fix: remove gauumbel softmax dependencies (#235)

src/ydata_synthetic/synthesizers/regular/cgan/model.py

@@ -21,7 +21,6 @@
 #Import ydata synthetic classes
 from ....synthesizers import TrainParameters
 from ....synthesizers.gan import ConditionalModel
-from ....utils.gumbel_softmax import GumbelSoftmaxActivation
 
 class CGAN(ConditionalModel):
     "CGAN model for discrete conditions"

src/ydata_synthetic/synthesizers/regular/cramergan/model.py

@@ -17,7 +17,6 @@
 from ....synthesizers import TrainParameters
 from ....synthesizers.gan import BaseModel
 from ....synthesizers.loss import Mode, gradient_penalty
-from ....utils.gumbel_softmax import GumbelSoftmaxActivation
 
 class CRAMERGAN(BaseModel):
 

src/ydata_synthetic/synthesizers/regular/cwgangp/model.py

@@ -15,7 +15,7 @@
 
 #Import ydata synthetic classes
 from ....synthesizers import TrainParameters
-from ....synthesizers.gan import BaseModel, ConditionalModel
+from ....synthesizers.gan import ConditionalModel
 from ....synthesizers.regular.wgangp.model import WGAN_GP
 
 class CWGANGP(ConditionalModel, WGAN_GP):

src/ydata_synthetic/synthesizers/regular/dragan/model.py

@@ -14,7 +14,6 @@
 #Import ydata synthetic classes
 from ....synthesizers.gan import BaseModel
 from ....synthesizers.loss import Mode, gradient_penalty
-from ....utils.gumbel_softmax import GumbelSoftmaxActivation
 
 class DRAGAN(BaseModel):
 

src/ydata_synthetic/synthesizers/regular/vanillagan/model.py

@@ -27,8 +27,7 @@ def __init__(self, model_parameters):
 
     def define_gan(self, activation_info: Optional[NamedTuple]):
         self.generator = Generator(self.batch_size).\
-            build_model(input_shape=(self.noise_dim,), dim=self.layers_dim, data_dim=self.data_dim,
-                        activation_info = activation_info, tau = self.tau)
+            build_model(input_shape=(self.noise_dim,), dim=self.layers_dim, data_dim=self.data_dim,)
 
         self.discriminator = Discriminator(self.batch_size).\
             build_model(input_shape=(self.data_dim,), dim=self.layers_dim)
@@ -136,14 +135,12 @@ class Generator(tf.keras.Model):
     def __init__(self, batch_size):
         self.batch_size=batch_size
 
-    def build_model(self, input_shape, dim, data_dim, activation_info: Optional[NamedTuple] = None, tau: Optional[float] = None):
+    def build_model(self, input_shape, dim, data_dim):
         input= Input(shape=input_shape, batch_size=self.batch_size)
         x = Dense(dim, activation='relu')(input)
         x = Dense(dim * 2, activation='relu')(x)
         x = Dense(dim * 4, activation='relu')(x)
         x = Dense(data_dim)(x)
-        if activation_info:
-            x = GumbelSoftmaxActivation(activation_info, tau=tau)(x)
         return Model(inputs=input, outputs=x)
 
 class Discriminator(tf.keras.Model):

src/ydata_synthetic/synthesizers/regular/wgan/model.py

@@ -18,7 +18,6 @@
 #Import ydata synthetic classes
 from ....synthesizers import TrainParameters
 from ....synthesizers.gan import BaseModel
-from ....utils.gumbel_softmax import GumbelSoftmaxActivation
 
 #Auxiliary Keras backend class to calculate the Random Weighted average
 #https://stackoverflow.com/questions/58133430/how-to-substitute-keras-layers-merge-merge-in-tensorflow-keras

src/ydata_synthetic/synthesizers/regular/wgangp/model.py

@@ -17,7 +17,6 @@
 #Import ydata synthetic classes
 from ....synthesizers import TrainParameters
 from ....synthesizers.gan import BaseModel
-from ....utils.gumbel_softmax import GumbelSoftmaxActivation
 
 class WGAN_GP(BaseModel):