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Tensorflow 1.x backend: layer-by-layer dropout rate setting for DeepONet #1792

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merged 4 commits into from
Jul 27, 2024
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Tensorflow 1.x backend: layer-by-layer dropout rate setting for DeepONet #1792

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5da3201
Tensorflow 1.x backend: layer-by-layer dropout rate setting for DeepONet
vl-dud Jul 3, 2024
de9a1ab
Convert dropout_rate to list
vl-dud Jul 7, 2024
b812c79
Fix formatting
vldud Jul 19, 2024
fd8427f
Fix formatting
vldud Jul 25, 2024
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94 changes: 80 additions & 14 deletions deepxde/nn/tensorflow_compat_v1/deeponet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -155,7 +155,13 @@ 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.
dropout_rate: If `dropout_rate` is a ``float`` between 0 and 1, then the
same rate is used in both trunk and branch nets. If `dropout_rate`
is a ``dict``, then the trunk net uses the rate `dropout_rate["trunk"]`,
and the branch net uses `dropout_rate["branch"]`. Both `dropout_rate["trunk"]`
and `dropout_rate["branch"]` should be ``float`` or lists of ``float``.
The list length should match the length of `layer_size_trunk` - 1 for the
trunk net and `layer_size_branch` - 2 for the branch net.
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 @@ -219,7 +225,31 @@ def __init__(
"stacked " + kernel_initializer
)
self.regularizer = regularizers.get(regularization)
self.dropout_rate = dropout_rate
if isinstance(dropout_rate, dict):
self.dropout_rate_branch = dropout_rate["branch"]
self.dropout_rate_trunk = dropout_rate["trunk"]
else:
self.dropout_rate_branch = self.dropout_rate_trunk = dropout_rate
if isinstance(self.dropout_rate_branch, list):
if not (len(layer_sizes_branch) - 2) == len(self.dropout_rate_branch):
raise ValueError(
"Number of dropout rates of branch net must be "
f"equal to {len(layer_sizes_branch) - 2}"
)
else:
self.dropout_rate_branch = [self.dropout_rate_branch] * (
len(layer_sizes_branch) - 2
)
if isinstance(self.dropout_rate_trunk, list):
if not (len(layer_sizes_trunk) - 1) == len(self.dropout_rate_trunk):
raise ValueError(
"Number of dropout rates of trunk net must be "
f"equal to {len(layer_sizes_trunk) - 1}"
)
else:
self.dropout_rate_trunk = [self.dropout_rate_trunk] * (
len(layer_sizes_trunk) - 1
)
self.use_bias = use_bias
self.stacked = stacked
self.trainable_branch = trainable_branch
Expand Down Expand Up @@ -303,9 +333,11 @@ def build_branch_net(self):
activation=self.activation_branch,
trainable=self.trainable_branch,
)
if self.dropout_rate > 0:
if self.dropout_rate_branch[i - 1] > 0:
y_func = tf.layers.dropout(
y_func, rate=self.dropout_rate, training=self.training
y_func,
rate=self.dropout_rate_branch[i - 1],
training=self.training,
)
y_func = self._stacked_dense(
y_func,
Expand All @@ -324,9 +356,11 @@ def build_branch_net(self):
regularizer=self.regularizer,
trainable=self.trainable_branch,
)
if self.dropout_rate > 0:
if self.dropout_rate_branch[i - 1] > 0:
y_func = tf.layers.dropout(
y_func, rate=self.dropout_rate, training=self.training
y_func,
rate=self.dropout_rate_branch[i - 1],
training=self.training,
)
y_func = self._dense(
y_func,
Expand All @@ -351,9 +385,9 @@ def build_trunk_net(self):
if isinstance(self.trainable_trunk, (list, tuple))
else self.trainable_trunk,
)
if self.dropout_rate > 0:
if self.dropout_rate_trunk[i - 1] > 0:
y_loc = tf.layers.dropout(
y_loc, rate=self.dropout_rate, training=self.training
y_loc, rate=self.dropout_rate_trunk[i - 1], training=self.training
)
return y_loc

Expand Down Expand Up @@ -454,7 +488,13 @@ 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.
dropout_rate: If `dropout_rate` is a ``float`` between 0 and 1, then the
same rate is used in both trunk and branch nets. If `dropout_rate`
is a ``dict``, then the trunk net uses the rate `dropout_rate["trunk"]`,
and the branch net uses `dropout_rate["branch"]`. Both `dropout_rate["trunk"]`
and `dropout_rate["branch"]` should be ``float`` or lists of ``float``.
The list length should match the length of `layer_size_trunk` - 1 for the
trunk net and `layer_size_branch` - 2 for the branch net.
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 @@ -504,7 +544,31 @@ 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
if isinstance(dropout_rate, dict):
self.dropout_rate_branch = dropout_rate["branch"]
self.dropout_rate_trunk = dropout_rate["trunk"]
else:
self.dropout_rate_branch = self.dropout_rate_trunk = dropout_rate
if isinstance(self.dropout_rate_branch, list):
if not (len(layer_size_branch) - 2) == len(self.dropout_rate_branch):
raise ValueError(
"Number of dropout rates of branch net must be "
f"equal to {len(layer_size_branch) - 2}"
)
else:
self.dropout_rate_branch = [self.dropout_rate_branch] * (
len(layer_size_branch) - 2
)
if isinstance(self.dropout_rate_trunk, list):
if not (len(layer_size_trunk) - 1) == len(self.dropout_rate_trunk):
raise ValueError(
"Number of dropout rates of trunk net must be "
f"equal to {len(layer_size_trunk) - 1}"
)
else:
self.dropout_rate_trunk = [self.dropout_rate_trunk] * (
len(layer_size_trunk) - 1
)
self._inputs = None

self.num_outputs = num_outputs
Expand Down Expand Up @@ -571,9 +635,11 @@ def build_branch_net(self):
kernel_initializer=self.kernel_initializer,
kernel_regularizer=self.regularizer,
)
if self.dropout_rate > 0:
if self.dropout_rate_branch[i - 1] > 0:
y_func = tf.layers.dropout(
y_func, rate=self.dropout_rate, training=self.training
y_func,
rate=self.dropout_rate_branch[i - 1],
training=self.training,
)
y_func = tf.layers.dense(
y_func,
Expand All @@ -596,9 +662,9 @@ def build_trunk_net(self):
kernel_initializer=self.kernel_initializer,
kernel_regularizer=self.regularizer,
)
if self.dropout_rate > 0:
if self.dropout_rate_trunk[i - 1] > 0:
y_loc = tf.layers.dropout(
y_loc, rate=self.dropout_rate, training=self.training
y_loc, rate=self.dropout_rate_trunk[i - 1], training=self.training
)
return y_loc

Expand Down
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