fix bug in xavier FC initialization, and README about SYNC_FREQ

owl/owl/net/net.py

@@ -233,8 +233,7 @@ def init_weights_with_filler(self):
         elif self.bias_filler.type == "uniform":
             npbias = np.random.uniform(self.bias_filler.min, self.bias_filler.max, self.bshape)
         elif self.bias_filler.type == "xavier":
-            fan_in = np.prod(self.in_shape[:])
-            scale = np.sqrt(float(3)/fan_in)
+            scale = np.sqrt(float(3)/self.fan_in)
             npbias = np.random.uniform(-scale, scale, self.bshape)
         self.bias = owl.from_numpy(npbias.astype(np.float32)).reshape(self.bshape)
 
@@ -590,7 +589,7 @@ def compute_size(self, from_btm, to_top):
         self.start_on_ori = to_top[self.top_names[0]]['start_on_ori']
         #set fan_in fan_out
         self.fan_out = self.inner_product_param.num_output
-        self.fan_in = from_btm[self.btm_names[0]]['out_shape'][0]
+        self.fan_in = np.prod(from_btm[self.btm_names[0]]['out_shape'][0:len(from_btm[self.btm_names[0]]['out_shape'])])
 
 
     def ff(self, act, phase):

scripts/learning/README.md

@@ -5,15 +5,16 @@ Train network
 
 Use following command to start training given Caffe's solver and configure file
 ```bash
-./net_trainer.py <solver_file> <SNAPSHOT> <NUM_GPU>
+./net_trainer.py <solver_file> <SNAPSHOT> <NUM_GPU> <SYNC_FREQ>
 ```
 * `solver_file` is the file name in Caffe's [solver](https://github.com/BVLC/caffe/blob/master/models/bvlc_googlenet/quick_solver.prototxt) format.
 * `SNAPSHOT` is the index of the snapshot to start with (default: 0). If SNAPSHOT is not equal to 0, it means we continue training from the formal snapshot
 * `NUM_GPU` is the number of gpu to use.
+* `SYNC_FREQ` is the option to hide IO time consuming, set SYN_FREQ > 1 usually have better speed but will consume more GPU RAM. Please propally set it to let Minerva run smoothly on your machine. (default: 1) 
 
 Example:
 ```bash
-./net_trainer.py /path/to/solver.txt 0 4
+./net_trainer.py /path/to/solver.txt 0 4 2
 ```
 
 Test network