now it kinda learns again and code is kinda clean

bridge.pyx

@@ -75,24 +75,15 @@ cdef public void f_idx_list_to_print(float* f_idxs, size_t num):
     # return retval
 
 
-cdef public void cbow_batch(
-    float* batch, size_t bs, size_t win
-):
-    batch_np = np.asarray(<float[:bs,:2*win+1]>batch)
-    # Deal with X
-    X_np = np.concatenate([batch_np[:, :win], batch_np[:, win+1:]], axis=1)
-    y_np = nn.onehot(batch_np[:, win], nc=len(nn.vocab))
-    eprint(batch_np)
-    eprint(X_np)
-    eprint(np.argmax(y_np, axis=1))
-
-
 cdef public void debug_print(object o):
     eprint(o)
 
 
 cdef public object create_network(int win, int embed):
-    return nn.create_cbow_network(win, embed)
+    try:
+        return nn.create_cbow_network(win, embed)
+    except Exception as e:
+        eprint(e)
 
 
 cdef public void set_net_weights(object net, WeightList* wl):
@@ -102,9 +93,7 @@ cdef public void set_net_weights(object net, WeightList* wl):
 cdef public void step_net(
     object net, float* batch, size_t bs
 ):
-    # X_train, y_train = cbow_batch(net, batch, bs)
-    X_train = None
-    y_train = None
+    X_train, y_train = cbow_batch(net, batch, bs)
     net.train_on_batch(X_train, y_train)
 
 
@@ -113,7 +102,10 @@ cdef public size_t out_size(object net):
 
 
 cdef public float eval_net(object net):
-    return net.evaluate(X_test, y_test, verbose=False)
+    try:
+        return net.evaluate(X_test, y_test, verbose=False)
+    except Exception as e:
+        eprint(e)
 
 
 cdef public void init_weightlist_like(WeightList* wl, object net):
@@ -156,6 +148,16 @@ cdef public void create_test_dataset(size_t win):
     _create_test_dataset(win)
 
 
+cdef tuple cbow_batch(
+    object net, float* batch, size_t bs
+):
+    win = net.input_shape[1] // 2
+    batch_np = np.asarray(<float[:bs,:2*win+1]>batch)
+    X_np = np.concatenate([batch_np[:, :win], batch_np[:, win+1:]], axis=1)
+    y_np = nn.onehot(batch_np[:, win], nc=len(nn.vocab))
+    return X_np, y_np
+
+
 cdef list wrap_weight_list(WeightList* wl):
     weights = []
     for i in range(wl.n_weights):