fix get_input_queue

example_cifar10.py

@@ -33,6 +33,7 @@ class Model(ModelDesc):
 
     def _get_cost(self, input_vars, is_training):
         image, label = input_vars
+        keep_prob = tf.constant(0.5 if is_training else 1.0)
 
         if is_training:
             image, label = tf.train.shuffle_batch(
@@ -40,7 +41,7 @@ class Model(ModelDesc):
                 num_threads=6, enqueue_many=True)
             tf.image_summary("train_image", image, 10)
 
-        l = Conv2D('conv1.1', image, out_channel=64, kernel_shape=3, padding='SAME')
+        l = Conv2D('conv1.1', image, out_channel=64, kernel_shape=3)
         l = Conv2D('conv1.2', l, out_channel=64, kernel_shape=3, nl=tf.identity)
         l = BatchNorm('bn1', l, is_training)
         l = tf.nn.relu(l)
@@ -56,8 +57,9 @@ class Model(ModelDesc):
         l = Conv2D('conv3.2', l, out_channel=128, kernel_shape=3, padding='VALID', nl=tf.identity)
         l = BatchNorm('bn3', l, is_training)
         l = tf.nn.relu(l)
-        l = FullyConnected('fc0', l, 512,
+        l = FullyConnected('fc0', l, 1024 + 512,
                            b_init=tf.constant_initializer(0.1))
+        l = tf.nn.dropout(l, keep_prob)
         l = FullyConnected('fc1', l, out_dim=512,
                            b_init=tf.constant_initializer(0.1))
         # fc will have activation summary by default. disable for the output layer
@@ -120,13 +122,13 @@ def get_config():
     lr = tf.train.exponential_decay(
         learning_rate=1e-2,
         global_step=get_global_step_var(),
-        decay_steps=dataset_train.size() * 30,
-        decay_rate=0.5, staircase=True, name='learning_rate')
+        decay_steps=dataset_train.size() * 40,
+        decay_rate=0.4, staircase=True, name='learning_rate')
     tf.scalar_summary('learning_rate', lr)
 
     return TrainConfig(
         dataset=dataset_train,
-        optimizer=tf.train.AdamOptimizer(lr),
+        optimizer=tf.train.AdamOptimizer(lr, epsilon=1e-3),
         callbacks=Callbacks([
             StatPrinter(),
             PeriodicSaver(),
@@ -135,7 +137,7 @@ def get_config():
         session_config=sess_config,
         model=Model(),
         step_per_epoch=step_per_epoch,
-        max_epoch=500,
+        max_epoch=300,
     )
 
 if __name__ == '__main__':

tensorpack/callbacks/summary.py

@@ -40,7 +40,7 @@ class StatHolder(object):
     def _print_stat(self):
         for k, v in sorted(self.stat_now.items(), key=operator.itemgetter(0)):
             if self.print_tag is None or k in self.print_tag:
-                logger.info('{}: {:.4f}'.format(k, v))
+                logger.info('{}: {:.5f}'.format(k, v))
 
     def _write_stat(self):
         tmp_filename = self.filename + '.tmp'

tensorpack/train/trainer.py

@@ -93,7 +93,7 @@ class QueueInputTrainer(Trainer):
     def train(self):
         model = self.model
         input_vars = model.get_input_vars()
-        input_queue = model.get_input_queue()
+        input_queue = model.get_input_queue(input_vars)
         enqueue_op = input_queue.enqueue(input_vars)
 
         def get_model_inputs():