Merge pull request #7 from ydataai/fix/betas

fix: betas

examples/cgan_example.py

@@ -2,6 +2,7 @@
 from ydata_synthetic.preprocessing.credit_fraud import transformations
 
 import pandas as pd
+import numpy as np
 from sklearn import cluster
 
 #Read the original data and have it preprocessed
@@ -45,6 +46,8 @@
 log_step = 100
 epochs = 200+1
 learning_rate = 5e-4
+beta_1 = 0.5
+beta_2 = 0.9
 models_dir = './cache'
 
 train_sample = fraud_w_classes.copy().reset_index(drop=True)
@@ -54,7 +57,7 @@
 train_sample[ data_cols ] = train_sample[ data_cols ] / 10 # scale to random noise size, one less thing to learn
 train_no_label = train_sample[ data_cols ]
 
-gan_args = [batch_size, learning_rate, noise_dim, train_sample.shape[1], 2, (0, 1), dim]
+gan_args = [batch_size, learning_rate, beta_1, beta_2, noise_dim, train_sample.shape[1], 2, (0, 1), dim]
 train_args = ['', label_cols[0], epochs, log_step, '']
 
 #Init the Conditional GAN providing the index of the label column as one of the arguments

src/ydata_synthetic/synthesizers/gan.py

@@ -7,7 +7,7 @@ def __init__(
             model_parameters
     ):
         self._model_parameters = model_parameters
-        [self.batch_size, self.lr, self.noise_dim,
+        [self.batch_size, self.lr, self.beta_1, self.beta_2, self.noise_dim,
          self.data_dim, self.layers_dim] = model_parameters
         self.define_gan()
 

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

@@ -13,7 +13,7 @@
 class CGAN():
 
     def __init__(self, model_parameters):
-        [self.batch_size, lr, self.noise_dim,
+        [self.batch_size, lr,self.beta_1, self.beta_2, self.noise_dim,
          self.data_dim, num_classes, self.classes, layers_dim] = model_parameters
 
         self.generator = Generator(self.batch_size, num_classes). \
@@ -22,7 +22,7 @@ def __init__(self, model_parameters):
         self.discriminator = Discriminator(self.batch_size, num_classes). \
             build_model(input_shape=(self.data_dim,), dim=layers_dim)
 
-        optimizer = Adam(lr, beta_1=0.5)
+        optimizer = Adam(lr, beta_1=self.beta_1, beta_2=self.beta_2)
 
         # Build and compile the discriminator
         self.discriminator.compile(loss='binary_crossentropy',

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

@@ -21,7 +21,7 @@ def define_gan(self):
         self.discriminator = Discriminator(self.batch_size).\
             build_model(input_shape=(self.data_dim,), dim=self.layers_dim)
 
-        optimizer = Adam(self.lr, 0.5)
+        optimizer = Adam(self.lr, beta_1=self.beta_1, beta_2=self.beta_2)
 
         # Build and compile the discriminator
         self.discriminator.compile(loss='binary_crossentropy',

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

@@ -41,8 +41,8 @@ def define_gan(self):
         self.critic = Critic(self.batch_size). \
             build_model(input_shape=(self.data_dim,), dim=self.layers_dim)
 
-        optimizer = Adam(self.lr, beta_1=0.5, beta_2=0.9)
-        self.critic_optimizer = Adam(self.lr, beta_1=0.5, beta_2=0.9)
+        optimizer = Adam(self.lr, beta_1=self.beta_1, beta_2=self.beta_2)
+        self.critic_optimizer = Adam(self.lr, beta_1=self.beta_1, beta_2=self.beta_2)
 
         # Build and compile the critic
         self.critic.compile(loss=self.wasserstein_loss,
@@ -111,7 +111,7 @@ def train(self, data, train_arguments):
             # Plot the progress
             print("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100 * d_loss[1], g_loss))
 
-            # If at save interval => save generated events
+            #If at save interval => save generated events
             if epoch % sample_interval == 0:
                 # Test here data generation step
                 # save model checkpoints