prefer get_variable over get_scalar_var

examples/CTC-TIMIT/train-timit.py

@@ -77,7 +77,7 @@ class Model(ModelDesc):
         summary.add_moving_summary(err, self.cost)
 
     def _get_optimizer(self):
-        lr = symbolic_functions.get_scalar_var('learning_rate', 5e-3, summary=True)
+        lr = tf.get_variable('learning_rate', initializer=5e-3, trainable=False)
         opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
         return optimizer.apply_grad_processors(
             opt, [GlobalNormClip(5), SummaryGradient()])

examples/Char-RNN/char-rnn.py

@@ -105,7 +105,7 @@ class Model(ModelDesc):
         summary.add_moving_summary(self.cost)
 
     def _get_optimizer(self):
-        lr = symbolic_functions.get_scalar_var('learning_rate', 2e-3, summary=True)
+        lr = tf.get_variable('learning_rate', initializer=2e-3, trainable=False)
         opt = tf.train.AdamOptimizer(lr)
         return optimizer.apply_grad_processors(opt, [GlobalNormClip(5)])
 

examples/DoReFa-Net/alexnet-dorefa.py

@@ -167,7 +167,7 @@ class Model(ModelDesc):
         add_moving_summary(cost, wd_cost, self.cost)
 
     def _get_optimizer(self):
-        lr = get_scalar_var('learning_rate', 1e-4, summary=True)
+        lr = tf.get_variable('learning_rate', initializer=1e-4, trainable=False)
         return tf.train.AdamOptimizer(lr, epsilon=1e-5)
 
 

examples/DynamicFilterNetwork/steering-filter.py

@@ -149,8 +149,7 @@ class Model(ModelDesc):
         summary.add_moving_summary(self.cost)
 
     def _get_optimizer(self):
-        lr = symbolic_functions.get_scalar_var('learning_rate', 1e-3, summary=True)
-        return tf.train.AdamOptimizer(lr)
+        return tf.train.AdamOptimizer(1e-3)
 
 
 class ThetaImages(ProxyDataFlow, RNGDataFlow):

examples/GAN/BEGAN.py

@@ -132,7 +132,7 @@ class Model(GANModelDesc):
         self.collect_variables()
 
     def _get_optimizer(self):
-        lr = symbolic_functions.get_scalar_var('learning_rate', 1e-4, summary=True)
+        lr = tf.get_variable('learning_rate', initializer=1e-4, trainable=False)
         opt = tf.train.AdamOptimizer(lr, beta1=0.5, beta2=0.9)
         return opt
 

examples/GAN/ConditionalGAN-mnist.py

@@ -96,8 +96,7 @@ class Model(GANModelDesc):
         self.collect_variables()
 
     def _get_optimizer(self):
-        lr = symbf.get_scalar_var('learning_rate', 2e-4, summary=True)
-        return tf.train.AdamOptimizer(lr, beta1=0.5, epsilon=1e-3)
+        return tf.train.AdamOptimizer(2e-4, beta1=0.5, epsilon=1e-3)
 
 
 def get_data():

examples/GAN/DiscoGAN-CelebA.py

@@ -159,8 +159,7 @@ class Model(GANModelDesc):
         add_moving_summary(recon_loss_A, recon_loss_B, rate, g_loss, d_loss, wd_g, wd_d)
 
     def _get_optimizer(self):
-        lr = symbolic_functions.get_scalar_var('learning_rate', 2e-4, summary=True)
-        return tf.train.AdamOptimizer(lr, beta1=0.5, epsilon=1e-3)
+        return tf.train.AdamOptimizer(2e-4, beta1=0.5, epsilon=1e-3)
 
 
 def get_celebA_data(datadir, styleA, styleB=None):

examples/GAN/Image2Image.py

@@ -134,7 +134,7 @@ class Model(GANModelDesc):
         self.collect_variables()
 
     def _get_optimizer(self):
-        lr = symbolic_functions.get_scalar_var('learning_rate', 2e-4, summary=True)
+        lr = tf.get_variable('learning_rate', initializer=2e-4, trainable=False)
         return tf.train.AdamOptimizer(lr, beta1=0.5, epsilon=1e-3)
 
 

examples/GAN/Improved-WGAN.py

@@ -83,8 +83,7 @@ class Model(DCGAN.Model):
         self.collect_variables()
 
     def _get_optimizer(self):
-        lr = symbolic_functions.get_scalar_var('learning_rate', 1e-4, summary=True)
-        opt = tf.train.AdamOptimizer(lr, beta1=0.5, beta2=0.9)
+        opt = tf.train.AdamOptimizer(1e-4, beta1=0.5, beta2=0.9)
         return opt
 
 

examples/GAN/WGAN.py

@@ -36,8 +36,7 @@ class Model(DCGAN.Model):
         add_moving_summary(self.d_loss, self.g_loss)
 
     def _get_optimizer(self):
-        lr = symbolic_functions.get_scalar_var('learning_rate', 1e-4, summary=True)
-        opt = tf.train.RMSPropOptimizer(lr)
+        opt = tf.train.RMSPropOptimizer(1e-4)
         return opt
 
         # An alternative way to implement the clipping:

examples/Inception/inception-bn.py

@@ -123,7 +123,7 @@ class Model(ModelDesc):
         add_moving_summary(wd_cost, self.cost)
 
     def _get_optimizer(self):
-        lr = get_scalar_var('learning_rate', 0.045, summary=True)
+        lr = tf.get_variable('learning_rate', initializer=0.045, trainable=False)
         return tf.train.MomentumOptimizer(lr, 0.9)
 
 

examples/Inception/inceptionv3.py

@@ -195,7 +195,7 @@ class Model(ModelDesc):
         add_moving_summary(loss1, loss2, wd_cost, self.cost)
 
     def _get_optimizer(self):
-        lr = get_scalar_var('learning_rate', 0.045, summary=True)
+        lr = tf.get_variable('learning_rate', initializer=0.045, trainable=False)
         return tf.train.AdamOptimizer(lr, epsilon=1e-3)
 
 

examples/PennTreebank/PTB-LSTM.py

@@ -109,7 +109,7 @@ class Model(ModelDesc):
                         s[1].h.assign(z), name='reset_lstm_state')
 
     def _get_optimizer(self):
-        lr = symbolic_functions.get_scalar_var('learning_rate', 1, summary=True)
+        lr = tf.get_variable('learning_rate', initializer=1.0, trainable=False)
         opt = tf.train.GradientDescentOptimizer(lr)
         return optimizer.apply_grad_processors(
             opt, [gradproc.GlobalNormClip(5)])

examples/ResNet/cifar10-resnet.py

@@ -116,7 +116,7 @@ class Model(ModelDesc):
         self.cost = tf.add_n([cost, wd_cost], name='cost')
 
     def _get_optimizer(self):
-        lr = get_scalar_var('learning_rate', 0.01, summary=True)
+        lr = tf.get_variable('learning_rate', initializer=0.01, trainable=False)
         opt = tf.train.MomentumOptimizer(lr, 0.9)
         return opt
 

examples/Saliency/CAM-resnet.py

@@ -68,7 +68,7 @@ class Model(ModelDesc):
         self.cost = tf.add_n([loss, wd_cost], name='cost')
 
     def _get_optimizer(self):
-        lr = get_scalar_var('learning_rate', 0.1, summary=True)
+        lr = tf.get_variable('learning_rate', initializer=0.1, trainable=False)
         opt = tf.train.MomentumOptimizer(lr, 0.9, use_nesterov=True)
         gradprocs = [gradproc.ScaleGradient(
             [('conv0.*', 0.1), ('group[0-2].*', 0.1)])]

examples/SimilarityLearning/mnist-embeddings.py

@@ -229,7 +229,7 @@ class EmbeddingModel(ModelDesc):
         return embeddings
 
     def _get_optimizer(self):
-        lr = symbf.get_scalar_var('learning_rate', 1e-4, summary=True)
+        lr = tf.get_variable('learning_rate', initializer=1e-4, trainable=False)
         return tf.train.GradientDescentOptimizer(lr)
 
 

examples/SpatialTransformer/mnist-addition.py

@@ -90,7 +90,7 @@ class Model(ModelDesc):
         self.cost = tf.add_n([wd_cost, cost], name='cost')
 
     def _get_optimizer(self):
-        lr = symbf.get_scalar_var('learning_rate', 5e-4, summary=True)
+        lr = tf.get_variable('learning_rate', initializer=5e-4, trainable=False)
         opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
         return optimizer.apply_grad_processors(
             opt, [

tensorpack/tfutils/symbolic_functions.py

@@ -145,7 +145,8 @@ def huber_loss(x, delta=1, name='huber_loss'):
     return tf.where(cond, l2, l1, name=name)
 
 
-# TODO remove this in the future
+# TODO deprecate this in the future
+# doesn't hurt to keep it here for now
 def get_scalar_var(name, init_value, summary=False, trainable=False):
     """
     Get a scalar float variable with certain initial value.