
config = tf.compat.v1.ConfigProto(gpu_options=tf.compat.v1.GPUOptions(
per_process_gpu_memory_fraction=0.1, allow_growth=True))
self.sess = tf.compat.v1.Session(config=config)
self.sess.run(tf.compat.v1.global_variables_initializer())
self.saver = tf.compat.v1.train.Saver()