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models_tf.py
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 1 16:15:14 2018
@author: Paris
"""
import tensorflow as tf
import numpy as np
import time
from pyDOE import lhs
tf.random.set_random_seed(1234)
np.random.seed(1234)
class Eikonal2DnetCV2:
# Initialize the class
def __init__(self, x, y, x_e, y_e, T_e, layers, CVlayers, C = 1.0, alpha = 1e-5, alphaL2 = 1e-6, jobs = 4):
X = np.concatenate([x, y], 1)
# X_e = np.concatenate([x_e, t_e], 1)
self.lb = X.min(0)
self.ub = X.max(0)
self.X = X
# self.X_e = X_e
self.x = x
self.y = y
self.T_e = T_e
self.x_e = x_e
self.y_e = y_e
self.layers = layers
self.CVlayers = CVlayers
# Initialize NN
self.weights, self.biases = self.initialize_NN(layers)
self.CVweights, self.CVbiases = self.initialize_NN(CVlayers)
self.C = tf.constant(C)
self.alpha = tf.constant(alpha)
self.alphaL2 = alphaL2
# tf placeholders and graph
self.sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=True,
intra_op_parallelism_threads=jobs,
inter_op_parallelism_threads=jobs,
device_count={'CPU': jobs}))
self.x_tf = tf.placeholder(tf.float32, shape=[None, self.x.shape[1]])
self.y_tf = tf.placeholder(tf.float32, shape=[None, self.y.shape[1]])
self.T_e_tf = tf.placeholder(tf.float32, shape=[None, self.T_e.shape[1]])
self.x_e_tf = tf.placeholder(tf.float32, shape=[None, self.x_e.shape[1]])
self.y_e_tf = tf.placeholder(tf.float32, shape=[None, self.y_e.shape[1]])
self.T_pred, self.CV_pred, self.f_T_pred, self.f_CV_pred = self.net_eikonal(self.x_tf, self.y_tf)
self.T_e_pred, self.CV_e_pred, self.f_T_e_pred, self.f_CV_e_pred = self.net_eikonal(self.x_e_tf, self.y_e_tf)
self.loss = tf.reduce_mean(tf.square(self.T_e_tf - self.T_e_pred)) + \
tf.reduce_mean(tf.square(self.f_T_e_pred)) + \
tf.reduce_mean(tf.square(self.f_T_pred)) + \
self.alpha*tf.reduce_mean(tf.square(self.f_CV_e_pred)) + \
self.alpha*tf.reduce_mean(tf.square(self.f_CV_pred)) + \
sum([self.alphaL2*tf.nn.l2_loss(w) for w in self.weights])
self.optimizer_Adam = tf.train.AdamOptimizer()
self.train_op_Adam = self.optimizer_Adam.minimize(self.loss)
# Define optimizer (use L-BFGS for better accuracy)
self.optimizer = tf.contrib.opt.ScipyOptimizerInterface(self.loss,
method = 'L-BFGS-B',
options = {'maxiter': 10000,
'maxfun': 50000,
'maxcor': 50,
'maxls': 50,
'ftol' : 1.0 * np.finfo(float).eps})
# Initialize Tensorflow variables
init = tf.global_variables_initializer()
self.sess.run(init)
# Initialize network weights and biases using Xavier initialization
def initialize_NN(self, layers):
# Xavier initialization
def xavier_init(size):
in_dim = size[0]
out_dim = size[1]
xavier_stddev = 1. / np.sqrt((in_dim + out_dim) / 2.)
return tf.Variable(tf.random_normal([in_dim, out_dim], dtype=tf.float32) * xavier_stddev, dtype=tf.float32)
weights = []
biases = []
num_layers = len(layers)
for l in range(0,num_layers-1):
W = xavier_init(size=[layers[l], layers[l+1]])
b = tf.Variable(tf.zeros([1,layers[l+1]], dtype=tf.float32), dtype=tf.float32)
weights.append(W)
biases.append(b)
return weights, biases
def neural_net(self, X, weights, biases):
num_layers = len(weights) + 1
H = 2.0*(X - self.lb)/(self.ub - self.lb) - 1.0
for l in range(0,num_layers-2):
W = weights[l]
b = biases[l]
H = tf.tanh(tf.add(tf.matmul(H, W), b))
W = weights[-1]
b = biases[-1]
Y = tf.add(tf.matmul(H, W), b)
return Y
def net_eikonal(self, x, y):
C = self.C
T = self.neural_net(tf.concat([x,y], 1), self.weights, self.biases)
CV = self.neural_net(tf.concat([x,y], 1), self.CVweights, self.CVbiases)
CV = C*tf.sigmoid(CV)
T_x = tf.gradients(T, x)[0]
T_y = tf.gradients(T, y)[0]
CV_x = tf.gradients(CV, x)[0]
CV_y = tf.gradients(CV, y)[0]
f_T = tf.sqrt(T_x**2 + T_y**2) - 1.0/CV
f_CV = tf.sqrt(CV_x**2 + CV_y**2)
return T, CV, f_T, f_CV
def callback(self, loss):
self.lossit.append(loss)
print('Loss: %.5e' % (loss))
def train(self):
tf_dict = {self.x_tf: self.x, self.y_tf: self.y,
self.x_e_tf: self.x_e, self.y_e_tf: self.y_e, self.T_e_tf: self.T_e}
# Call SciPy's L-BFGS otpimizer
self.optimizer.minimize(self.sess,
feed_dict = tf_dict,
fetches = [self.loss],
loss_callback = self.callback)
def train_Adam(self, nIter):
self.lossit = []
tf_dict = {self.x_tf: self.x, self.y_tf: self.y,
self.x_e_tf: self.x_e, self.y_e_tf: self.y_e, self.T_e_tf: self.T_e}
start_time = time.time()
for it in range(nIter):
self.sess.run(self.train_op_Adam, tf_dict)
loss_value = self.sess.run(self.loss, tf_dict)
self.lossit.append(loss_value)
# Print
if it % 10 == 0:
elapsed = time.time() - start_time
C_value = np.exp(self.sess.run(self.C))
print('It: %d, Loss: %.3e, C: %.3f, Time: %.2f' %
(it, loss_value, C_value, elapsed))
start_time = time.time()
self.optimizer.minimize(self.sess,
feed_dict = tf_dict,
fetches = [self.loss],
loss_callback = self.callback)
def train_Adam_minibatch(self, nIter, size = 50):
self.lossit = []
start_time = time.time()
for it in range(nIter):
X = lhs(2, size)
tf_dict = {self.x_tf: X[:,:1], self.y_tf: X[:,1:],
self.x_e_tf: self.x_e, self.y_e_tf: self.y_e, self.T_e_tf: self.T_e}
self.sess.run(self.train_op_Adam, tf_dict)
loss_value = self.sess.run(self.loss, tf_dict)
self.lossit.append(loss_value)
# Print
if it % 10 == 0:
elapsed = time.time() - start_time
C_value = np.exp(self.sess.run(self.C))
print('It: %d, Loss: %.3e, C: %.3f, Time: %.2f' %
(it, loss_value, C_value, elapsed))
start_time = time.time()
def predict(self, x_star, y_star):
tf_dict = {self.x_tf: x_star, self.y_tf: y_star,
self.x_e_tf: self.x_e, self.y_e_tf: self.y_e}
T_star = self.sess.run(self.T_pred, tf_dict)
CV_star = self.sess.run(self.CV_pred, tf_dict)
return T_star, CV_star
def get_adaptive_points(self, N = 1000, M = 10):
X = lhs(2, N)
tf_dict = {self.x_tf: X[:,:1], self.y_tf: X[:,1:],
self.x_e_tf: self.x_e, self.y_e_tf: self.y_e}
f_T_star = self.sess.run(self.f_T_pred, tf_dict)
ind = f_T_star[:,0].argsort()[-M:]
return X[ind], f_T_star[ind]
class Eikonal3DnetCV2:
# Initialize the class
def __init__(self, X, normals,X_e, T_e, layers, CVlayers, Tmax, C = 1.0, alpha = 1e-5, alphaL2 = 1e-6, jobs = 4):
# X_e = np.concatenate([x_e, t_e], 1)
self.Tmax = Tmax
self.lb = X.min(0)
self.ub = X.max(0)
self.X = X
self.normals = normals
# self.X_e = X_e
self.T_e = T_e
self.X_e = X_e
self.layers = layers
self.CVlayers = CVlayers
# Initialize NN
self.weights, self.biases = self.initialize_NN(layers)
self.CVweights, self.CVbiases = self.initialize_NN(CVlayers)
self.C = tf.constant(C)
self.alpha = tf.constant(alpha)
self.alphaL2 = alphaL2
# tf placeholders and graph
self.sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=True,
intra_op_parallelism_threads=jobs,
inter_op_parallelism_threads=jobs,
device_count={'CPU': jobs}))
self.X_tf = tf.placeholder(tf.float32, shape=[None, self.X.shape[1]])
self.normals_tf = tf.placeholder(tf.float32, shape=[None, self.X.shape[1]])
self.T_e_tf = tf.placeholder(tf.float32, shape=[None, self.T_e.shape[1]])
self.X_e_tf = tf.placeholder(tf.float32, shape=[None, self.X_e.shape[1]])
self.T_pred, self.CV_pred, self.f_T_pred, self.f_CV_pred, self.f_N_pred = self.net_eikonal(self.X_tf, self.normals_tf)
self.T_e_pred, self.CV_e_pred = self.net_data(self.X_e_tf)
self.pde_loss = tf.reduce_mean(tf.square(self.f_T_pred))
self.normal_loss = 1e-2*tf.reduce_mean(tf.square(self.f_N_pred))
self.loss = tf.reduce_mean(tf.square(self.T_e_tf - self.T_e_pred)) + \
self.pde_loss + \
self.normal_loss + \
self.alpha*tf.reduce_mean(tf.square(self.f_CV_pred)) + \
sum([self.alphaL2*tf.nn.l2_loss(w) for w in self.weights])
self.optimizer_Adam = tf.train.AdamOptimizer()
self.train_op_Adam = self.optimizer_Adam.minimize(self.loss)
# Define optimizer (use L-BFGS for better accuracy)
self.optimizer = tf.contrib.opt.ScipyOptimizerInterface(self.loss,
method = 'L-BFGS-B',
options = {'maxiter': 10000,
'maxfun': 50000,
'maxcor': 50,
'maxls': 50,
'ftol' : 1.0 * np.finfo(float).eps})
# Initialize Tensorflow variables
init = tf.global_variables_initializer()
self.sess.run(init)
# Initialize network weights and biases using Xavier initialization
def initialize_NN(self, layers):
# Xavier initialization
def xavier_init(size):
in_dim = size[0]
out_dim = size[1]
xavier_stddev = 1. / np.sqrt((in_dim + out_dim) / 2.)
return tf.Variable(tf.random_normal([in_dim, out_dim], dtype=tf.float32) * xavier_stddev, dtype=tf.float32)
weights = []
biases = []
num_layers = len(layers)
for l in range(0,num_layers-1):
W = xavier_init(size=[layers[l], layers[l+1]])
b = tf.Variable(tf.zeros([1,layers[l+1]], dtype=tf.float32), dtype=tf.float32)
weights.append(W)
biases.append(b)
return weights, biases
def neural_net(self, X, weights, biases):
num_layers = len(weights) + 1
H = 2.0*(X - self.lb)/(self.ub - self.lb) - 1.0
for l in range(0,num_layers-2):
W = weights[l]
b = biases[l]
H = tf.tanh(tf.add(tf.matmul(H, W), b))
W = weights[-1]
b = biases[-1]
Y = tf.add(tf.matmul(H, W), b)
return Y
def net_eikonal(self, X, normals):
C = self.C
T = self.neural_net(X, self.weights, self.biases)
CV = self.neural_net(X, self.CVweights, self.CVbiases)
CV = C*tf.sigmoid(CV)
T_x = tf.gradients(T, X)[0]
CV_x = tf.gradients(CV, X)[0]
f_T = CV*tf.norm(self.Tmax*(T_x), axis = -1, keepdims = True) - 1.0
f_CV = tf.norm(CV_x, axis = -1)
f_N = self.C*self.Tmax*tf.reduce_sum(T_x*normals, axis = -1)
#f_N = 0.0
return T, CV, f_T, f_CV, f_N
def net_data(self, X):
C = self.C
T = self.neural_net(X, self.weights, self.biases)
CV = self.neural_net(X, self.CVweights, self.CVbiases)
CV = C*tf.sigmoid(CV)
return T, CV
def callback(self, loss):
self.lossit.append(loss)
print('Loss: %.5e' % (loss))
def train(self):
tf_dict = {self.X_tf: self.X,
self.normals_tf: self.normals,
self.X_e_tf: self.X_e,
self.T_e_tf: self.T_e}
# Call SciPy's L-BFGS otpimizer
self.optimizer.minimize(self.sess,
feed_dict = tf_dict,
fetches = [self.loss],
loss_callback = self.callback)
def train_Adam(self, nIter):
self.lossit = []
tf_dict = {self.X_tf: self.X,
self.normals_tf: self.normals,
self.X_e_tf: self.X_e,
self.T_e_tf: self.T_e}
start_time = time.time()
for it in range(nIter):
self.sess.run(self.train_op_Adam, tf_dict)
loss_value = self.sess.run(self.loss, tf_dict)
self.lossit.append(loss_value)
# Print
if it % 10 == 0:
elapsed = time.time() - start_time
C_value = np.exp(self.sess.run(self.C))
print('It: %d, Loss: %.3e, C: %.3f, Time: %.2f' %
(it, loss_value, C_value, elapsed))
start_time = time.time()
self.optimizer.minimize(self.sess,
feed_dict = tf_dict,
fetches = [self.loss],
loss_callback = self.callback)
def train_Adam_minibatch(self, nEpoch, size = 50):
self.lossit = []
start_time = time.time()
idx_global = np.arange(self.X.shape[0])
np.random.shuffle(idx_global)
splits = np.array_split(idx_global, idx_global.shape[0]//size)
for ep in range(nEpoch):
for it, idx in enumerate(splits):
tf_dict = {self.X_tf: self.X[idx],
self.normals_tf: self.normals[idx],
self.X_e_tf: self.X_e,
self.T_e_tf: self.T_e}
self.sess.run(self.train_op_Adam, tf_dict)
loss_value = self.sess.run(self.loss, tf_dict)
self.lossit.append(loss_value)
# Print
if it % 10 == 0:
elapsed = time.time() - start_time
pde_loss = self.sess.run(self.pde_loss, tf_dict)
normal_loss = self.sess.run(self.normal_loss, tf_dict)
print('Epoch: %d, It: %d, Loss: %.3e, pde loss: %.3e, normal_loss: %.3e, Time: %.2f' %
(ep, it + ep*idx_global.shape[0]//size, loss_value, pde_loss, normal_loss, elapsed))
start_time = time.time()
def predict(self, X_star):
tf_dict = {self.X_e_tf: X_star}
T_star = self.sess.run(self.T_e_pred, tf_dict)
CV_star = self.sess.run(self.CV_e_pred, tf_dict)
return T_star, CV_star
def get_adaptive_points(self, N = 1000, M = 10):
X = lhs(2, N)
tf_dict = {self.x_tf: X[:,:1], self.y_tf: X[:,1:],
self.x_e_tf: self.x_e, self.y_e_tf: self.y_e}
f_T_star = self.sess.run(self.f_T_pred, tf_dict)
ind = f_T_star[:,0].argsort()[-M:]
return X[ind], f_T_star[ind]