Update Util

Util/Util.py

@@ -113,35 +113,29 @@ def gen_spin(size=50, n=7, n_class=7, scale=4, one_hot=True):
             ys[ix] = i % n_class
         if not one_hot:
             return xs, ys
-        z = []
-        for yy in ys:
-            z.append([0 if i != yy else 1 for i in range(n_class)])
-        return xs, np.array(z)
+        return xs, np.array(ys[..., None] == np.arange(n_class), dtype=np.int8)
 
     @staticmethod
     def gen_random(size=100, n_dim=2, n_class=2, one_hot=True):
-        xs = np.random.randn(size, n_dim)
+        xs = np.random.randn(size, n_dim).astype(np.float32)
         ys = np.random.randint(n_class, size=size).astype(np.int8)
         if not one_hot:
             return xs, ys
-        z = []
-        for yy in ys:
-            z.append([0 if i != yy else 1 for i in range(n_class)])
-        return xs, np.array(z, dtype=np.int8)
+        return xs, np.array(ys == np.arange(n_class), dtype=np.int8)
 
     @staticmethod
     def gen_two_clusters(size=100, n_dim=2, center=0, dis=2, scale=1, one_hot=True):
         center1 = (np.random.random(n_dim) + center - 0.5) * scale + dis
         center2 = (np.random.random(n_dim) + center - 0.5) * scale - dis
         cluster1 = (np.random.randn(size, n_dim) + center1) * scale
         cluster2 = (np.random.randn(size, n_dim) + center2) * scale
-        data = np.vstack((cluster1, cluster2))
+        data = np.vstack((cluster1, cluster2)).astype(np.float32)
         labels = np.array([1] * size + [0] * size)
         _indices = np.random.permutation(size * 2)
         data, labels = data[_indices], labels[_indices]
         if not one_hot:
             return data, labels
-        labels = np.array([[0, 1] if label == 1 else [1, 0] for label in labels])
+        labels = np.array([[0, 1] if label == 1 else [1, 0] for label in labels], dtype=np.int8)
         return data, labels
 
     @staticmethod