def construct_network(context, characters, hidden, mult_hidden):
print "Setting up memory..."
X = T.bvector('X')
Y = T.bvector('Y')
alpha = T.cast(T.fscalar('alpha'), dtype=theano.config.floatX)
lr = T.cast(T.fscalar('lr'), dtype=theano.config.floatX)
print "Initialising weights..."
W_char_hidden = U.create_shared(U.initial_weights(characters, hidden))
f_char_hidden = U.create_shared(U.initial_weights(characters, mult_hidden))
b_hidden = U.create_shared(U.initial_weights(hidden))
Wf_hidden = U.create_shared(U.initial_weights(hidden, mult_hidden))
fW_hidden = U.create_shared(U.initial_weights(mult_hidden, hidden))
W_hidden_predict = U.create_shared(U.initial_weights(hidden, characters))
b_predict = U.create_shared(U.initial_weights(characters))
print "Constructing graph..."
hidden = make_hidden(hidden, W_char_hidden[X], f_char_hidden[X], Wf_hidden,
fW_hidden, b_hidden)
predictions = T.nnet.softmax(T.dot(hidden, W_hidden_predict) + b_predict)
weights = [
W_char_hidden, f_char_hidden, b_hidden, Wf_hidden, fW_hidden,
W_hidden_predict, b_predict
]
cost = -T.mean(T.log(predictions)[T.arange(Y.shape[0]), Y])
gparams = T.grad(cost, weights)
deltas = [U.create_shared(np.zeros(w.get_value().shape)) for w in weights]
updates = [(param, param - (alpha * delta + gparam * lr))
for param, delta, gparam in zip(weights, deltas, gparams)
] + [(delta, alpha * delta + gparam * lr)
for delta, gparam in zip(deltas, gparams)]
return X, Y, alpha, lr, updates, predictions, weights
def __init__(self, ne, de, cs, nh, nc, L2_reg = 0.0, rng = np.random.RandomState()):
self.nc = nc
self.hiddenLayer = Layer(de*cs, nh, rng = rng)
self.outputLayer = Layer(nh, nc)
self.emb = theano.shared(rng.normal(loc = 0.0, scale = 0.01, size = (ne, de)).astype(theano.config.floatX))
A = rng.normal(loc = 0.0, scale = 0.01, size = (nc, nc)).astype(theano.config.floatX)
self.A = theano.shared(value = A, name = 'A', borrow = True)
self.params = self.hiddenLayer.params + self.outputLayer.params + [self.emb, self.A]
self.names = ['Wh', 'bh', 'w', 'b', 'emb', 'A']
idxs = T.imatrix('idxs')
x = self.emb[idxs].reshape((idxs.shape[0], de*cs))
y = T.bvector('y')
ans = T.bvector('ans')
INF = 1e9
result, updates1 = theano.scan(fn = self.one_step, sequences = x, outputs_info = [theano.shared(0.0), theano.shared(-INF), theano.shared(-INF), theano.shared(-INF), None, None, None, None])
self.decode = theano.function(inputs = [idxs], outputs = result, updates = updates1)
score, updates2 = theano.scan(fn = self.two_step, sequences = [x, dict(input = y, taps = [-1, 0]), dict(input = ans, taps = [-1, 0])], outputs_info = theano.shared(0.0))
cost = score[-1]
gradients = T.grad(cost, self.params)
lr = T.scalar('lr')
for p, g in zip(self.params, gradients):
updates2[p] = p + lr * g
self.fit = theano.function(inputs = [idxs, y, ans, lr], outputs = cost, updates = updates2)
self.normalize = theano.function(inputs = [], updates = {self.emb: self.emb / T.sqrt((self.emb**2).sum(axis=1)).dimshuffle(0, 'x')})
def test_param_allow_downcast_int(self):
a = tensor.wvector('a') # int16
b = tensor.bvector('b') # int8
c = tensor.bscalar('c') # int8
f = pfunc([
Param(a, allow_downcast=True),
Param(b, allow_downcast=False),
Param(c, allow_downcast=None)
], (a + b + c))
# Both values are in range. Since they're not ndarrays (but lists),
# they will be converted, and their value checked.
assert numpy.all(f([3], [6], 1) == 10)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
self.assertRaises(TypeError, f, [3], numpy.array([6], dtype='int16'),
1)
# Value too big for a, silently ignored
assert numpy.all(f([2**20], numpy.ones(1, dtype='int8'), 1) == 2)
# Value too big for b, raises TypeError
self.assertRaises(TypeError, f, [3], [312], 1)
# Value too big for c, raises TypeError
self.assertRaises(TypeError, f, [3], [6], 806)
def test_param_allow_downcast_int(self):
a = tensor.wvector("a") # int16
b = tensor.bvector("b") # int8
c = tensor.bscalar("c") # int8
f = pfunc(
[
In(a, allow_downcast=True),
In(b, allow_downcast=False),
In(c, allow_downcast=None),
],
(a + b + c),
)
# Both values are in range. Since they're not ndarrays (but lists),
# they will be converted, and their value checked.
assert np.all(f([3], [6], 1) == 10)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
with pytest.raises(TypeError):
f([3], np.array([6], dtype="int16"), 1)
# Value too big for a, silently ignored
assert np.all(f([2**20], np.ones(1, dtype="int8"), 1) == 2)
# Value too big for b, raises TypeError
with pytest.raises(TypeError):
f([3], [312], 1)
# Value too big for c, raises TypeError
with pytest.raises(TypeError):
f([3], [6], 806)
def test_allow_input_downcast_int(self):
a = tensor.wvector("a") # int16
b = tensor.bvector("b") # int8
c = tensor.bscalar("c") # int8
f = pfunc([a, b, c], (a + b + c), allow_input_downcast=True)
# Value too big for a, b, or c, silently ignored
assert f([2**20], [1], 0) == 1
assert f([3], [312], 0) == 59
assert f([3], [1], 806) == 42
g = pfunc([a, b, c], (a + b + c), allow_input_downcast=False)
# All values are in range. Since they're not ndarrays (but lists
# or scalars), they will be converted, and their value checked.
assert np.all(g([3], [6], 0) == 9)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
with pytest.raises(TypeError):
g([3], np.array([6], dtype="int16"), 0)
# Value too big for b, raises TypeError
with pytest.raises(TypeError):
g([3], [312], 0)
h = pfunc([a, b, c], (a + b + c)) # Default: allow_input_downcast=None
# Everything here should behave like with False
assert np.all(h([3], [6], 0) == 9)
with pytest.raises(TypeError):
h([3], np.array([6], dtype="int16"), 0)
with pytest.raises(TypeError):
h([3], [312], 0)
def build_loss(self, env_spec, policy):
obs = env_spec.observation_space.new_tensor_variable('obs', extra_dims=1)
next_obs = env_spec.observation_space.new_tensor_variable('next_obs', extra_dims=1)
act = env_spec.action_space.new_tensor_variable('act', extra_dims=1)
ret = T.vector('disc_n_return')
term = T.bvector('terminal')
if self.prioritized_replay:
isw = T.vector('importance_sample_weights')
if self.double_dqn:
next_a = policy.actions_sym(next_obs)
next_q = policy.target_q_at_a_sym(next_obs, next_a)
else:
next_q = policy.target_max_q_sym(next_obs)
disc_next_q = (self.discount ** self.reward_horizon) * next_q
y = ret + (1 - term) * disc_next_q
q = policy.q_at_a_sym(obs, act)
d = y - q
losses = 0.5 * d ** 2
if self.delta_clip is not None:
# Huber loss:
b = self.delta_clip * (abs(d) - self.delta_clip / 2)
losses = T.switch(abs(d) <= self.delta_clip, losses, b)
if self.prioritized_replay:
losses = isw * losses
loss = T.mean(losses)
td_abs_errors = T.clip(abs(d), 0, self.delta_clip)
input_list = [obs, next_obs, act, ret, term]
if self.prioritized_replay:
input_list.append(isw)
return input_list, loss, td_abs_errors
def test_param_allow_downcast_int(self):
a = tensor.wvector('a') # int16
b = tensor.bvector('b') # int8
c = tensor.bscalar('c') # int8
f = pfunc([Param(a, allow_downcast=True),
Param(b, allow_downcast=False),
Param(c, allow_downcast=None)],
(a + b + c))
# Both values are in range. Since they're not ndarrays (but lists),
# they will be converted, and their value checked.
assert numpy.all(f([3], [6], 1) == 10)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
self.assertRaises(TypeError, f,
[3], numpy.array([6], dtype='int16'), 1)
# Value too big for a, silently ignored
assert numpy.all(f([2 ** 20], numpy.ones(1, dtype='int8'), 1) == 2)
# Value too big for b, raises TypeError
self.assertRaises(TypeError, f, [3], [312], 1)
# Value too big for c, raises TypeError
self.assertRaises(TypeError, f, [3], [6], 806)
def test_allow_input_downcast_int(self):
a = tensor.wvector('a') # int16
b = tensor.bvector('b') # int8
c = tensor.bscalar('c') # int8
f = pfunc([a, b, c], (a + b + c), allow_input_downcast=True)
# Value too big for a, b, or c, silently ignored
assert f([2 ** 20], [1], 0) == 1
assert f([3], [312], 0) == 59
assert f([3], [1], 806) == 42
g = pfunc([a, b, c], (a + b + c), allow_input_downcast=False)
# All values are in range. Since they're not ndarrays (but lists
# or scalars), they will be converted, and their value checked.
assert numpy.all(g([3], [6], 0) == 9)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
self.assertRaises(TypeError, g,
[3], numpy.array([6], dtype='int16'), 0)
# Value too big for b, raises TypeError
self.assertRaises(TypeError, g, [3], [312], 0)
h = pfunc([a, b, c], (a + b + c)) # Default: allow_input_downcast=None
# Everything here should behave like with False
assert numpy.all(h([3], [6], 0) == 9)
self.assertRaises(TypeError, h,
[3], numpy.array([6], dtype='int16'), 0)
self.assertRaises(TypeError, h, [3], [312], 0)
def construct_network(context,characters,hidden,mult_hidden):
print "Setting up memory..."
X = T.bvector('X')
Y = T.bvector('Y')
alpha = T.cast(T.fscalar('alpha'),dtype=theano.config.floatX)
lr = T.cast(T.fscalar('lr'), dtype=theano.config.floatX)
print "Initialising weights..."
W_char_hidden = U.create_shared(U.initial_weights(characters,hidden))
f_char_hidden = U.create_shared(U.initial_weights(characters,mult_hidden))
b_hidden = U.create_shared(U.initial_weights(hidden))
Wf_hidden = U.create_shared(U.initial_weights(hidden,mult_hidden))
fW_hidden = U.create_shared(U.initial_weights(mult_hidden,hidden))
W_hidden_predict = U.create_shared(U.initial_weights(hidden,characters))
b_predict = U.create_shared(U.initial_weights(characters))
print "Constructing graph..."
hidden = make_hidden(
hidden,
W_char_hidden[X],
f_char_hidden[X],
Wf_hidden,
fW_hidden,
b_hidden
)
predictions = T.nnet.softmax(T.dot(hidden,W_hidden_predict) + b_predict)
weights = [
W_char_hidden,
f_char_hidden,
b_hidden,
Wf_hidden,
fW_hidden,
W_hidden_predict,
b_predict
]
cost = -T.mean(T.log(predictions)[T.arange(Y.shape[0]),Y])
gparams = T.grad(cost,weights)
deltas = [ U.create_shared(np.zeros(w.get_value().shape)) for w in weights ]
updates = [
( param, param - ( alpha * delta + gparam * lr ) )
for param,delta,gparam in zip(weights,deltas,gparams)
] + [
( delta, alpha * delta + gparam * lr)
for delta,gparam in zip(deltas,gparams)
]
return X,Y,alpha,lr,updates,predictions,weights
def __init__(self,
state_format,
actions_number,
gamma=0.99,
learning_rate=0.00025,
ddqn=False,
**kwargs):
self.inputs = dict()
self.learning_rate = learning_rate
architecture = kwargs
self.loss_history = []
self.misc_state_included = (state_format["s_misc"] > 0)
self.gamma = np.float64(gamma)
self.inputs["S0"] = tensor.tensor4("S0")
self.inputs["S1"] = tensor.tensor4("S1")
self.inputs["A"] = tensor.ivector("Action")
self.inputs["R"] = tensor.vector("Reward")
self.inputs["Nonterminal"] = tensor.bvector("Nonterminal")
if self.misc_state_included:
self.inputs["S0_misc"] = tensor.matrix("S0_misc")
self.inputs["S1_misc"] = tensor.matrix("S1_misc")
self.misc_len = state_format["s_misc"]
else:
self.misc_len = None
# save it for the evaluation reshape
# TODO get rid of this?
self.single_image_input_shape = (1, ) + tuple(state_format["s_img"])
architecture["img_input_shape"] = (None, ) + tuple(
state_format["s_img"])
architecture["misc_len"] = self.misc_len
architecture["output_size"] = actions_number
if self.misc_state_included:
self.network, input_layers, _ = self._initialize_network(
img_input=self.inputs["S0"],
misc_input=self.inputs["S0_misc"],
**architecture)
self.frozen_network, _, alternate_inputs = self._initialize_network(
img_input=self.inputs["S1"],
misc_input=self.inputs["S1_misc"],
**architecture)
else:
self.network, input_layers, _ = self._initialize_network(
img_input=self.inputs["S0"], **architecture)
self.frozen_network, _, alternate_inputs = self._initialize_network(
img_input=self.inputs["S1"], **architecture)
self.alternate_input_mappings = {}
for layer, input in zip(input_layers, alternate_inputs):
self.alternate_input_mappings[layer] = input
# print "Network initialized."
self._compile(ddqn)
def __init__(self, param_dict):
self.param_dict = param_dict
self.training_batch_size = param_dict['training_batch_size']
nkerns = param_dict['nkerns']
recept_width = param_dict['recept_width']
pool_width = param_dict['pool_width']
stride = param_dict['stride']
dropout_prob = param_dict['dropout_prob']
weight_decay = param_dict['l2_reg']
activation = param_dict['activation']
weights_variance = param_dict['weights_variance']
n_channels = param_dict['n_channels']
n_timesteps = param_dict['n_timesteps']
n_fbins = param_dict['n_fbins']
global_pooling = param_dict['global_pooling']
rng = np.random.RandomState(23455)
self.training_mode = T.iscalar('training_mode')
self.x = T.tensor4('x')
self.y = T.bvector('y')
self.batch_size = theano.shared(self.training_batch_size)
self.input = self.x.reshape((self.batch_size, 1, n_channels * n_fbins, n_timesteps))
self.feature_extractor = FeatureExtractor(rng, self.input, nkerns, recept_width, pool_width, stride,
self.training_mode,
dropout_prob[0],
activation, weights_variance, n_channels, n_timesteps, n_fbins,
global_pooling)
self.classifier = SoftmaxLayer(rng=rng, input=self.feature_extractor.output, n_in=nkerns[-1],
training_mode=self.training_mode, dropout_prob=dropout_prob[-1])
self.weights = self.feature_extractor.weights + self.classifier.weights
# ---------------------- BACKPROP
self.cost = self.classifier.cross_entropy_cost(self.y)
self.cost = self.classifier.cross_entropy_cost(self.y)
L2_sqr = sum((weight ** 2).sum() for weight in self.weights[::2])
self.grads = T.grad(self.cost + weight_decay * L2_sqr, self.weights)
self.updates = self.adadelta_updates(self.grads, self.weights)
# self.updates = self.nesterov_momentum(self.grads, self.weights)
# --------------------- FUNCTIONS
self.train_model = theano.function([self.x, self.y, Param(self.training_mode, default=1)],
outputs=self.cost,
updates=self.updates)
self.validate_model = theano.function([self.x, self.y, Param(self.training_mode, default=0)],
self.cost)
self.test_model = theano.function([self.x, Param(self.training_mode, default=0)],
self.classifier.p_y_given_x[:, 1])
def __init__(self, nkerns, recept_width, pool_width,
dropout_prob, training_batch_size, activation, n_timesteps=1000, dim=18):
if activation == 'tanh':
activation_function = lambda x: T.tanh(x)
elif activation == 'relu':
activation_function = lambda x: T.maximum(0.0, x)
else:
raise ValueError('unknown activation function')
self.training_batch_size = training_batch_size
rng = np.random.RandomState(23455)
self.training_mode = T.iscalar('training_mode')
self.x = T.matrix('x')
self.y = T.bvector('y')
self.batch_size = theano.shared(self.training_batch_size)
# 18@1*1000
self.layer0_input = self.x.reshape((self.batch_size, dim, 1, n_timesteps))
# image 18 @ 1*1000
# c1: nkerns[0] @ 1* (1000 - recept_width[0] + 1)
# s2: nkerns[0] @ 1 * c1 / pool_width[0]
layer0 = ConvPoolLayer(rng, input=self.layer0_input,
image_shape=(None, dim, 1, n_timesteps),
filter_shape=(nkerns[0], dim, 1, recept_width[0]),
poolsize=(1, pool_width[0]), activation_function=activation_function)
# c3: nkerns[1] @ 1 * (s2 - recept_width[1] + 1)
# s4 nkerns[1] @ 1 * c3 / pool_width
input_layer1_width = (n_timesteps - recept_width[0] + 1) / pool_width[0]
layer1 = ConvPoolLayer(rng, input=layer0.output,
image_shape=(None, nkerns[0], 1, input_layer1_width),
filter_shape=(nkerns[1], nkerns[0], 1, recept_width[1]),
poolsize=(1, pool_width[1]), activation_function=activation_function)
# s4:(batch_size, nkerns[1], 1, s4) -> flatten(2) -> (batch_size, nkerns[1]* 1 * s4)
layer2_input = layer1.output.flatten(2)
input_layer2_size = (input_layer1_width - recept_width[1] + 1) / pool_width[1]
# c5: 120@1*1
self.layer2 = HiddenLayer(rng=rng, input=layer2_input,
n_in=nkerns[1] * 1 * input_layer2_size, n_out=nkerns[2],
training_mode=self.training_mode,
dropout_prob=dropout_prob, activation_function=activation_function)
# f6/output
self.layer3 = LogisticRegressionLayer(input=self.layer2.output, n_in=nkerns[2], n_out=2,
training_mode=self.training_mode, dropout_prob=dropout_prob)
self.params = self.layer3.params + self.layer2.params + layer1.params + layer0.params
def __init__(self, rng, hiddenLayerList, n_out):
"""Initialize the parameters for the multilayer perceptron
:type rng: numpy.random.RandomState
:param rng: a random number generator used to initialize weights
:type hiddenLayerList: [HiddenLayer instances]
:param hiddenLayerList: A list of hidden layers
:type n_out: int
:param n_out: number of output units, the dimension of the space in
which the labels lie
"""
# connect hidden layers (no need to, they're already connected outside when building them)
self.hiddenLayers=hiddenLayerList
# prevLy=hiddenLayerList[0]
# prevLy.input=input
# for ly in hiddenLayerList[1:]:
# ly.input=prevLy.output
# prevLy=ly
# The logistic regression layer gets as input the hidden units of the hidden layer
self.logRegressionLayer = LogisticRegression(
input=hiddenLayerList[-1].output,
n_in=hiddenLayerList[-1].inOutDim[1],
n_out=n_out)
# symbolic variables for data
self.X=self.hiddenLayers[0].input # training data
self.y=T.bvector('y') # labels for training data
# L1 norm ; one regularization option is to enforce L1 norm to be small
self.L1 = abs(self.logRegressionLayer.W).sum()
for ly in self.hiddenLayers:
self.L1 += abs(ly.W).sum()
# square of L2 norm ; one regularization option is to enforce square of L2 norm to be small
self.L2_sqr = (self.logRegressionLayer.W ** 2).sum()
for ly in self.hiddenLayers:
self.L2_sqr += (ly.W ** 2).sum()
# negative log likelihood of the MLP is given by the negative log likelihood of the output
# of the model, computed in the logistic regression layer
self.negative_log_likelihood = self.logRegressionLayer.negative_log_likelihood
# same holds for the function computing the number of errors
self.errors = self.logRegressionLayer.errors
# the parameters of the model are the parameters of the all layers
self.params=self.logRegressionLayer.params
for ly in self.hiddenLayers:
self.params+=ly.params
def __init__(self, nkerns, recept_width, pool_width, stride, dropout_prob, l2_reg, training_batch_size, activation,
weights_variance, n_timesteps,
dim, objective_function):
self.training_batch_size = training_batch_size
self.objective_function = objective_function
rng = np.random.RandomState(23455)
self.training_mode = T.iscalar('training_mode')
self.x = T.matrix('x')
self.y = T.bvector('y')
self.batch_size = theano.shared(training_batch_size)
self.input = self.x.reshape((self.batch_size, 1, dim, n_timesteps))
self.feature_extractor = FeatureExtractor(rng, self.input, nkerns, recept_width, pool_width, stride,
self.training_mode,
dropout_prob[0],
activation, weights_variance, n_timesteps, dim)
self.classifier = LogisticRegressionLayer(rng=rng, input=self.feature_extractor.output, n_in=nkerns[-1],
training_mode=self.training_mode, dropout_prob=dropout_prob[1])
self.params = self.feature_extractor.params + self.classifier.params
# ---------------------- BACKPROP
if self.objective_function == 'cross_entropy':
self.cost = self.classifier.cross_entropy_cost(self.y)
elif self.objective_function == 'auc':
self.cost = self.classifier.auc_cost(self.y)
else:
raise ValueError('wrong objective function')
L2_sqr = sum((param ** 2).sum() for param in self.params[::2])
self.grads = T.grad(self.cost + l2_reg * L2_sqr, self.params)
self.updates = self._adadelta_updates(self.grads)
# --------------------- FUNCTIONS
tp, tn, fp, fn = self.classifier.confusion_matrix(self.y)
self.train_model = theano.function([self.x, self.y, Param(self.training_mode, default=1)],
updates=self.updates)
self.validate_model = theano.function([self.x, self.y, Param(self.training_mode, default=0)],
[self.cost, tp, tn, fp, fn])
self.test_model = theano.function([self.x, Param(self.training_mode, default=0)],
self.classifier.p_y_given_x.flatten())
def make_train_functions():
P = Parameters()
X = T.bvector('X')
Y = T.ivector('Y')
aux = {}
predict = model.build(
P,
input_size=128,
embedding_size=64,
controller_size=256,
stack_size=256,
output_size=128,
)
output = predict(X,aux=aux)
error = - T.log(output[T.arange(Y.shape[0]),((128+1 + Y)%(128+1))])
error = error[-(Y.shape[0]/2):]
parameters = P.values()
gradients = T.grad(T.sum(error),wrt=parameters)
shapes = [ p.get_value().shape for p in parameters ]
count = theano.shared(np.float32(0))
acc_grads = [
theano.shared(np.zeros(s,dtype=np.float32))
for s in shapes
]
acc_update = [ (a,a+g) for a,g in zip(acc_grads,gradients) ] +\
[ (count,count + np.float32(1)) ]
acc_clear = [ (a,np.float32(0) * a) for a in acc_grads ] +\
[ (count,np.int32(0)) ]
avg_grads = [ (g / count) for g in acc_grads ]
avg_grads = [ clip(g,1) for g in acc_grads ]
acc = theano.function(
inputs=[X,Y],
outputs=T.mean(error),
updates = acc_update,
)
update = theano.function(
inputs=[],
updates=updates.adadelta(parameters,avg_grads,learning_rate=1e-8) + acc_clear
)
test = theano.function(
inputs=[X],
outputs=T.argmax(output,axis=1)[-(X.shape[0]/2):],
)
return acc,update,test
def __init__(self, game_params, arch_params, solver_params, trained_model, sn_dir):
params=None
if trained_model:
params = common.load_params(trained_model)
self.lr_func = create_learning_rate_func(solver_params)
self.x_h_0 = tt.fvector('x_h_0')
self.v_h_0 = tt.fvector('v_h_0')
self.t_h_0 = tt.fvector('t_h_0')
self.x_t_0 = tt.fmatrix('x_t_0')
self.v_t_0 = tt.fmatrix('v_t_0')
self.a_t_0 = tt.fmatrix('a_t_0')
self.t_t_0 = tt.fvector('t_t_0')
self.time_steps = tt.fvector('t_0')
self.exist = tt.bvector('exist')
self.is_leader = tt.fvector('is_leader')
self.x_goal = tt.fvector('x_goal')
self.turn_vec_h = tt.fvector('turn_vec_h')
self.turn_vec_t = tt.fvector('turn_vec_t')
self.n_steps = tt.iscalar('n_steps')
self.lr = tt.fscalar('lr')
self.sn_dir = sn_dir
self.game_params = game_params
self.arch_params = arch_params
self.solver_params = solver_params
self.model = CONTROLLER(self.x_h_0,
self.v_h_0,
self.t_h_0,
self.x_t_0,
self.v_t_0,
self.a_t_0,
self.t_t_0,
self.time_steps,
self.exist,
self.is_leader,
self.x_goal,
self.turn_vec_h,
self.turn_vec_t,
self.n_steps,
self.lr,
self.game_params,
self.arch_params,
self.solver_params,
params)
def __init__(self, state_format, actions_number, gamma=0.99, learning_rate=0.00025, ddqn=False, **kwargs):
self.inputs = dict()
self.learning_rate = learning_rate
architecture = kwargs
self.loss_history = []
self.misc_state_included = (state_format["s_misc"] > 0)
self.gamma = np.float64(gamma)
self.inputs["S0"] = tensor.tensor4("S0")
self.inputs["S1"] = tensor.tensor4("S1")
self.inputs["A"] = tensor.ivector("Action")
self.inputs["R"] = tensor.vector("Reward")
self.inputs["Nonterminal"] = tensor.bvector("Nonterminal")
if self.misc_state_included:
self.inputs["S0_misc"] = tensor.matrix("S0_misc")
self.inputs["S1_misc"] = tensor.matrix("S1_misc")
self.misc_len = state_format["s_misc"]
else:
self.misc_len = None
# save it for the evaluation reshape
# TODO get rid of this?
self.single_image_input_shape = (1,) + tuple(state_format["s_img"])
architecture["img_input_shape"] = (None,) + tuple(state_format["s_img"])
architecture["misc_len"] = self.misc_len
architecture["output_size"] = actions_number
if self.misc_state_included:
self.network, input_layers, _ = self._initialize_network(img_input=self.inputs["S0"],
misc_input=self.inputs["S0_misc"],
**architecture)
self.frozen_network, _, alternate_inputs = self._initialize_network(img_input=self.inputs["S1"],
misc_input=self.inputs["S1_misc"],
**architecture)
else:
self.network, input_layers, _ = self._initialize_network(img_input=self.inputs["S0"], **architecture)
self.frozen_network, _, alternate_inputs = self._initialize_network(img_input=self.inputs["S1"],
**architecture)
self.alternate_input_mappings = {}
for layer, input in zip(input_layers, alternate_inputs):
self.alternate_input_mappings[layer] = input
# print "Network initialized."
self._compile(ddqn)
def make_train_functions():
P = Parameters()
X = T.bvector('X')
Y = T.ivector('Y')
aux = {}
predict = model.build(
P,
input_size=128,
embedding_size=64,
controller_size=256,
stack_size=256,
output_size=128,
)
output = predict(X, aux=aux)
error = -T.log(output[T.arange(Y.shape[0]), ((128 + 1 + Y) % (128 + 1))])
error = error[-(Y.shape[0] / 2):]
parameters = P.values()
gradients = T.grad(T.sum(error), wrt=parameters)
shapes = [p.get_value().shape for p in parameters]
count = theano.shared(np.float32(0))
acc_grads = [theano.shared(np.zeros(s, dtype=np.float32)) for s in shapes]
acc_update = [ (a,a+g) for a,g in zip(acc_grads,gradients) ] +\
[ (count,count + np.float32(1)) ]
acc_clear = [ (a,np.float32(0) * a) for a in acc_grads ] +\
[ (count,np.int32(0)) ]
avg_grads = [(g / count) for g in acc_grads]
avg_grads = [clip(g, 1) for g in acc_grads]
acc = theano.function(
inputs=[X, Y],
outputs=T.mean(error),
updates=acc_update,
)
update = theano.function(
inputs=[],
updates=updates.adadelta(parameters, avg_grads, learning_rate=1e-8) +
acc_clear)
test = theano.function(
inputs=[X],
outputs=T.argmax(output, axis=1)[-(X.shape[0] / 2):],
)
return acc, update, test
def inputs(self):
return {'call_type': tensor.bvector('call_type'),
'origin_call': tensor.ivector('origin_call'),
'origin_stand': tensor.bvector('origin_stand'),
'taxi_id': tensor.wvector('taxi_id'),
'timestamp': tensor.ivector('timestamp'),
'day_type': tensor.bvector('day_type'),
'missing_data': tensor.bvector('missing_data'),
'latitude': tensor.matrix('latitude'),
'longitude': tensor.matrix('longitude'),
'latitude_mask': tensor.matrix('latitude_mask'),
'longitude_mask': tensor.matrix('longitude_mask'),
'week_of_year': tensor.bvector('week_of_year'),
'day_of_week': tensor.bvector('day_of_week'),
'qhour_of_day': tensor.bvector('qhour_of_day'),
'destination_latitude': tensor.vector('destination_latitude'),
'destination_longitude': tensor.vector('destination_longitude')}
def inputs(self):
return {
'call_type': tensor.bvector('call_type'),
'origin_call': tensor.ivector('origin_call'),
'origin_stand': tensor.bvector('origin_stand'),
'taxi_id': tensor.wvector('taxi_id'),
'timestamp': tensor.ivector('timestamp'),
'day_type': tensor.bvector('day_type'),
'missing_data': tensor.bvector('missing_data'),
'latitude': tensor.matrix('latitude'),
'longitude': tensor.matrix('longitude'),
'latitude_mask': tensor.matrix('latitude_mask'),
'longitude_mask': tensor.matrix('longitude_mask'),
'week_of_year': tensor.bvector('week_of_year'),
'day_of_week': tensor.bvector('day_of_week'),
'qhour_of_day': tensor.bvector('qhour_of_day'),
'destination_latitude': tensor.vector('destination_latitude'),
'destination_longitude': tensor.vector('destination_longitude')
}
def __init__(self, nkerns, recept_width, pool_width, stride, dropout_prob, l2_reg, training_batch_size, activation,
weights_variance, n_timesteps, dim):
self.training_batch_size = training_batch_size
rng = np.random.RandomState(23455)
self.training_mode = T.iscalar('training_mode')
self.x = T.matrix('x')
self.y = T.bvector('y')
self.batch_size = theano.shared(training_batch_size)
self.input = self.x.reshape((self.batch_size, 1, dim, n_timesteps))
self.feature_extractor = FeatureExtractor(rng, self.input, nkerns, recept_width, pool_width, stride,
self.training_mode,
dropout_prob[0],
activation, weights_variance, n_timesteps, dim)
self.classifier = SoftmaxLayer(rng=rng, input=self.feature_extractor.output,
n_in=nkerns[-1], n_out=3,
training_mode=self.training_mode, dropout_prob=dropout_prob[1])
self.params = self.feature_extractor.params + self.classifier.params
# ---------------------- BACKPROP
self.cost = self.classifier.cross_entropy_cost(self.y)
L2_sqr = sum((param ** 2).sum() for param in self.params[::2])
self.grads = T.grad(self.cost + l2_reg * L2_sqr, self.params)
self.updates = self._adadelta_updates(self.grads)
# --------------------- FUNCTIONS
self.train_model = theano.function([self.x, self.y, Param(self.training_mode, default=1)],
self.cost,
updates=self.updates)
self.validate_model = theano.function([self.x, self.y, Param(self.training_mode, default=0)],
self.cost)
self.test_model = theano.function([self.x, Param(self.training_mode, default=0)],
self.classifier.p_y_given_x)
def GetProbFunctions(num_features, learning_rate=1e-4, ret_updates=True):
adjustment_var = T.bmatrix(name='Adjustment matrix')
features_var = T.fmatrix(name='Features')
mask_var = T.bvector(name='Filter mask')
reward_var = T.scalar(name='Reward')
net = BuildGraphNetwork(adjustment_var, features_var, mask_var,
num_features)
desc = lasagne.layers.get_output(net['desc'])
prob = msoftmax(theano.gradient.grad_clip(desc, -1, 1))
reward_grad = reward_var / prob
params = lasagne.layers.get_all_params(net['desc'], trainable=True)
grads = theano.grad(None, params, known_grads={prob: reward_grad})
updates = lasagne.updates.momentum(grads,
params,
learning_rate=learning_rate)
action_fn = theano.function([adjustment_var, features_var, mask_var], prob)
if ret_updates:
updates_fn = theano.function(
[adjustment_var, features_var, mask_var, reward_var], [],
updates=updates,
allow_input_downcast=True)
return net, action_fn, updates_fn
else:
return net, action_fn
def __init__(self, game_params, arch_params, solver_params, trained_model,
sn_dir):
params = None
if trained_model:
params = common.load_params(trained_model)
self.lr_func = create_learning_rate_func(solver_params)
self.x_h_0 = tt.fvector('x_h_0')
self.v_h_0 = tt.fvector('v_h_0')
self.t_h_0 = tt.fvector('t_h_0')
self.x_t_0 = tt.fmatrix('x_t_0')
self.v_t_0 = tt.fmatrix('v_t_0')
self.a_t_0 = tt.fmatrix('a_t_0')
self.t_t_0 = tt.fvector('t_t_0')
self.time_steps = tt.fvector('t_0')
self.exist = tt.bvector('exist')
self.is_leader = tt.fvector('is_leader')
self.x_goal = tt.fvector('x_goal')
self.turn_vec_h = tt.fvector('turn_vec_h')
self.turn_vec_t = tt.fvector('turn_vec_t')
self.n_steps = tt.iscalar('n_steps')
self.lr = tt.fscalar('lr')
self.sn_dir = sn_dir
self.game_params = game_params
self.arch_params = arch_params
self.solver_params = solver_params
self.model = CONTROLLER(self.x_h_0, self.v_h_0, self.t_h_0, self.x_t_0,
self.v_t_0, self.a_t_0, self.t_t_0,
self.time_steps, self.exist, self.is_leader,
self.x_goal, self.turn_vec_h, self.turn_vec_t,
self.n_steps, self.lr, self.game_params,
self.arch_params, self.solver_params, params)
def __init__(self, rng, n_in, n_out, n_h, n_layers, f_act=leaky_relu, obj='single', dropout_rate = 0):
'''
:param rng: Numpy RandomState
:param n_in: Input dimension (int)
:param n_out: Output dimension (int)
:param n_h: Hidden dimension (int)
:param n_layers: Number of hidden layers (int)
:param f_act: Hidden-to-hidden activation function
:param f_out: Output activation function
'''
if obj=='single':
f_out = softmax
elif obj=='multi':
f_out = sigmoid
self.x = T.vector()
# construct hidden layers
assert(n_layers>=1)
first_hiddenLayer = HiddenLayer(
rng=rng,
input=self.x,
predict_input=self.x,
n_in=n_in,
n_out=n_h,
activation=f_act,
dropout_rate = dropout_rate,
nametag='0'
)
self.hidden_layers = [first_hiddenLayer]
self.p = first_hiddenLayer.params[:]
for i in range(n_layers-1):
cur_hiddenLayer = ResNetLayer(
rng=rng,
input=self.hidden_layers[-1].output,
predict_input=self.hidden_layers[-1].predict_output,
n_h=n_h,
activation=f_act,
dropout_rate = dropout_rate,
nametag=str(i+1)
)
self.hidden_layers.append(cur_hiddenLayer)
self.p.extend(cur_hiddenLayer.params[:])
# params for output layer
self.outputLayer = HiddenLayer(
rng=rng,
input=self.hidden_layers[-1].output,
predict_input=self.hidden_layers[-1].predict_output,
n_in=n_h,
n_out=n_out,
activation=f_out,
dropout_rate = 0,
nametag='o'
)
self.p.extend(self.outputLayer.params[:])
self.n_layers = n_layers + 1
self.obj = obj
if obj=='single':
self.y = T.bscalar('y')
self.o = self.outputLayer.output
self.cost = T.nnet.categorical_crossentropy(self.o, T.eye(n_out)[self.y])
self.accuracy = T.switch(T.eq(T.argmax(self.o), self.y), 1., 0.)
self.prediction = np.argmax(self.o)
elif obj=='multi':
self.y = T.bvector('y')
self.o = self.outputLayer.output
self.cost = T.nnet.binary_crossentropy(self.o, self.y).mean()
self.prediction = T.argsort(self.o)
self.accuracy = self.y[T.argmax(self.o)]
self.accuracy3 = (1.0/3.0) * (self.y[self.prediction[-3]]+self.y[self.prediction[-2]]+self.y[self.prediction[-1]])
self.accuracy5 = (1.0/5.0) * (self.y[self.prediction[-5]]+self.y[self.prediction[-4]]+self.y[self.prediction[-3]]+self.y[self.prediction[-2]]+self.y[self.prediction[-1]])
self.optimiser = sgd_optimizer(self, 'ResNet')
def __init__(self,
atari_env,
state_dimension,
action_dimension,
monitor_env=False,
learning_rate=0.001,
critic_update=10,
train_step=1,
gamma=0.95,
eps_max=1.0,
eps_min=0.1,
eps_decay=10000,
n_epochs=10000,
batch_size=32,
buffer_size=50000):
self.env = gym.make(atari_env)
if monitor_env:
None
self.state_dimension = state_dimension
self.action_dimension = action_dimension
self.learning_rate = learning_rate
self.critic_update = critic_update
self.train_step = train_step
self.gamma = gamma
self.eps_max = eps_max
self.eps_min = eps_min
self.eps_decay = eps_decay
self.n_epochs = n_epochs
self.batch_size = batch_size
self.buffer_size = buffer_size
self.experience_replay = []
def q_network(state):
input_state = InputLayer(input_var=state,
shape=(None, self.state_dimension[0],
self.state_dimension[1],
self.state_dimension[2]))
input_state = DimshuffleLayer(input_state, pattern=(0, 3, 1, 2))
conv = Conv2DLayer(input_state,
num_filters=32,
filter_size=(8, 8),
stride=(4, 4),
nonlinearity=rectify)
conv = Conv2DLayer(conv,
num_filters=64,
filter_size=(4, 4),
stride=(2, 2),
nonlinearity=rectify)
conv = Conv2DLayer(conv,
num_filters=64,
filter_size=(3, 3),
stride=(1, 1),
nonlinearity=rectify)
flatten = FlattenLayer(conv)
dense = DenseLayer(flatten, num_units=512, nonlinearity=rectify)
q_values = DenseLayer(dense,
num_units=self.action_dimension,
nonlinearity=linear)
return q_values
self.X_state = T.ftensor4()
self.X_action = T.bvector()
self.X_reward = T.fvector()
self.X_next_state = T.ftensor4()
self.X_done = T.bvector()
self.X_action_hot = to_one_hot(self.X_action, self.action_dimension)
self.q_ = q_network(self.X_state)
self.q = get_output(self.q_)
self.q_target_ = q_network(self.X_next_state)
self.q_target = get_output(self.q_target_)
self.q_max = T.max(self.q_target, axis=1)
self.action = T.argmax(self.q, axis=1)
self.mu = theano.function(inputs=[self.X_state],
outputs=self.action,
allow_input_downcast=True)
self.loss = squared_error(
self.X_reward + self.gamma * self.q_max * (1.0 - self.X_done),
T.batched_dot(self.q, self.X_action_hot))
self.loss = self.loss.mean()
self.params = get_all_params(self.q_)
self.grads = T.grad(self.loss, self.params)
self.normed_grads = total_norm_constraint(self.grads, 1.0)
self.updates = rmsprop(self.normed_grads,
self.params,
learning_rate=self.learning_rate)
self.update_network = theano.function(inputs=[
self.X_state, self.X_action, self.X_reward, self.X_next_state,
self.X_done
],
outputs=self.loss,
updates=self.updates,
allow_input_downcast=True)
def __init__(self,n_hidden,embedding_dimention=50,feature_dimention=61):
##n_in: sequence lstm 的输入维度
##n_hidden: lstm for candi and zp 的隐层维度
#repre_active = ReLU
repre_active = linear
self.params = []
self.w_embedding = init_weight_file(args.embedding,args.embedding_dimention)
self.params.append(self.w_embedding)
self.zp_x_pre_index = T.imatrix("zp_x_pre")
self.zp_x_post_index = T.imatrix("zp_x_post")
zp_x_pre_newshape = (T.shape(self.zp_x_pre_index)[0],args.embedding_dimention)
self.embedding_sub_zp_pre = self.w_embedding[self.zp_x_pre_index.flatten()]
self.zp_x_pre = T.reshape(self.embedding_sub_zp_pre,zp_x_pre_newshape)
zp_x_post_newshape = (T.shape(self.zp_x_post_index)[0],args.embedding_dimention)
self.embedding_sub_zp_post = self.w_embedding[self.zp_x_post_index.flatten()]
self.zp_x_post = T.reshape(self.embedding_sub_zp_post,zp_x_post_newshape)
zp_nn_pre = LSTM(embedding_dimention,n_hidden,self.zp_x_pre)
self.params += zp_nn_pre.params
zp_nn_post = LSTM(embedding_dimention,n_hidden,self.zp_x_post)
self.params += zp_nn_post.params
attention_pre_on_post = softmax((zp_nn_pre.nn_out*zp_nn_post.all_hidden).sum(axis=1))[0]
attention_post_on_pre = softmax((zp_nn_post.nn_out*zp_nn_pre.all_hidden).sum(axis=1))[0]
zp_post = T.sum(attention_pre_on_post[:,None]*zp_nn_post.all_hidden,axis=0)
zp_pre = T.sum(attention_post_on_pre[:,None]*zp_nn_pre.all_hidden,axis=0)
#self.zp_out = T.concatenate((zp_nn_pre.nn_out,zp_nn_post.nn_out))
self.zp_out = T.concatenate((zp_post,zp_pre))
self.zp_out_output = self.zp_out
### get sequence output for NP ###
self.np_x_post_index = T.itensor3("np_x")
self.np_x_postc_index = T.itensor3("np_x")
self.np_x_pre_index = T.itensor3("np_x")
self.np_x_prec_index = T.itensor3("np_x")
np_x_post_newshape = (T.shape(self.np_x_post_index)[0],T.shape(self.np_x_post_index)[1],args.embedding_dimention)
self.embedding_sub_np_x_post = self.w_embedding[self.np_x_post_index.flatten()]
self.np_x_post = T.reshape(self.embedding_sub_np_x_post,np_x_post_newshape)
np_x_postc_newshape = (T.shape(self.np_x_postc_index)[0],T.shape(self.np_x_postc_index)[1],args.embedding_dimention)
self.embedding_sub_np_x_postc = self.w_embedding[self.np_x_postc_index.flatten()]
self.np_x_postc = T.reshape(self.embedding_sub_np_x_postc,np_x_postc_newshape)
np_x_pre_newshape = (T.shape(self.np_x_pre_index)[0],T.shape(self.np_x_pre_index)[1],args.embedding_dimention)
self.embedding_sub_np_x_pre = self.w_embedding[self.np_x_pre_index.flatten()]
self.np_x_pre = T.reshape(self.embedding_sub_np_x_pre,np_x_pre_newshape)
np_x_prec_newshape = (T.shape(self.np_x_prec_index)[0],T.shape(self.np_x_prec_index)[1],args.embedding_dimention)
self.embedding_sub_np_x_prec = self.w_embedding[self.np_x_prec_index.flatten()]
self.np_x_prec = T.reshape(self.embedding_sub_np_x_prec,np_x_prec_newshape)
self.mask_pre = T.matrix("mask")
self.mask_prec = T.matrix("mask")
self.mask_post = T.matrix("mask")
self.mask_postc = T.matrix("mask")
self.np_nn_pre = sub_LSTM_batch(embedding_dimention,n_hidden,self.np_x_pre,self.np_x_prec,self.mask_pre,self.mask_prec)
self.params += self.np_nn_pre.params
self.np_nn_post = sub_LSTM_batch(embedding_dimention,n_hidden,self.np_x_post,self.np_x_postc,self.mask_post,self.mask_postc)
self.params += self.np_nn_post.params
self.np_nn_post_output = self.np_nn_post.nn_out
self.np_nn_pre_output = self.np_nn_pre.nn_out
self.np_out = T.concatenate((self.np_nn_post_output,self.np_nn_pre_output),axis=1)
np_nn_f = LSTM(n_hidden*2,n_hidden*2,self.np_out)
self.params += np_nn_f.params
np_nn_b = LSTM(n_hidden*2,n_hidden*2,self.np_out[::-1])
self.params += np_nn_b.params
self.bi_np_out = T.concatenate((np_nn_f.all_hidden,np_nn_b.all_hidden[::-1]),axis=1)
self.np_out_output = self.bi_np_out
#self.get_np_out = theano.function(inputs=[self.np_x_pre,self.np_x_prec,self.np_x_post,self.np_x_postc,self.mask_pre,self.mask_prec,self.mask_post,self.mask_postc],outputs=[self.np_out_output])
self.feature = T.matrix("feature")
self.feature_layer = Layer(feature_dimention,n_hidden,self.feature,repre_active)
self.params += self.feature_layer.params
w_attention_zp,b_attention = init_weight(n_hidden*2,1,pre="attention_zp",ones=False)
self.params += [w_attention_zp,b_attention]
#w_attention_np,b_u = init_weight(n_hidden*2,1,pre="attention_np",ones=False)
#self.params += [w_attention_np]
w_attention_np_rnn,b_u = init_weight(n_hidden*4,1,pre="attention_np_rnn",ones=False)
self.params += [w_attention_np_rnn]
w_attention_feature,b_u = init_weight(n_hidden,1,pre="attention_feature",ones=False)
self.params += [w_attention_feature]
#self.calcu_attention = tanh(T.dot(self.zp_out_output,w_attention_zp) + T.dot(self.np_out,w_attention_np) + T.dot(self.feature_layer.output,w_attention_feature) + b_attention)
#self.calcu_attention = tanh(T.dot(self.np_out_output,w_attention_np_rnn) + T.dot(self.zp_out_output,w_attention_zp) + T.dot(self.np_out,w_attention_np) + T.dot(self.feature_layer.output,w_attention_feature) + b_attention)
self.calcu_attention = tanh(T.dot(self.np_out_output,w_attention_np_rnn) + T.dot(self.zp_out_output,w_attention_zp) + T.dot(self.feature_layer.output,w_attention_feature) + b_attention)
#self.calcu_attention = tanh(T.dot(self.np_out_output,w_attention_np_rnn) + T.dot(self.zp_out_output,w_attention_zp) + T.dot(self.np_out,w_attention_np) + b_attention)
self.attention = softmax(T.transpose(self.calcu_attention,axes=(1,0)))[0]
self.out = self.attention
self.get_out = theano.function(inputs=[self.zp_x_pre_index,self.zp_x_post_index,self.np_x_pre_index,self.np_x_prec_index,self.np_x_post_index,self.np_x_postc_index,self.mask_pre,self.mask_prec,self.mask_post,self.mask_postc,self.feature],outputs=[self.out],on_unused_input='warn')
l1_norm_squared = sum([(w**2).sum() for w in self.params])
l2_norm_squared = sum([(abs(w)).sum() for w in self.params])
lmbda_l1 = 0.0
#lmbda_l2 = 0.001
lmbda_l2 = 0.0
t = T.bvector()
cost = -(T.log((self.out*t).sum()))
lr = T.scalar()
updates = lasagne.updates.sgd(cost, self.params, lr)
#updates = lasagne.updates.adadelta(cost, self.params)
self.train_step = theano.function(
inputs=[self.zp_x_pre_index,self.zp_x_post_index,self.np_x_pre_index,self.np_x_prec_index,self.np_x_post_index,self.np_x_postc_index,self.mask_pre,self.mask_prec,self.mask_post,self.mask_postc,self.feature,t,lr],
outputs=[cost],
on_unused_input='warn',
updates=updates)
def fit(self, data, sample_store=10000000):
'''
Trains the network.
Parameters
--------
data : pandas.DataFrame
Training data. It contains the transactions of the sessions. It has one column for session IDs, one for item IDs and one for the timestamp of the events (unix timestamps).
It must have a header. Column names are arbitrary, but must correspond to the ones you set during the initialization of the network (session_key, item_key, time_key properties).
sample_store : int
If additional negative samples are used (n_sample > 0), the efficiency of GPU utilization can be sped up, by precomputing a large batch of negative samples (and recomputing when necessary).
This parameter regulizes the size of this precomputed ID set. Its value is the maximum number of int values (IDs) to be stored. Precomputed IDs are stored in the RAM.
For the most efficient computation, a balance must be found between storing few examples and constantly interrupting GPU computations for a short time vs. computing many examples and interrupting GPU computations for a long time (but rarely).
'''
self.predict = None
self.error_during_train = False
itemids = data[self.item_key].unique()
self.n_items = len(itemids)
self.itemidmap = pd.Series(data=np.arange(self.n_items), index=itemids)
data = pd.merge(data, pd.DataFrame({self.item_key:itemids, 'ItemIdx':self.itemidmap[itemids].values}), on=self.item_key, how='inner')
offset_sessions = self.init(data)
if self.n_sample:
pop = data.groupby('ItemId').size()
pop = pop[self.itemidmap.index.values].values**self.sample_alpha
pop = pop.cumsum() / pop.sum()
pop[-1] = 1
if sample_store:
generate_length = sample_store // self.n_sample
if generate_length <= 1:
sample_store = 0
print('No example store was used')
else:
neg_samples = self.generate_neg_samples(pop, generate_length)
sample_pointer = 0
else:
print('No example store was used')
X = T.ivector()
Y = T.ivector()
M = T.iscalar()
R = T.bvector()
H_new, Y_pred, sparams, full_params, sidxs = self.model(X, self.H, M, R, Y, self.dropout_p_hidden, self.dropout_p_embed)
cost = (M/self.batch_size) * self.loss_function(Y_pred, M)
params = [self.Wx if self.embedding or self.constrained_embedding else self.Wx[1:], self.Wh, self.Wrz, self.Bh]
updates = self.RMSprop(cost, params, full_params, sparams, sidxs)
for i in range(len(self.H)):
updates[self.H[i]] = H_new[i]
train_function = function(inputs=[X, Y, M, R], outputs=cost, updates=updates, allow_input_downcast=True)
base_order = np.argsort(data.groupby(self.session_key)[self.time_key].min().values) if self.time_sort else np.arange(len(offset_sessions)-1)
data_items = data.ItemIdx.values
for epoch in range(self.n_epochs):
for i in range(len(self.layers)):
self.H[i].set_value(np.zeros((self.batch_size,self.layers[i]), dtype=theano.config.floatX), borrow=True)
c = []
cc = []
session_idx_arr = np.random.permutation(len(offset_sessions)-1) if self.train_random_order else base_order
iters = np.arange(self.batch_size)
maxiter = iters.max()
start = offset_sessions[session_idx_arr[iters]]
end = offset_sessions[session_idx_arr[iters]+1]
finished = False
while not finished:
minlen = (end-start).min()
out_idx = data_items[start]
for i in range(minlen-1):
in_idx = out_idx
out_idx = data_items[start+i+1]
if self.n_sample:
if sample_store:
if sample_pointer == generate_length:
neg_samples = self.generate_neg_samples(pop, generate_length)
sample_pointer = 0
sample = neg_samples[sample_pointer]
sample_pointer += 1
else:
sample = self.generate_neg_samples(pop, 1)
y = np.hstack([out_idx, sample])
else:
y = out_idx
if self.n_sample:
if sample_pointer == generate_length:
generate_samples()
sample_pointer = 0
sample_pointer += 1
reset = (start+i+1 == end-1)
cost = train_function(in_idx, y, len(iters), reset)
c.append(cost)
cc.append(len(iters))
if np.isnan(cost):
print(str(epoch) + ': NaN error!')
self.error_during_train = True
return
start = start+minlen-1
finished_mask = (end-start<=1)
n_finished = finished_mask.sum()
iters[finished_mask] = maxiter + np.arange(1,n_finished+1)
maxiter += n_finished
valid_mask = (iters < len(offset_sessions)-1)
n_valid = valid_mask.sum()
if (n_valid == 0) or (n_valid < 2 and self.n_sample == 0):
finished = True
break
mask = finished_mask & valid_mask
sessions = session_idx_arr[iters[mask]]
start[mask] = offset_sessions[sessions]
end[mask] = offset_sessions[sessions+1]
iters = iters[valid_mask]
start = start[valid_mask]
end = end[valid_mask]
if n_valid < len(valid_mask):
for i in range(len(self.H)):
tmp = self.H[i].get_value(borrow=True)
tmp = tmp[valid_mask]
self.H[i].set_value(tmp, borrow=True)
c = np.array(c)
cc = np.array(cc)
avgc = np.sum(c * cc) / np.sum(cc)
if np.isnan(avgc):
print('Epoch {}: NaN error!'.format(str(epoch)))
self.error_during_train = True
return
print('Epoch{}\tloss: {:.6f}'.format(epoch, avgc))
def _init_model(self, in_size, out_size, slot_sizes, db, \
n_hid=10, learning_rate_sl=0.005, learning_rate_rl=0.005, batch_size=32, ment=0.1, \
inputtype='full', sl='e2e', rl='e2e'):
self.in_size = in_size
self.out_size = out_size
self.slot_sizes = slot_sizes
self.batch_size = batch_size
self.learning_rate = learning_rate_rl
self.n_hid = n_hid
self.r_hid = self.n_hid
self.sl = sl
self.rl = rl
table = db.table
counts = db.counts
m_unk = [db.inv_counts[s][-1] for s in dialog_config.inform_slots]
prior = [db.priors[s] for s in dialog_config.inform_slots]
unknown = [db.unks[s] for s in dialog_config.inform_slots]
ids = [db.ids[s] for s in dialog_config.inform_slots]
input_var, turn_mask, act_mask, reward_var = T.ftensor3('in'), T.bmatrix('tm'), \
T.btensor3('am'), T.fvector('r')
T_var, N_var = T.as_tensor_variable(table), T.as_tensor_variable(
counts)
db_index_var = T.imatrix('db')
db_index_switch = T.bvector('s')
l_mask_in = L.InputLayer(shape=(None, None), input_var=turn_mask)
flat_mask = T.reshape(turn_mask,
(turn_mask.shape[0] * turn_mask.shape[1], 1))
def _smooth(p):
p_n = p + EPS
return p_n / (p_n.sum(axis=1)[:, np.newaxis])
def _add_unk(p, m, N):
# p: B x V, m- num missing, N- total, p0: 1 x V
t_unk = T.as_tensor_variable(float(m) / N)
ps = p * (1. - t_unk)
return T.concatenate([ps, T.tile(t_unk, (ps.shape[0], 1))], axis=1)
def kl_divergence(p, q):
p_n = _smooth(p)
return -T.sum(q * T.log(p_n), axis=1)
# belief tracking
l_in = L.InputLayer(shape=(None, None, self.in_size),
input_var=input_var)
p_vars = []
pu_vars = []
phi_vars = []
p_targets = []
phi_targets = []
hid_in_vars = []
hid_out_vars = []
bt_loss = T.as_tensor_variable(0.)
kl_loss = []
x_loss = []
self.trackers = []
for i, s in enumerate(dialog_config.inform_slots):
hid_in = T.fmatrix('h')
l_rnn = L.GRULayer(l_in, self.r_hid, hid_init=hid_in, \
mask_input=l_mask_in,
grad_clipping=10.) # B x H x D
l_b_in = L.ReshapeLayer(l_rnn,
(input_var.shape[0] * input_var.shape[1],
self.r_hid)) # BH x D
hid_out = L.get_output(l_rnn)[:, -1, :]
p_targ = T.ftensor3('p_target_' + s)
p_t = T.reshape(
p_targ,
(p_targ.shape[0] * p_targ.shape[1], self.slot_sizes[i]))
phi_targ = T.fmatrix('phi_target' + s)
phi_t = T.reshape(phi_targ,
(phi_targ.shape[0] * phi_targ.shape[1], 1))
l_b = L.DenseLayer(l_b_in,
self.slot_sizes[i],
nonlinearity=lasagne.nonlinearities.softmax)
l_phi = L.DenseLayer(l_b_in,
1,
nonlinearity=lasagne.nonlinearities.sigmoid)
phi = T.clip(L.get_output(l_phi), 0.01, 0.99)
p = L.get_output(l_b)
p_u = _add_unk(p, m_unk[i], db.N)
kl_loss.append(
T.sum(flat_mask.flatten() * kl_divergence(p, p_t)) /
T.sum(flat_mask))
x_loss.append(
T.sum(flat_mask *
lasagne.objectives.binary_crossentropy(phi, phi_t)) /
T.sum(flat_mask))
bt_loss += kl_loss[-1] + x_loss[-1]
p_vars.append(p)
pu_vars.append(p_u)
phi_vars.append(phi)
p_targets.append(p_targ)
phi_targets.append(phi_targ)
hid_in_vars.append(hid_in)
hid_out_vars.append(hid_out)
self.trackers.append(l_b)
self.trackers.append(l_phi)
self.bt_params = L.get_all_params(self.trackers)
def check_db(pv, phi, Tb, N):
O = T.alloc(0., pv[0].shape[0], Tb.shape[0]) # BH x T.shape[0]
for i, p in enumerate(pv):
p_dc = T.tile(phi[i], (1, Tb.shape[0]))
O += T.log(p_dc*(1./db.table.shape[0]) + \
(1.-p_dc)*(p[:,Tb[:,i]]/N[np.newaxis,:,i]))
Op = T.exp(O) #+EPS # BH x T.shape[0]
Os = T.sum(Op, axis=1)[:, np.newaxis] # BH x 1
return Op / Os
def entropy(p):
p = _smooth(p)
return -T.sum(p * T.log(p), axis=-1)
def weighted_entropy(p, q, p0, unks, idd):
w = T.dot(idd, q.transpose()) # Pi x BH
u = p0[np.newaxis, :] * (q[:, unks].sum(axis=1)[:, np.newaxis]
) # BH x Pi
p_tilde = w.transpose() + u
return entropy(p_tilde)
p_db = check_db(pu_vars, phi_vars, T_var, N_var) # BH x T.shape[0]
if inputtype == 'entropy':
H_vars = [weighted_entropy(pv,p_db,prior[i],unknown[i],ids[i]) \
for i,pv in enumerate(p_vars)]
H_db = entropy(p_db)
phv = [ph[:, 0] for ph in phi_vars]
t_in = T.stacklists(H_vars + phv + [H_db]).transpose() # BH x 2M+1
t_in_resh = T.reshape(t_in, (turn_mask.shape[0], turn_mask.shape[1], \
t_in.shape[1])) # B x H x 2M+1
l_in_pol = L.InputLayer(
shape=(None,None,2*len(dialog_config.inform_slots)+1), \
input_var=t_in_resh)
else:
in_reshaped = T.reshape(input_var,
(input_var.shape[0]*input_var.shape[1], \
input_var.shape[2]))
prev_act = in_reshaped[:, -len(dialog_config.inform_slots):]
t_in = T.concatenate(pu_vars + phi_vars + [p_db, prev_act],
axis=1) # BH x D-sum+A
t_in_resh = T.reshape(t_in, (turn_mask.shape[0], turn_mask.shape[1], \
t_in.shape[1])) # B x H x D-sum
l_in_pol = L.InputLayer(shape=(None,None,sum(self.slot_sizes)+ \
3*len(dialog_config.inform_slots)+ \
table.shape[0]), input_var=t_in_resh)
pol_in = T.fmatrix('pol-h')
l_pol_rnn = L.GRULayer(l_in_pol,
n_hid,
hid_init=pol_in,
mask_input=l_mask_in,
grad_clipping=10.) # B x H x D
pol_out = L.get_output(l_pol_rnn)[:, -1, :]
l_den_in = L.ReshapeLayer(
l_pol_rnn,
(turn_mask.shape[0] * turn_mask.shape[1], n_hid)) # BH x D
l_out = L.DenseLayer(l_den_in, self.out_size, \
nonlinearity=lasagne.nonlinearities.softmax) # BH x A
self.network = l_out
self.pol_params = L.get_all_params(self.network)
self.params = self.bt_params + self.pol_params
# db loss
p_db_reshaped = T.reshape(
p_db, (turn_mask.shape[0], turn_mask.shape[1], table.shape[0]))
p_db_final = p_db_reshaped[:, -1, :] # B x T.shape[0]
p_db_final = _smooth(p_db_final)
ix = T.tile(T.arange(p_db_final.shape[0]),
(db_index_var.shape[1], 1)).transpose()
sample_probs = p_db_final[ix, db_index_var] # B x K
if dialog_config.SUCCESS_MAX_RANK == 1:
log_db_probs = T.log(sample_probs).sum(axis=1)
else:
cum_probs,_ = theano.scan(fn=lambda x, prev: x+prev, \
outputs_info=T.zeros_like(sample_probs[:,0]), \
sequences=sample_probs[:,:-1].transpose())
cum_probs = T.clip(cum_probs.transpose(), 0., 1. - 1e-5) # B x K-1
log_db_probs = T.log(sample_probs).sum(
axis=1) - T.log(1. - cum_probs).sum(axis=1) # B
log_db_probs = log_db_probs * db_index_switch
# rl
probs = L.get_output(self.network) # BH x A
probs = _smooth(probs)
out_probs = T.reshape(probs, (turn_mask.shape[0], turn_mask.shape[1],
self.out_size)) # B x H x A
log_probs = T.log(out_probs)
act_probs = (log_probs * act_mask).sum(axis=2) # B x H
ep_probs = (act_probs * turn_mask).sum(axis=1) # B
H_probs = -T.sum(T.sum(out_probs * log_probs, axis=2), axis=1) # B
self.act_loss = -T.mean(ep_probs * reward_var)
self.db_loss = -T.mean(log_db_probs * reward_var)
self.reg_loss = -T.mean(ment * H_probs)
self.loss = self.act_loss + self.db_loss + self.reg_loss
self.inps = [input_var, turn_mask, act_mask, reward_var, db_index_var, db_index_switch, \
pol_in] + hid_in_vars
self.obj_fn = theano.function(self.inps,
self.loss,
on_unused_input='warn')
self.act_fn = theano.function([input_var,turn_mask,pol_in]+hid_in_vars, \
[out_probs,p_db,pol_out]+pu_vars+phi_vars+hid_out_vars, on_unused_input='warn')
self.debug_fn = theano.function(self.inps, [probs, p_db, self.loss],
on_unused_input='warn')
self._rl_train_fn(self.learning_rate)
## sl
sl_loss = 0. + bt_loss - T.mean(ep_probs)
if self.sl == 'e2e':
sl_updates = lasagne.updates.rmsprop(sl_loss, self.params, \
learning_rate=learning_rate_sl, epsilon=1e-4)
sl_updates_with_mom = lasagne.updates.apply_momentum(sl_updates)
elif self.sl == 'bel':
sl_updates = lasagne.updates.rmsprop(sl_loss, self.bt_params, \
learning_rate=learning_rate_sl, epsilon=1e-4)
sl_updates_with_mom = lasagne.updates.apply_momentum(sl_updates)
else:
sl_updates = lasagne.updates.rmsprop(sl_loss, self.pol_params, \
learning_rate=learning_rate_sl, epsilon=1e-4)
sl_updates_with_mom = lasagne.updates.apply_momentum(sl_updates)
sl_inps = [input_var, turn_mask, act_mask, pol_in
] + p_targets + phi_targets + hid_in_vars
self.sl_train_fn = theano.function(sl_inps, [sl_loss]+kl_loss+x_loss, updates=sl_updates, \
on_unused_input='warn')
self.sl_obj_fn = theano.function(sl_inps,
sl_loss,
on_unused_input='warn')
def __init__(
self,
rng,
batchsize=100,
activation=relu
):
import char_load
(num_sent, char_cnt, word_cnt, max_word_len, max_sen_len,\
k_chr, k_wrd, x_chr, x_wrd, y) = char_load.read("tweets_clean.txt")
dim_word = 30
dim_char = 5
cl_word = 300
cl_char = 50
k_word = k_wrd
k_char = k_chr
data_train_word,\
data_test_word,\
data_train_char,\
data_test_char,\
target_train,\
target_test\
= train_test_split(x_wrd, x_chr, y, random_state=1234, test_size=0.1)
x_train_word = theano.shared(np.asarray(data_train_word, dtype='int16'), borrow=True)
x_train_char = theano.shared(np.asarray(data_train_char, dtype='int16'), borrow=True)
y_train = theano.shared(np.asarray(target_train, dtype='int8'), borrow=True)
x_test_word = theano.shared(np.asarray(data_test_word, dtype='int16'), borrow=True)
x_test_char = theano.shared(np.asarray(data_test_char, dtype='int16'), borrow=True)
y_test = theano.shared(np.asarray(target_test, dtype='int8'), borrow=True)
self.n_train_batches = x_train_word.get_value(borrow=True).shape[0] / batchsize
self.n_test_batches = x_test_word.get_value(borrow=True).shape[0] / batchsize
"""symbol definition"""
index = T.iscalar()
x_wrd = T.wmatrix('x_wrd')
x_chr = T.wtensor3('x_chr')
y = T.bvector('y')
train = T.iscalar('train')
"""network definition"""
layer_char_embed_input = x_chr#.reshape((batchsize, max_sen_len, max_word_len))
layer_char_embed = EmbedIDLayer(
rng,
layer_char_embed_input,
n_input=char_cnt,
n_output=dim_char
)
layer1_input = layer_char_embed.output.reshape(
(batchsize*max_sen_len, 1, max_word_len, dim_char)
)
layer1 = ConvolutionalLayer(
rng,
layer1_input,
filter_shape=(cl_char, 1, k_char, dim_char),# cl_charフィルタ数
image_shape=(batchsize*max_sen_len, 1, max_word_len, dim_char)
)
layer2 = MaxPoolingLayer(
layer1.output,
poolsize=(max_word_len-k_char+1, 1)
)
layer_word_embed_input = x_wrd #.reshape((batchsize, max_sen_len))
layer_word_embed = EmbedIDLayer(
rng,
layer_word_embed_input,
n_input=word_cnt,
n_output=dim_word
)
layer3_word_input = layer_word_embed.output.reshape((batchsize, 1, max_sen_len, dim_word))
layer3_char_input = layer2.output.reshape((batchsize, 1, max_sen_len, cl_char))
layer3_input = T.concatenate(
[layer3_word_input,
layer3_char_input],
axis=3
)#.reshape((batchsize, 1, max_sen_len, dim_word+cl_char))
layer3 = ConvolutionalLayer(
rng,
layer3_input,
filter_shape=(cl_word, 1, k_word, dim_word + cl_char),#1は入力チャネル数
image_shape=(batchsize, 1, max_sen_len, dim_word + cl_char),
activation=activation
)
layer4 = MaxPoolingLayer(
layer3.output,
poolsize=(max_sen_len-k_word+1, 1)
)
layer5_input = layer4.output.reshape((batchsize, cl_word))
layer5 = FullyConnectedLayer(
rng,
dropout(rng, layer5_input, train),
n_input=cl_word,
n_output=50,
activation=activation
)
layer6_input = layer5.output
layer6 = FullyConnectedLayer(
rng,
dropout(rng, layer6_input, train, p=0.1),
n_input=50,
n_output=2,
activation=None
)
result = Result(layer6.output, y)
loss = result.negative_log_likelihood()
accuracy = result.accuracy()
params = layer6.params\
+layer5.params\
+layer3.params\
+layer_word_embed.params\
+layer1.params\
+layer_char_embed.params
updates = RMSprop(learning_rate=0.001, params=params).updates(loss)
self.train_model = theano.function(
inputs=[index],
outputs=[loss, accuracy],
updates=updates,
givens={
x_wrd: x_train_word[index*batchsize: (index+1)*batchsize],
x_chr: x_train_char[index*batchsize: (index+1)*batchsize],
y: y_train[index*batchsize: (index+1)*batchsize],
train: np.cast['int32'](1)
}
)
self.test_model = theano.function(
inputs=[index],
outputs=[loss, accuracy],
givens={
x_wrd: x_test_word[index*batchsize: (index+1)*batchsize],
x_chr: x_test_char[index*batchsize: (index+1)*batchsize],
y: y_test[index*batchsize: (index+1)*batchsize],
train: np.cast['int32'](0)
}
)
def __init__(self, n_hidden, embedding_dimention=50, feature_dimention=61):
##n_in: sequence lstm 的输入维度
##n_hidden: lstm for candi and zp 的隐层维度
self.params = []
self.zp_x_pre = T.matrix("zp_x_pre")
self.zp_x_post = T.matrix("zp_x_post")
zp_nn_pre = LSTM(embedding_dimention, n_hidden, self.zp_x_pre)
#zp_nn_pre = LSTM(embedding_dimention,n_hidden,self.zp_x_pre_dropout)
self.params += zp_nn_pre.params
zp_nn_post = LSTM(embedding_dimention, n_hidden, self.zp_x_post)
#zp_nn_post = LSTM(embedding_dimention,n_hidden,self.zp_x_post_dropout)
self.params += zp_nn_post.params
self.zp_out = T.concatenate((zp_nn_pre.nn_out, zp_nn_post.nn_out))
self.zp_out_output = self.zp_out
### get sequence output for NP ###
self.np_x_post = T.tensor3("np_x")
self.np_x_postc = T.tensor3("np_x")
self.np_x_pre = T.tensor3("np_x")
self.np_x_prec = T.tensor3("np_x")
self.mask_pre = T.matrix("mask")
self.mask_prec = T.matrix("mask")
self.mask_post = T.matrix("mask")
self.mask_postc = T.matrix("mask")
self.np_nn_pre = sub_LSTM_batch(embedding_dimention, n_hidden,
self.np_x_pre, self.np_x_prec,
self.mask_pre, self.mask_prec)
self.params += self.np_nn_pre.params
self.np_nn_post = sub_LSTM_batch(embedding_dimention, n_hidden,
self.np_x_post, self.np_x_postc,
self.mask_post, self.mask_postc)
self.params += self.np_nn_post.params
self.np_nn_post_output = self.np_nn_post.nn_out
self.np_nn_pre_output = self.np_nn_pre.nn_out
self.np_out = T.concatenate(
(self.np_nn_post_output, self.np_nn_pre_output), axis=1)
#np_nn_f = LSTM(n_hidden*2,n_hidden*2,self.np_out)
np_nn_f = RNN(n_hidden * 2, n_hidden * 2, self.np_out)
self.params += np_nn_f.params
#np_nn_b = LSTM(n_hidden*2,n_hidden*2,self.np_out[::-1])
np_nn_b = RNN(n_hidden * 2, n_hidden * 2, self.np_out[::-1])
self.params += np_nn_b.params
self.bi_np_out = T.concatenate(
(np_nn_f.all_hidden, np_nn_b.all_hidden[::-1]), axis=1)
self.np_out_output = self.bi_np_out
self.get_np_out = theano.function(inputs=[
self.np_x_pre, self.np_x_prec, self.np_x_post, self.np_x_postc,
self.mask_pre, self.mask_prec, self.mask_post, self.mask_postc
],
outputs=[self.np_out_output])
#self.feature = T.matrix("feature")
#self.feature_layer = Layer(feature_dimention,n_hidden,self.feature,repre_active)
#self.params += self.feature_layer.params
w_attention_zp, b_attention = init_weight(n_hidden * 2,
1,
pre="attention_zp",
ones=False)
self.params += [w_attention_zp, b_attention]
w_attention_np, b_u = init_weight(n_hidden * 2,
1,
pre="attention_np",
ones=False)
self.params += [w_attention_np]
w_attention_np_rnn, b_u = init_weight(n_hidden * 4,
1,
pre="attention_np_rnn",
ones=False)
self.params += [w_attention_np_rnn]
#w_attention_feature,b_u = init_weight(n_hidden,1,pre="attention_feature",ones=False)
#self.params += [w_attention_feature]
#self.calcu_attention = tanh(T.dot(self.np_out_output,w_attention_np_rnn) + T.dot(self.zp_out_output,w_attention_zp) + T.dot(self.np_out,w_attention_np) + T.dot(self.feature_layer.output,w_attention_feature) + b_attention)
self.calcu_attention = tanh(
T.dot(self.np_out_output, w_attention_np_rnn) +
T.dot(self.zp_out_output, w_attention_zp) +
T.dot(self.np_out, w_attention_np) + b_attention)
#self.calcu_attention = tanh(T.dot(self.zp_out_output,w_attention_zp) + T.dot(self.np_out,w_attention_np) + b_attention)
self.attention = softmax(T.transpose(self.calcu_attention,
axes=(1, 0)))[0]
#self.attention = T.transpose(self.calcu_attention,axes=(1,0))[0]
t = T.bvector()
max_attention = (self.attention * t).max()
self.out = self.attention
self.get_out = theano.function(inputs=[
self.zp_x_pre, self.zp_x_post, self.np_x_pre, self.np_x_prec,
self.np_x_post, self.np_x_postc, self.mask_pre, self.mask_prec,
self.mask_post, self.mask_postc
],
outputs=[self.out],
on_unused_input='warn')
self.get_max = theano.function(inputs=[
self.zp_x_pre, self.zp_x_post, self.np_x_pre, self.np_x_prec,
self.np_x_post, self.np_x_postc, self.mask_pre, self.mask_prec,
self.mask_post, self.mask_postc, t
],
outputs=[max_attention],
on_unused_input='warn')
l1_norm_squared = sum([(w**2).sum() for w in self.params])
l2_norm_squared = sum([(abs(w)).sum() for w in self.params])
lmbda_l1 = 0.0
#lmbda_l2 = 0.001
lmbda_l2 = 0.0
cost = ((1 - t) * (1 - max_attention + self.out)).sum()
#cost = -(T.log((self.out*t).sum()))
lr = T.scalar()
updates = lasagne.updates.sgd(cost, self.params, lr)
#updates = lasagne.updates.adadelta(cost, self.params)
#updates = lasagne.updates.adam(cost, self.params)
self.train_step = theano.function(inputs=[
self.zp_x_pre, self.zp_x_post, self.np_x_pre, self.np_x_prec,
self.np_x_post, self.np_x_postc, self.mask_pre, self.mask_prec,
self.mask_post, self.mask_postc, t, lr
],
outputs=[cost],
on_unused_input='warn',
updates=updates)
def __init__(self,
nkerns,
recept_width,
pool_width,
dropout_prob,
training_batch_size,
activation,
n_timesteps=1000,
dim=18):
if activation == 'tanh':
activation_function = lambda x: T.tanh(x)
elif activation == 'relu':
activation_function = lambda x: T.maximum(0.0, x)
else:
raise ValueError('unknown activation function')
self.training_batch_size = training_batch_size
rng = np.random.RandomState(23455)
self.training_mode = T.iscalar('training_mode')
self.x = T.matrix('x')
self.y = T.bvector('y')
self.batch_size = theano.shared(self.training_batch_size)
# 18@1*1000
self.layer0_input = self.x.reshape(
(self.batch_size, dim, 1, n_timesteps))
# image 18 @ 1*1000
# c1: nkerns[0] @ 1* (1000 - recept_width[0] + 1)
# s2: nkerns[0] @ 1 * c1 / pool_width[0]
layer0 = ConvPoolLayer(rng,
input=self.layer0_input,
image_shape=(None, dim, 1, n_timesteps),
filter_shape=(nkerns[0], dim, 1,
recept_width[0]),
poolsize=(1, pool_width[0]),
activation_function=activation_function)
# c3: nkerns[1] @ 1 * (s2 - recept_width[1] + 1)
# s4 nkerns[1] @ 1 * c3 / pool_width
input_layer1_width = (n_timesteps - recept_width[0] +
1) / pool_width[0]
layer1 = ConvPoolLayer(rng,
input=layer0.output,
image_shape=(None, nkerns[0], 1,
input_layer1_width),
filter_shape=(nkerns[1], nkerns[0], 1,
recept_width[1]),
poolsize=(1, pool_width[1]),
activation_function=activation_function)
# s4:(batch_size, nkerns[1], 1, s4) -> flatten(2) -> (batch_size, nkerns[1]* 1 * s4)
layer2_input = layer1.output.flatten(2)
input_layer2_size = (input_layer1_width - recept_width[1] +
1) / pool_width[1]
# c5: 120@1*1
self.layer2 = HiddenLayer(rng=rng,
input=layer2_input,
n_in=nkerns[1] * 1 * input_layer2_size,
n_out=nkerns[2],
training_mode=self.training_mode,
dropout_prob=dropout_prob,
activation_function=activation_function)
# f6/output
self.layer3 = LogisticRegressionLayer(input=self.layer2.output,
n_in=nkerns[2],
n_out=2,
training_mode=self.training_mode,
dropout_prob=dropout_prob)
self.params = self.layer3.params + self.layer2.params + layer1.params + layer0.params
def initialize(self, policy, env_spec, sample_size, horizon,
mid_batch_reset):
if mid_batch_reset and policy.recurrent:
raise NotImplementedError
obs = env_spec.observation_space.new_tensor_variable('obs',
extra_dims=1)
act = env_spec.action_space.new_tensor_variable('act', extra_dims=1)
adv = T.vector('adv')
ret = T.vector('ret')
old_value = T.vector('old_value')
dist = policy.distribution
old_dist_info = {
k: T.matrix('old_%s' % k)
for k in dist.dist_info_keys
}
self._dist_info_keys = dist.dist_info_keys
state_info = {k: T.matrix(k) for k in policy.state_info_keys}
self._state_info_keys = policy.state_info_keys
new_dist_info = policy.dist_info_sym(obs, state_info_vars=state_info)
new_value = policy.value_sym(obs, state_info_vars=state_info)
self._lr_mult = theano.shared(np.array(1., dtype=theano.config.floatX),
name='lr_mult')
if mid_batch_reset and not policy.recurrent:
self._use_valids = False
valids = None # will be ignored inside valids_mean()
else:
self._use_valids = True
valids = T.bvector('valids') # dtype int8
v_err = (new_value - ret)**2
v_loss = self.v_loss_coeff * valids_mean(v_err, valids)
ent = policy.distribution.entropy_sym(new_dist_info)
ent_loss = -self.ent_loss_coeff * valids_mean(ent, valids)
pi_loss = \
self.pi_loss(policy, act, adv, old_dist_info, new_dist_info, valids)
losses = (pi_loss, v_loss, ent_loss)
pi_kl = valids_mean(dist.kl_sym(old_dist_info, new_dist_info), valids)
v_kl = valids_mean((new_value - old_value)**2, valids)
constraints = (pi_kl, v_kl)
input_list = [obs, act, adv, ret, old_value]
old_dist_info_list = [old_dist_info[k] for k in dist.dist_info_keys]
state_info_list = [state_info[k] for k in policy.state_info_keys]
input_list += old_dist_info_list + state_info_list
opt_examples = dict(
advantages=np.array(1, dtype=adv.dtype),
returns=np.array(1, dtype=ret.dtype),
)
if self._use_valids:
input_list.append(valids)
opt_examples["valids"] = np.array(1, dtype=np.int8)
self.optimizer.initialize(
inputs=input_list,
losses=losses,
constraints=constraints,
target=policy,
lr_mult=self._lr_mult,
)
self._opt_buf = buffer_with_segs_view(opt_examples,
sample_size,
horizon,
shared=False)
self._batch_size = sample_size
self._mid_batch_reset = mid_batch_reset
self._horizon = horizon
self.policy = policy
def inputs(self):
return {
'call_type':
tensor.bvector('call_type'),
'origin_call':
tensor.ivector('origin_call'),
'origin_stand':
tensor.bvector('origin_stand'),
'taxi_id':
tensor.wvector('taxi_id'),
'timestamp':
tensor.ivector('timestamp'),
'day_type':
tensor.bvector('day_type'),
'missing_data':
tensor.bvector('missing_data'),
'latitude':
tensor.matrix('latitude'),
'longitude':
tensor.matrix('longitude'),
'destination_latitude':
tensor.vector('destination_latitude'),
'destination_longitude':
tensor.vector('destination_longitude'),
'travel_time':
tensor.ivector('travel_time'),
'first_k_latitude':
tensor.matrix('first_k_latitude'),
'first_k_longitude':
tensor.matrix('first_k_longitude'),
'last_k_latitude':
tensor.matrix('last_k_latitude'),
'last_k_longitude':
tensor.matrix('last_k_longitude'),
'input_time':
tensor.ivector('input_time'),
'week_of_year':
tensor.bvector('week_of_year'),
'day_of_week':
tensor.bvector('day_of_week'),
'qhour_of_day':
tensor.bvector('qhour_of_day'),
'candidate_call_type':
tensor.bvector('candidate_call_type'),
'candidate_origin_call':
tensor.ivector('candidate_origin_call'),
'candidate_origin_stand':
tensor.bvector('candidate_origin_stand'),
'candidate_taxi_id':
tensor.wvector('candidate_taxi_id'),
'candidate_timestamp':
tensor.ivector('candidate_timestamp'),
'candidate_day_type':
tensor.bvector('candidate_day_type'),
'candidate_missing_data':
tensor.bvector('candidate_missing_data'),
'candidate_latitude':
tensor.matrix('candidate_latitude'),
'candidate_longitude':
tensor.matrix('candidate_longitude'),
'candidate_destination_latitude':
tensor.vector('candidate_destination_latitude'),
'candidate_destination_longitude':
tensor.vector('candidate_destination_longitude'),
'candidate_travel_time':
tensor.ivector('candidate_travel_time'),
'candidate_first_k_latitude':
tensor.matrix('candidate_first_k_latitude'),
'candidate_first_k_longitude':
tensor.matrix('candidate_first_k_longitude'),
'candidate_last_k_latitude':
tensor.matrix('candidate_last_k_latitude'),
'candidate_last_k_longitude':
tensor.matrix('candidate_last_k_longitude'),
'candidate_input_time':
tensor.ivector('candidate_input_time'),
'candidate_week_of_year':
tensor.bvector('candidate_week_of_year'),
'candidate_day_of_week':
tensor.bvector('candidate_day_of_week'),
'candidate_qhour_of_day':
tensor.bvector('candidate_qhour_of_day')
}
def get_updates_functions(self):
tind = T.ivector('ind')
if self.NMF_updates == 'beta':
print "Standard rules for beta-divergence"
H_update = T.set_subtensor(self.H[tind[3]:tind[4], ],
updates.beta_H(self.X_buff[tind[1]:tind[2], ],
self.W[tind[0]],
self.H[tind[3]:tind[4], ],
self.beta))
W_update = T.set_subtensor(self.W[tind[0]],
updates.beta_W(self.X_buff[tind[1]:tind[2], ],
self.W[tind[0]],
self.H[tind[3]:tind[4], ],
self.beta))
self.trainH = theano.function(inputs=[tind],
outputs=[],
updates={self.H: H_update},
name="trainH",
allow_input_downcast=True)
self.trainW = theano.function(inputs=[tind],
outputs=[],
updates={self.W: W_update},
name="trainW",
allow_input_downcast=True)
if self.NMF_updates == 'groupNMF':
tcomp = T.ivector('comp')
tlambda = T.fvector('lambda')
tcard = T.bvector('card')
print "Group NMF with class specific rules for beta-divergence"
if self.dist_mode=='iter':
tparams = [tind, tcomp, tlambda, tcard]
print "Compute contraint distances once per iteration"
H_update = T.set_subtensor(self.H[tind[3]:tind[4], ],
updates.group_H(self.X_buff[tind[1]:tind[2], ],
self.W[tind[0]],
self.H,
self.beta,
tparams))
W_update = T.set_subtensor(self.W[tind[0]],
updates.group_W_nosum(self.X_buff[tind[1]:tind[2], ],
self.W,
self.H[tind[3]:tind[4], ],
self.cls_sums[tind[5]],
self.ses_sums[tind[6]],
self.beta,
tparams))
self.trainH = theano.function(inputs=[tind,
tcomp,
tlambda,
tcard],
outputs=[],
updates={self.H: H_update},
name="trainH",
on_unused_input='ignore',
allow_input_downcast=True)
self.trainW = theano.function(inputs=[tind,
tcomp,
tlambda,
tcard],
outputs=[],
updates={self.W: W_update},
name="trainW",
on_unused_input='ignore',
allow_input_downcast=True)
else:
print "Compute contraint distances at each segment update"
tSc = T.ivector('Sc')
tCs = T.ivector('Cs')
tparams = [tind, tcomp, tlambda, tSc, tCs, tcard]
H_update = T.set_subtensor(self.H[tind[3]:tind[4], ],
updates.group_H(self.X_buff[tind[1]:tind[2], ],
self.W[tind[0]],
self.H,
self.beta,
tparams))
W_update = T.set_subtensor(self.W[tind[0]],
updates.group_W(self.X_buff[tind[1]:tind[2], ],
self.W,
self.H[tind[3]:tind[4], ],
self.beta,
tparams))
self.trainH = theano.function(inputs=[tind,
tcomp,
tlambda,
tSc,
tCs,
tcard],
outputs=[],
updates={self.H: H_update},
name="trainH",
on_unused_input='ignore',
allow_input_downcast=True)
self.trainW = theano.function(inputs=[tind,
tcomp,
tlambda,
tSc,
tCs,
tcard],
outputs=[],
updates={self.W: W_update},
name="trainW",
on_unused_input='ignore',
allow_input_downcast=True)
if self.NMF_updates == 'noiseNMF':
tcomp = T.ivector('comp')
tlambda = T.fvector('lambda')
tcard = T.bvector('card')
print "Group NMF with noise reference rules for beta-divergence"
tSc = T.ivector('Sc')
tCs = T.ivector('Cs')
tparams = [tind, tcomp, tlambda, tSc, tCs, tcard]
H_update = T.set_subtensor(self.H[tind[3]:tind[4], ],
updates.group_H(self.X_buff[tind[1]:tind[2], ],
self.W[tind[0]],
self.H,
self.beta,
tparams))
W_update = T.set_subtensor(self.W[tind[0]],
updates.noise_W(self.X_buff[tind[1]:tind[2], ],
self.W,
self.Wn,
self.H[tind[3]:tind[4], ],
self.beta,
tparams))
self.trainH = theano.function(inputs=[tind,
tcomp,
tlambda,
tSc,
tCs,
tcard],
outputs=[],
updates={self.H: H_update},
name="trainH",
on_unused_input='ignore',
allow_input_downcast=True)
self.trainW = theano.function(inputs=[tind,
tcomp,
tlambda,
tSc,
tCs,
tcard],
outputs=[],
updates={self.W: W_update},
name="trainW",
on_unused_input='ignore',
allow_input_downcast=True)
def create_iter_functions(dataset, output_layer,
batch_size=MINIBATCH_SIZE
):
print("Creating IterFunctions...")
batch_index = T.iscalar('batch_index')
X_batch = T.imatrix('x')
# See http://stackoverflow.com/questions/25166657/index-gymnastics-inside-a-theano-function
# And https://bitbucket.org/kostialopuhin/word-models/src/ba4b00bb03c7eee83b11dc729fd4f6a58ab21fb6/word_embeddings.py?at=default
vectors = dataset['language']['vectors']
#X_batch_flat_vectors = vectors[X_batch].reshape( (X_batch.shape[0], -1) ) # next line is more explicit, for safety
X_batch_flat_vectors = vectors[X_batch].reshape( (X_batch.shape[0], vectors.shape[1]*X_batch.shape[1] ) )
#Y_batch = T.ivector('y')
Y_batch = T.bvector('y') # This is smaller...
batch_slice = slice(
batch_index * batch_size, (batch_index + 1) * batch_size
)
# Output layer vector position assignment :
# a = NotMissing
# b = Missing (complex)
# c-x = Missing a simple word (take shift into account)
def loss(output):
# This pulls out log(output) at the correct index position for each element of the mini-batch,
# and takes the mean
return -T.mean(T.log(output)[T.arange(Y_batch.shape[0]), Y_batch])
loss_train = loss(output_layer.get_output(X_batch_flat_vectors))
loss_eval = loss(output_layer.get_output(X_batch_flat_vectors, deterministic=True)) # deterministic=True turns off dropout
# But this (for the first runs) easy to model as a soft-max thing
# from 0=(nogap), 1=(complex), 2..(small_limit+2)=small-word
pred = T.argmax(
output_layer.get_output(X_batch_flat_vectors, deterministic=True), axis=1
)
accuracy = T.mean(T.eq(pred, Y_batch), dtype=theano.config.floatX) # Would otherwise use float64
all_params = lasagne.layers.get_all_params(output_layer)
#updates = lasagne.updates.nesterov_momentum(
# loss_train, all_params, learning_rate, momentum
#)
#def adagrad(loss, all_params, learning_rate=1.0, epsilon=1e-6):
#updates = lasagne.updates.adagrad(
# loss_train, all_params #, learning_rate, momentum
#)
#def adadelta(loss, all_params, learning_rate=1.0, rho=0.95, epsilon=1e-6):
updates = lasagne.updates.adadelta(
loss_train, all_params #, learning_rate, momentum
)
iters={}
if 'train' in dataset:
d=dataset['train']
iters['train'] = theano.function(
[batch_index], loss_train,
updates=updates,
givens={
X_batch: d['X'][batch_slice],
Y_batch: d['Y'][batch_slice],
},
)
if 'valid' in dataset:
d=dataset['valid']
iters['valid'] = theano.function(
[batch_index], [loss_eval, accuracy],
givens={
X_batch: d['X'][batch_slice],
Y_batch: d['Y'][batch_slice],
},
)
if 'test' in dataset:
d=dataset['test']
iters['test'] = theano.function(
[batch_index], [loss_eval, accuracy],
givens={
X_batch: d['X'][batch_slice],
Y_batch: d['Y'][batch_slice],
},
)
return iters
def construct_network(context,characters,hidden):
print "Setting up memory..."
X = T.bmatrix('X')
Y = T.bvector('Y')
zeros = np.zeros(characters,dtype=np.int8)
zeros[0] = 1
zeros[1] = 1
alpha = T.cast(T.fscalar('alpha'),dtype=theano.config.floatX)
lr = T.cast(T.fscalar('lr'),dtype=theano.config.floatX)
Ws_char_to_hidden = [
U.create_shared(
U.initial_weights(characters,hidden),
name='char[%d]'%i
) for i in xrange(context)
]
mat = Ws_char_to_hidden[0].get_value()
mat[0] = 0
Ws_char_to_hidden[0].set_value(mat)
W_hidden_to_hidden_i = U.create_shared(U.initial_weights(hidden,hidden) + np.eye(hidden))
b_hidden_i = U.create_shared(U.initial_weights(hidden))
W_hidden_to_hidden_o = U.create_shared(U.initial_weights(hidden,hidden) + np.eye(hidden))
b_hidden_o = U.create_shared(U.initial_weights(hidden))
W_hidden_to_predict = U.create_shared(U.initial_weights(hidden,characters))
b_predict = U.create_shared(U.initial_weights(characters))
W_predict_to_hidden = U.create_shared(U.initial_weights(characters,hidden))
gen_weight_mask = U.create_shared(zeros,name='mask')
print "Constructing graph..."
hidden_inputs = make_char_outputs(X,Ws_char_to_hidden)
hidden_outputs,predictions = make_hidden_predict_outputs(
hidden,characters,
hidden_inputs,
gen_weight_mask[X[:,0]],
W_hidden_to_hidden_i,
b_hidden_i,
W_hidden_to_hidden_o,
b_hidden_o,
W_hidden_to_predict,
b_predict,
W_predict_to_hidden
)
weights = Ws_char_to_hidden + [
W_hidden_to_hidden_i,
b_hidden_i,
W_hidden_to_hidden_o,
b_hidden_o,
W_hidden_to_predict,
b_predict,
W_predict_to_hidden
]
cost = -T.mean(T.log(predictions)[T.arange(Y.shape[0]),Y])
gparams = T.grad(cost,weights)
deltas = [ U.create_shared(np.zeros(w.get_value().shape)) for w in weights ]
updates = [
( param, param - ( alpha * delta + gparam * lr ) )
for param,delta,gparam in zip(weights,deltas,gparams)
] + [
( delta, alpha * delta + gparam * lr)
for delta,gparam in zip(deltas,gparams)
]
return X,Y,alpha,lr,updates,predictions,weights
num_units = n_state,
nonlinearity = tanh,
W = Normal(0.1, 0.0),
b = Constant(0.0))
q_values = DenseLayer(dense_2,
num_units = n_action,
nonlinearity = None,
W = Normal(0.1, 0.0),
b = Constant(0.0))
return q_values
X_next_state = T.fmatrix()
X_state = T.fmatrix()
X_action = T.bvector()
X_reward = T.fvector()
X_done = T.bvector()
X_action_hot = to_one_hot(X_action, n_action)
q_ = q_network(X_state); q = get_output(q_)
q_target_ = q_network(X_next_state); q_target = get_output(q_target_)
q_max = T.max(q_target, axis=1)
action = T.argmax(q, axis=1)
mu = theano.function(inputs = [X_state],
outputs = action,
allow_input_downcast = True)
loss = squared_error(X_reward + gamma * q_max * (1.0 - X_done), T.batched_dot(q, X_action_hot))
def __init__(self,n_hidden,embedding_dimention=50,feature_dimention=61):
##n_in: sequence lstm 的输入维度
##n_hidden: lstm for candi and zp 的隐层维度
#repre_active = ReLU
repre_active = linear
self.params = []
self.zp_x_pre = T.matrix("zp_x_pre")
self.zp_x_post = T.matrix("zp_x_post")
zp_nn_pre = LSTM(embedding_dimention,n_hidden,self.zp_x_pre)
self.params += zp_nn_pre.params
zp_nn_post = LSTM(embedding_dimention,n_hidden,self.zp_x_post)
self.params += zp_nn_post.params
danwei = theano.shared(np.eye(8, dtype=theano.config.floatX))
H_pre = zp_nn_pre.all_hidden
H_post = zp_nn_post.all_hidden
Ws1_pre,heihei = init_weight(n_hidden,n_hidden,pre="Ws1_pre_zp",ones=False)
Ws2_pre,heihei = init_weight(8,n_hidden,pre="Ws2_pre_zp",ones=False)
self.params += [Ws1_pre,Ws2_pre]
A_pre = softmax(T.dot(Ws2_pre,T.dot(Ws1_pre,T.transpose(H_pre))))
P_pre = T.dot(A_pre,T.transpose(A_pre))-danwei
f_norm_pre = (P_pre**2).sum()
zp_out_pre = T.mean(T.dot(A_pre,H_pre),axis=0)
Ws1_post,heihei = init_weight(n_hidden,n_hidden,pre="Ws1_post_zp",ones=False)
Ws2_post,heihei = init_weight(8,n_hidden,pre="Ws2_post_zp",ones=False)
self.params += [Ws1_post,Ws2_post]
A_post = softmax(T.dot(Ws2_post,T.dot(Ws1_post,T.transpose(H_post))))
P_post = T.dot(A_post,T.transpose(A_post))-danwei
f_norm_post = (P_post**2).sum()
zp_out_post = T.mean(T.dot(A_post,H_post),axis=0)
f_norm = f_norm_pre + f_norm_post
#self.zp_out = T.concatenate((zp_nn_pre.nn_out,zp_nn_post.nn_out))
self.zp_out = T.concatenate((zp_out_pre,zp_out_post))
self.zp_out_output = self.zp_out
### get sequence output for NP ###
self.np_x_post = T.tensor3("np_x")
self.np_x_postc = T.tensor3("np_x")
self.np_x_pre = T.tensor3("np_x")
self.np_x_prec = T.tensor3("np_x")
self.mask_pre = T.matrix("mask")
self.mask_prec = T.matrix("mask")
self.mask_post = T.matrix("mask")
self.mask_postc = T.matrix("mask")
self.np_nn_pre = sub_LSTM_batch(embedding_dimention,n_hidden,self.np_x_pre,self.np_x_prec,self.mask_pre,self.mask_prec)
self.params += self.np_nn_pre.params
self.np_nn_post = sub_LSTM_batch(embedding_dimention,n_hidden,self.np_x_post,self.np_x_postc,self.mask_post,self.mask_postc)
self.params += self.np_nn_post.params
self.np_nn_post_output = self.np_nn_post.nn_out
self.np_nn_pre_output = self.np_nn_pre.nn_out
self.np_out = T.concatenate((self.np_nn_post_output,self.np_nn_pre_output),axis=1)
#np_nn_f = LSTM(n_hidden*2,n_hidden*2,self.np_out)
#self.params += np_nn_f.params
#np_nn_b = LSTM(n_hidden*2,n_hidden*2,self.np_out[::-1])
#self.params += np_nn_b.params
#self.bi_np_out = T.concatenate((np_nn_f.all_hidden,np_nn_b.all_hidden[::-1]),axis=1)
#self.np_out_output = self.bi_np_out
#self.get_np_out = theano.function(inputs=[self.np_x_pre,self.np_x_prec,self.np_x_post,self.np_x_postc,self.mask_pre,self.mask_prec,self.mask_post,self.mask_postc],outputs=[self.np_out_output])
self.feature = T.matrix("feature")
self.feature_layer = Layer(feature_dimention,n_hidden,self.feature,repre_active)
self.params += self.feature_layer.params
w_attention_zp,b_attention = init_weight(n_hidden*2,1,pre="attention_zp",ones=False)
self.params += [w_attention_zp,b_attention]
w_attention_np,b_u = init_weight(n_hidden*2,1,pre="attention_np",ones=False)
self.params += [w_attention_np]
#w_attention_np_rnn,b_u = init_weight(n_hidden*4,1,pre="attention_np_rnn",ones=False)
#self.params += [w_attention_np_rnn]
w_attention_feature,b_u = init_weight(n_hidden,1,pre="attention_feature",ones=False)
self.params += [w_attention_feature]
self.calcu_attention = tanh(T.dot(self.zp_out_output,w_attention_zp) + T.dot(self.np_out,w_attention_np) + T.dot(self.feature_layer.output,w_attention_feature) + b_attention)
#self.calcu_attention = tanh(T.dot(self.np_out_output,w_attention_np_rnn) + T.dot(self.zp_out_output,w_attention_zp) + T.dot(self.np_out,w_attention_np) + T.dot(self.feature_layer.output,w_attention_feature) + b_attention)
#self.calcu_attention = tanh(T.dot(self.np_out_output,w_attention_np_rnn) + T.dot(self.zp_out_output,w_attention_zp) + T.dot(self.np_out,w_attention_np) + b_attention)
self.attention = softmax(T.transpose(self.calcu_attention,axes=(1,0)))[0]
self.out = self.attention
self.get_out = theano.function(inputs=[self.zp_x_pre,self.zp_x_post,self.np_x_pre,self.np_x_prec,self.np_x_post,self.np_x_postc,self.mask_pre,self.mask_prec,self.mask_post,self.mask_postc,self.feature],outputs=[self.out],on_unused_input='warn')
l1_norm_squared = sum([(w**2).sum() for w in self.params])
l2_norm_squared = sum([(abs(w)).sum() for w in self.params])
lmbda_l1 = 0.0
#lmbda_l2 = 0.001
lmbda_l2 = 0.0
t = T.bvector()
cost = -(T.log((self.out*t).sum())) + f_norm
lr = T.scalar()
updates = lasagne.updates.sgd(cost, self.params, lr)
#updates = lasagne.updates.adadelta(cost, self.params)
self.train_step = theano.function(
inputs=[self.zp_x_pre,self.zp_x_post,self.np_x_pre,self.np_x_prec,self.np_x_post,self.np_x_postc,self.mask_pre,self.mask_prec,self.mask_post,self.mask_postc,self.feature,t,lr],
outputs=[cost],
on_unused_input='warn',
updates=updates)
def __init__(
self,
rng,
batchsize=100,
activation=relu
):
import char_load
(num_sent, char_cnt, word_cnt, max_word_len, max_sen_len, \
k_chr, k_wrd, x_chr, x_wrd, y) = char_load.read("tweets_clean.txt")
dim_word = 30
dim_char = 5
cl_word = 300
cl_char = 50
k_word = k_wrd
k_char = k_chr
data_train_word, \
data_test_word, \
data_train_char, \
data_test_char, \
target_train, \
target_test \
= train_test_split(x_wrd, x_chr, y, random_state=1234, test_size=0.1)
x_train_word = theano.shared(np.asarray(data_train_word, dtype='int16'), borrow=True)
x_train_char = theano.shared(np.asarray(data_train_char, dtype='int16'), borrow=True)
y_train = theano.shared(np.asarray(target_train, dtype='int8'), borrow=True)
x_test_word = theano.shared(np.asarray(data_test_word, dtype='int16'), borrow=True)
x_test_char = theano.shared(np.asarray(data_test_char, dtype='int16'), borrow=True)
y_test = theano.shared(np.asarray(target_test, dtype='int8'), borrow=True)
self.n_train_batches = x_train_word.get_value(borrow=True).shape[0] / batchsize
self.n_test_batches = x_test_word.get_value(borrow=True).shape[0] / batchsize
"""symbol definition"""
index = T.iscalar()
x_wrd = T.wmatrix('x_wrd')
x_chr = T.wtensor3('x_chr')
y = T.bvector('y')
train = T.iscalar('train')
"""network definition"""
layer_char_embed_input = x_chr # .reshape((batchsize, max_sen_len, max_word_len))
layer_char_embed = EmbedIDLayer(
rng,
layer_char_embed_input,
n_input=char_cnt,
n_output=dim_char
)
layer1_input = layer_char_embed.output.reshape(
(batchsize * max_sen_len, 1, max_word_len, dim_char)
)
layer1 = ConvolutionalLayer(
rng,
layer1_input,
filter_shape=(cl_char, 1, k_char, dim_char), # cl_charフィルタ数
image_shape=(batchsize * max_sen_len, 1, max_word_len, dim_char)
)
layer2 = MaxPoolingLayer(
layer1.output,
poolsize=(max_word_len - k_char + 1, 1)
)
layer_word_embed_input = x_wrd # .reshape((batchsize, max_sen_len))
layer_word_embed = EmbedIDLayer(
rng,
layer_word_embed_input,
n_input=word_cnt,
n_output=dim_word
)
layer3_word_input = layer_word_embed.output.reshape((batchsize, 1, max_sen_len, dim_word))
layer3_char_input = layer2.output.reshape((batchsize, 1, max_sen_len, cl_char))
layer3_input = T.concatenate(
[layer3_word_input,
layer3_char_input],
axis=3
) # .reshape((batchsize, 1, max_sen_len, dim_word+cl_char))
layer3 = ConvolutionalLayer(
rng,
layer3_input,
filter_shape=(cl_word, 1, k_word, dim_word + cl_char), # 1は入力チャネル数
image_shape=(batchsize, 1, max_sen_len, dim_word + cl_char),
activation=activation
)
layer4 = MaxPoolingLayer(
layer3.output,
poolsize=(max_sen_len - k_word + 1, 1)
)
layer5_input = layer4.output.reshape((batchsize, cl_word))
layer5 = FullyConnectedLayer(
rng,
dropout(rng, layer5_input, train),
n_input=cl_word,
n_output=50,
activation=activation
)
layer6_input = layer5.output
layer6 = FullyConnectedLayer(
rng,
dropout(rng, layer6_input, train, p=0.1),
n_input=50,
n_output=2,
activation=None
)
result = Result(layer6.output, y)
loss = result.negative_log_likelihood()
accuracy = result.accuracy()
params = layer6.params \
+ layer5.params \
+ layer3.params \
+ layer_word_embed.params \
+ layer1.params \
+ layer_char_embed.params
updates = RMSprop(learning_rate=0.001, params=params).updates(loss)
self.train_model = theano.function(
inputs=[index],
outputs=[loss, accuracy],
updates=updates,
givens={
x_wrd: x_train_word[index * batchsize: (index + 1) * batchsize],
x_chr: x_train_char[index * batchsize: (index + 1) * batchsize],
y: y_train[index * batchsize: (index + 1) * batchsize],
train: np.cast['int32'](1)
}
)
self.test_model = theano.function(
inputs=[index],
outputs=[loss, accuracy],
givens={
x_wrd: x_test_word[index * batchsize: (index + 1) * batchsize],
x_chr: x_test_char[index * batchsize: (index + 1) * batchsize],
y: y_test[index * batchsize: (index + 1) * batchsize],
train: np.cast['int32'](0)
}
)
def __init__(self, n_hidden, embedding_dimention=50):
##n_in: sequence lstm 的输入维度
##n_hidden: lstm for candi and zp 的隐层维度
##n_hidden_sequence: sequence lstm的隐层维度 因为要同zp的结合做dot，所以其维度要是n_hidden的2倍
## 即 n_hidden_sequence = 2 * n_hidden
self.params = []
self.zp_x_pre = T.matrix("zp_x_pre")
self.zp_x_post = T.matrix("zp_x_post")
#self.zp_x_pre_dropout = _dropout_from_layer(self.zp_x_pre)
#self.zp_x_post_dropout = _dropout_from_layer(self.zp_x_post)
zp_nn_pre = GRU(embedding_dimention, n_hidden, self.zp_x_pre)
#zp_nn_pre = LSTM(embedding_dimention,n_hidden,self.zp_x_pre_dropout)
self.params += zp_nn_pre.params
zp_nn_post = GRU(embedding_dimention, n_hidden, self.zp_x_post)
#zp_nn_post = LSTM(embedding_dimention,n_hidden,self.zp_x_post_dropout)
self.params += zp_nn_post.params
self.zp_out = T.concatenate((zp_nn_pre.nn_out, zp_nn_post.nn_out))
self.ZP_layer = Layer(n_hidden * 2, n_hidden * 2, self.zp_out, ReLU)
self.zp_out_output = self.ZP_layer.output
#self.zp_out_dropout = _dropout_from_layer(T.concatenate((zp_nn_pre.nn_out,zp_nn_post.nn_out)))
self.get_zp_out = theano.function(
inputs=[self.zp_x_pre, self.zp_x_post],
outputs=[self.ZP_layer.output])
### get sequence output for NP ###
self.np_x = T.tensor3("np_x")
self.np_x_post = T.tensor3("np_x")
self.np_x_pre = T.tensor3("np_x")
#self.np_x_dropout = _dropout_from_layer(self.np_x)
self.mask = T.matrix("mask")
self.mask_pre = T.matrix("mask")
self.mask_post = T.matrix("mask")
self.np_nn_x = RNN_batch(embedding_dimention, n_hidden, self.np_x,
self.mask)
self.params += self.np_nn_x.params
self.np_nn_pre = GRU_batch(embedding_dimention, n_hidden,
self.np_x_pre, self.mask_pre)
self.params += self.np_nn_pre.params
self.np_nn_post = GRU_batch(embedding_dimention, n_hidden,
self.np_x_post, self.mask_post)
self.params += self.np_nn_post.params
#self.np_nn_out = LSTM_batch(embedding_dimention,n_hidden*2,self.np_x,self.mask)
#self.np_nn_out = LSTM_batch(embedding_dimention,n_hidden*2,self.np_x_dropout,self.mask)
#self.params += self.np_nn_out.params
#self.np_out = self.np_nn.nn_out
self.np_nn_x_output = (self.np_nn_x.all_hidden).mean(axis=1)
self.np_nn_post_output = self.np_nn_post.nn_out
self.np_nn_pre_output = self.np_nn_pre.nn_out
self.np_out = T.concatenate(
(self.np_nn_x_output, self.np_nn_post_output,
self.np_nn_pre_output),
axis=1)
self.NP_layer = Layer(n_hidden * 3, n_hidden * 2, self.np_out, ReLU)
self.np_out_output = self.NP_layer.output
self.np_x_head = T.transpose(self.np_x, axes=(1, 0, 2))[-1]
self.get_np_head = theano.function(inputs=[self.np_x],
outputs=[self.np_x_head])
self.get_np = theano.function(inputs=[
self.np_x, self.np_x_pre, self.np_x_post, self.mask, self.mask_pre,
self.mask_post
],
outputs=[self.np_out])
self.get_np_out = theano.function(inputs=[
self.np_x, self.np_x_pre, self.np_x_post, self.mask, self.mask_pre,
self.mask_post
],
outputs=[self.np_out_output])
w_attention_zp, b_attention = init_weight(n_hidden * 2,
1,
pre="attention_hidden",
ones=False)
self.params += [w_attention_zp, b_attention]
w_attention_np, b_u = init_weight(n_hidden * 2,
1,
pre="attention_zp",
ones=False)
self.params += [w_attention_np]
self.calcu_attention = tanh(
T.dot(self.np_out_output, w_attention_np) +
T.dot(self.zp_out_output, w_attention_zp) + b_attention)
self.attention = softmax(T.transpose(self.calcu_attention,
axes=(1, 0)))[0]
self.get_attention = theano.function(inputs=[
self.zp_x_pre, self.zp_x_post, self.np_x, self.np_x_pre,
self.np_x_post, self.mask, self.mask_pre, self.mask_post
],
outputs=[self.attention])
new_zp = T.sum(self.attention[:, None] * self.np_x_head, axis=0)
self.get_new_zp = theano.function(inputs=[
self.zp_x_pre, self.zp_x_post, self.np_x, self.np_x_pre,
self.np_x_post, self.mask, self.mask_pre, self.mask_post
],
outputs=[new_zp])
#### *** HOP *** ####
self.w_hop_zp, self.b_hop_zp = init_weight(n_hidden * 2 +
embedding_dimention,
n_hidden * 2,
pre="hop_")
self.params += [self.w_hop_zp, self.b_hop_zp]
## hop 1 ##
self.zp_hop_1_init = T.concatenate(
(zp_nn_pre.nn_out, zp_nn_post.nn_out, new_zp))
self.zp_hop_1 = ReLU(
T.dot(self.zp_hop_1_init, self.w_hop_zp) + self.b_hop_zp)
self.calcu_attention_hop_1 = tanh(
T.dot(self.np_out_output, w_attention_np) +
T.dot(self.zp_hop_1, w_attention_zp) + b_attention)
self.attention_hop_1 = softmax(
T.transpose(self.calcu_attention_hop_1, axes=(1, 0)))[0]
self.get_attention_hop_1 = theano.function(
inputs=[
self.zp_x_pre, self.zp_x_post, self.np_x, self.np_x_pre,
self.np_x_post, self.mask, self.mask_pre, self.mask_post
],
outputs=[self.attention_hop_1])
self.out = self.attention_hop_1
self.get_out = theano.function(inputs=[
self.zp_x_pre, self.zp_x_post, self.np_x, self.np_x_pre,
self.np_x_post, self.mask, self.mask_pre, self.mask_post
],
outputs=[self.out])
l1_norm_squared = sum([(w**2).sum() for w in self.params])
l2_norm_squared = sum([(abs(w)).sum() for w in self.params])
lmbda_l1 = 0.0
#lmbda_l2 = 0.001
lmbda_l2 = 0.0
t = T.bvector()
cost = -(T.log((self.out * t).sum()))
#cost = -(T.log((self.out_dropout*t).sum()))
#cost = 1-((self.out*t).sum())
lr = T.scalar()
#grads = T.grad(cost, self.params)
#updates = [(param, param-lr*grad)
# for param, grad in zip(self.params, grads)]
#updates = lasagne.updates.sgd(cost, self.params, lr)
updates = lasagne.updates.adadelta(cost, self.params)
self.train_step = theano.function(inputs=[
self.zp_x_pre, self.zp_x_post, self.np_x, self.np_x_pre,
self.np_x_post, self.mask, self.mask_pre, self.mask_post, t, lr
],
outputs=[cost],
on_unused_input='warn',
updates=updates)
def build_loss(self, env_spec, policy):
obs = env_spec.observation_space.new_tensor_variable('obs',
extra_dims=1)
next_obs = env_spec.observation_space.new_tensor_variable('next_obs',
extra_dims=1)
act = env_spec.action_space.new_tensor_variable('act', extra_dims=1)
ret = T.vector('disc_n_return')
term = T.bvector('terminal')
if self.prioritized_replay:
isw = T.vector('importance_sample_weights')
z_np = np.linspace(self.V_min,
self.V_max,
policy.n_atoms,
dtype=theano.config.floatX)
z = theano.shared(z_np)
z_contracted = theano.shared(
(self.discount**self.reward_horizon) * z_np)
policy.incorporate_z(
z) # (policy sets n_atoms, but algo sets vmin,vmax)
delta_z = (self.V_max - self.V_min) / (policy.n_atoms - 1)
# Yeah this is difficult to read and know if it's right.
# (tested it vs numpy loop and numpy vectorized form in another script)
z_contracted_bc = z_contracted.dimshuffle('x', 0) # (bc: broadcast)
z_cntrct_term = (1 - term.dimshuffle(0, 'x')) * z_contracted_bc
# z_cntrct_term is 2D tensor, with contracted z-values repeated for
# each data point (each row), and zero'd wherever terminal is True
ret_bc = ret.dimshuffle(0, 'x')
z_next = T.clip(ret_bc + z_cntrct_term, self.V_min, self.V_max)
# each row (data entry) in z_next had all z_values shifted by
# corresponding return
# must compare every pair of base z atom with next z atom
z_next_bc = z_next.dimshuffle(0, 1, 'x')
z_bc = z.dimshuffle('x', 'x', 0)
abs_diff_on_delta = abs(z_next_bc - z_bc) / delta_z
projection_coeffs = T.clip(1 - abs_diff_on_delta, 0, 1) # (mostly 0's)
# projection coefficients is a 3-D tensor.
# dim-0: independent data entries (gets scanned/looped over in batched_dot)
# dim-1: corresponds to z_next atoms (gets summed over in batched_dot)
# dim-2: corresponds to base z atoms (becomes dim-1 after batched_dot)
if self.double_dqn:
next_act = policy.actions_sym(next_obs)
next_Z = policy.target_Z_at_a_sym(next_obs, next_act)
else:
next_Z = policy.target_max_Z_sym(next_obs)
# lower case z refers to the domain of atoms,
# capital Z refers to the probabilities for given state and action
# projected_next_Z = T.batched_dot(next_Z, projection_coeffs)
# NOTE: use of batched_dot somehow breaks the gradient (Theano 0.9);
# so, do the broadcasting and summing manually (until Theano 1.0)
next_Z_bc = T.shape_padright(next_Z)
next_Z_x_coeff = projection_coeffs * next_Z_bc
projected_next_Z = next_Z_x_coeff.sum(axis=1)
predicted_Z = policy.Z_at_a_sym(obs, act)
predicted_Z = T.clip(predicted_Z, 1e-6, 1) # (NaN-guard)
losses = -T.sum(projected_next_Z * T.log(predicted_Z),
axis=1) # CrossEnt
if self.prioritized_replay:
losses = isw * losses
loss = T.mean(losses)
projected_next_Z = T.clip(projected_next_Z, 1e-6, 1) # (NaN-guard)
KL_divs = T.sum(
projected_next_Z * T.log(projected_next_Z / predicted_Z),
axis=1,
)
KL_divs = T.clip(KL_divs, 1e-6, 1e6) # avoid < 0 from NaN-guard
input_list = [obs, next_obs, act, ret, term]
if self.prioritized_replay:
input_list.append(isw)
return input_list, loss, KL_divs
def fit(self, data, test=None, sample_store=10000000):
'''
Trains the network.
Parameters
--------
data : pandas.DataFrame
Training data. It contains the transactions of the sessions. It has one column for session IDs, one for item IDs and one for the timestamp of the events (unix timestamps).
It must have a header. Column names are arbitrary, but must correspond to the ones you set during the initialization of the network (session_key, item_key, time_key properties).
sample_store : int
If additional negative samples are used (n_sample > 0), the efficiency of GPU utilization can be sped up, by precomputing a large batch of negative samples (and recomputing when necessary).
This parameter regulizes the size of this precomputed ID set. Its value is the maximum number of int values (IDs) to be stored. Precomputed IDs are stored in the RAM.
For the most efficient computation, a balance must be found between storing few examples and constantly interrupting GPU computations for a short time vs. computing many examples and interrupting GPU computations for a long time (but rarely).
'''
self.predict = None
self.error_during_train = False
itemids = data[self.item_key].unique()
self.n_items = len(itemids)
self.itemidmap = pd.Series(data=np.arange(self.n_items), index=itemids)
data = pd.merge(data,
pd.DataFrame({
self.item_key: itemids,
'ItemIdx': self.itemidmap[itemids].values
}),
on=self.item_key,
how='inner')
offset_sessions = self.init_data(data)
if self.n_sample:
pop = data.groupby(self.item_key).size()
pop = pop[self.itemidmap.index.values].values**self.sample_alpha
pop = pop.cumsum() / pop.sum()
pop[-1] = 1
if sample_store:
generate_length = sample_store // self.n_sample
if generate_length <= 1:
sample_store = 0
print('No example store was used')
else:
neg_samples = self.generate_neg_samples(
pop, generate_length)
sample_pointer = 0
else:
print('No example store was used')
X = T.ivector()
Y = T.ivector()
M = T.iscalar()
R = T.bvector()
H_new, Y_pred, sparams, full_params, sidxs = self.model(
X, self.H, M, R, Y, self.dropout_p_hidden, self.dropout_p_embed)
cost = (M / self.batch_size) * self.loss_function(Y_pred, M)
params = [
self.Wx if self.embedding or self.constrained_embedding else
self.Wx[1:], self.Wh, self.Wrz, self.Bh
]
updates = self.RMSprop(cost, params, full_params, sparams, sidxs)
for i in range(len(self.H)):
updates[self.H[i]] = H_new[i]
train_function = function(inputs=[X, Y, M, R],
outputs=cost,
updates=updates,
allow_input_downcast=True)
base_order = np.argsort(
data.groupby(self.session_key)[self.time_key].min().values
) if self.time_sort else np.arange(len(offset_sessions) - 1)
data_items = data.ItemIdx.values
for epoch in range(self.n_epochs):
sc = time.clock()
st = time.time()
for i in range(len(self.layers)):
self.H[i].set_value(np.zeros((self.batch_size, self.layers[i]),
dtype=theano.config.floatX),
borrow=True)
c = []
cc = []
session_idx_arr = np.random.permutation(
len(offset_sessions) -
1) if self.train_random_order else base_order
iters = np.arange(self.batch_size)
maxiter = iters.max()
start = offset_sessions[session_idx_arr[iters]]
end = offset_sessions[session_idx_arr[iters] + 1]
finished = False
while not finished:
minlen = (end - start).min()
out_idx = data_items[start]
for i in range(minlen - 1):
in_idx = out_idx
out_idx = data_items[start + i + 1]
if self.n_sample:
if sample_store:
if sample_pointer == generate_length:
neg_samples = self.generate_neg_samples(
pop, generate_length)
sample_pointer = 0
sample = neg_samples[sample_pointer]
sample_pointer += 1
else:
sample = self.generate_neg_samples(pop, 1)
y = np.hstack([out_idx, sample])
else:
y = out_idx
if self.n_sample:
if sample_pointer == generate_length:
generate_samples()
sample_pointer = 0
sample_pointer += 1
reset = (start + i + 1 == end - 1)
cost = train_function(in_idx, y, len(iters), reset)
c.append(cost)
cc.append(len(iters))
if np.isnan(cost):
print(str(epoch) + ': NaN error!')
self.error_during_train = True
return
start = start + minlen - 1
finished_mask = (end - start <= 1)
n_finished = finished_mask.sum()
iters[finished_mask] = maxiter + np.arange(1, n_finished + 1)
maxiter += n_finished
valid_mask = (iters < len(offset_sessions) - 1)
n_valid = valid_mask.sum()
if (n_valid == 0) or (n_valid < 2 and self.n_sample == 0):
finished = True
break
mask = finished_mask & valid_mask
sessions = session_idx_arr[iters[mask]]
start[mask] = offset_sessions[sessions]
end[mask] = offset_sessions[sessions + 1]
iters = iters[valid_mask]
start = start[valid_mask]
end = end[valid_mask]
if n_valid < len(valid_mask):
for i in range(len(self.H)):
tmp = self.H[i].get_value(borrow=True)
tmp = tmp[valid_mask]
self.H[i].set_value(tmp, borrow=True)
c = np.array(c)
cc = np.array(cc)
avgc = np.sum(c * cc) / np.sum(cc)
if np.isnan(avgc):
print('Epoch {}: NaN error!'.format(str(epoch)))
self.error_during_train = True
return
print('Epoch{}\tloss: {:.6f}'.format(epoch, avgc), 'time: ',
(time.clock() - sc), 'c / ', (time.time() - st), 's')
def _init_model(self, in_size, out_size, slot_sizes, db, \
n_hid=10, learning_rate_sl=0.005, learning_rate_rl=0.005, batch_size=32, ment=0.1, \
inputtype='full', sl='e2e', rl='e2e'):
self.in_size = in_size
self.out_size = out_size
self.slot_sizes = slot_sizes
self.batch_size = batch_size
self.learning_rate = learning_rate_rl
self.n_hid = n_hid
self.r_hid = self.n_hid
self.sl = sl
self.rl = rl
table = db.table
counts = db.counts
m_unk = [db.inv_counts[s][-1] for s in dialog_config.inform_slots]
prior = [db.priors[s] for s in dialog_config.inform_slots]
unknown = [db.unks[s] for s in dialog_config.inform_slots]
ids = [db.ids[s] for s in dialog_config.inform_slots]
input_var, turn_mask, act_mask, reward_var = T.ftensor3('in'), T.bmatrix('tm'), \
T.btensor3('am'), T.fvector('r')
T_var, N_var = T.as_tensor_variable(table), T.as_tensor_variable(counts)
db_index_var = T.imatrix('db')
db_index_switch = T.bvector('s')
l_mask_in = L.InputLayer(shape=(None,None), input_var=turn_mask)
flat_mask = T.reshape(turn_mask, (turn_mask.shape[0]*turn_mask.shape[1],1))
def _smooth(p):
p_n = p+EPS
return p_n/(p_n.sum(axis=1)[:,np.newaxis])
def _add_unk(p,m,N):
# p: B x V, m- num missing, N- total, p0: 1 x V
t_unk = T.as_tensor_variable(float(m)/N)
ps = p*(1.-t_unk)
return T.concatenate([ps, T.tile(t_unk, (ps.shape[0],1))], axis=1)
def kl_divergence(p,q):
p_n = _smooth(p)
return -T.sum(q*T.log(p_n), axis=1)
# belief tracking
l_in = L.InputLayer(shape=(None,None,self.in_size), input_var=input_var)
p_vars = []
pu_vars = []
phi_vars = []
p_targets = []
phi_targets = []
hid_in_vars = []
hid_out_vars = []
bt_loss = T.as_tensor_variable(0.)
kl_loss = []
x_loss = []
self.trackers = []
for i,s in enumerate(dialog_config.inform_slots):
hid_in = T.fmatrix('h')
l_rnn = L.GRULayer(l_in, self.r_hid, hid_init=hid_in, \
mask_input=l_mask_in,
grad_clipping=10.) # B x H x D
l_b_in = L.ReshapeLayer(l_rnn,
(input_var.shape[0]*input_var.shape[1], self.r_hid)) # BH x D
hid_out = L.get_output(l_rnn)[:,-1,:]
p_targ = T.ftensor3('p_target_'+s)
p_t = T.reshape(p_targ,
(p_targ.shape[0]*p_targ.shape[1],self.slot_sizes[i]))
phi_targ = T.fmatrix('phi_target'+s)
phi_t = T.reshape(phi_targ, (phi_targ.shape[0]*phi_targ.shape[1], 1))
l_b = L.DenseLayer(l_b_in, self.slot_sizes[i],
nonlinearity=lasagne.nonlinearities.softmax)
l_phi = L.DenseLayer(l_b_in, 1,
nonlinearity=lasagne.nonlinearities.sigmoid)
phi = T.clip(L.get_output(l_phi), 0.01, 0.99)
p = L.get_output(l_b)
p_u = _add_unk(p, m_unk[i], db.N)
kl_loss.append(T.sum(flat_mask.flatten()*kl_divergence(p, p_t))/T.sum(flat_mask))
x_loss.append(T.sum(flat_mask*lasagne.objectives.binary_crossentropy(phi,phi_t))/
T.sum(flat_mask))
bt_loss += kl_loss[-1] + x_loss[-1]
p_vars.append(p)
pu_vars.append(p_u)
phi_vars.append(phi)
p_targets.append(p_targ)
phi_targets.append(phi_targ)
hid_in_vars.append(hid_in)
hid_out_vars.append(hid_out)
self.trackers.append(l_b)
self.trackers.append(l_phi)
self.bt_params = L.get_all_params(self.trackers)
def check_db(pv, phi, Tb, N):
O = T.alloc(0.,pv[0].shape[0],Tb.shape[0]) # BH x T.shape[0]
for i,p in enumerate(pv):
p_dc = T.tile(phi[i], (1, Tb.shape[0]))
O += T.log(p_dc*(1./db.table.shape[0]) + \
(1.-p_dc)*(p[:,Tb[:,i]]/N[np.newaxis,:,i]))
Op = T.exp(O)#+EPS # BH x T.shape[0]
Os = T.sum(Op, axis=1)[:,np.newaxis] # BH x 1
return Op/Os
def entropy(p):
p = _smooth(p)
return -T.sum(p*T.log(p), axis=-1)
def weighted_entropy(p,q,p0,unks,idd):
w = T.dot(idd,q.transpose()) # Pi x BH
u = p0[np.newaxis,:]*(q[:,unks].sum(axis=1)[:,np.newaxis]) # BH x Pi
p_tilde = w.transpose()+u
return entropy(p_tilde)
p_db = check_db(pu_vars, phi_vars, T_var, N_var) # BH x T.shape[0]
if inputtype=='entropy':
H_vars = [weighted_entropy(pv,p_db,prior[i],unknown[i],ids[i]) \
for i,pv in enumerate(p_vars)]
H_db = entropy(p_db)
phv = [ph[:,0] for ph in phi_vars]
t_in = T.stacklists(H_vars+phv+[H_db]).transpose() # BH x 2M+1
t_in_resh = T.reshape(t_in, (turn_mask.shape[0], turn_mask.shape[1], \
t_in.shape[1])) # B x H x 2M+1
l_in_pol = L.InputLayer(
shape=(None,None,2*len(dialog_config.inform_slots)+1), \
input_var=t_in_resh)
else:
in_reshaped = T.reshape(input_var,
(input_var.shape[0]*input_var.shape[1], \
input_var.shape[2]))
prev_act = in_reshaped[:,-len(dialog_config.inform_slots):]
t_in = T.concatenate(pu_vars+phi_vars+[p_db,prev_act],
axis=1) # BH x D-sum+A
t_in_resh = T.reshape(t_in, (turn_mask.shape[0], turn_mask.shape[1], \
t_in.shape[1])) # B x H x D-sum
l_in_pol = L.InputLayer(shape=(None,None,sum(self.slot_sizes)+ \
3*len(dialog_config.inform_slots)+ \
table.shape[0]), input_var=t_in_resh)
pol_in = T.fmatrix('pol-h')
l_pol_rnn = L.GRULayer(l_in_pol, n_hid, hid_init=pol_in,
mask_input=l_mask_in,
grad_clipping=10.) # B x H x D
pol_out = L.get_output(l_pol_rnn)[:,-1,:]
l_den_in = L.ReshapeLayer(l_pol_rnn,
(turn_mask.shape[0]*turn_mask.shape[1], n_hid)) # BH x D
l_out = L.DenseLayer(l_den_in, self.out_size, \
nonlinearity=lasagne.nonlinearities.softmax) # BH x A
self.network = l_out
self.pol_params = L.get_all_params(self.network)
self.params = self.bt_params + self.pol_params
# db loss
p_db_reshaped = T.reshape(p_db, (turn_mask.shape[0],turn_mask.shape[1],table.shape[0]))
p_db_final = p_db_reshaped[:,-1,:] # B x T.shape[0]
p_db_final = _smooth(p_db_final)
ix = T.tile(T.arange(p_db_final.shape[0]),(db_index_var.shape[1],1)).transpose()
sample_probs = p_db_final[ix,db_index_var] # B x K
if dialog_config.SUCCESS_MAX_RANK==1:
log_db_probs = T.log(sample_probs).sum(axis=1)
else:
cum_probs,_ = theano.scan(fn=lambda x, prev: x+prev, \
outputs_info=T.zeros_like(sample_probs[:,0]), \
sequences=sample_probs[:,:-1].transpose())
cum_probs = T.clip(cum_probs.transpose(), 0., 1.-1e-5) # B x K-1
log_db_probs = T.log(sample_probs).sum(axis=1) - T.log(1.-cum_probs).sum(axis=1) # B
log_db_probs = log_db_probs * db_index_switch
# rl
probs = L.get_output(self.network) # BH x A
probs = _smooth(probs)
out_probs = T.reshape(probs, (turn_mask.shape[0],turn_mask.shape[1],self.out_size)) # B x H x A
log_probs = T.log(out_probs)
act_probs = (log_probs*act_mask).sum(axis=2) # B x H
ep_probs = (act_probs*turn_mask).sum(axis=1) # B
H_probs = -T.sum(T.sum(out_probs*log_probs,axis=2),axis=1) # B
self.act_loss = -T.mean(ep_probs*reward_var)
self.db_loss = -T.mean(log_db_probs*reward_var)
self.reg_loss = -T.mean(ment*H_probs)
self.loss = self.act_loss + self.db_loss + self.reg_loss
self.inps = [input_var, turn_mask, act_mask, reward_var, db_index_var, db_index_switch, \
pol_in] + hid_in_vars
self.obj_fn = theano.function(self.inps, self.loss, on_unused_input='warn')
self.act_fn = theano.function([input_var,turn_mask,pol_in]+hid_in_vars, \
[out_probs,p_db,pol_out]+pu_vars+phi_vars+hid_out_vars, on_unused_input='warn')
self.debug_fn = theano.function(self.inps, [probs, p_db, self.loss], on_unused_input='warn')
self._rl_train_fn(self.learning_rate)
## sl
sl_loss = 0. + bt_loss - T.mean(ep_probs)
if self.sl=='e2e':
sl_updates = lasagne.updates.rmsprop(sl_loss, self.params, \
learning_rate=learning_rate_sl, epsilon=1e-4)
sl_updates_with_mom = lasagne.updates.apply_momentum(sl_updates)
elif self.sl=='bel':
sl_updates = lasagne.updates.rmsprop(sl_loss, self.bt_params, \
learning_rate=learning_rate_sl, epsilon=1e-4)
sl_updates_with_mom = lasagne.updates.apply_momentum(sl_updates)
else:
sl_updates = lasagne.updates.rmsprop(sl_loss, self.pol_params, \
learning_rate=learning_rate_sl, epsilon=1e-4)
sl_updates_with_mom = lasagne.updates.apply_momentum(sl_updates)
sl_inps = [input_var, turn_mask, act_mask, pol_in] + p_targets + phi_targets + hid_in_vars
self.sl_train_fn = theano.function(sl_inps, [sl_loss]+kl_loss+x_loss, updates=sl_updates, \
on_unused_input='warn')
self.sl_obj_fn = theano.function(sl_inps, sl_loss, on_unused_input='warn')
def __init__(self,
ne,
de,
cs,
nh,
nc,
L2_reg=0.0,
rng=np.random.RandomState()):
self.nc = nc
self.hiddenLayer = Layer(de * cs, nh, rng=rng)
self.outputLayer = Layer(nh, nc)
self.emb = theano.shared(
rng.normal(loc=0.0, scale=0.01,
size=(ne, de)).astype(theano.config.floatX))
A = rng.normal(loc=0.0, scale=0.01,
size=(nc, nc)).astype(theano.config.floatX)
self.A = theano.shared(value=A, name='A', borrow=True)
self.params = self.hiddenLayer.params + self.outputLayer.params + [
self.emb, self.A
]
self.names = ['Wh', 'bh', 'w', 'b', 'emb', 'A']
idxs = T.imatrix('idxs')
x = self.emb[idxs].reshape((idxs.shape[0], de * cs))
y = T.bvector('y')
ans = T.bvector('ans')
INF = 1e9
result, updates1 = theano.scan(fn=self.one_step,
sequences=x,
outputs_info=[
theano.shared(0.0),
theano.shared(-INF),
theano.shared(-INF),
theano.shared(-INF), None, None,
None, None
])
self.decode = theano.function(inputs=[idxs],
outputs=result,
updates=updates1)
score, updates2 = theano.scan(fn=self.two_step,
sequences=[
x,
dict(input=y, taps=[-1, 0]),
dict(input=ans, taps=[-1, 0])
],
outputs_info=theano.shared(0.0))
cost = score[-1]
gradients = T.grad(cost, self.params)
lr = T.scalar('lr')
for p, g in zip(self.params, gradients):
updates2[p] = p + lr * g
self.fit = theano.function(inputs=[idxs, y, ans, lr],
outputs=cost,
updates=updates2)
self.normalize = theano.function(
inputs=[],
updates={
self.emb:
self.emb / T.sqrt(
(self.emb**2).sum(axis=1)).dimshuffle(0, 'x')
})
def inputs(self):
return {
"call_type": tensor.bvector("call_type"),
"origin_call": tensor.ivector("origin_call"),
"origin_stand": tensor.bvector("origin_stand"),
"taxi_id": tensor.wvector("taxi_id"),
"timestamp": tensor.ivector("timestamp"),
"day_type": tensor.bvector("day_type"),
"missing_data": tensor.bvector("missing_data"),
"latitude": tensor.matrix("latitude"),
"longitude": tensor.matrix("longitude"),
"destination_latitude": tensor.vector("destination_latitude"),
"destination_longitude": tensor.vector("destination_longitude"),
"travel_time": tensor.ivector("travel_time"),
"first_k_latitude": tensor.matrix("first_k_latitude"),
"first_k_longitude": tensor.matrix("first_k_longitude"),
"last_k_latitude": tensor.matrix("last_k_latitude"),
"last_k_longitude": tensor.matrix("last_k_longitude"),
"input_time": tensor.ivector("input_time"),
"week_of_year": tensor.bvector("week_of_year"),
"day_of_week": tensor.bvector("day_of_week"),
"qhour_of_day": tensor.bvector("qhour_of_day"),
"candidate_call_type": tensor.bvector("candidate_call_type"),
"candidate_origin_call": tensor.ivector("candidate_origin_call"),
"candidate_origin_stand": tensor.bvector("candidate_origin_stand"),
"candidate_taxi_id": tensor.wvector("candidate_taxi_id"),
"candidate_timestamp": tensor.ivector("candidate_timestamp"),
"candidate_day_type": tensor.bvector("candidate_day_type"),
"candidate_missing_data": tensor.bvector("candidate_missing_data"),
"candidate_latitude": tensor.matrix("candidate_latitude"),
"candidate_longitude": tensor.matrix("candidate_longitude"),
"candidate_destination_latitude": tensor.vector("candidate_destination_latitude"),
"candidate_destination_longitude": tensor.vector("candidate_destination_longitude"),
"candidate_travel_time": tensor.ivector("candidate_travel_time"),
"candidate_first_k_latitude": tensor.matrix("candidate_first_k_latitude"),
"candidate_first_k_longitude": tensor.matrix("candidate_first_k_longitude"),
"candidate_last_k_latitude": tensor.matrix("candidate_last_k_latitude"),
"candidate_last_k_longitude": tensor.matrix("candidate_last_k_longitude"),
"candidate_input_time": tensor.ivector("candidate_input_time"),
"candidate_week_of_year": tensor.bvector("candidate_week_of_year"),
"candidate_day_of_week": tensor.bvector("candidate_day_of_week"),
"candidate_qhour_of_day": tensor.bvector("candidate_qhour_of_day"),
}
def __init__(self,
input,
in_layer_shape,
layer2_in = 1000,
n_out = 11,
use_adagrad = True,
patch_size=64,
activation=NeuralActivations.Rectifier,
layer1_nout=11,
exp_id=1,
quiet=False,
n_classes=11,
save_file=None,
mem_alloc="CPU",
momentum=1.,
enable_standardization=False,
rng=None):
"""
Initialize the parameters for the multilayer perceptron
:type rng: numpy.random.RandomState
:param rng: a random number generator used to initialize weights
:type input: theano.tensor.TensorType
:param input: symbolic variable that describes the input of the architecture (one minibatch)
:type n_in: int
:param n_in: number of input units, the dimension of the space in which the datapoints lie.
:type n_hidden: int
:param n_hidden: number of hidden units
:type in_layer_shape: list
:param in_layer_shape: the shape of the first layer - format is :
(no of patches, no of pixels per patch, no of batches, number of
hidden units for locally connected hidden layer 1)
:type layer2_in: list
:param layer2_in: No of hidden units in the second hidden layer.
:type shared_weights: use shared weights across the image
:param shared_weights: boolean parameter to enable/disable the usage of
shared weights
:type n_out: int
:param n_out: number of output units, the dimension of the space in which
the labels lie.
"""
self.input = input
if rng == None:
rng = numpy.random.RandomState(1234)
self.monitor = Monitor()
self.learning_rate = 0.001
self.exp_id = exp_id
self.y = T.bvector('y') # the labels are presented as 1D vector of int32
self.mem_alloc = mem_alloc
self.rng = rng
self.ds = Dataset()
self.n_out = n_out
self.momentum = momentum
self.save_file = save_file
self.in_layer_shape = in_layer_shape
self.layer2_in = layer2_in
self.patch_size = patch_size
self.layer1_nout = layer1_nout
self.locally_connected_layer = None
self.fully_connected_layer = None
self.activation = activation
self.n_hiddens_layer2 = (self.layer1_nout * in_layer_shape[0], layer2_in)
self.n_classes = n_classes
self.state = "train"
self.enable_standardization = enable_standardization
self.out_dir = "out/"
self.grads = []
self.test_scores = []
#Whether to turn on or off the messages.
self.quiet = quiet
self.test_set_x = None
self.valid_set_x = None
self.test_set_y = None
self.valid_set_y = None
self.setup_hidden_layers(activation, in_layer_shape,
self.n_hiddens_layer2, n_out)
self.use_adagrad = use_adagrad
#Error for patches with object in it:
self.obj_patch_error_percent = 0
# the W matrix of the inputVectors as used in [1]
targetEmbeddings = theano.shared(
np.random.uniform(-1, 1, (vocabSize, embeddingSize)))
# the W' matrix of the outputVectors as used in [1]
contextEmbeddings = theano.shared(
np.random.normal(scale=1.0 / np.sqrt(vocabSize),
size=(embeddingSize, vocabSize)))
# A |batchSize x 2| dimensional matrix, having (traget, context) pairs for
# a batch (including) -ve samples. This is the input to the training function .
targetContext = T.imatrix()
# the |batchSize x 1| vector, trainig labels (also an input to the training
# function), whether the context word matches the target word or not
isContext = T.bvector()
batchMatchScores = []
for i in range(batchSize):
matchScore = T.dot(targetEmbeddings[targetContext[i][0], :],
contextEmbeddings[:, targetContext[i][1]])
batchMatchScores.append(matchScore)
objective = isContext * T.log(T.nnet.sigmoid(batchMatchScores)) + (
1 - isContext) * T.log(1 - T.nnet.sigmoid(batchMatchScores))
loss = -T.mean(objective)
# TRAINING FUNCTION
from lasagne.updates import nesterov_momentum
inputs = [],
outputs = T.mean(T.neq(T.argmax(predictions, axis=1), Y)),
updates = updates,
givens = {
X: data,
Y: labels,
}
)
return train_model
if __name__ == '__main__':
print "Setting up memory..."
X = T.bmatrix('X')
Y = T.bvector('Y')
Ws_char_to_hidden = [ U.create_shared(U.initial_weights(CHARACTERS,HIDDEN),name='yeah%d'%i) for i in xrange(CONTEXT) ]
b_hidden = U.create_shared(U.initial_weights(HIDDEN))
W_hidden_to_hidden = U.create_shared(U.initial_weights(HIDDEN,HIDDEN))
W_hidden_to_predict = U.create_shared(U.initial_weights(HIDDEN,CHARACTERS))
b_predict = U.create_shared(U.initial_weights(CHARACTERS))
tunables = Ws_char_to_hidden + [
b_hidden,
W_hidden_to_hidden,
W_hidden_to_predict,
b_predict
]
print "Constructing graph..."
hidden_inputs = make_hidden_inputs(X,Ws_char_to_hidden,b_hidden)
hidden_outputs = make_hidden_outputs(hidden_inputs,W_hidden_to_hidden)
def policy_network(state):
input_state = InputLayer(input_var=state, shape=(None, n_input))
dense_1 = DenseLayer(input_state, num_units=n_input, nonlinearity=tanh)
dense_2 = DenseLayer(dense_1, num_units=n_input, nonlinearity=tanh)
probs = DenseLayer(dense_2, num_units=n_output, nonlinearity=softmax)
return probs
X_state = T.fmatrix()
X_action = T.bvector()
X_reward = T.fvector()
X_action_hot = to_one_hot(X_action, n_output)
prob_values = policy_network(X_state)
policy_ = get_output(prob_values)
policy = theano.function(inputs=[X_state],
outputs=policy_,
allow_input_downcast=True)
loss = categorical_crossentropy(policy_, X_action_hot) * X_reward
loss = loss.mean()
params = get_all_params(prob_values)
def __init__(self, n_hidden, embedding_dimention=50, feature_dimention=61):
##n_in: sequence lstm 的输入维度
##n_hidden: lstm for candi and zp 的隐层维度
self.params = []
self.w_embedding = init_weight_file(args.embedding,
args.embedding_dimention)
self.params.append(self.w_embedding)
self.zp_x_pre_index = T.imatrix("zp_x_pre")
self.zp_x_post_index = T.imatrix("zp_x_post")
zp_x_pre_newshape = (T.shape(self.zp_x_pre_index)[0],
args.embedding_dimention)
self.embedding_sub_zp_pre = self.w_embedding[
self.zp_x_pre_index.flatten()]
self.zp_x_pre = T.reshape(self.embedding_sub_zp_pre, zp_x_pre_newshape)
zp_x_post_newshape = (T.shape(self.zp_x_post_index)[0],
args.embedding_dimention)
self.embedding_sub_zp_post = self.w_embedding[
self.zp_x_post_index.flatten()]
self.zp_x_post = T.reshape(self.embedding_sub_zp_post,
zp_x_post_newshape)
zp_nn_pre = LSTM(embedding_dimention, n_hidden, self.zp_x_pre)
self.params += zp_nn_pre.params
zp_nn_post = LSTM(embedding_dimention, n_hidden, self.zp_x_post)
self.params += zp_nn_post.params
danwei = theano.shared(np.eye(8, dtype=theano.config.floatX))
H_pre = zp_nn_pre.all_hidden
H_post = zp_nn_post.all_hidden
Ws1_pre, heihei = init_weight(n_hidden,
n_hidden,
pre="Ws1_pre_zp",
ones=False)
Ws2_pre, heihei = init_weight(8,
n_hidden,
pre="Ws2_pre_zp",
ones=False)
self.params += [Ws1_pre, Ws2_pre]
A_pre = softmax(T.dot(Ws2_pre, T.dot(Ws1_pre, T.transpose(H_pre))))
P_pre = T.dot(A_pre, T.transpose(A_pre)) - danwei
#norm_pre, _ = theano.scan(lambda i, tmp: T.dot(P_pre[i], P_pre[i]) + tmp,
# sequences = T.arange(P_pre.shape[0]),
# outputs_info = np.asarray(0., dtype=theano.config.floatX))
#f_norm_pre = T.sum(norm_pre[-1])
f_norm_pre = (P_pre**2).sum()
zp_out_pre = T.mean(T.dot(A_pre, H_pre), axis=0)
Ws1_post, heihei = init_weight(n_hidden,
n_hidden,
pre="Ws1_post_zp",
ones=False)
Ws2_post, heihei = init_weight(8,
n_hidden,
pre="Ws2_post_zp",
ones=False)
self.params += [Ws1_post, Ws2_post]
A_post = softmax(T.dot(Ws2_post, T.dot(Ws1_post, T.transpose(H_post))))
P_post = T.dot(A_post, T.transpose(A_post)) - danwei
#norm_post, _ = theano.scan(lambda i, tmp: T.dot(P_post[i], P_post[i]) + tmp,
# sequences = T.arange(P_post.shape[0]),
# outputs_info = np.asarray(0., dtype=theano.config.floatX))
#f_norm_post = T.sum(norm_post[-1])
f_norm_post = (P_post**2).sum()
zp_out_post = T.mean(T.dot(A_post, H_post), axis=0)
f_norm = f_norm_pre + f_norm_post
#self.zp_out = T.concatenate((zp_nn_pre.nn_out,zp_nn_post.nn_out))
self.zp_out = T.concatenate((zp_out_pre, zp_out_post))
self.zp_out_output = self.zp_out
### get sequence output for NP ###
self.np_x_post_index = T.itensor3("np_x")
self.np_x_postc_index = T.itensor3("np_x")
self.np_x_pre_index = T.itensor3("np_x")
self.np_x_prec_index = T.itensor3("np_x")
np_x_post_newshape = (T.shape(self.np_x_post_index)[0],
T.shape(self.np_x_post_index)[1],
args.embedding_dimention)
self.embedding_sub_np_x_post = self.w_embedding[
self.np_x_post_index.flatten()]
self.np_x_post = T.reshape(self.embedding_sub_np_x_post,
np_x_post_newshape)
np_x_postc_newshape = (T.shape(self.np_x_postc_index)[0],
T.shape(self.np_x_postc_index)[1],
args.embedding_dimention)
self.embedding_sub_np_x_postc = self.w_embedding[
self.np_x_postc_index.flatten()]
self.np_x_postc = T.reshape(self.embedding_sub_np_x_postc,
np_x_postc_newshape)
np_x_pre_newshape = (T.shape(self.np_x_pre_index)[0],
T.shape(self.np_x_pre_index)[1],
args.embedding_dimention)
self.embedding_sub_np_x_pre = self.w_embedding[
self.np_x_pre_index.flatten()]
self.np_x_pre = T.reshape(self.embedding_sub_np_x_pre,
np_x_pre_newshape)
np_x_prec_newshape = (T.shape(self.np_x_prec_index)[0],
T.shape(self.np_x_prec_index)[1],
args.embedding_dimention)
self.embedding_sub_np_x_prec = self.w_embedding[
self.np_x_prec_index.flatten()]
self.np_x_prec = T.reshape(self.embedding_sub_np_x_prec,
np_x_prec_newshape)
self.mask_pre = T.matrix("mask")
self.mask_prec = T.matrix("mask")
self.mask_post = T.matrix("mask")
self.mask_postc = T.matrix("mask")
self.np_nn_pre = sub_LSTM_batch(embedding_dimention, n_hidden,
self.np_x_pre, self.np_x_prec,
self.mask_pre, self.mask_prec)
self.params += self.np_nn_pre.params
self.np_nn_post = sub_LSTM_batch(embedding_dimention, n_hidden,
self.np_x_post, self.np_x_postc,
self.mask_post, self.mask_postc)
self.params += self.np_nn_post.params
self.np_nn_post_output = self.np_nn_post.nn_out
self.np_nn_pre_output = self.np_nn_pre.nn_out
self.np_out = T.concatenate(
(self.np_nn_post_output, self.np_nn_pre_output), axis=1)
np_nn_f = LSTM(n_hidden * 2, n_hidden * 2, self.np_out)
self.params += np_nn_f.params
np_nn_b = LSTM(n_hidden * 2, n_hidden * 2, self.np_out[::-1])
self.params += np_nn_b.params
self.bi_np_out = T.concatenate(
(np_nn_f.all_hidden, np_nn_b.all_hidden[::-1]), axis=1)
self.np_out_output = self.bi_np_out
#self.get_np_out = theano.function(inputs=[self.np_x_pre,self.np_x_prec,self.np_x_post,self.np_x_postc,self.mask_pre,self.mask_prec,self.mask_post,self.mask_postc],outputs=[self.np_out_output])
#self.feature = T.matrix("feature")
#self.feature_layer = Layer(feature_dimention,n_hidden,self.feature,repre_active)
#self.params += self.feature_layer.params
w_attention_zp, b_attention = init_weight(n_hidden * 2,
1,
pre="attention_zp",
ones=False)
self.params += [w_attention_zp, b_attention]
w_attention_np, b_u = init_weight(n_hidden * 2,
1,
pre="attention_np",
ones=False)
#self.params += [w_attention_np]
w_attention_np_rnn, b_u = init_weight(n_hidden * 4,
1,
pre="attention_np_rnn",
ones=False)
self.params += [w_attention_np_rnn]
#np_out_dropout = _dropout_from_layer(self.np_out_output)
#zp_out_dropout = _dropout_from_layer(self.zp_out_output)
#np_dropout = _dropout_from_layer(self.np_out)
#self.calcu_attention_dropout = tanh(T.dot(np_out_dropout,w_attention_np_rnn) + T.dot(zp_out_dropout,w_attention_zp) + T.dot(np_dropout,w_attention_np) + b_attention)
#self.calcu_attention = tanh(T.dot(self.np_out_output,w_attention_np_rnn) + T.dot(self.zp_out_output,w_attention_zp) + T.dot(self.np_out,w_attention_np) + b_attention)
self.calcu_attention = tanh(
T.dot(self.np_out_output, w_attention_np_rnn) +
T.dot(self.zp_out_output, w_attention_zp) + b_attention)
self.attention = softmax(T.transpose(self.calcu_attention,
axes=(1, 0)))[0]
#self.attention_dropout = softmax(T.transpose(self.calcu_attention_dropout,axes=(1,0)))[0]
self.out = self.attention
#self.out_dropout = self.attention_dropout
self.get_out = theano.function(inputs=[
self.zp_x_pre_index, self.zp_x_post_index, self.np_x_pre_index,
self.np_x_prec_index, self.np_x_post_index, self.np_x_postc_index,
self.mask_pre, self.mask_prec, self.mask_post, self.mask_postc
],
outputs=[self.out],
on_unused_input='warn')
l1_norm_squared = sum([(w**2).sum() for w in self.params])
l2_norm_squared = sum([(abs(w)).sum() for w in self.params])
lmbda_l1 = 0.0
#lmbda_l2 = 0.001
lmbda_l2 = 0.0
t = T.bvector()
cost = -(T.log((self.out * t).sum())) + f_norm
#cost = -(T.log((self.out_dropout*t).sum()))
lr = T.scalar()
updates = lasagne.updates.sgd(cost, self.params, lr)
#updates = lasagne.updates.adadelta(cost, self.params)
self.train_step = theano.function(inputs=[
self.zp_x_pre_index, self.zp_x_post_index, self.np_x_pre_index,
self.np_x_prec_index, self.np_x_post_index, self.np_x_postc_index,
self.mask_pre, self.mask_prec, self.mask_post, self.mask_postc, t,
lr
],
outputs=[cost],
on_unused_input='warn',
updates=updates)