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Resizing layer is no longer an experimental Layer #2

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528183f
Resizing layer is no longer an experimental Layer
Marcos-L Jun 18, 2024
ffef74c
Update MTVAE.py
Marcos-L Jun 18, 2024
8a3b43b
Update MTVAE_1conv2d.py
Marcos-L Jun 18, 2024
7dd6d5b
Update Shallownet_1conv2d.py
Marcos-L Jun 18, 2024
7c4364b
Update Shallownet_1conv2d_rff.py
Marcos-L Jun 18, 2024
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2 changes: 1 addition & 1 deletion EEG_Tensorflow_models/Models/DMTL_BCI.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
from tensorflow.keras.layers import Input, Flatten
from tensorflow.keras.constraints import max_norm
from tensorflow.keras import backend as K
from tensorflow.keras.layers.experimental.preprocessing import Resizing
from tensorflow.keras.layers import Resizing
from tensorflow.keras.losses import CategoricalCrossentropy
from tensorflow.keras.regularizers import l1_l2

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2 changes: 1 addition & 1 deletion EEG_Tensorflow_models/Models/MTVAE.py
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Expand Up @@ -5,7 +5,7 @@
from tensorflow.keras.layers import Input, Flatten, Reshape
from tensorflow.keras.constraints import max_norm
from tensorflow.keras import backend as K
from tensorflow.keras.layers.experimental.preprocessing import Resizing
from tensorflow.keras.layers import Resizing
from tensorflow.keras.losses import CategoricalCrossentropy
from tensorflow.keras.regularizers import l1_l2
import tensorflow as tf
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4 changes: 2 additions & 2 deletions EEG_Tensorflow_models/Models/MTVAE_1conv2d.py
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Expand Up @@ -5,7 +5,7 @@
from tensorflow.keras.layers import Input, Flatten, Reshape
from tensorflow.keras.constraints import max_norm
from tensorflow.keras import backend as K
from tensorflow.keras.layers.experimental.preprocessing import Resizing
from tensorflow.keras.layers import Resizing
from tensorflow.keras.losses import CategoricalCrossentropy
from tensorflow.keras.regularizers import l1_l2
import tensorflow as tf
Expand Down Expand Up @@ -56,4 +56,4 @@ def MTVAE_1conv2d(nb_classes, Chans = 22, Samples = 250, dropoutRate = 0.5, l1 =

KL = -0.5 * tf.keras.backend.sum( 1 + var_flat - tf.keras.backend.exp(var_flat) - tf.keras.backend.square(mu_flat),axis=-1)
model.add_loss(tf.keras.backend.mean(KL)/var_flat.shape[-1])#Chans*Samples)
return model
return model
2 changes: 1 addition & 1 deletion EEG_Tensorflow_models/Models/Shallownet_1conv2d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
from tensorflow.keras.layers import Input, Flatten
from tensorflow.keras.constraints import max_norm
from tensorflow.keras import backend as K
from tensorflow.keras.layers.experimental.preprocessing import Resizing
from tensorflow.keras.layers import Resizing
from tensorflow.keras.losses import CategoricalCrossentropy
from tensorflow.keras.regularizers import l1_l2

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2 changes: 1 addition & 1 deletion EEG_Tensorflow_models/Models/Shallownet_1conv2d_rff.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
from tensorflow.keras.layers import Input, Flatten
from tensorflow.keras.constraints import max_norm
from tensorflow.keras import backend as K
from tensorflow.keras.layers.experimental.preprocessing import Resizing
from tensorflow.keras.layers import Resizing
from tensorflow.keras.losses import CategoricalCrossentropy
from tensorflow.keras.regularizers import l1_l2

Expand Down