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Tensorflow 1.x backend: add dropout to DeepONet #1579

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merged 3 commits into from
Jun 25, 2024
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Tensorflow 1.x backend: add dropout to DeepONet #1579

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71f1a46
Add dropout to DeepONet
vl-dud Dec 3, 2023
5c5b317
Fix training argument
vl-dud Jun 21, 2024
bac0539
Use self.training in tf.layers.dropout for DeepONetCartesianProd
vl-dud Jun 24, 2024
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31 changes: 28 additions & 3 deletions deepxde/nn/tensorflow_compat_v1/deeponet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -155,6 +155,7 @@ class DeepONet(NN):
both trunk and branch nets. If `activation` is a ``dict``, then the trunk
net uses the activation `activation["trunk"]`, and the branch net uses
`activation["branch"]`.
dropout_rate (float): The dropout rate, between 0 and 1.
trainable_branch: Boolean.
trainable_trunk: Boolean or a list of booleans.
num_outputs (integer): Number of outputs. In case of multiple outputs, i.e., `num_outputs` > 1,
Expand Down Expand Up @@ -192,6 +193,7 @@ def __init__(
activation,
kernel_initializer,
regularization=None,
dropout_rate=0,
use_bias=True,
stacked=False,
trainable_branch=True,
Expand All @@ -217,6 +219,7 @@ def __init__(
"stacked " + kernel_initializer
)
self.regularizer = regularizers.get(regularization)
self.dropout_rate = dropout_rate
self.use_bias = use_bias
self.stacked = stacked
self.trainable_branch = trainable_branch
Expand Down Expand Up @@ -300,6 +303,10 @@ def build_branch_net(self):
activation=self.activation_branch,
trainable=self.trainable_branch,
)
if self.dropout_rate > 0:
y_func = tf.layers.dropout(
y_func, rate=self.dropout_rate, training=self.trainable_branch
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)
y_func = self._stacked_dense(
y_func,
1,
Expand All @@ -317,6 +324,10 @@ def build_branch_net(self):
regularizer=self.regularizer,
trainable=self.trainable_branch,
)
if self.dropout_rate > 0:
y_func = tf.layers.dropout(
y_func, rate=self.dropout_rate, training=self.trainable_branch
)
y_func = self._dense(
y_func,
self.layer_size_func[-1],
Expand All @@ -331,15 +342,22 @@ def build_trunk_net(self):
if self._input_transform is not None:
y_loc = self._input_transform(y_loc)
for i in range(1, len(self.layer_size_loc)):
trainable = (
self.trainable_trunk[i - 1]
if isinstance(self.trainable_trunk, (list, tuple))
else self.trainable_trunk
)
y_loc = self._dense(
y_loc,
self.layer_size_loc[i],
activation=self.activation_trunk,
regularizer=self.regularizer,
trainable=self.trainable_trunk[i - 1]
if isinstance(self.trainable_trunk, (list, tuple))
else self.trainable_trunk,
trainable=trainable,
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)
if self.dropout_rate > 0:
y_loc = tf.layers.dropout(
y_loc, rate=self.dropout_rate, training=trainable
)
return y_loc

def merge_branch_trunk(self, branch, trunk):
Expand Down Expand Up @@ -439,6 +457,7 @@ class DeepONetCartesianProd(NN):
both trunk and branch nets. If `activation` is a ``dict``, then the trunk
net uses the activation `activation["trunk"]`, and the branch net uses
`activation["branch"]`.
dropout_rate (float): The dropout rate, between 0 and 1.
num_outputs (integer): Number of outputs. In case of multiple outputs, i.e., `num_outputs` > 1,
`multi_output_strategy` below should be set.
multi_output_strategy (str or None): ``None``, "independent", "split_both", "split_branch" or
Expand Down Expand Up @@ -474,6 +493,7 @@ def __init__(
activation,
kernel_initializer,
regularization=None,
dropout_rate=0,
num_outputs=1,
multi_output_strategy=None,
):
Expand All @@ -487,6 +507,7 @@ def __init__(
self.activation_branch = self.activation_trunk = activations.get(activation)
self.kernel_initializer = initializers.get(kernel_initializer)
self.regularizer = regularizers.get(regularization)
self.dropout_rate = dropout_rate
self._inputs = None

self.num_outputs = num_outputs
Expand Down Expand Up @@ -553,6 +574,8 @@ def build_branch_net(self):
kernel_initializer=self.kernel_initializer,
kernel_regularizer=self.regularizer,
)
if self.dropout_rate > 0:
y_func = tf.layers.dropout(y_func, rate=self.dropout_rate)
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y_func = tf.layers.dense(
y_func,
self.layer_size_func[-1],
Expand All @@ -574,6 +597,8 @@ def build_trunk_net(self):
kernel_initializer=self.kernel_initializer,
kernel_regularizer=self.regularizer,
)
if self.dropout_rate > 0:
y_loc = tf.layers.dropout(y_loc, rate=self.dropout_rate)
return y_loc

def merge_branch_trunk(self, branch, trunk):
Expand Down