testing a simple shiftnet

examples/python/tensorflow/run.py

@@ -100,7 +100,7 @@ def batch_norm_grad(op, dy, mean, var):
 
 
 def run_batchnorm():
-    C, H, W, B = 1, 4, 4, 4
+    C, H, W, B = 32, 14, 14, 64
     np.random.seed(0)
     # Placeholders
     x = tf.placeholder(tf.float32, shape=[C, H, W, B])
@@ -117,7 +117,8 @@ def run_batchnorm():
     sess = tf.InteractiveSession()
     sess.run(tf.global_variables_initializer())
     result = sess.run([y, m, v], feed_dict = {x: hx, g: hg, b: hb})
-    #print(result[0], result[1], result[2])
+    print(result[1])
+    print(np.mean(hx, (1, 2, 3)))
     grads = tf.test.compute_gradient([x, g, b], [(C, H, W, B), (C, ), (C, )], y, (C, H, W, B),
                                      extra_feed_dict = {x: hx, g: hg, b: hb})
     dx_t, dx_n = grads[0]