73 lines
1.5 KiB
Cython
73 lines
1.5 KiB
Cython
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)
|