Updated optimizer

Author: mehfuzh

config.yaml

@@ -19,4 +19,4 @@ embedded_intent_classifier:
     # drop rate to avoid overfitting
     drop_rate: 0.5
     # early stop training in case of not improving
-    early_stopping: True
+    early_stopping: False

data/demo.json

@@ -0,0 +1,31 @@
+{
+    "examples": {
+        "intents": [
+            {
+                "text": "about",
+                "intent": "about"
+            },
+            {
+                "text": "I want to learn about the platform",
+                "intent": "about"
+            },
+            {
+                "text": "learn more",
+                "intent": "about"
+            },
+            {
+                "text": "start",
+                "intent": "start"
+            },
+            {
+                "text": "hello",
+                "intent": "start"
+            },
+            {
+                "text": "hi",
+                "intent": "start"
+            }
+        ]
+    },
+    "lang": "en"
+}

smartloop/core/nlu/classifiers/embedding_intent_classifier.py

@@ -107,15 +107,15 @@ def __fit(self, model, features, intent):
         if self.cfg.logs:
             callbacks.append(TensorBoard(log_dir=log_dir))
 
-        early_stopping = self.cfg.embedded_intent_classifier.get('early_stopping', False)
+        early_stopping = self.cfg.embedded_intent_classifier.get('early_stopping')
 
         if early_stopping:
-            callbacks.append(EarlyStopping(monitor='accuracy', mode='max', patience=10))
+            callbacks.append(EarlyStopping(monitor='accuracy', patience=20))
 
         model.fit(
             features,
             intent,
-            validation_split=0.2,
+            validation_split=0.3,
             epochs=self.cfg.epochs,
             callbacks=[
                 callbacks
@@ -204,7 +204,7 @@ def __build_model(self, input_dim, output_dim, input_length, neurons, drop_rate,
             decay_steps=100,
             decay_rate=0.9)
 
-        optimizer = keras.optimizers.Adam(learning_rate=lr_schedule)
+        optimizer = keras.optimizers.RMSprop(learning_rate=lr_schedule)
 
         model.compile(
             loss=keras.losses.SparseCategoricalCrossentropy(),

smartloop/version.py

@@ -1 +1 @@
-__version__ = '1.0.7'
+__version__ = '1.0.8'