Datasets:
HenryAI
/
KerasBERTv1-Data

Dataset card Data Studio Files Community
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4.99k
__________________________________________________________________________________________________
leaky_re_lu_31 (LeakyReLU) (None, None, 256) 0 weight_normalization_31[0][0]
__________________________________________________________________________________________________
leaky_re_lu_37 (LeakyReLU) (None, None, 256) 0 weight_normalization_38[0][0]
__________________________________________________________________________________________________
leaky_re_lu_43 (LeakyReLU) (None, None, 256) 0 weight_normalization_45[0][0]
__________________________________________________________________________________________________
weight_normalization_32 (Weight (None, None, 1024) 169985 leaky_re_lu_31[0][0]
__________________________________________________________________________________________________
weight_normalization_39 (Weight (None, None, 1024) 169985 leaky_re_lu_37[0][0]
__________________________________________________________________________________________________
weight_normalization_46 (Weight (None, None, 1024) 169985 leaky_re_lu_43[0][0]
__________________________________________________________________________________________________
leaky_re_lu_32 (LeakyReLU) (None, None, 1024) 0 weight_normalization_32[0][0]
__________________________________________________________________________________________________
leaky_re_lu_38 (LeakyReLU) (None, None, 1024) 0 weight_normalization_39[0][0]
__________________________________________________________________________________________________
leaky_re_lu_44 (LeakyReLU) (None, None, 1024) 0 weight_normalization_46[0][0]
__________________________________________________________________________________________________
weight_normalization_33 (Weight (None, None, 1024) 169985 leaky_re_lu_32[0][0]
__________________________________________________________________________________________________
weight_normalization_40 (Weight (None, None, 1024) 169985 leaky_re_lu_38[0][0]
__________________________________________________________________________________________________
weight_normalization_47 (Weight (None, None, 1024) 169985 leaky_re_lu_44[0][0]
__________________________________________________________________________________________________
leaky_re_lu_33 (LeakyReLU) (None, None, 1024) 0 weight_normalization_33[0][0]
__________________________________________________________________________________________________
leaky_re_lu_39 (LeakyReLU) (None, None, 1024) 0 weight_normalization_40[0][0]
__________________________________________________________________________________________________
leaky_re_lu_45 (LeakyReLU) (None, None, 1024) 0 weight_normalization_47[0][0]
__________________________________________________________________________________________________
weight_normalization_34 (Weight (None, None, 1024) 5244929 leaky_re_lu_33[0][0]
__________________________________________________________________________________________________
weight_normalization_41 (Weight (None, None, 1024) 5244929 leaky_re_lu_39[0][0]
__________________________________________________________________________________________________
weight_normalization_48 (Weight (None, None, 1024) 5244929 leaky_re_lu_45[0][0]
__________________________________________________________________________________________________
leaky_re_lu_34 (LeakyReLU) (None, None, 1024) 0 weight_normalization_34[0][0]
__________________________________________________________________________________________________
leaky_re_lu_40 (LeakyReLU) (None, None, 1024) 0 weight_normalization_41[0][0]
__________________________________________________________________________________________________
leaky_re_lu_46 (LeakyReLU) (None, None, 1024) 0 weight_normalization_48[0][0]
__________________________________________________________________________________________________
weight_normalization_35 (Weight (None, None, 1) 3075 leaky_re_lu_34[0][0]
__________________________________________________________________________________________________
weight_normalization_42 (Weight (None, None, 1) 3075 leaky_re_lu_40[0][0]
__________________________________________________________________________________________________
weight_normalization_49 (Weight (None, None, 1) 3075 leaky_re_lu_46[0][0]
==================================================================================================
Total params: 16,924,107
Trainable params: 16,924,086
Non-trainable params: 21
__________________________________________________________________________________________________
Defining the loss functions
Generator Loss
The generator architecture uses a combination of two losses
Mean Squared Error:
This is the standard MSE generator loss calculated between ones and the outputs from the discriminator with N layers.
Feature Matching Loss:
This loss involves extracting the outputs of every layer from the discriminator for both the generator and ground truth and compare each layer output k using Mean Absolute Error.
Discriminator Loss
The discriminator uses the Mean Absolute Error and compares the real data predictions with ones and generated predictions with zeros.
# Generator loss
def generator_loss(real_pred, fake_pred):
\"\"\"Loss function for the generator.
Args:
real_pred: Tensor, output of the ground truth wave passed through the discriminator.
fake_pred: Tensor, output of the generator prediction passed through the discriminator.
Returns:
Loss for the generator.
\"\"\"
gen_loss = []
for i in range(len(fake_pred)):
gen_loss.append(mse(tf.ones_like(fake_pred[i][-1]), fake_pred[i][-1]))
return tf.reduce_mean(gen_loss)
def feature_matching_loss(real_pred, fake_pred):
\"\"\"Implements the feature matching loss.
Args:
real_pred: Tensor, output of the ground truth wave passed through the discriminator.
fake_pred: Tensor, output of the generator prediction passed through the discriminator.