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Updating mne_bids version #586

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merged 25 commits into from
May 29, 2024
Merged

Updating mne_bids version #586

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d017c00
Updating mne_bids version
brunaafl Apr 29, 2024
4720506
Merge branch 'develop' into fix_bids
bruAristimunha Apr 30, 2024
1462c77
Accepting mne 1.7
brunaafl Apr 30, 2024
092377a
Updating whats new file
brunaafl Apr 30, 2024
768df28
Merge branch 'develop' into fix_bids
bruAristimunha May 2, 2024
7dfddb7
Enforce moabb 1.7
brunaafl May 3, 2024
0558baf
Enforce moabb 1.7
brunaafl May 8, 2024
4f2df9e
Merge branch 'develop' into fix_bids
bruAristimunha May 17, 2024
9c26b63
Adding edfio as a dependency
brunaafl May 17, 2024
8e8a110
Merge branch 'develop' into fix_bids
bruAristimunha May 27, 2024
6430dde
Update pyproject.toml
brunaafl May 27, 2024
1035a93
Update pyproject.toml
bruAristimunha May 27, 2024
94c0f6d
Updating versions: keras, scikeras, tensorflow, h5py
brunaafl May 27, 2024
92c3cea
Updating versions: keras, scikeras, tensorflow, h5py
brunaafl May 27, 2024
c9c838b
Defining tensorflow-io-gcs-filesystem version
brunaafl May 27, 2024
ea1ac0e
Defining tensorflow-io-gcs-filesystem version
brunaafl May 27, 2024
ae644ac
Updating to Keras 3 optimizers
brunaafl May 28, 2024
74e9c81
Updating code Keras3
brunaafl May 28, 2024
b229a72
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] May 28, 2024
df2e4ba
Updating code Keras3
brunaafl May 28, 2024
458cbff
Merge remote-tracking branch 'origin/fix_bids' into fix_bids
brunaafl May 28, 2024
c10ec6a
Updating to Keras 3 functions
brunaafl May 29, 2024
12b3e3c
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] May 29, 2024
94c1dbf
Merge branch 'develop' into fix_bids
bruAristimunha May 29, 2024
ea62ae6
Merge branch 'develop' into fix_bids
bruAristimunha May 29, 2024
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2 changes: 2 additions & 0 deletions docs/source/whats_new.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -49,6 +49,7 @@ Bugs
- Fix number of sessions in doc of :class:`moabb.datasets.Sosulski2019` (:gh:`565` by `Pierre Guetschel`_)
- Fix `code` column of :class:`moabb.datasets.CastillosCVEP100` and :class:`moabb.datasets.CastillosCVEP100` (:gh:`567` by `Pierre Guetschel`_)
- MAINT updating the packages pre-release (:gh:`578` by `Bruno Aristimunha`_)
- Fix mne_bids version incompatibility with mne (:gh:`586` by `Bruna Lopes`_)
- Updating the parameters of the SSVEP_TRCA method (:gh:`589` by `Bruno Aristimunha`_)
- Fix and updating the parameters for the benchmark function (:gh:`588` by `Bruno Aristimunha`_)
- Fix :class:`moabb.datasets.preprocessing.SetRawAnnotations` setting incorrect annotations when the dataset's interval does not start at 0 (:gh:`607` by `Pierre Guetschel`_)
Expand Down Expand Up @@ -449,4 +450,5 @@ API changes
.. _Jordy Thielen: https://github.com/thijor
.. _Sebastien Velut: https://github.com/swetbear
.. _Brian Irvine: https://github.com/brianjohannes
.. _Bruna Lopes: https://github.com/brunaafl
.. _Yash Chauhan: https://github.com/jiggychauhi
2 changes: 1 addition & 1 deletion examples/pipelines_DL/Keras_DeepConvNet.yml
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Expand Up @@ -25,7 +25,7 @@ pipeline:
loss: "sparse_categorical_crossentropy"
optimizer:
- name: Adam
from: tensorflow.keras.optimizers.legacy
from: tensorflow.keras.optimizers
parameters:
learning_rate: 0.001
epochs: 2
Expand Down
2 changes: 1 addition & 1 deletion examples/pipelines_DL/Keras_EEGITNet.yml
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Expand Up @@ -25,7 +25,7 @@ pipeline:
loss: "sparse_categorical_crossentropy"
optimizer:
- name: Adam
from: tensorflow.keras.optimizers.legacy
from: tensorflow.keras.optimizers
parameters:
learning_rate: 0.001
epochs: 2
Expand Down
2 changes: 1 addition & 1 deletion examples/pipelines_DL/Keras_EEGNeX.yml
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Expand Up @@ -25,7 +25,7 @@ pipeline:
loss: "sparse_categorical_crossentropy"
optimizer:
- name: Adam
from: tensorflow.keras.optimizers.legacy
from: tensorflow.keras.optimizers
parameters:
learning_rate: 0.001
epochs: 2
Expand Down
2 changes: 1 addition & 1 deletion examples/pipelines_DL/Keras_EEGNet_8_2.yml
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Expand Up @@ -25,7 +25,7 @@ pipeline:
loss: "sparse_categorical_crossentropy"
optimizer:
- name: Adam
from: tensorflow.keras.optimizers.legacy
from: tensorflow.keras.optimizers
parameters:
learning_rate: 0.001
epochs: 2
Expand Down
2 changes: 1 addition & 1 deletion examples/pipelines_DL/Keras_EEGTCNet.yml
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Expand Up @@ -25,7 +25,7 @@ pipeline:
loss: "sparse_categorical_crossentropy"
optimizer:
- name: Adam
from: tensorflow.keras.optimizers.legacy
from: tensorflow.keras.optimizers
parameters:
learning_rate: 0.001
epochs: 2
Expand Down
2 changes: 1 addition & 1 deletion examples/pipelines_DL/Keras_ShallowConvNet.yml
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Expand Up @@ -25,7 +25,7 @@ pipeline:
loss: "sparse_categorical_crossentropy"
optimizer:
- name: Adam
from: tensorflow.keras.optimizers.legacy
from: tensorflow.keras.optimizers
parameters:
learning_rate: 0.001
epochs: 2
Expand Down
28 changes: 15 additions & 13 deletions moabb/pipelines/deep_learning.py
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Expand Up @@ -10,7 +10,6 @@
from typing import Any, Dict

import tensorflow as tf
from keras import backend as K
from keras.constraints import max_norm
from keras.layers import (
Activation,
Expand All @@ -31,7 +30,9 @@
Permute,
)
from keras.models import Model, Sequential
from keras.ops import pad
from scikeras.wrappers import KerasClassifier
from tensorflow.keras import backend as K

from moabb.pipelines.utils_deep_model import EEGNet, EEGNet_TC, TCN_block

Expand Down Expand Up @@ -728,15 +729,15 @@ def _keras_build_fn(self, compile_kwargs: Dict[str, Any]):

# ================================

paddings = tf.constant([[0, 0], [0, 0], [3, 0], [0, 0]])
block = tf.pad(block_in, paddings, "CONSTANT")
paddings = [[0, 0], [0, 0], [3, 0], [0, 0]]
block = pad(block_in, paddings, "constant")
block = DepthwiseConv2D(
(1, 4), padding="valid", depth_multiplier=1, dilation_rate=(1, 1)
)(block)
block = BatchNormalization()(block)
block = Activation("elu")(block)
block = Dropout(0.4)(block)
block = tf.pad(block, paddings, "CONSTANT")
block = pad(block, paddings, "constant")
block = DepthwiseConv2D(
(1, 4), padding="valid", depth_multiplier=1, dilation_rate=(1, 1)
)(block)
Expand All @@ -745,15 +746,16 @@ def _keras_build_fn(self, compile_kwargs: Dict[str, Any]):
block = Dropout(self.drop_rate)(block)
block_out = Add()([block_in, block])

paddings = tf.constant([[0, 0], [0, 0], [6, 0], [0, 0]])
block = tf.pad(block_out, paddings, "CONSTANT")
paddings = [[0, 0], [0, 0], [6, 0], [0, 0]]
block = pad(block_out, paddings, "constant")
block = DepthwiseConv2D(
(1, 4), padding="valid", depth_multiplier=1, dilation_rate=(1, 2)
)(block)
block = BatchNormalization()(block)
block = Activation("elu")(block)
block = Dropout(self.drop_rate)(block)
block = tf.pad(block, paddings, "CONSTANT")
print(block.dtype)
block = pad(block, paddings, "constant")
block = DepthwiseConv2D(
(1, 4), padding="valid", depth_multiplier=1, dilation_rate=(1, 2)
)(block)
Expand All @@ -762,15 +764,15 @@ def _keras_build_fn(self, compile_kwargs: Dict[str, Any]):
block = Dropout(self.drop_rate)(block)
block_out = Add()([block_out, block])

paddings = tf.constant([[0, 0], [0, 0], [12, 0], [0, 0]])
block = tf.pad(block_out, paddings, "CONSTANT")
paddings = [[0, 0], [0, 0], [12, 0], [0, 0]]
block = pad(block_out, paddings, "constant")
block = DepthwiseConv2D(
(1, 4), padding="valid", depth_multiplier=1, dilation_rate=(1, 4)
)(block)
block = BatchNormalization()(block)
block = Activation("elu")(block)
block = Dropout(self.drop_rate)(block)
block = tf.pad(block, paddings, "CONSTANT")
block = pad(block, paddings, "constant")
block = DepthwiseConv2D(
(1, 4), padding="valid", depth_multiplier=1, dilation_rate=(1, 4)
)(block)
Expand All @@ -779,15 +781,15 @@ def _keras_build_fn(self, compile_kwargs: Dict[str, Any]):
block = Dropout(self.drop_rate)(block)
block_out = Add()([block_out, block])

paddings = tf.constant([[0, 0], [0, 0], [24, 0], [0, 0]])
block = tf.pad(block_out, paddings, "CONSTANT")
paddings = [[0, 0], [0, 0], [24, 0], [0, 0]]
block = pad(block_out, paddings, "constant")
block = DepthwiseConv2D(
(1, 4), padding="valid", depth_multiplier=1, dilation_rate=(1, 8)
)(block)
block = BatchNormalization()(block)
block = Activation("elu")(block)
block = Dropout(self.drop_rate)(block)
block = tf.pad(block, paddings, "CONSTANT")
block = pad(block, paddings, "constant")
block = DepthwiseConv2D(
(1, 4), padding="valid", depth_multiplier=1, dilation_rate=(1, 8)
)(block)
Expand Down
2 changes: 1 addition & 1 deletion pipelines/Keras_DeepConvNet.yml
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Expand Up @@ -27,7 +27,7 @@ pipeline:
loss: "sparse_categorical_crossentropy"
optimizer:
- name: Adam
from: tensorflow.keras.optimizers.legacy
from: tensorflow.keras.optimizers
parameters:
learning_rate: 0.001
epochs: 300
Expand Down
2 changes: 1 addition & 1 deletion pipelines/Keras_EEGITNet.yml
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Expand Up @@ -27,7 +27,7 @@ pipeline:
loss: "sparse_categorical_crossentropy"
optimizer:
- name: Adam
from: tensorflow.keras.optimizers.legacy
from: tensorflow.keras.optimizers
parameters:
learning_rate: 0.001
epochs: 300
Expand Down
2 changes: 1 addition & 1 deletion pipelines/Keras_EEGNeX.yml
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Expand Up @@ -27,7 +27,7 @@ pipeline:
loss: "sparse_categorical_crossentropy"
optimizer:
- name: Adam
from: tensorflow.keras.optimizers.legacy
from: tensorflow.keras.optimizers
parameters:
learning_rate: 0.001
epochs: 300
Expand Down
2 changes: 1 addition & 1 deletion pipelines/Keras_EEGNet_8_2.yml
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Expand Up @@ -27,7 +27,7 @@ pipeline:
loss: "sparse_categorical_crossentropy"
optimizer:
- name: Adam
from: tensorflow.keras.optimizers.legacy
from: tensorflow.keras.optimizers
parameters:
learning_rate: 0.001
epochs: 300
Expand Down
2 changes: 1 addition & 1 deletion pipelines/Keras_EEGTCNet.yml
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Expand Up @@ -27,7 +27,7 @@ pipeline:
loss: "sparse_categorical_crossentropy"
optimizer:
- name: Adam
from: tensorflow.keras.optimizers.legacy
from: tensorflow.keras.optimizers
parameters:
learning_rate: 0.001
epochs: 300
Expand Down
2 changes: 1 addition & 1 deletion pipelines/Keras_ShallowConvNet.yml
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Expand Up @@ -27,7 +27,7 @@ pipeline:
loss: "sparse_categorical_crossentropy"
optimizer:
- name: Adam
from: tensorflow.keras.optimizers.legacy
from: tensorflow.keras.optimizers
parameters:
learning_rate: 0.001
epochs: 300
Expand Down
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