def test_train():
image_data, label_data = readfile()
im = get_image(image_data)
label = get_label(label_data)
a = FeatureMap(32, 1, 1)
w1 = FilterLayer(5, 1, 6, 1)
b = FeatureMap(28, 6, 1)
c = FeatureMap(14, 6, 1)
w2 = FilterLayer(5, 6, 16, 1)
d = FeatureMap(10, 16, 1)
e = FeatureMap(5, 16, 1)
w3 = FilterLayer(5, 16, 120, 1)
f = FeatureMap(1, 120, 1)
connection_map(a, b, w1)
connection_map(b, c)
connection_map(c, d, w2)
connection_map(d, e)
connection_map(e, f, w3)
net = Net(120, [84], 10)
for i in range(10):
print(i)
a.set_out_val(ConOp.add_zero_circle(im[i].reshape((1, 28, 28)), 2))
label_v = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
label_v[label[i]] = 1
b.cal_net_out()
b.cal_out_val()
c.pooling_cal_net_val()
c.pooling_cal_out_val()
d.cal_net_out()
d.cal_out_val()
e.pooling_cal_net_val()
e.pooling_cal_out_val()
f.cal_net_out()
f.cal_out_val()
con_out = f.out_val
net_input = con_out.reshape(120)
net.cal_out(net_input)
net.cal_err_term(label_v)
net.cal_err_out()
err_out_val = np.array(net.err_out).reshape((120, 1, 1))
f.set_err_term(err_out_val)
e.cal_err_out()
e.cal_err_term()
d.get_err_out_from_pooling()
d.cal_err_term()
c.cal_err_out()
c.cal_err_term()
b.get_err_out_from_pooling()
b.cal_err_term()
w1.cal_dw()
w2.cal_dw()
e.cal_dw()
c.cal_dw()
net.cal_dw()
w1.update_w(0.01)
w2.update_w(0.01)
e.update_w(0.01)
c.update_w(0.01)
net.update_w(0.01)
for i in range(10):
a.set_out_val(ConOp.add_zero_circle(im[i].reshape((1, 28, 28)), 2))
label_v = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
label_v[label[i]] = 1
b.cal_net_out()
b.cal_out_val()
c.pooling_cal_net_val()
c.pooling_cal_out_val()
d.cal_net_out()
d.cal_out_val()
e.pooling_cal_net_val()
e.pooling_cal_out_val()
f.cal_net_out()
f.cal_out_val()
con_out = f.out_val
net_input = con_out.reshape(120)
print(net.cal_out(net_input))
def test_train1():
image_data, label_data = readfile()
im = get_image(image_data)
label = get_label(label_data)
a = FeatureMap(32, 1, 1)
w1 = FilterLayer(5, 1, 6, 1)
b = FeatureMap(28, 6, 1)
c = FeatureMap(14, 6, 1)
w2 = FilterLayer(5, 6, 16, 1)
d = FeatureMap(10, 16, 1)
e = FeatureMap(5, 16, 1)
w3 = FilterLayer(5, 16, 120, 1)
f = FeatureMap(1, 120, 1)
connection_map(a, b, w1)
connection_map(b, c)
connection_map(c, d, w2)
connection_map(d, e)
connection_map(e, f, w3)
net = Net(120, [84], 10)
for i in range(1000):
a.set_out_val(ConOp.add_zero_circle(im[i].reshape((1, 28, 28)), 2))
label_v = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
label_v[label[i]] = 1
b.cal_net_out()
b.cal_out_val()
c.pooling_cal_net_val()
c.pooling_cal_out_val()
d.cal_net_out()
d.cal_out_val()
e.pooling_cal_net_val()
e.pooling_cal_out_val()
f.cal_net_out()
f.cal_out_val()
con_out = f.out_val
net_input = con_out.reshape(120)
net.cal_out(net_input)
net.cal_err_term(label_v)
net.cal_err_out()
err_out_val = np.array(net.err_out).reshape((120, 1, 1))
f.set_err_term(err_out_val)
e.cal_err_out()
e.cal_err_term()
d.get_err_out_from_pooling()
d.cal_err_term()
c.cal_err_out()
c.cal_err_term()
b.get_err_out_from_pooling()
b.cal_err_term()
w1.cal_dw()
w_list = w1.w_list
dw = w1.dw
for i in range(len(w_list)):
deep, row, col = w_list[i].shape
for index_deep in range(deep):
for r in range(row):
for co in range(col):
es = 0.00001
w_list[i][index_deep][r][co] += es
w1.set_w(w_list)
b.cal_net_out()
b.cal_out_val()
c.pooling_cal_net_val()
c.pooling_cal_out_val()
d.cal_net_out()
d.cal_out_val()
e.pooling_cal_net_val()
e.pooling_cal_out_val()
f.cal_net_out()
f.cal_out_val()
con_out = f.out_val
net_input = con_out.reshape(120)
out1 = net.cal_out(net_input)
err1 = cal_err(out1, label_v)
w_list[i][index_deep][r][co] -= 2 * es
w1.set_w(w_list)
b.cal_net_out()
b.cal_out_val()
c.pooling_cal_net_val()
c.pooling_cal_out_val()
d.cal_net_out()
d.cal_out_val()
e.pooling_cal_net_val()
e.pooling_cal_out_val()
f.cal_net_out()
f.cal_out_val()
con_out = f.out_val
net_input = con_out.reshape(120)
out2 = net.cal_out(net_input)
err2 = cal_err(out2, label_v)
w_list[i][index_deep][r][co] += es
w1.set_w(w_list)
print((err1 - err2) / (2 * es))
print(dw[i][index_deep][r][co])
for i in range(10):
label_v = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
label_v[label[i]] = 1
print(label[i])
print(net.cal_out(im[i]))