124 lines
2.9 KiB
Cython
124 lines
2.9 KiB
Cython
cimport numpy as np
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import numpy as np
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import mynet as mn
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from libc.stdlib cimport malloc
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ctr = []
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X_train, y_train, X_test, y_test = mn.load_mnist()
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opt = mn.SGDOptimizer(lr=0.1)
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cdef extern from "numpy/arrayobject.h":
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object PyArray_SimpleNewFromData(
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int nd, long* dims, int typenum, void* data
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)
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void *PyArray_DATA(np.ndarray arr)
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ctypedef public struct Dense:
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long[2] shape
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int ownmem
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float* W
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float* b
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ctypedef public struct Network:
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Py_ssize_t n_layers;
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Dense* layers;
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cdef public char * greeting():
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return f'The value is {3**3**3}'.encode('utf-8')
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cdef public void debug_print(object o):
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print(o.flags)
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cdef public np.ndarray[np.float32_t, ndim=2, mode='c'] predict(
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object net,
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np.ndarray[np.float32_t, ndim=2, mode='c'] X
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):
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try:
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return net(X)
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except Exception as e:
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print(e)
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cdef public object create_network():
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return mn.Network((784, 10), mn.relu, mn.sigmoid, mn.bin_x_entropy)
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cdef public object combo_net(list nets):
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return mn.combo_net(nets)
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cdef public object make_like(object neta, object netb):
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netb.be_like(neta)
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cdef public void step_net(
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object net,
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float* batch_data,
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Py_ssize_t batch_size
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):
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cdef Py_ssize_t in_dim = net.geometry[0]
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cdef Py_ssize_t out_dim = net.geometry[-1]
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batch = np.asarray(<float[:batch_size,:in_dim+out_dim]>batch_data)
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net.step(batch[:, :in_dim], batch[:, in_dim:], opt)
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cdef public float eval_net(
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object net
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):
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return net.evaluate(X_test, y_test, 'cls')
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cdef public np.ndarray[np.float32_t, ndim=2, mode='c'] mnist_batch(
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Py_ssize_t bs
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):
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idx = np.random.choice(len(X_train), bs, replace=False)
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arr = np.concatenate([X_train[idx], y_train[idx]], axis=1)
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return arr
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cdef public void inspect_array(
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np.ndarray[np.float32_t, ndim=2, mode='c'] a
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):
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print(a.flags)
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print(a.dtype)
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print(a.sum())
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cdef public void be_like_cified(
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object net,
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Network* c_net
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):
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"""WARNING this function makes an assumption that `net` and `c_net`
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have the same shape and hopefully is going to crash horribly otherwise."""
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for i, l in enumerate(net.layers):
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w1, w2 = l.W.shape
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l.W[:] = <float[:w1,:w2]>c_net.layers[i].W
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l.b[:] = <float[:w2]>c_net.layers[i].b
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cdef public void cify_network(
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object net, Network* c_net
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):
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"""WARNING `c_net` is valid as long as `net` is
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Whoever has `c_net` is responsible for freeing c_net.layers list
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Layers themselves don't need any de-init.
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"""
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c_net.n_layers = len(net.layers)
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c_net.layers = <Dense*>malloc(len(net.layers) * sizeof(Dense))
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for i, l in enumerate(net.layers):
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w1, w2 = l.W.shape
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c_net.layers[i].shape[0] = w1
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c_net.layers[i].shape[1] = w2
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c_net.layers[i].W = <float*>PyArray_DATA(l.W)
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c_net.layers[i].b = <float*>PyArray_DATA(l.b)
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c_net.layers[i].ownmem = 0
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