it started working so commit it

library.pyx

@@ -0,0 +1,72 @@
+cimport numpy as np
+import numpy as np
+import mynet as mn
+
+
+ctr = []
+X_train, y_train, X_test, y_test = mn.load_mnist()
+
+
+cdef public char * greeting():
+    return f'The value is {3**3**3}'.encode('utf-8')
+
+
+cdef public void debug_print(object o):
+    print(o.flags)
+    # print(o)
+
+
+cdef public np.ndarray[np.float32_t, ndim=2, mode='c'] dot(
+    np.ndarray[np.float32_t, ndim=2, mode='c'] x,
+    np.ndarray[np.float32_t, ndim=2, mode='c'] y
+):
+    return x @ y
+
+
+cdef public np.ndarray[np.float32_t, ndim=2, mode='c'] predict(
+    object net,
+    np.ndarray[np.float32_t, ndim=2, mode='c'] X
+):
+    try:
+        return net(X)
+    except Exception as e:
+        print(e)
+
+
+cdef public object create_network():
+    return mn.Network((784, 10), mn.relu, mn.sigmoid, mn.bin_x_entropy)
+
+
+cdef public object combo_net(list nets):
+    return mn.combo_net(nets)
+
+
+cdef public object make_like(object neta, object netb):
+    netb.be_like(neta)
+
+
+cdef public void step_net(
+    object net,
+    np.ndarray[np.float32_t, ndim=2, mode='c'] batch
+):
+    opt = mn.SGDOptimizer(lr=0.1)
+    net.step(batch[:, :784], batch[:, 784:], opt)
+
+
+cdef public float eval_net(
+    object net
+):
+    return net.evaluate(X_test, y_test, 'cls')
+
+
+cdef public np.ndarray[np.float32_t, ndim=2, mode='c'] mnist_batch(
+    Py_ssize_t bs
+):
+    idx = np.random.choice(len(X_train), bs, replace=False)
+    arr = np.concatenate([X_train[idx], y_train[idx]], axis=1)
+    return arr
+
+cdef public float arrsum(
+    np.ndarray[np.float32_t, ndim=2, mode='c'] a
+):
+    return np.sum(a)