Documentation for v0.1 version (#199)
* Adding Equations, solving typos * improve _code.rst * the team rst and restuctore index.rst * fixing errors --------- Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>
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Dario Coscia
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Nicola Demo
Nicola Demo
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3f9305d475
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8b7b61b3bd
@@ -11,11 +11,11 @@ class FeedForward(torch.nn.Module):
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perceptron.
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:param int input_dimensions: The number of input components of the model.
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Expected tensor shape of the form (*, input_dimensions), where *
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means any number of dimensions including none.
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Expected tensor shape of the form :math:`(*, d)`, where *
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means any number of dimensions including none, and :math:`d` the ``input_dimensions``.
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:param int output_dimensions: The number of output components of the model.
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Expected tensor shape of the form (*, output_dimensions), where *
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means any number of dimensions including none.
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Expected tensor shape of the form :math:`(*, d)`, where *
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means any number of dimensions including none, and :math:`d` the ``output_dimensions``.
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:param int inner_size: number of neurons in the hidden layer(s). Default is
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20.
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:param int n_layers: number of hidden layers. Default is 2.
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@@ -23,18 +23,24 @@ class FeedForward(torch.nn.Module):
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:class:`torch.nn.Module` is passed, this is used as activation function
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after any layers, except the last one. If a list of Modules is passed,
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they are used as activation functions at any layers, in order.
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:param iterable(int) layers: a list containing the number of neurons for
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any hidden layers. If specified, the parameters `n_layers` e
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`inner_size` are not considered.
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:param bool bias: If `True` the MLP will consider some bias.
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:param list(int) | tuple(int) layers: a list containing the number of neurons for
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any hidden layers. If specified, the parameters ``n_layers`` e
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``inner_size`` are not considered.
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:param bool bias: If ``True`` the MLP will consider some bias.
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"""
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def __init__(self, input_dimensions, output_dimensions, inner_size=20,
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n_layers=2, func=nn.Tanh, layers=None, bias=True):
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def __init__(self,
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input_dimensions,
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output_dimensions,
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inner_size=20,
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n_layers=2,
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func=nn.Tanh,
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layers=None,
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bias=True):
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"""
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"""
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super().__init__()
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if not isinstance(input_dimensions, int):
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raise ValueError('input_dimensions expected to be int.')
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self.input_dimension = input_dimensions
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@@ -52,8 +58,7 @@ class FeedForward(torch.nn.Module):
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self.layers = []
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for i in range(len(tmp_layers) - 1):
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self.layers.append(
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nn.Linear(tmp_layers[i], tmp_layers[i + 1], bias=bias)
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)
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nn.Linear(tmp_layers[i], tmp_layers[i + 1], bias=bias))
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if isinstance(func, list):
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self.functions = func
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@@ -76,10 +81,10 @@ class FeedForward(torch.nn.Module):
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"""
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Defines the computation performed at every call.
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:param x: .
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:type x: :class:`pina.LabelTensor`
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:param x: The tensor to apply the forward pass.
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:type x: torch.Tensor
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:return: the output computed by the model.
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:rtype: LabelTensor
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:rtype: torch.Tensor
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"""
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return self.model(x)
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@@ -93,18 +98,18 @@ class ResidualFeedForward(torch.nn.Module):
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.. seealso::
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**Original reference**: Wang, Sifan, Yujun Teng, and Paris Perdikaris.
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"Understanding and mitigating gradient flow pathologies in physics-informed
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neural networks." SIAM Journal on Scientific Computing 43.5 (2021): A3055-A3081.
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*Understanding and mitigating gradient flow pathologies in physics-informed
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neural networks*. SIAM Journal on Scientific Computing 43.5 (2021): A3055-A3081.
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DOI: `10.1137/20M1318043
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<https://epubs.siam.org/doi/abs/10.1137/20M1318043>`_
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:param int input_dimensions: The number of input components of the model.
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Expected tensor shape of the form (*, input_dimensions), where *
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means any number of dimensions including none.
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Expected tensor shape of the form :math:`(*, d)`, where *
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means any number of dimensions including none, and :math:`d` the ``input_dimensions``.
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:param int output_dimensions: The number of output components of the model.
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Expected tensor shape of the form (*, output_dimensions), where *
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means any number of dimensions including none.
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Expected tensor shape of the form :math:`(*, d)`, where *
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means any number of dimensions including none, and :math:`d` the ``output_dimensions``.
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:param int inner_size: number of neurons in the hidden layer(s). Default is
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20.
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:param int n_layers: number of hidden layers. Default is 2.
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@@ -112,14 +117,21 @@ class ResidualFeedForward(torch.nn.Module):
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:class:`torch.nn.Module` is passed, this is used as activation function
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after any layers, except the last one. If a list of Modules is passed,
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they are used as activation functions at any layers, in order.
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:param bool bias: If `True` the MLP will consider some bias.
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:param bool bias: If ``True`` the MLP will consider some bias.
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:param list | tuple transformer_nets: a list or tuple containing the two
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torch.nn.Module which act as transformer network. The input dimension
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of the network must be the same as ``input_dimensions``, and the output
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dimension must be the same as ``inner_size``.
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"""
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def __init__(self, input_dimensions, output_dimensions, inner_size=20,
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n_layers=2, func=nn.Tanh, bias=True, transformer_nets=None):
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def __init__(self,
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input_dimensions,
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output_dimensions,
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inner_size=20,
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n_layers=2,
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func=nn.Tanh,
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bias=True,
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transformer_nets=None):
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"""
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"""
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super().__init__()
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@@ -135,26 +147,37 @@ class ResidualFeedForward(torch.nn.Module):
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# check transformer nets
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if transformer_nets is None:
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transformer_nets = [
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EnhancedLinear(nn.Linear(in_features=input_dimensions, out_features=inner_size),
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nn.Tanh()),
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EnhancedLinear(nn.Linear(in_features=input_dimensions, out_features=inner_size),
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nn.Tanh())
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]
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EnhancedLinear(
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nn.Linear(in_features=input_dimensions,
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out_features=inner_size), nn.Tanh()),
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EnhancedLinear(
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nn.Linear(in_features=input_dimensions,
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out_features=inner_size), nn.Tanh())
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]
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elif isinstance(transformer_nets, (list, tuple)):
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if len(transformer_nets) != 2:
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raise ValueError('transformer_nets needs to be a list of len two.')
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raise ValueError(
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'transformer_nets needs to be a list of len two.')
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for net in transformer_nets:
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if not isinstance(net, nn.Module):
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raise ValueError('transformer_nets needs to be a list of torch.nn.Module.')
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raise ValueError(
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'transformer_nets needs to be a list of torch.nn.Module.'
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)
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x = torch.rand(10, input_dimensions)
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try:
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out = net(x)
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except RuntimeError:
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raise ValueError('transformer network input incompatible with input_dimensions.')
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raise ValueError(
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'transformer network input incompatible with input_dimensions.'
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)
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if out.shape[-1] != inner_size:
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raise ValueError('transformer network output incompatible with inner_size.')
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raise ValueError(
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'transformer network output incompatible with inner_size.'
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)
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else:
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RuntimeError('Runtime error for transformer nets, check official documentation.')
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RuntimeError(
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'Runtime error for transformer nets, check official documentation.'
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)
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# assign variables
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self.input_dimension = input_dimensions
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@@ -170,9 +193,10 @@ class ResidualFeedForward(torch.nn.Module):
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self.layers = []
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for i in range(len(tmp_layers) - 1):
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self.layers.append(
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nn.Linear(tmp_layers[i], tmp_layers[i + 1], bias=bias)
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)
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self.last_layer = nn.Linear(tmp_layers[len(tmp_layers) - 1], output_dimensions, bias=bias)
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nn.Linear(tmp_layers[i], tmp_layers[i + 1], bias=bias))
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self.last_layer = nn.Linear(tmp_layers[len(tmp_layers) - 1],
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output_dimensions,
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bias=bias)
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if isinstance(func, list):
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self.functions = func()
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@@ -184,28 +208,27 @@ class ResidualFeedForward(torch.nn.Module):
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unique_list = []
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for layer, func in zip(self.layers, self.functions):
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unique_list.append(EnhancedLinear(layer=layer,
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activation=func))
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unique_list.append(EnhancedLinear(layer=layer, activation=func))
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self.inner_layers = torch.nn.Sequential(*unique_list)
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def forward(self, x):
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"""
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Defines the computation performed at every call.
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:param x: .
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:type x: :class:`pina.LabelTensor`
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:param x: The tensor to apply the forward pass.
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:type x: torch.Tensor
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:return: the output computed by the model.
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:rtype: LabelTensor
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:rtype: torch.Tensor
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"""
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# enhance the input with transformer
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input_ = []
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for nets in self.transformer_nets:
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input_.append(nets(x))
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# skip connections pass
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for layer in self.inner_layers.children():
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x = layer(x)
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x = (1. - x) * input_[0] + x * input_[1]
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# last layer
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return self.last_layer(x)
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return self.last_layer(x)
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