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variable name fix FeedForward model

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This commit is contained in:
Dario Coscia
2023-09-13 12:48:55 +02:00
committed by Nicola Demo
parent 17464ceca9
commit 5a4c114d48
3 changed files with 29 additions and 29 deletions
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8
pina/model/feed_forward.py
View File

@@ -28,16 +28,16 @@ class FeedForward(torch.nn.Module):
`inner_size` are not considered.
:param bool bias: If `True` the MLP will consider some bias.
"""
def __init__(self, input_dimensons, output_dimensions, inner_size=20,
def __init__(self, input_dimensions, output_dimensions, inner_size=20,
n_layers=2, func=nn.Tanh, layers=None, bias=True):
"""
"""
super().__init__()
if not isinstance(input_dimensons, int):
raise ValueError('input_dimensons expected to be int.')
self.input_dimension = input_dimensons
if not isinstance(input_dimensions, int):
raise ValueError('input_dimensions expected to be int.')
self.input_dimension = input_dimensions
if not isinstance(output_dimensions, int):
raise ValueError('output_dimensions expected to be int.')

20
tests/test_model/test_deeponet.py
View File

@@ -11,8 +11,8 @@ input_ = LabelTensor(data, input_vars)
def test_constructor():
branch_net = FeedForward(input_dimensons=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensons=2, output_dimensions=10)
branch_net = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=2, output_dimensions=10)
DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=['a'],
@@ -22,8 +22,8 @@ def test_constructor():
def test_constructor_fails_when_invalid_inner_layer_size():
branch_net = FeedForward(input_dimensons=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensons=2, output_dimensions=8)
branch_net = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=2, output_dimensions=8)
with pytest.raises(ValueError):
DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
@@ -33,8 +33,8 @@ def test_constructor_fails_when_invalid_inner_layer_size():
aggregator='*')
def test_forward_extract_str():
branch_net = FeedForward(input_dimensons=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensons=2, output_dimensions=10)
branch_net = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=2, output_dimensions=10)
model = DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=['a'],
@@ -44,8 +44,8 @@ def test_forward_extract_str():
model(input_)
def test_forward_extract_int():
branch_net = FeedForward(input_dimensons=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensons=2, output_dimensions=10)
branch_net = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=2, output_dimensions=10)
model = DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=[0],
@@ -55,8 +55,8 @@ def test_forward_extract_int():
model(data)
def test_forward_extract_str_wrong():
branch_net = FeedForward(input_dimensons=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensons=2, output_dimensions=10)
branch_net = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=2, output_dimensions=10)
model = DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=['a'],

30
tests/test_model/test_mionet.py
View File

@@ -11,9 +11,9 @@ input_ = LabelTensor(data, input_vars)
def test_constructor():
branch_net1 = FeedForward(input_dimensons=1, output_dimensions=10)
branch_net2 = FeedForward(input_dimensons=2, output_dimensions=10)
trunk_net = FeedForward(input_dimensons=1, output_dimensions=10)
branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
branch_net2 = FeedForward(input_dimensions=2, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=1, output_dimensions=10)
networks = {branch_net1 : ['x'],
branch_net2 : ['x', 'y'],
trunk_net : ['z']}
@@ -23,9 +23,9 @@ def test_constructor():
def test_constructor_fails_when_invalid_inner_layer_size():
branch_net1 = FeedForward(input_dimensons=1, output_dimensions=10)
branch_net2 = FeedForward(input_dimensons=2, output_dimensions=10)
trunk_net = FeedForward(input_dimensons=1, output_dimensions=12)
branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
branch_net2 = FeedForward(input_dimensions=2, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=1, output_dimensions=12)
networks = {branch_net1 : ['x'],
branch_net2 : ['x', 'y'],
trunk_net : ['z']}
@@ -35,9 +35,9 @@ def test_constructor_fails_when_invalid_inner_layer_size():
aggregator='*')
def test_forward_extract_str():
branch_net1 = FeedForward(input_dimensons=1, output_dimensions=10)
branch_net2 = FeedForward(input_dimensons=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensons=1, output_dimensions=10)
branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=1, output_dimensions=10)
networks = {branch_net1 : ['a'],
branch_net2 : ['b'],
trunk_net : ['c']}
@@ -47,9 +47,9 @@ def test_forward_extract_str():
model(input_)
def test_forward_extract_int():
branch_net1 = FeedForward(input_dimensons=1, output_dimensions=10)
branch_net2 = FeedForward(input_dimensons=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensons=1, output_dimensions=10)
branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=1, output_dimensions=10)
networks = {branch_net1 : [0],
branch_net2 : [1],
trunk_net : [2]}
@@ -59,9 +59,9 @@ def test_forward_extract_int():
model(data)
def test_forward_extract_str_wrong():
branch_net1 = FeedForward(input_dimensons=1, output_dimensions=10)
branch_net2 = FeedForward(input_dimensons=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensons=1, output_dimensions=10)
branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=1, output_dimensions=10)
networks = {branch_net1 : ['a'],
branch_net2 : ['b'],
trunk_net : ['c']}