Update main.py

main.py

@@ -601,14 +601,17 @@ concat_layer_level1_2 = tf.keras.layers.LeakyReLU()(concat_layer_level1_2)
 concat_layer_level = tf.keras.layers.concatenate([concat_layer_level1_1,concat_layer_level1_2])
 sub_layer = tf.keras.layers.Dense(300,name="sub_layer")(concat_layer_level)
 sub_layer = tf.keras.layers.LeakyReLU()(sub_layer)
-
+sub_layer = tf.keras.layers.Dropout(rate=0.2)(sub_layer)
 # con  = tf.keras.layers.Dropout(rate=0.2)(sub_layer)
 
 # Concatenate all input branches
 concat_layer = tf.keras.layers.concatenate([s1,sub_layer ])
 
 # Add dense and output layers
-f1= tf.keras.layers.Dense(1000)(concat_layer)
+f1= tf.keras.layers.Dense(2000)(concat_layer)
+f1 = tf.keras.layers.LeakyReLU()(f1)
+
+f1 = tf.keras.layers.Dense(1000)(f1)
 f1 = tf.keras.layers.LeakyReLU()(f1)
 
 f1 = tf.keras.layers.Dense(500)(f1)
@@ -680,8 +683,8 @@ class MyCustomCallback(tf.keras.callbacks.Callback):
 
 k = model.fit(X_train,
           y_train,
-          epochs=6,
-          batch_size=1024,
+          epochs=10,
+          batch_size=2048,
           validation_data=(X_test, y_test),
           callbacks=[MyCustomCallback(),tf.keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10),tensorboard_callback],
           verbose=1