def main():
Ws, bs = get_params(['model_reinforcement.pickle', 'model.pickle'])
Ws_s, bs_s = train.get_parameters(Ws=Ws, bs=bs)
f_pred = get_predict(Ws_s, bs_s)
f_train = get_update(Ws_s, bs_s)
i, n, l, c = 0, 0.0, 0.0, 0.0
base_learning_rate = 1e-2
t0 = time.time()
while True:
learning_rate = base_learning_rate * math.exp(-(time.time() - t0) / 86400)
r = game(f_pred, f_train, learning_rate)
if r is None:
continue
i += 1
n_t, l_t, c_t = r
n = n*0.999 + n_t
l = l*0.999 + l_t*n_t
c = c*0.999 + c_t*n_t
print '%6d %9.5f %9.5f %9.5f' % (i, learning_rate, l / n, c / n)
if i % 100 == 0:
print 'dumping model...'
dump(Ws_s, bs_s)
def main():
Ws, bs = get_params(['model_reinforcement.pickle', 'model.pickle'])
Ws_s, bs_s = train.get_parameters(Ws=Ws, bs=bs)
f_pred = get_predict(Ws_s, bs_s)
f_train = get_update(Ws_s, bs_s)
i, n, l, c = 0, 0.0, 0.0, 0.0
base_learning_rate = 1e-2
t0 = time.time()
while True:
learning_rate = base_learning_rate * math.exp(
-(time.time() - t0) / 86400)
r = game(f_pred, f_train, learning_rate)
if r is None:
continue
i += 1
n_t, l_t, c_t = r
n = n * 0.999 + n_t
l = l * 0.999 + l_t * n_t
c = c * 0.999 + c_t * n_t
print '%6d %9.5f %9.5f %9.5f' % (i, learning_rate, l / n, c / n)
if i % 100 == 0:
print 'dumping model...'
dump(Ws_s, bs_s)
def get_model_from_pickle(fn):
f = open(fn)
Ws, bs = pickle.load(f)
Ws_s, bs_s = train.get_parameters(Ws=Ws, bs=bs)
x, p = train.get_model(Ws_s, bs_s)
predict = theano.function(inputs=[x], outputs=p)
return predict
def get_model_from_pickle(fn):
f = open(fn)
Ws, bs = pickle.load(f)
Ws_s, bs_s = train.get_parameters(Ws=Ws, bs=bs)
x, p = train.get_model(Ws_s, bs_s)
predict = theano.function(
inputs=[x],
outputs=p)
return predict
def predict():
cur_pvs = np.zeros([24, 19], dtype=np.float32)
paras = train.get_parameters("vapor_press_trained_parameters.npz")
pvs = get_a_line()
while True:
time.sleep(5)
line = next(pvs)
cur_pvs = np.delete(cur_pvs, -1, axis=0)
cur_pvs = np.insert(cur_pvs, 0, line, axis=0)
cur_pvs_trans = cur_pvs.reshape(-1, 1)
pre_val = train.forward_propagation(cur_pvs_trans, paras)
sess = tf.Session()
val = sess.run(pre_val)
val = val * 50 + 50
print(val)
