Merge branch 'develop' of github.com:ECP-CANDLE/Benchmarks into develop

Author: jmohdyusof

examples/xform-smiles/srt_baseline_keras.py

@@ -55,15 +55,15 @@ def run(params):
     optimizer = optimizers.deserialize({'class_name': params['optimizer'], 'config': {}})
 
     # I don't know why we set base_lr. It doesn't appear to be used.
-    if 'base_lr' in params and params['base_lr'] > 0:
-        base_lr = params['base_lr']
-    else:
-        base_lr = K.get_value(optimizer.lr)
+    # if 'base_lr' in params and params['base_lr'] > 0:
+    #     base_lr = params['base_lr']
+    # else:
+    #     base_lr = K.get_value(optimizer.lr)
 
     if 'learning_rate' in params and params['learning_rate'] > 0:
         K.set_value(optimizer.lr, params['learning_rate'])
         print('Done setting optimizer {} learning rate to {}'.format(
-            params['optimizer'],params['learning_rate']))
+            params['optimizer'], params['learning_rate']))
 
     model.compile(loss='mean_squared_error',
                   optimizer=optimizer,
@@ -76,12 +76,12 @@ def run(params):
     reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.75, patience=20, verbose=1, mode='auto', epsilon=0.0001, cooldown=3, min_lr=0.000000001)
     early_stop = EarlyStopping(monitor='val_loss', patience=100, verbose=1, mode='auto')
 
-    history = model.fit(x_train, y_train,
-                        batch_size=params['batch_size'],
-                        epochs=params['epochs'],
-                        verbose=1,
-                        validation_data=(x_val, y_val),
-                        callbacks=[checkpointer, csv_logger, reduce_lr, early_stop])
+    model.fit(x_train, y_train,
+              batch_size=params['batch_size'],
+              epochs=params['epochs'],
+              verbose=1,
+              validation_data=(x_val, y_val),
+              callbacks=[checkpointer, csv_logger, reduce_lr, early_stop])
 
     model.load_weights('smile_regress.autosave.model.h5')
 

examples/xform-smiles/srt_baseline_keras2.py

@@ -55,15 +55,15 @@ def run(params):
     optimizer = optimizers.deserialize({'class_name': params['optimizer'], 'config': {}})
 
     # I don't know why we set base_lr. It doesn't appear to be used.
-    if 'base_lr' in params and params['base_lr'] > 0:
-        base_lr = params['base_lr']
-    else:
-        base_lr = K.get_value(optimizer.lr)
+    # if 'base_lr' in params and params['base_lr'] > 0:
+    #     base_lr = params['base_lr']
+    # else:
+    #     base_lr = K.get_value(optimizer.lr)
 
     if 'learning_rate' in params and params['learning_rate'] > 0:
         K.set_value(optimizer.lr, params['learning_rate'])
         print('Done setting optimizer {} learning rate to {}'.format(
-            params['optimizer'],params['learning_rate']))
+            params['optimizer'], params['learning_rate']))
 
     model.compile(loss='mean_squared_error',
                   optimizer=optimizer,
@@ -87,6 +87,7 @@ def run(params):
 
     return history
 
+
 def main():
     params = initialize_parameters()
     run(params)