def main():
Ws, bs = get_params(['model_reinforcement.pickle', 'model.pickle'])
Ws_s, bs_s = load.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 train():
Xc_train, Xc_test, Xr_train, Xr_test, Xp_train, Xp_test = get_data(
['x', 'xr', 'xp'])
for board in [Xc_train[0], Xp_train[0]]:
for row in range(8):
print(' '.join('%2d' % x
for x in board[(row * 8):((row + 1) * 8)]))
print
n_in = 12 * 64
Ws_s, bs_s = load.get_parameters(n_in=n_in, n_hidden_units=[2048] * 3)
minibatch_size = min(MINIBATCH_SIZE, Xc_train.shape[0])
train = get_function(Ws_s, bs_s, update=True, dropout=False)
test = get_function(Ws_s, bs_s, update=False, dropout=False)
best_test_loss = float('inf')
base_learning_rate = 0.03
t0 = time.time()
i = 0
while True:
i += 1
learning_rate = floatX(base_learning_rate *
math.exp(-(time.time() - t0) / 86400))
minibatch_index = random.randint(
0,
int(Xc_train.shape[0] / minibatch_size) - 1)
lo, hi = minibatch_index * minibatch_size, (minibatch_index +
1) * minibatch_size
loss, reg, loss_a, loss_b, loss_c = train(Xc_train[lo:hi],
Xr_train[lo:hi],
Xp_train[lo:hi],
learning_rate)
zs = [loss, loss_a, loss_b, loss_c, reg]
print('iteration %6d learning rate %12.9f: %s' %
(i, learning_rate, '\t'.join(['%12.9f' % z for z in zs])))
if i % 200 == 0:
test_loss, test_reg, _, _, _ = test(Xc_test, Xr_test, Xp_test,
learning_rate)
print('test loss %12.9f' % test_loss)
if test_loss < best_test_loss:
print('new record!')
best_test_loss = test_loss
print('dumping pickled model')
def values(zs):
return [z.get_value(borrow=True) for z in zs]
with open('cupy_model.pickle', 'wb') as f:
pickle.dump((values(Ws_s), values(bs_s)), f)
def get_model_from_pickle(fn):
f = open(fn, 'rb')
Ws, bs = pickle.load(f, encoding='latin1')
Ws_s, bs_s = load.get_parameters(Ws=Ws, bs=bs)
x, p = load.get_model(Ws_s, bs_s)
predict = theano.function(inputs=[x], outputs=p)
return predict
def getModel(fn):
#gets the model from the pickle file and turns it into a function
f = open(fn, 'rb')
Ws, bs = pickle.load(f)
Ws_s, bs_s = load.get_parameters(Ws=Ws, bs=bs)
x, p = load.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 = load.get_parameters(Ws=Ws, bs=bs)
x, p = load.get_model(Ws_s, bs_s)
predict = theano.function(
inputs=[x],
outputs=p)
return predict
def train():
Xc_train, Xc_test, Xr_train, Xr_test, Xp_train, Xp_test = get_data(['x', 'xr', 'xp'])
for board in [Xc_train[0], Xp_train[0]]:
for row in xrange(8):
print ' '.join('%2d' % x for x in board[(row*8):((row+1)*8)])
print
n_in = 12 * 64
Ws_s, bs_s = load.get_parameters(n_in=n_in, n_hidden_units=[2048] * 3)
minibatch_size = min(MINIBATCH_SIZE, Xc_train.shape[0])
train = get_function(Ws_s, bs_s, update=True, dropout=False)
test = get_function(Ws_s, bs_s, update=False, dropout=False)
best_test_loss = float('inf')
base_learning_rate = 0.03
t0 = time.time()
i = 0
while True:
i += 1
learning_rate = floatX(base_learning_rate * math.exp(-(time.time() - t0) / 86400))
minibatch_index = random.randint(0, int(Xc_train.shape[0] / minibatch_size) - 1)
lo, hi = minibatch_index * minibatch_size, (minibatch_index + 1) * minibatch_size
loss, reg, loss_a, loss_b, loss_c = train(Xc_train[lo:hi], Xr_train[lo:hi], Xp_train[lo:hi], learning_rate)
zs = [loss, loss_a, loss_b, loss_c, reg]
print 'iteration %6d learning rate %12.9f: %s' % (i, learning_rate, '\t'.join(['%12.9f' % z for z in zs]))
if i % 200 == 0:
test_loss, test_reg, _, _, _ = test(Xc_test, Xr_test, Xp_test, learning_rate)
print 'test loss %12.9f' % test_loss
if test_loss < best_test_loss:
print 'new record!'
best_test_loss = test_loss
print 'dumping pickled model'
f = open('model.pickle', 'w')
def values(zs):
return [z.get_value(borrow=True) for z in zs]
pickle.dump((values(Ws_s), values(bs_s)), f)
f.close()
def get_model_from_pickle(fn):
"""
Loads and returns model from pickled file which stores weights and biases.
:param fn: filename
:return: the theano function object representing the model.
"""
f = open(fn, 'rb')
# load weights Ws and biases bs.
# Ws is a list of 3 nd arrays.
# 1st nd-array: 768 x 2048 (weights for input to first hidden layer)
# 2nd nd-array: 2048 x 2048
# 3rd nd-array: 2048-dimensional vector to provide single output value.
Ws, bs = pickle.load(f, encoding='bytes')
Ws_s, bs_s = load.get_parameters(Ws=Ws, bs=bs)
x, p = load.get_model(Ws_s, bs_s)
# predict is a function object, computing outputs from inputs (based on
# the function defined in load.py's get_model function.
predict = theano.function(inputs=[x], outputs=p)
return predict
def train():
#Get test and train data for each paramter of
#parent, observed and random moves
Xc_train, Xc_test, Xr_train, Xr_test, Xp_train, Xp_test = get_data(
['x', 'xr', 'xp'])
#Print the board representation to be passed in
for board in [Xc_train[0], Xp_train[0]]:
for row in range(8):
print(' '.join('%2d' % x
for x in board[(row * 8):((row + 1) * 8)]))
print("\n")
n_in = 12 * 64
#Get the parmeters for training
Ws_s, bs_s = load.get_parameters(n_in=n_in, n_hidden_units=[2048] * 3)
minibatch_size = min(MINIBATCH_SIZE, Xc_train.shape[0])
#Get the training and test sets
train = get_function(Ws_s, bs_s, update=True, dropout=False)
test = get_function(Ws_s, bs_s, update=False, dropout=False)
#Set initail values for
#test loss and the base learning rate and number of iterations
best_test_loss = float('inf')
base_learning_rate = 0.03
t0 = time.time()
i = 0
#Train loop
while True:
i += 1
#calculate the learning rate
learning_rate = floatX(base_learning_rate *
math.exp(-(time.time() - t0) / 86400))
#calculate the training loss
minibatch_index = random.randint(
0,
int(Xc_train.shape[0] / minibatch_size) - 1)
lo, hi = minibatch_index * minibatch_size, (minibatch_index +
1) * minibatch_size
loss, reg, loss_a, loss_b, loss_c = train(Xc_train[lo:hi],
Xr_train[lo:hi],
Xp_train[lo:hi],
learning_rate)
zs = [loss, loss_a, loss_b, loss_c, reg]
#Print the learning rate and current loss
print("iteration %6d learning rate %12.9f: %s" %
(i, learning_rate, '\t'.join(["%12.9f" % z for z in zs])))
#every 200 iterations check if the test loss is better than the best loss recorded
if i % 200 == 0:
test_loss, test_reg, _, _, _ = test(Xc_test, Xr_test, Xp_test,
learning_rate)
#Print he test loss
print("test loss %12.9f" % test_loss)
#if test loss is better than the best loss then dump model parameters to model.pickle
if test_loss < best_test_loss:
print("new record!")
best_test_loss = test_loss
print("dumping pickled model")
f = open('model.pickle', 'wb')
def values(zs):
return [z.get_value(borrow=True) for z in zs]
pickle.dump((values(Ws_s), values(bs_s)), f)
f.close()
def training():
global train, test
Xc_train, Xc_test, Xp_train, Xp_test, Xd_train, Xd_test, Xa_train, Xa_test = get_data(
['x', 'xp', 'xd', 'xa'])
Xc_train = [x.flatten() for x in Xc_train]
Xc_test = [x.flatten() for x in Xc_test]
Xp_train = [x.flatten() for x in Xp_train]
Xp_test = [x.flatten() for x in Xp_test]
n_in = 12 * 64
Xtrain = [
x for x in numpy.concatenate(Xc_train),
numpy.concatenate(Xp_train),
Xd_train.flatten(),
Xa_train.flatten()
]
Xtest = [
x for x in numpy.concatenate(Xc_test),
numpy.concatenate(Xp_test),
Xd_test.flatten(),
Xa_test.flatten()
]
Ws_s, bs_s = load.get_parameters(n_in=n_in, n_hidden_units=[2048] * 3)
minibatch_size = min(MINIBATCH_SIZE, len(Xp_train))
train = get_function(Ws_s, bs_s, update=True, dropout=False)
test = get_function(Ws_s, bs_s, update=False, dropout=False)
best_test_loss = float('inf')
base_learning_rate = 0.03
t0 = time.time()
i = 0
while True:
i += 1
learning_rate = floatX(base_learning_rate *
math.exp(-(time.time() - t0) / 86400))
print learning_rate
minibatch_index = random.randint(
0,
int(len(Xp_train) / minibatch_size) - 1)
print minibatch_index
lo, hi = minibatch_index * minibatch_size, (minibatch_index +
1) * minibatch_size
print lo, hi
loss, reg, loss_a, loss_b, loss_c = train(Xc_train[lo:hi],
Xp_train[lo:hi],
Xd_train[lo:hi],
Xa_train[lo:hi],
learning_rate)
print loss
zs = [loss, loss_a, loss_b, loss_c, reg]
print 'iteration %6d learning rate %12.9f: %s' % (
i, learning_rate, '\t'.join(['%12.9f' % z for z in zs]))
if i % 200 == 0:
test_loss, test_reg, _, _, _ = test(Xc_test, Xr_test, Xp_test,
learning_rate)
print 'test loss %12.9f' % test_loss
if test_loss < best_test_loss:
print 'new record!'
best_test_loss = test_loss
print 'dumping pickled model'
f = open('model.pickle', 'w')
def values(zs):
return [z.get_value(borrow=True) for z in zs]
pickle.dump((values(Ws_s), values(bs_s)), f)
f.close()
