"""
Stefania Fresca, MOX Laboratory, Politecnico di Milano
April 2019
"""
import tensorflow as tf
import numpy as np
import scipy.io as sio
import time
import os
from Net import Net
import utils
seed = 374
np.random.seed(seed)
class DecNet(Net):
def __init__(self, config):
Net.__init__(self, config)
self.n = config['n']
self.n_params = config['n_params']
self.size = 5
self.n_layers = 10 # hidden layers - 1
self.n_neurons = 50
self.n_h = config['n_h']
def get_data(self):
with tf.name_scope('data'):
self.X = tf.placeholder(tf.float32, shape = [None, self.N_h])
self.Y = tf.placeholder(tf.float32, shape = [None, self.n_params])
dataset = tf.data.Dataset.from_tensor_slices((self.X, self.Y))
dataset = dataset.batch(self.batch_size)
iterator = dataset.make_initializable_iterator()
self.init = iterator.initializer
self.output, self.params = iterator.get_next()
def inference(self):
# at testing time the encoder function is discarded
fc_n = tf.layers.dense(self.params,
self.n_neurons,
activation = tf.nn.elu,
kernel_initializer = tf.keras.initializers.he_uniform())
for i in range(self.n_layers):
fc_n = tf.layers.dense(fc_n,
self.n_neurons,
activation = tf.nn.elu,
kernel_initializer = tf.keras.initializers.he_uniform())
u_n = tf.layers.dense(fc_n,
self.n,
activation = tf.nn.elu,
kernel_initializer = tf.keras.initializers.he_uniform())
fc1_t = tf.layers.dense(u_n, 256, activation = tf.nn.elu, kernel_initializer = tf.keras.initializers.he_uniform(), name = 'fc1_t')
fc2_t = tf.layers.dense(fc1_t, self.N_h, activation = tf.nn.elu, kernel_initializer = tf.keras.initializers.he_uniform(), name = 'fc2_t')
fc2_t = tf.reshape(fc2_t, [-1, self.n_h, self.n_h, 64])
conv1_t = tf.layers.conv2d_transpose(inputs = fc2_t,
filters = 64,
kernel_size = [self.size, self.size],
padding = 'SAME',
strides = 2,
kernel_initializer = tf.keras.initializers.he_uniform(),
activation = tf.nn.elu,
name = 'conv1_t')
conv2_t = tf.layers.conv2d_transpose(inputs = conv1_t,
filters = 32,
kernel_size = [self.size, self.size],
padding = 'SAME',
strides = 2,
kernel_initializer = tf.keras.initializers.he_uniform(),
activation = tf.nn.elu,
name = 'conv2_t')
conv3_t = tf.layers.conv2d_transpose(inputs = conv2_t,
filters = 16,
kernel_size = [self.size, self.size],
padding = 'SAME',
strides = 2,
kernel_initializer = tf.keras.initializers.he_uniform(),
activation = tf.nn.elu,
name = 'conv3_t')
conv4_t = tf.layers.conv2d_transpose(inputs = conv3_t,
filters = 1,
kernel_size = [self.size, self.size],
padding = 'SAME',
strides = 1,
kernel_initializer = tf.keras.initializers.he_uniform(),
name = 'conv4_t')
feature_dim_dec = conv4_t.shape[1] * conv4_t.shape[2] * conv4_t.shape[3]
self.u_h = tf.reshape(conv4_t, [-1, feature_dim_dec])
def loss(self, u_h):
with tf.name_scope('loss'):
self.loss = self.omega_h * tf.reduce_mean(tf.reduce_sum(tf.pow(self.output - u_h, 2), axis = 1))
def build(self):
self.get_data()
self.inference()
self.loss(self.u_h)
def test_once(self, sess, init):
start_time = time.time()
sess.run(init, feed_dict = {self.X : self.S_test, self.Y : self.params_test})
total_loss = 0
n_batches = 0
self.U_h = np.zeros(self.S_test.shape)
print('------------ TESTING ------------')
try:
while True:
l, u_h = sess.run([self.loss, self.u_h])
self.U_h[self.batch_size * n_batches : self.batch_size * (n_batches + 1)] = u_h
total_loss += l
n_batches += 1
except tf.errors.OutOfRangeError:
pass
print('Average loss on testing set: {0}'.format(total_loss / n_batches))
print('Took: {0} seconds'.format(time.time() - start_time))
#@profile
def test_all(self):
list = [v for v in tf.global_variables() if '_t' or 'dense' in v.name]
saver = tf.train.Saver(var_list = list)
if (self.large):
S_train = utils.read_large_data(self.train_mat)
else:
S_train = utils.read_data(self.train_mat)
idxs = np.random.permutation(S_train.shape[0])
S_train = S_train[idxs]
S_max, S_min = utils.max_min(S_train, self.n_train)
del S_train
print('Loading testing snapshot matrix...')
if (self.large):
self.S_test = utils.read_large_data(self.test_mat)
else:
self.S_test = utils.read_data(self.test_mat)
utils.scaling(self.S_test, S_max, S_min)
if (self.zero_padding):
self.S_test = utils.zero_pad(self.S_test, self.p)
print('Loading testing parameters...')
self.params_test = utils.read_params(self.test_params)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(os.path.dirname(self.checkpoints_folder + '/checkpoint'))
if ckpt and ckpt.model_checkpoint_path:
print(ckpt.model_checkpoint_path)
saver.restore(sess, ckpt.model_checkpoint_path)
self.test_once(sess, self.init)
utils.inverse_scaling(self.U_h, S_max, S_min)
utils.inverse_scaling(self.S_test, S_max, S_min)
n_test = self.S_test.shape[0] // self.N_t
err = np.zeros((n_test, 1))
for i in range(n_test):
num = np.sqrt(np.mean(np.linalg.norm(self.S_test[i * self.N_t : (i + 1) * self.N_t] - self.U_h[i * self.N_t : (i + 1) * self.N_t], 2, axis = 1) ** 2))
den = np.sqrt(np.mean(np.linalg.norm(self.S_test[i * self.N_t : (i + 1) * self.N_t], 2, axis = 1) ** 2))
err[i] = num / den
print('Error indicator epsilon_rel: {0}'.format(np.mean(err)))
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