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module.py
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module.py
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from .nn import NN
from .. import activations
from .. import initializers
from .. import regularizers
from ... import config
from ...backend import tf
from ...utils import timing
class MIONet(NN):
"""Multiple-input operator network with two input functions."""
def __init__(
self,
layer_sizes_branch1,
layer_sizes_branch2,
layer_sizes_trunk,
activation,
kernel_initializer,
regularization,
):
super().__init__()
self.layer_branch1 = layer_sizes_branch1
self.layer_branch2 = layer_sizes_branch2
self.layer_trunk = layer_sizes_trunk
if isinstance(activation, dict):
self.activation_branch1 = activations.get(activation["branch1"])
self.activation_branch2 = activations.get(activation["branch2"])
self.activation_trunk = activations.get(activation["trunk"])
else:
self.activation_branch1 = (
self.activation_branch2
) = self.activation_trunk = activations.get(activation)
self.kernel_initializer = initializers.get(kernel_initializer)
self.regularizer = regularizers.get(regularization)
self._inputs = None
@property
def inputs(self):
return self._inputs
@property
def outputs(self):
return self.y
@property
def targets(self):
return self.target
@timing
def build(self):
print("Building MIONet...")
self.X_func1 = tf.placeholder(config.real(tf), [None, self.layer_branch1[0]])
self.X_func2 = tf.placeholder(config.real(tf), [None, self.layer_branch2[0]])
self.X_loc = tf.placeholder(config.real(tf), [None, self.layer_trunk[0]])
self._inputs = [self.X_func1, self.X_func2, self.X_loc]
# Branch net 1
if callable(self.layer_branch1[1]):
# User-defined network
y_func1 = self.layer_branch1[1](self.X_func1)
else:
y_func1 = self._net(
self.X_func1, self.layer_branch1[1:], self.activation_branch1
)
# Branch net 2
if callable(self.layer_branch2[1]):
# User-defined network
y_func2 = self.layer_branch2[1](self.X_func2)
else:
y_func2 = self._net(
self.X_func2, self.layer_branch2[1:], self.activation_branch2
)
# Trunk net
y_loc = self._net(self.X_loc, self.layer_trunk[1:], self.activation_trunk)
# Dot product
self.y = tf.multiply(y_func1, y_loc)
self.y = tf.multiply(self.y, y_func2)
self.y = tf.reduce_sum(self.y, 1, keepdims=True)
b = tf.Variable(tf.zeros(1))
self.y += b
self.target = tf.placeholder(config.real(tf), [None, 1])
self.built = True
def _net(self, X, layer, activation):
output = X
for i in range(len(layer) - 1):
output = tf.layers.dense(
output,
layer[i],
activation=activation,
kernel_regularizer=self.regularizer,
)
return tf.layers.dense(output, layer[-1], kernel_regularizer=self.regularizer)
class MIONetCartesianProd(MIONet):
"""MIONet with two input functions for Cartesian product format."""
@timing
def build(self):
print("Building MIONetCartesianProd...")
self.X_func1 = tf.placeholder(config.real(tf), [None, self.layer_branch1[0]])
self.X_func2 = tf.placeholder(config.real(tf), [None, self.layer_branch2[0]])
self.X_loc = tf.placeholder(config.real(tf), [None, self.layer_trunk[0]])
self._inputs = [self.X_func1, self.X_func2, self.X_loc]
# Branch net 1
if callable(self.layer_branch1[1]):
# User-defined network
y_func1 = self.layer_branch1[1](self.X_func1)
else:
y_func1 = self._net(
self.X_func1, self.layer_branch1[1:], self.activation_branch1
)
# Branch net 2
if callable(self.layer_branch2[1]):
# User-defined network
y_func2 = self.layer_branch2[1](self.X_func2)
else:
y_func2 = self._net(
self.X_func2, self.layer_branch2[1:], self.activation_branch2
)
# Trunk net
y_loc = self._net(self.X_loc, self.layer_trunk[1:], self.activation_trunk)
# Dot product
self.y = tf.multiply(y_func1, y_func2)
self.y = tf.einsum("ip,jp->ij", self.y, y_loc)
b = tf.Variable(tf.zeros(1))
self.y += b
if self._output_transform is not None:
self.y = self._output_transform(self._inputs, self.y)
self.target = tf.placeholder(config.real(tf), [None, None])
self.built = True
class MIONet_CNN(NN):
"""Multiple-input operator network with two input functions."""
def __init__(
self,
layer_sizes_branch1,
layer_sizes_branch2,
layer_sizes_trunk,
layer_sizes_dot,
activation,
kernel_initializer,
regularization,
):
super().__init__()
self.layer_branch1 = layer_sizes_branch1
self.layer_branch2 = layer_sizes_branch2
self.layer_trunk = layer_sizes_trunk
self.layer_dot = layer_sizes_dot
if isinstance(activation, dict):
self.activation_branch1 = activations.get(activation["branch1"])
self.activation_branch2 = activations.get(activation["branch2"])
self.activation_trunk = activations.get(activation["trunk"])
else:
self.activation_branch1 = (
self.activation_branch2
) = self.activation_trunk = activations.get(activation)
self.kernel_initializer = initializers.get(kernel_initializer)
self.regularizer = regularizers.get(regularization)
self._inputs = None
@property
def inputs(self):
return self._inputs
@property
def outputs(self):
return self.y
@property
def targets(self):
return self.target
@timing
def build(self):
print("Building MIONet...")
self.X_func1 = tf.placeholder(config.real(tf), [None, self.layer_branch1[0]])
self.X_func2 = tf.placeholder(config.real(tf), [None, self.layer_branch2[0]])
self.X_loc = tf.placeholder(config.real(tf), [None,1849,self.layer_trunk[0]])
self._inputs = [self.X_func1, self.X_func2, self.X_loc]
# Branch net 1
if callable(self.layer_branch1[1]):
# User-defined network
y_func1 = self.layer_branch1[1](self.X_func1)
else:
y_func1 = self._net(
self.X_func1, self.layer_branch1[1:], self.activation_branch1
)
# Branch net 2
if callable(self.layer_branch2[1]):
# User-defined network
y_func2 = self.layer_branch2[1](self.X_func2)
else:
y_func2 = self._net(
self.X_func2, self.layer_branch2[1:], self.activation_branch2
)
# Trunk net
if callable(self.layer_trunk[1]):
# User-defined network
y_loc = self.layer_trunk[1](self.X_loc)
else:
y_loc = self._net(self.X_loc, self.layer_trunk[1:], self.activation_trunk)
# Dot product
if callable(self.layer_dot[0]):
# User-defined network
self.y = self.layer_dot[0](tf.concat((tf.expand_dims(y_func1, axis=2),tf.expand_dims(y_func2, axis=2), tf.expand_dims(y_loc, axis=2)), 2))
else:
self.y = tf.multiply(y_func1, y_loc)
self.y = tf.multiply(self.y, y_func2)
self.y = tf.reduce_sum(self.y, 1, keepdims=True)
b = tf.Variable(tf.zeros(1))
self.y += b
self.target = tf.placeholder(config.real(tf), [None,1849])
self.built = True
def _net(self, X, layer, activation):
output = X
for i in range(len(layer) - 1):
output = tf.layers.dense(
output,
layer[i],
activation=activation,
kernel_regularizer=self.regularizer,
)
return tf.layers.dense(output, layer[-1], kernel_regularizer=self.regularizer)
class MIONet_CNN_no_average(NN):
"""Multiple-input operator network with two input functions."""
def __init__(
self,
layer_sizes_branch1,
layer_sizes_trunk,
layer_sizes_dot,
activation,
kernel_initializer,
regularization,
):
super().__init__()
self.layer_branch1 = layer_sizes_branch1
self.layer_trunk = layer_sizes_trunk
self.layer_dot = layer_sizes_dot
if isinstance(activation, dict):
self.activation_branch1 = activations.get(activation["branch1"])
self.activation_trunk = activations.get(activation["trunk"])
else:
self.activation_branch1 = self.activation_trunk = activations.get(activation)
self.kernel_initializer = initializers.get(kernel_initializer)
self.regularizer = regularizers.get(regularization)
self._inputs = None
@property
def inputs(self):
return self._inputs
@property
def outputs(self):
return self.y
@property
def targets(self):
return self.target
@timing
def build(self):
print("Building MIONet...")
self.X_func1 = tf.placeholder(config.real(tf), [None, self.layer_branch1[0]])
self.X_loc = tf.placeholder(config.real(tf), [None,841,self.layer_trunk[0]])
self._inputs = [self.X_func1, self.X_loc]
# Branch net 1
if callable(self.layer_branch1[1]):
# User-defined network
y_func1 = self.layer_branch1[1](self.X_func1)
else:
y_func1 = self._net(
self.X_func1, self.layer_branch1[1:], self.activation_branch1
)
# Trunk net
if callable(self.layer_trunk[1]):
# User-defined network
y_loc = self.layer_trunk[1](self.X_loc)
else:
y_loc = self._net(self.X_loc, self.layer_trunk[1:], self.activation_trunk)
# Dot product
if callable(self.layer_dot[0]):
# User-defined network
self.y = self.layer_dot[0](tf.concat((tf.expand_dims(y_func1, axis=2), tf.expand_dims(y_loc, axis=2)), 2))
else:
self.y = tf.multiply(y_func1, y_loc)
self.y = tf.reduce_sum(self.y, 1, keepdims=True)
b = tf.Variable(tf.zeros(1))
self.y += b
self.target = tf.placeholder(config.real(tf), [None, None])
self.built = True
def _net(self, X, layer, activation):
output = X
for i in range(len(layer) - 1):
output = tf.layers.dense(
output,
layer[i],
activation=activation,
kernel_regularizer=self.regularizer,
)
return tf.layers.dense(output, layer[-1], kernel_regularizer=self.regularizer)