def load_model(self, location, scale_file="", logp=True):
"""Read the autoencoder from a tensorflow file"""
self._graph = tf.Graph()
self._sess = tf.Session(graph=self._graph)
if LOAD_PROTOBUF:
with tf.gfile.GFile(location + '/final_variables_frz', 'rb') as f:
gdef = tf.GraphDef()
gdef.ParseFromString(f.read())
with self._graph.as_default():
tf.import_graph_def(gdef)
pfx = 'import/'
else:
with self._graph.as_default():
saver = tf.train.import_meta_graph(location +
'/final_variables.meta')
saver.restore(self._sess, location + '/final_variables')
pfx = ''
# hooks to the decoder
self.i_q = self._graph.get_tensor_by_name(pfx + 'i_q:0')
self.o_x = self._graph.get_tensor_by_name(pfx + 'decode:0')
# hooks to the encoder
self.i_x = self._graph.get_tensor_by_name(pfx + 'i_x:0')
self.o_q = self._graph.get_tensor_by_name(pfx + 'encode:0')
# The simulation inputs and outputs in the unscaled space
# (the prior conditioning of the data put it on [-1,1])
if scale_file:
self.logp = logp
raw_scale = np.loadtxt(scale_file, skiprows=1, delimiter=',')
self.scale = raw_scale[:, 0:4]
unshifted = unshift(self.o_x, self.scale[:, 0:4])
if logp:
self.o_s = tf.concat([
tf.expand_dims(unshifted[:, 0], -1),
tf.expand_dims(tf.math.exp(unshifted[:, 1]), -1),
unshifted[:, 2:]
],
axis=1)
else:
self.o_s = unshifted
else:
self.scale = None
self.o_s = self.o_x
self.o_dxdq = atu.vector_gradient_dep(self.o_x, self.i_q)
self.o_dsdq = atu.vector_gradient_dep(self.o_s, self.i_q)
self.dtype = self.i_x.dtype
def make_goal(self, data):
q = self.encode(data)
pred = self.decode(q)
loss = tf.losses.mean_squared_error(data, pred)
if self.cae_lambda != 0:
dqdx = atu.vector_gradient_dep(q,data)
cae = tf.constant(self.cae_lambda,dtype=self.dtype) \
* tf.norm(dqdx, axis=(1,2))
return loss + tf.metrics.mean(cae)[0] # CHECK
else:
return loss
def build_flux(self):
"""This should really go into the child LatentFlow."""
with self._graph.as_default():
self.i_XA = tf.placeholder(name='i_XA',
shape=(None, 2),
dtype=self.dtype)
self.i_XB = tf.placeholder(name='i_XB',
shape=(None, 2),
dtype=self.dtype)
self.i_q2 = tf.placeholder(name='i_q2',
shape=(None, 2),
dtype=self.dtype)
self.o_s2 = atu.replicate_subgraph(self.o_s, {self.i_q: self.i_q2})
TA, pA, rhoA, rho_hA = tf.split(self.o_s, 4, axis=-1)
TB, pB, rhoB, rho_hB = tf.split(self.o_s2, 4, axis=-1)
self.o_F = self.flux(TA, pA, rhoA, rho_hA, TB, pB, rhoB, rho_hB,
self.i_XA, self.i_XB)
self.o_KF = tf.concat([
atu.vector_gradient_dep(self.o_F, self.i_q),
atu.vector_gradient_dep(self.o_F, self.i_q2)
],
axis=-1)
self._sess.run(tf.variables_initializer(self._vars.values()))
def build_dae(self, method='BWEuler'):
"""Builds the differential algebraic equation and sets up the system
lhs(q)=rhs
K = d lhs / dq
"""
aii = {'BWEuler': 1.0, 'Trap': 0.5}[method]
with self._graph.as_default():
aii = self.regvar('aii', aii)
Dt = self.regvar('Dt', 1.0)
T, p, rho, rho_h = tf.split(self.o_s, 4, axis=-1)
m, r = self.m_and_r(T, p, rho, rho_h)
self.m = m
self.r = r
self.lhs = m - Dt * aii * r
self.rhs = m + (1.0 - aii) * Dt * r
self.K_lhs = atu.vector_gradient_dep(self.lhs, self.i_q)
# Initialize parameters
ini = tf.variables_initializer(self._vars.values())
self._sess.run(ini)