param2 = np.array([0.099])
# ----
# Training ----
model = ParamNN(rateTrain=rateTrain,
lr=lr,
index=index,
size=size,
dataMode=dataMode)
llosses, grads, preparam, exactparam = model.train(nItr=nItr,
alpha=alpha,
param2=param2)
# Plot ----
myPlot = burgers2Dplot.Plot(dataMode=dataMode, trialID=index)
#myPlot.Loss1Data(llosses[0], labels=['test'], savename=f'poNN2d_testloss1_d{size}_part7_{param2}')
#myPlot.Loss1Data(llosses[1], labels=['test'], savename=f'poNN2d_testloss2_d{size}_part5')
#myPlot.Loss1Data(grads[0], labels=['test'], savename=f'poNN2d_testgrad1_d{size}_part5')
#myPlot.Loss1Data(grads[1], labels=['test'], savename=f'poNN2d_testgrad2_d{size}_part5')
myPlot.paramCls(preparam[0],
np.array([1.0]),
savename=f'poNN2d_testparam1_d{size}_part7_{param2}')
myPlot.paramCls(preparam[1],
exactparam,
savename=f'poNN2d_testparam2_d{size}_part7_{param2}')
# ----
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()