def get_updates(nnet, train_obj, trainable_params):
implemented_solvers = ("nesterov", "adagrad", "adadelta", "adam")
if not hasattr(nnet, "solver") or nnet.solver not in implemented_solvers:
nnet.sgd_solver = "nesterov"
else:
nnet.sgd_solver = nnet.solver
if nnet.sgd_solver == "nesterov":
updates = l_updates.nesterov_momentum(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate,
momentum=0.9)
elif nnet.sgd_solver == "adagrad":
updates = l_updates.adagrad(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate)
elif nnet.sgd_solver == "adadelta":
updates = l_updates.adadelta(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate)
elif nnet.sgd_solver == "adam":
updates = l_updates.adam(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate)
return updates
def create_train_func(layers, lr=0.01):
# dims: batch, sequence, vocabulary
X = T.tensor3('X')
X_batch = T.tensor3('X_batch')
# dims: target
y = T.ivector('y')
y_batch = T.ivector('y_batch')
y_hat = get_output(layers['l_out'], X, deterministic=False)
train_loss = T.mean(categorical_crossentropy(y_hat, y), axis=0)
params = get_all_params(layers['l_out'], trainable=True)
updates = adagrad(train_loss, params, lr)
train_func = theano.function(
inputs=[theano.In(X_batch), theano.In(y_batch)],
outputs=train_loss,
updates=updates,
givens={
X: X_batch,
y: y_batch,
},
)
return train_func
def generate_theano_func(args, network, penalty, input_dict, target_var):
prediction = get_output(network, input_dict)
# loss = T.mean( target_var * ( T.log(target_var) - prediction ))
loss = T.mean(categorical_crossentropy(prediction, target_var))
# loss += 0.0001 * sum (T.sum(layer_params ** 2) for layer_params in get_all_params(network) )
# penalty = sum ( T.sum(lstm_param**2) for lstm_param in lstm_params )
# penalty = regularize_layer_params(l_forward_1_lstm, l2)
# penalty = T.sum(lstm_param**2 for lstm_param in lstm_params)
# penalty = 0.0001 * sum (T.sum(layer_params ** 2) for layer_params in get_all_params(l_forward_1) )
loss = loss + penalty
params = get_all_params(network, trainable=True)
if args.optimizer == "sgd":
updates = sgd(loss, params, learning_rate=args.step)
elif args.optimizer == "adagrad":
updates = adagrad(loss, params, learning_rate=args.step)
elif args.optimizer == "adadelta":
updates = adadelta(loss, params, learning_rate=args.step)
elif args.optimizer == "nesterov":
updates = nesterov_momentum(loss, params, learning_rate=args.step)
elif args.optimizer == "rms":
updates = rmsprop(loss, params, learning_rate=args.step)
elif args.optimizer == "adam":
updates = adam(loss, params, learning_rate=args.step)
else:
raise "Need set optimizer correctly"
test_prediction = get_output(network, input_dict, deterministic=True)
# test_prediction = get_output(network, deterministic=True)
# test_loss = T.mean( target_var * ( T.log(target_var) - test_prediction))
test_loss = T.mean(categorical_crossentropy(test_prediction, target_var))
train_fn = theano.function(
[input1_var, input1_mask_var, input2_var, input2_mask_var, target_var],
loss,
updates=updates,
allow_input_downcast=True,
)
if args.task == "sts":
val_fn = theano.function(
[input1_var, input1_mask_var, input2_var, input2_mask_var, target_var],
[test_loss, test_prediction],
allow_input_downcast=True,
)
elif args.task == "ent":
# test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var), dtype=theano.config.floatX)
test_acc = T.mean(categorical_accuracy(test_prediction, target_var))
val_fn = theano.function(
[input1_var, input1_mask_var, input2_var, input2_mask_var, target_var],
[test_loss, test_acc],
allow_input_downcast=True,
)
return train_fn, val_fn
def build_updates(grad, params, optimization, learning_rate): """ setup optimization algorithm """ if optimization['optimizer'] == 'sgd': update_op = updates.sgd(grad, params, learning_rate=learning_rate) elif optimization['optimizer'] == 'nesterov_momentum': if momenum in optimization: momentum = optimization['momentum'] else: momentum = 0.9 update_op = updates.nesterov_momentum(grad, params, learning_rate=learning_rate, momentum=momentum) elif optimization['optimizer'] == 'adagrad': update_op = updates.adagrad(grad, params, learning_rate=learning_rate) elif optimization['optimizer'] == 'rmsprop': if 'rho' in optimization: rho = optimization['rho'] else: rho = 0.9 update_op = updates.rmsprop(grad, params, learning_rate=learning_rate, rho=rho) elif optimization['optimizer'] == 'adam': if 'beta1' in optimization: beta1 = optimization['beta1'] else: beta1 = 0.9 if 'beta2' in optimization: beta2 = optimization['beta2'] else: beta2 = 0.999 update_op = updates.adam(grad, params, learning_rate=learning_rate, beta1=beta1, beta2=beta2) return update_op
def optimize(grads, params):
if state['optim_method'] == 'adam':
updates = adam(grads, params, lrt, state['momentum'])
elif state['optim_method'] == 'adagrad':
updates = adagrad(grads, params, lrt)
elif state['optim_method'] == 'sgd':
updates = sgd(grads, params, lrt)
return updates
def adagrad_momentum(grads,
params,
learning_rate=1.0,
momentum=0.9,
epsilon=1e-06):
return apply_nesterov_momentum(adagrad(grads, params, learning_rate,
epsilon),
params=params,
momentum=momentum)
def create_iter_functions(self,
dataset,
output_layer,
X_tensor_type=T.matrix):
batch_index = T.iscalar('batch_index')
X_batch = X_tensor_type('x')
y_batch = T.ivector('y')
batch_slice = slice(batch_index * self.batch_size,
(batch_index + 1) * self.batch_size)
objective = Objective(output_layer,
loss_function=categorical_crossentropy)
loss_train = objective.get_loss(X_batch, target=y_batch)
loss_eval = objective.get_loss(X_batch,
target=y_batch,
deterministic=True)
pred = T.argmax(output_layer.get_output(X_batch, deterministic=True),
axis=1)
proba = output_layer.get_output(X_batch, deterministic=True)
accuracy = T.mean(T.eq(pred, y_batch), dtype=theano.config.floatX)
all_params = get_all_params(output_layer)
updates = adagrad(loss_train, all_params, self.lr, self.epsilon)
iter_train = theano.function(
[batch_index],
loss_train,
updates=updates,
givens={
X_batch: dataset['X_train'][batch_slice],
y_batch: dataset['y_train'][batch_slice],
},
on_unused_input='ignore',
)
iter_valid = None
if self.use_valid:
iter_valid = theano.function(
[batch_index],
[loss_eval, accuracy, proba],
givens={
X_batch: dataset['X_valid'][batch_slice],
y_batch: dataset['y_valid'][batch_slice],
},
)
return dict(train=iter_train, valid=iter_valid)
def test_workflow(self):
input_var = theano.shared(self.x)
with pm.Model() as model:
inp = layers.InputLayer(self.x.shape, input_var=input_var)
out = BayesDenseLayer(inp, 1, nonlinearity=to.identity)
pm.Normal('y', mu=get_output(out), sd=self.sd, observed=self.y)
elbo, _, upd_rng, vp = sample_elbo(model, samples=1)
upd_adam = updates.adagrad(-elbo, vp.params)
upd_rng.update(upd_adam)
step = theano.function([], elbo, updates=upd_rng)
for i in range(100):
step()
self.assertRaises(ValueError, get_output, out, deterministic=True)
preds = get_output(out, vp=vp, deterministic=True)
np.testing.assert_allclose(preds.eval(), self.y, rtol=0, atol=1)
def get_cost_updates(self, corruption_level, learning_rate, noise = 0.0, momentum=0):
""" This function computes the cost and the updates for one trainng
step of the dA """
tilde_x = self.get_corrupted_input(self.x, corruption_level, noise)
y = self.get_hidden_values(tilde_x)
z = self.get_reconstructed_input(y)
L = - T.sum(self.desired * T.log(z) + (1 - self.desired) * T.log(1 - z), axis=1)
cost = T.mean(L)
# adagrad with momentum on cost
updates_ada = adagrad(cost, self.params, learning_rate=learning_rate)
updates = apply_momentum(updates_ada, self.params, momentum=momentum)
return (cost, updates)
def get_updates(nnet, train_obj, trainable_params, solver=None):
implemented_solvers = ("sgd", "momentum", "nesterov", "adagrad", "rmsprop",
"adadelta", "adam", "adamax")
if solver not in implemented_solvers:
nnet.sgd_solver = "adam"
else:
nnet.sgd_solver = solver
if nnet.sgd_solver == "sgd":
updates = l_updates.sgd(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate)
elif nnet.sgd_solver == "momentum":
updates = l_updates.momentum(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate,
momentum=Cfg.momentum)
elif nnet.sgd_solver == "nesterov":
updates = l_updates.nesterov_momentum(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate,
momentum=Cfg.momentum)
elif nnet.sgd_solver == "adagrad":
updates = l_updates.adagrad(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate)
elif nnet.sgd_solver == "rmsprop":
updates = l_updates.rmsprop(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate,
rho=Cfg.rho)
elif nnet.sgd_solver == "adadelta":
updates = l_updates.adadelta(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate,
rho=Cfg.rho)
elif nnet.sgd_solver == "adam":
updates = l_updates.adam(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate)
elif nnet.sgd_solver == "adamax":
updates = l_updates.adamax(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate)
return updates
def create_iter_functions(self, dataset, output_layer, X_tensor_type=T.matrix):
batch_index = T.iscalar('batch_index')
X_batch = X_tensor_type('x')
y_batch = T.ivector('y')
batch_slice = slice(batch_index * self.batch_size, (batch_index + 1) * self.batch_size)
objective = Objective(output_layer, loss_function=categorical_crossentropy)
loss_train = objective.get_loss(X_batch, target=y_batch)
loss_eval = objective.get_loss(X_batch, target=y_batch, deterministic=True)
pred = T.argmax(output_layer.get_output(X_batch, deterministic=True), axis=1)
proba = output_layer.get_output(X_batch, deterministic=True)
accuracy = T.mean(T.eq(pred, y_batch), dtype=theano.config.floatX)
all_params = get_all_params(output_layer)
updates = adagrad(loss_train, all_params, self.lr, self.rho)
iter_train = theano.function(
[batch_index], loss_train,
updates=updates,
givens={
X_batch: dataset['X_train'][batch_slice],
y_batch: dataset['y_train'][batch_slice],
},
on_unused_input='ignore',
)
iter_valid = None
if self.use_valid:
iter_valid = theano.function(
[batch_index], [loss_eval, accuracy, proba],
givens={
X_batch: dataset['X_valid'][batch_slice],
y_batch: dataset['y_valid'][batch_slice],
},
)
return dict(train=iter_train, valid=iter_valid)
def __init__(self, num_units=2):
"""
:param num_units: output vector dimension
"""
def loss_func(all_predicted):
def distance_sq(x1, x2):
return T.sum(T.sqr(x1 - x2))
d1 = distance_sq(all_predicted[:, 0], all_predicted[:, 1])
d2 = distance_sq(all_predicted[:, 0], all_predicted[:, 2])
alpha = 1e-2
return T.maximum(d1 + alpha, 0) - T.maximum(d2 + alpha, 0)
super().__init__(
sound_shape=(20, 100),
num_units=num_units,
main_layer_class=Speech2VecLayer,
loss_func=loss_func,
updates_func=lambda loss, weights: adagrad(loss, weights, learning_rate=0.001)
)
def model_class(ds, paths, param_arch, param_cost, param_updates, param_train):
# create a log file containing the architecture configuration
formatter = logging.Formatter('%(message)s')
logger = logging.getLogger('log_config')
if 'start_from_epoch' in param_train:
name_tmp = 'config_from_epoch=%04d.log' % (
param_train['start_from_epoch'])
else:
name_tmp = 'config.log'
path_tmp = os.path.join(paths['exp'], name_tmp)
if not os.path.isfile(path_tmp):
handler = logging.FileHandler(
path_tmp,
mode='w') # to append at the end of the file use: mode='a'
else:
raise Exception('[e] the log file ', name_tmp, ' already exists!')
handler.setFormatter(formatter)
handler.setLevel(logging.INFO)
logger.addHandler(handler)
logger.setLevel(logging.INFO)
# input dimensions
dim_desc = ds.descs_train[0].shape[1]
dim_labels = ds.labels_train[0].shape[0]
print(dim_labels)
# architecture definition:
print(("[i] architecture definition... "), end=' ')
tic = time.time()
if param_arch['type'] == 0:
desc, patch_op, cla, net, logger = arch_class_00(
dim_desc, dim_labels, param_arch, logger)
elif param_arch['type'] == 1:
desc, patch_op, cla, net, logger = arch_class_01(
dim_desc, dim_labels, param_arch, logger)
elif param_arch['type'] == 2:
desc, patch_op, cla, net, logger = arch_class_02(
dim_desc, dim_labels, param_arch, logger)
else:
raise Exception('[e] architecture not supported!')
print(("%02.2fs" % (time.time() - tic)))
# cost function definition:
print(("[i] cost function definition... "), end=' ')
tic = time.time()
pred = LL.get_output(cla, deterministic=True) # in case we use dropout
feat = LL.get_output(net)
target = T.ivector('target')
# data term
if param_cost['cost_func'] == 'cross_entropy':
if param_arch['non_linearity'] == 'softmax':
cost_dataterm = T.mean(
LO.categorical_crossentropy(pred, target)
) # in the original code we were using *.mean() instead of T.mean(*)
elif param_arch['non_linearity'] == 'log_softmax':
cost_dataterm = T.mean(
categorical_crossentropy_logdomain(pred, target))
elif param_cost['cost_func'] == 'cross_entropy_stable':
if param_arch['non_linearity'] == 'softmax':
cost_dataterm = T.mean(
categorical_crossentropy_stable(pred, target))
else:
raise Exception(
'[e] the chosen cost function is not implemented for the chosen non-linearity!'
)
else:
raise Exception('[e] the chosen cost function is not supported!')
# classification accuracy
acc = LO.categorical_accuracy(pred, target).mean()
# regularization
cost_reg = param_cost['mu'] * LR.regularize_network_params(cla, LR.l2)
# cost function
cost = cost_dataterm + cost_reg
# get params
params = LL.get_all_params(cla)
# gradient definition
grad = T.grad(cost, params)
grad_norm = T.nlinalg.norm(T.concatenate([g.flatten() for g in grad]), 2)
print(("%02.2fs" % (time.time() - tic)))
# updates definition:
print(("[i] gradient updates definition... "), end=' ')
tic = time.time()
if param_updates['method'] == 'momentum':
if param_updates.get('learning_rate') is not None:
learning_rate = param_updates['learning_rate'] # default: 1.0
else:
raise Exception('[e] missing learning_rate parameter!')
if param_updates.get('momentum') is not None:
momentum = param_updates['momentum'] # default: 0.9
else:
raise Exception('[e] missing learning_rate parameter!')
updates = LU.momentum(grad, params, learning_rate, momentum)
elif param_updates['method'] == 'adagrad':
if param_updates.get('learning_rate') is not None:
learning_rate = param_updates['learning_rate'] # default: 1.0
else:
raise Exception('[e] missing learning_rate parameter!')
updates = LU.adagrad(grad, params, learning_rate)
elif param_updates['method'] == 'adadelta':
if param_updates.get('learning_rate') is not None:
learning_rate = param_updates['learning_rate'] # default: 1.0
else:
raise Exception('[e] missing learning_rate parameter!')
updates = LU.adadelta(grad, params, learning_rate)
elif param_updates['method'] == 'adam':
if param_updates.get('learning_rate') is not None:
learning_rate = param_updates['learning_rate'] # default: 1e-03
else:
raise Exception('[e] missing learning_rate parameter!')
if param_updates.get('beta1') is not None:
beta1 = param_updates['beta1'] # default: 0.9
else:
raise Exception('[e] missing beta1 parameter!')
if param_updates.get('beta2') is not None:
beta2 = param_updates['beta2'] # default: 0.999
else:
raise Exception('[e] missing beta2 parameter!')
if param_updates.get('epsilon') is not None:
epsilon = param_updates['epsilon'] # default: 1e-08
else:
raise Exception('[e] missing epsilon parameter!')
updates = LU.adam(grad, params, learning_rate, beta1, beta2, epsilon)
else:
raise Exception('[e] updates method not supported!')
print(("%02.2fs" % (time.time() - tic)))
# train / test functions:
funcs = dict()
print(("[i] compiling function 'train'... "), end=' ')
tic = time.time()
funcs['train'] = theano.function(
[desc.input_var, patch_op.input_var, target],
[cost, cost_dataterm, cost_reg, grad_norm, acc],
updates=updates,
allow_input_downcast=True,
on_unused_input='warn')
print(("%02.2fs" % (time.time() - tic)))
print(("[i] compiling function 'fwd'... "), end=' ')
tic = time.time()
funcs['fwd'] = theano.function(
[desc.input_var, patch_op.input_var, target], [cost, grad_norm, acc],
allow_input_downcast=True,
on_unused_input='ignore')
print(("%02.2fs" % (time.time() - tic)))
print(("[i] compiling function 'pred'... "), end=' ')
tic = time.time()
funcs['pred'] = theano.function(
[desc.input_var, patch_op.input_var, target], [pred],
allow_input_downcast=True,
on_unused_input='ignore')
print(("%02.2fs" % (time.time() - tic)))
print(("[i] compiling function 'feat'... "), end=' ')
tic = time.time()
funcs['feat'] = theano.function(
[desc.input_var, patch_op.input_var, target], [feat],
allow_input_downcast=True,
on_unused_input='ignore')
print(("%02.2fs" % (time.time() - tic)))
# save cost function parameters to a config file
logger.info('\nCost function parameters:')
logger.info(' cost function = %s' % param_cost['cost_func'])
logger.info(' mu = %e' % param_cost['mu'])
# save updates parameters to a config file
logger.info('\nUpdates parameters:')
logger.info(' method = %s' % param_updates['method'])
logger.info(' learning rate = %e' % param_updates['learning_rate'])
if param_updates['method'] == 'momentum':
logger.info(' momentum = %e' % param_updates['momentum'])
if param_updates['method'] == 'adam':
logger.info(' beta1 = %e' % param_updates['beta1'])
logger.info(' beta2 = %e' % param_updates['beta2'])
logger.info(' epsilon = %e' % param_updates['epsilon'])
# save training parameters to a config file
logger.info('\nTraining parameters:')
logger.info(' epoch size = %d' % ds.epoch_size)
return funcs, cla, updates
def run_dnn(learning_rate=0.001, dnn_strategy='mix', possitive_punishment=1):
#input_var = T.TensorType('float32', ((False,) * 3))() # Notice the () at the end
input_var = T.ftensor3('X')
target_var = T.imatrix('y')
features_type = 16
perioid = 20
features_dim = features_type * perioid
network = build_mix(input_var, 1, features_type, features_dim, perioid, activity=sigmoid)
if dnn_strategy == 'dnn':
build_dnn(input_var, 1, features_type, features_dim, perioid, activity=sigmoid)
elif dnn_strategy == 'conv1d':
build_conv1d(input_var, 1, features_type, features_dim, perioid, activity=sigmoid)
elif dnn_strategy == 'cascade':
build_cascade(input_var, 1, features_type, features_dim, perioid, activity=sigmoid)
elif dnn_strategy == 'lstm':
build_lstm(input_var, 1, features_type, features_dim, perioid, activity=sigmoid)
elif dnn_strategy == 'partitioned':
build_partitioned(input_var, 1, features_type, features_dim, perioid, activity=sigmoid)
elif dnn_strategy == 'mix':
pass
else:
raise AttributeError("This dnn_strategy is not supported!")
l_output = get_output(network)
loss = self_binary_crossentropy(l_output, target_var, possitive_punishment=possitive_punishment).mean()
train_acc = binary_accuracy(l_output, target_var).mean()
all_params = get_all_params(network, trainable=True)
updates = adagrad(loss, all_params, learning_rate=learning_rate)
train = theano.function([input_var, target_var], [loss, train_acc], updates=updates)
test_prediction = get_output(network, deterministic=True)
test_loss = self_binary_crossentropy(test_prediction, target_var, possitive_punishment=possitive_punishment).mean()
test_acc = binary_accuracy(test_prediction, target_var).mean()
#calculate win rate
win_rate_result1 = []
win_rate_result2 = []
for win_rate_threhold in [0.5, 0.6, 0.7, 0.8, 0.9]:
tmp1 = T.sum(T.switch(T.and_(T.gt(test_prediction, win_rate_threhold), T.eq(target_var, 1)), 1, 0), dtype=theano.config.floatX)
tmp2 = T.sum(T.switch(T.gt(test_prediction, win_rate_threhold), 1, 0), dtype=theano.config.floatX)
test_win_rate = (tmp1 + 0.00001) / (tmp2 + 0.00001)
win_rate_result1.append(test_win_rate)
win_rate_result2.append(tmp1)
val = theano.function([input_var, target_var], [test_prediction, test_loss, test_acc, T.as_tensor_variable(win_rate_result1), T.as_tensor_variable(win_rate_result2)])
_, _, _, _, X_train, y_train, X_val, y_val, _, _ = load_dataset('../../data/800core')
'''
test_data_list = []
test_label_list = []
for ix in range(103):
file_name = '../../data/test_dis/data_' + str(ix) + '.txt'
tmp_test_data, tmp_test_label, _, _, _, _, _, _ = load_dataset(file_name)
test_data_list.append(tmp_test_data)
test_label_list.append(tmp_test_label)
'''
num_epochs = 150
batch_size = 128
for epoch in xrange(num_epochs):
train_err = 0
train_acc = 0
train_batches = 0
start_time = time.time()
#train
for batch in iterate_minibatches(X_train, y_train, batch_size):
inputs, targets = batch
err, acc= train(inputs, targets)
train_err += err
train_acc += acc
train_batches += 1
#validate
_, val_err, val_acc, val_wr1, val_wr2 = val(X_val, y_val)
# Then we print the results for this epoch:
for ix in range(len([0.5, 0.6, 0.7, 0.8, 0.9])):
sys.stdout.write(" validation win rate :\t\t{}\n".format(val_wr1[ix]))
sys.stdout.write(" validation possitive num:\t\t{}\n".format(val_wr2[ix]))
sys.stdout.write("Epoch {} of {} took {:.3f}s\n".format(
epoch + 1, num_epochs, time.time() - start_time))
sys.stdout.write(" training loss:\t\t{}\n".format(train_err / train_batches))
sys.stdout.write(" training accuracy:\t\t{}\n".format(train_acc / train_batches))
sys.stdout.write(" validation loss:\t\t{}\n".format(val_err/1))
sys.stdout.write(" validation accuracy:\t\t{} %\n".format(val_acc * 100))
sys.stdout.write('\n')
sys.stdout.flush()
#sotre for gpu
with open('../../model/' + dnn_strategy + '/' + 'learning_rate' + str(learning_rate) + '_punishment' + str(possitive_punishment) + '_epoch' + str(epoch) + '.model', 'w') as f:
cPickle.dump(network, f, protocol=cPickle.HIGHEST_PROTOCOL)
print 'Done!'
cost_d = -T.mean(T.log(prob_T)) - T.mean(T.log(1. - prob_F)) #### generator cost_g = -T.mean(T.log(prob_F)) #### cost variables to be considered cost_str = ['E_T', 'E_F', 'P_T', 'P_F'] cost_var = [E_T, E_F, P_T, P_F] ############################ # Gradient & Optimization ############################ #### Parameter updates # updates_d = sgd(cost_d, params_d, lr) # updates_g = sgd(cost_g, params_g, lr) updates_d = adagrad(cost_d, params_d, lr) updates_g = adagrad(cost_g, params_g, lr) #### Barchnorm state updates batchnorm_updates_d = disc_model.get_updates() print '# D batchnorm updates: %d' % (len(batchnorm_updates_d)) batchnorm_updates_g = gen_model.get_updates() print '# G batchnorm updates: %d' % (len(batchnorm_updates_g)) #### Collect all updates updates_d.update(batchnorm_updates_d) updates_g.update(batchnorm_updates_g) updates_all = OrderedDict() updates_all.update(updates_d)
def multi_task_classifier(args, input_var, target_var, wordEmbeddings, seqlen, num_feats, lambda_val = 0.5 * 1e-4):
print("Building multi task model with 1D Convolution")
vocab_size = wordEmbeddings.shape[1]
wordDim = wordEmbeddings.shape[0]
kw = 2
num_filters = seqlen-kw+1
stride = 1
filter_size=wordDim
pool_size=num_filters
input = InputLayer((None, seqlen, num_feats),input_var=input_var)
batchsize, _, _ = input.input_var.shape
emb = EmbeddingLayer(input, input_size=vocab_size, output_size=wordDim, W=wordEmbeddings.T)
reshape = ReshapeLayer(emb, (batchsize, seqlen, num_feats*wordDim))
conv1d_1 = DimshuffleLayer(Conv1DLayer(reshape, num_filters=num_filters, filter_size=wordDim, stride=1,
nonlinearity=tanh,W=GlorotUniform()), (0,2,1))
maxpool_1 = MaxPool1DLayer(conv1d_1, pool_size=pool_size)
hid_1 = DenseLayer(maxpool_1, num_units=args.hiddenDim, nonlinearity=sigmoid)
network_1 = DenseLayer(hid_1, num_units=2, nonlinearity=softmax)
conv1d_2 = DimshuffleLayer(Conv1DLayer(reshape, num_filters=num_filters, filter_size=wordDim, stride=1,
nonlinearity=tanh,W=GlorotUniform()), (0,2,1))
maxpool_2 = MaxPool1DLayer(conv1d_2, pool_size=pool_size)
hid_2 = DenseLayer(maxpool_2, num_units=args.hiddenDim, nonlinearity=sigmoid)
network_2 = DenseLayer(hid_2, num_units=4, nonlinearity=softmax)
conv1d_3 = DimshuffleLayer(Conv1DLayer(reshape, num_filters=num_filters, filter_size=wordDim, stride=1,
nonlinearity=tanh,W=GlorotUniform()), (0,2,1))
maxpool_3 = MaxPool1DLayer(conv1d_3, pool_size=pool_size)
hid_3 = DenseLayer(maxpool_3, num_units=args.hiddenDim, nonlinearity=sigmoid)
network_3 = DenseLayer(hid_3, num_units=3, nonlinearity=softmax)
conv1d_4 = DimshuffleLayer(Conv1DLayer(reshape, num_filters=num_filters, filter_size=wordDim, stride=1,
nonlinearity=tanh,W=GlorotUniform()), (0,2,1))
maxpool_4 = MaxPool1DLayer(conv1d_4, pool_size=pool_size)
hid_4 = DenseLayer(maxpool_4, num_units=args.hiddenDim, nonlinearity=sigmoid)
network_4 = DenseLayer(hid_4, num_units=3, nonlinearity=softmax)
conv1d_5 = DimshuffleLayer(Conv1DLayer(reshape, num_filters=num_filters, filter_size=wordDim, stride=1,
nonlinearity=tanh,W=GlorotUniform()), (0,2,1))
maxpool_5 = MaxPool1DLayer(conv1d_5, pool_size=pool_size)
hid_5 = DenseLayer(maxpool_5, num_units=args.hiddenDim, nonlinearity=sigmoid)
network_5 = DenseLayer(hid_5, num_units=2, nonlinearity=softmax)
conv1d_6 = DimshuffleLayer(Conv1DLayer(reshape, num_filters=num_filters, filter_size=wordDim, stride=1,
nonlinearity=tanh,W=GlorotUniform()), (0,2,1))
maxpool_6 = MaxPool1DLayer(conv1d_6, pool_size=pool_size)
hid_6 = DenseLayer(maxpool_6, num_units=args.hiddenDim, nonlinearity=sigmoid)
network_6 = DenseLayer(hid_6, num_units=4, nonlinearity=softmax)
conv1d_7 = DimshuffleLayer(Conv1DLayer(reshape, num_filters=num_filters, filter_size=wordDim, stride=1,
nonlinearity=tanh,W=GlorotUniform()), (0,2,1))
maxpool_7 = MaxPool1DLayer(conv1d_7, pool_size=pool_size)
hid_7 = DenseLayer(maxpool_7, num_units=args.hiddenDim, nonlinearity=sigmoid)
network_7 = DenseLayer(hid_7, num_units=3, nonlinearity=softmax)
conv1d_8 = DimshuffleLayer(Conv1DLayer(reshape, num_filters=num_filters, filter_size=wordDim, stride=1,
nonlinearity=tanh,W=GlorotUniform()), (0,2,1))
maxpool_8 = MaxPool1DLayer(conv1d_8, pool_size=pool_size)
hid_8 = DenseLayer(maxpool_8, num_units=args.hiddenDim, nonlinearity=sigmoid)
network_8 = DenseLayer(hid_8, num_units=3, nonlinearity=softmax)
# Is this important?
network_1_out, network_2_out, network_3_out, network_4_out, \
network_5_out, network_6_out, network_7_out, network_8_out = \
get_output([network_1, network_2, network_3, network_4, network_5, network_6, network_7, network_8])
loss_1 = T.mean(binary_crossentropy(network_1_out,target_var)) + regularize_layer_params_weighted({emb:lambda_val, conv1d_1:lambda_val,
hid_1:lambda_val, network_1:lambda_val} , l2)
updates_1 = adagrad(loss_1, get_all_params(network_1, trainable=True), learning_rate=args.step)
train_fn_1 = theano.function([input_var, target_var], loss_1, updates=updates_1, allow_input_downcast=True)
val_acc_1 = T.mean(binary_accuracy(get_output(network_1, deterministic=True), target_var))
val_fn_1 = theano.function([input_var, target_var], val_acc_1, allow_input_downcast=True)
loss_2 = T.mean(categorical_crossentropy(network_2_out,target_var)) + regularize_layer_params_weighted({emb:lambda_val, conv1d_2:lambda_val,
hid_2:lambda_val, network_2:lambda_val} , l2)
updates_2 = adagrad(loss_2, get_all_params(network_2, trainable=True), learning_rate=args.step)
train_fn_2 = theano.function([input_var, target_var], loss_2, updates=updates_2, allow_input_downcast=True)
val_acc_2 = T.mean(categorical_accuracy(get_output(network_2, deterministic=True), target_var))
val_fn_2 = theano.function([input_var, target_var], val_acc_2, allow_input_downcast=True)
loss_3 = T.mean(categorical_crossentropy(network_3_out,target_var)) + regularize_layer_params_weighted({emb:lambda_val, conv1d_3:lambda_val,
hid_3:lambda_val, network_3:lambda_val} , l2)
updates_3 = adagrad(loss_3, get_all_params(network_3, trainable=True), learning_rate=args.step)
train_fn_3 = theano.function([input_var, target_var], loss_3, updates=updates_3, allow_input_downcast=True)
val_acc_3 = T.mean(categorical_accuracy(get_output(network_3, deterministic=True), target_var))
val_fn_3 = theano.function([input_var, target_var], val_acc_3, allow_input_downcast=True)
loss_4 = T.mean(categorical_crossentropy(network_4_out,target_var)) + regularize_layer_params_weighted({emb:lambda_val, conv1d_4:lambda_val,
hid_4:lambda_val, network_4:lambda_val} , l2)
updates_4 = adagrad(loss_4, get_all_params(network_4, trainable=True), learning_rate=args.step)
train_fn_4 = theano.function([input_var, target_var], loss_4, updates=updates_4, allow_input_downcast=True)
val_acc_4 = T.mean(categorical_accuracy(get_output(network_4, deterministic=True), target_var))
val_fn_4 = theano.function([input_var, target_var], val_acc_4, allow_input_downcast=True)
loss_5 = T.mean(binary_crossentropy(network_5_out,target_var)) + regularize_layer_params_weighted({emb:lambda_val, conv1d_5:lambda_val,
hid_5:lambda_val, network_5:lambda_val} , l2)
updates_5 = adagrad(loss_5, get_all_params(network_5, trainable=True), learning_rate=args.step)
train_fn_5 = theano.function([input_var, target_var], loss_5, updates=updates_5, allow_input_downcast=True)
val_acc_5 = T.mean(binary_accuracy(get_output(network_5, deterministic=True), target_var))
val_fn_5 = theano.function([input_var, target_var], val_acc_5, allow_input_downcast=True)
loss_6 = T.mean(categorical_crossentropy(network_6_out,target_var)) + regularize_layer_params_weighted({emb:lambda_val, conv1d_6:lambda_val,
hid_6:lambda_val, network_6:lambda_val} , l2)
updates_6 = adagrad(loss_6, get_all_params(network_6, trainable=True), learning_rate=args.step)
train_fn_6 = theano.function([input_var, target_var], loss_6, updates=updates_6, allow_input_downcast=True)
val_acc_6 = T.mean(categorical_accuracy(get_output(network_6, deterministic=True), target_var))
val_fn_6 = theano.function([input_var, target_var], val_acc_6, allow_input_downcast=True)
loss_7 = T.mean(categorical_crossentropy(network_7_out,target_var)) + regularize_layer_params_weighted({emb:lambda_val, conv1d_7:lambda_val,
hid_7:lambda_val, network_7:lambda_val} , l2)
updates_7 = adagrad(loss_7, get_all_params(network_7, trainable=True), learning_rate=args.step)
train_fn_7 = theano.function([input_var, target_var], loss_7, updates=updates_7, allow_input_downcast=True)
val_acc_7 = T.mean(categorical_accuracy(get_output(network_7, deterministic=True), target_var))
val_fn_7 = theano.function([input_var, target_var], val_acc_7, allow_input_downcast=True)
loss_8 = T.mean(categorical_crossentropy(network_8_out,target_var)) + regularize_layer_params_weighted({emb:lambda_val, conv1d_8:lambda_val,
hid_8:lambda_val, network_8:lambda_val} , l2)
updates_8 = adagrad(loss_8, get_all_params(network_8, trainable=True), learning_rate=args.step)
train_fn_8 = theano.function([input_var, target_var], loss_8, updates=updates_8, allow_input_downcast=True)
val_acc_8 = T.mean(categorical_accuracy(get_output(network_8, deterministic=True), target_var))
val_fn_8 = theano.function([input_var, target_var], val_acc_8, allow_input_downcast=True)
return train_fn_1, val_fn_1, network_1, train_fn_2, val_fn_2, network_2, train_fn_3, val_fn_3, \
network_3, train_fn_4, val_fn_4, network_4, train_fn_5, val_fn_5, network_5, \
train_fn_6, val_fn_6, network_6, train_fn_7, val_fn_7, network_7, train_fn_8, val_fn_8, network_8
def event_span_classifier(args, input_var, input_mask_var, target_var, wordEmbeddings, seqlen):
print("Building model with LSTM")
vocab_size = wordEmbeddings.shape[1]
wordDim = wordEmbeddings.shape[0]
GRAD_CLIP = wordDim
args.lstmDim = 150
input = InputLayer((None, seqlen),input_var=input_var)
batchsize, seqlen = input.input_var.shape
input_mask = InputLayer((None, seqlen),input_var=input_mask_var)
emb = EmbeddingLayer(input, input_size=vocab_size, output_size=wordDim, W=wordEmbeddings.T)
#emb.params[emb_1.W].remove('trainable')
lstm = LSTMLayer(emb, num_units=args.lstmDim, mask_input=input_mask, grad_clipping=GRAD_CLIP,
nonlinearity=tanh)
lstm_back = LSTMLayer(
emb, num_units=args.lstmDim, mask_input=input_mask, grad_clipping=GRAD_CLIP,
nonlinearity=tanh, backwards=True)
slice_forward = SliceLayer(lstm, indices=-1, axis=1) # out_shape (None, args.lstmDim)
slice_backward = SliceLayer(lstm_back, indices=0, axis=1) # out_shape (None, args.lstmDim)
concat = ConcatLayer([slice_forward, slice_backward])
hid = DenseLayer(concat, num_units=args.hiddenDim, nonlinearity=sigmoid)
network = DenseLayer(hid, num_units=2, nonlinearity=softmax)
prediction = get_output(network)
loss = T.mean(binary_crossentropy(prediction,target_var))
lambda_val = 0.5 * 1e-4
layers = {emb:lambda_val, lstm:lambda_val, hid:lambda_val, network:lambda_val}
penalty = regularize_layer_params_weighted(layers, l2)
loss = loss + penalty
params = get_all_params(network, trainable=True)
if args.optimizer == "sgd":
updates = sgd(loss, params, learning_rate=args.step)
elif args.optimizer == "adagrad":
updates = adagrad(loss, params, learning_rate=args.step)
elif args.optimizer == "adadelta":
updates = adadelta(loss, params, learning_rate=args.step)
elif args.optimizer == "nesterov":
updates = nesterov_momentum(loss, params, learning_rate=args.step)
elif args.optimizer == "rms":
updates = rmsprop(loss, params, learning_rate=args.step)
elif args.optimizer == "adam":
updates = adam(loss, params, learning_rate=args.step)
else:
raise "Need set optimizer correctly"
test_prediction = get_output(network, deterministic=True)
test_loss = T.mean(binary_crossentropy(test_prediction,target_var))
train_fn = theano.function([input_var, input_mask_var,target_var],
loss, updates=updates, allow_input_downcast=True)
test_acc = T.mean(binary_accuracy(test_prediction, target_var))
val_fn = theano.function([input_var, input_mask_var, target_var], [test_loss, test_acc], allow_input_downcast=True)
return train_fn, val_fn, network
l4=MaxPool2DLayer(l3,(2,2))
l5=Conv2DLayer(l4,96,(5,5),nonlinearity=very_leaky_rectify,W=GlorotUniform('relu'))
l6=MaxPool2DLayer(l5,(3,3))
l7=DenseLayer(l6,512,nonlinearity=very_leaky_rectify,W=lasagne.init.GlorotNormal())
#l7_5=cyclicpool(l7)
#l7_5=lasagne.layers.DropoutLayer(l7)
l8=DenseLayer(l7,2,nonlinearity=softmax)
rate=theano.shared(.0002)
params = lasagne.layers.get_all_params(l8)
prediction = lasagne.layers.get_output(l8)
loss = lasagne.objectives.categorical_crossentropy(prediction,y1)
loss = loss.mean()
updates_sgd = adagrad(loss, params, learning_rate=rate)
updates = apply_nesterov_momentum(updates_sgd, params, momentum=0.9)
train_model = theano.function([x1,y1],outputs=loss,updates=updates)
pred = theano.function([x1,y1],outputs=lasagne.objectives.categorical_crossentropy(prediction,y1))
# pred=theano.function([x1,y1],outputs=prediction,on_unused_input='ignore')
### begin to train
renewtrain=len(train_x)/batchsize
renewtest=len(test_x)/batchsize
for i in range(15000):
if i>325 and i<3000:
rate.set_value(.001)
def event_span_classifier(args, input_var, target_var, wordEmbeddings, seqlen, num_feats):
print("Building model with 1D Convolution")
vocab_size = wordEmbeddings.shape[1]
wordDim = wordEmbeddings.shape[0]
kw = 2
num_filters = seqlen-kw+1
stride = 1
#important context words as channels
#CNN_sentence config
filter_size=wordDim
pool_size=seqlen-filter_size+1
input = InputLayer((None, seqlen, num_feats),input_var=input_var)
batchsize, _, _ = input.input_var.shape
emb = EmbeddingLayer(input, input_size=vocab_size, output_size=wordDim, W=wordEmbeddings.T)
#emb.params[emb.W].remove('trainable') #(batchsize, seqlen, wordDim)
#print get_output_shape(emb)
reshape = ReshapeLayer(emb, (batchsize, seqlen, num_feats*wordDim))
#print get_output_shape(reshape)
conv1d = Conv1DLayer(reshape, num_filters=num_filters, filter_size=wordDim, stride=1,
nonlinearity=tanh,W=GlorotUniform()) #nOutputFrame = num_flters,
#nOutputFrameSize = (num_feats*wordDim-filter_size)/stride +1
#print get_output_shape(conv1d)
conv1d = DimshuffleLayer(conv1d, (0,2,1))
#print get_output_shape(conv1d)
pool_size=num_filters
maxpool = MaxPool1DLayer(conv1d, pool_size=pool_size)
#print get_output_shape(maxpool)
#forward = FlattenLayer(maxpool)
#print get_output_shape(forward)
hid = DenseLayer(maxpool, num_units=args.hiddenDim, nonlinearity=sigmoid)
network = DenseLayer(hid, num_units=2, nonlinearity=softmax)
prediction = get_output(network)
loss = T.mean(binary_crossentropy(prediction,target_var))
lambda_val = 0.5 * 1e-4
layers = {emb:lambda_val, conv1d:lambda_val, hid:lambda_val, network:lambda_val}
penalty = regularize_layer_params_weighted(layers, l2)
loss = loss + penalty
params = get_all_params(network, trainable=True)
if args.optimizer == "sgd":
updates = sgd(loss, params, learning_rate=args.step)
elif args.optimizer == "adagrad":
updates = adagrad(loss, params, learning_rate=args.step)
elif args.optimizer == "adadelta":
updates = adadelta(loss, params, learning_rate=args.step)
elif args.optimizer == "nesterov":
updates = nesterov_momentum(loss, params, learning_rate=args.step)
elif args.optimizer == "rms":
updates = rmsprop(loss, params, learning_rate=args.step)
elif args.optimizer == "adam":
updates = adam(loss, params, learning_rate=args.step)
else:
raise "Need set optimizer correctly"
test_prediction = get_output(network, deterministic=True)
test_loss = T.mean(binary_crossentropy(test_prediction,target_var))
train_fn = theano.function([input_var, target_var],
loss, updates=updates, allow_input_downcast=True)
test_acc = T.mean(binary_accuracy(test_prediction, target_var))
val_fn = theano.function([input_var, target_var], [test_loss, test_acc], allow_input_downcast=True)
return train_fn, val_fn, network
def build_network_2dconv(args, input_var, target_var, wordEmbeddings, maxlen=60):
print("Building model with 2D Convolution")
vocab_size = wordEmbeddings.shape[1]
wordDim = wordEmbeddings.shape[0]
num_filters = 100
stride = 1
# CNN_sentence config
filter_size = (3, wordDim)
pool_size = (maxlen - 3 + 1, 1)
input = InputLayer((None, maxlen), input_var=input_var)
batchsize, seqlen = input.input_var.shape
emb = EmbeddingLayer(input, input_size=vocab_size, output_size=wordDim, W=wordEmbeddings.T)
emb.params[emb.W].remove("trainable") # (batchsize, maxlen, wordDim)
reshape = ReshapeLayer(emb, (batchsize, 1, maxlen, wordDim))
conv2d = Conv2DLayer(
reshape,
num_filters=num_filters,
filter_size=(filter_size),
stride=stride,
nonlinearity=rectify,
W=GlorotUniform(),
) # (None, 100, 34, 1)
maxpool = MaxPool2DLayer(conv2d, pool_size=pool_size) # (None, 100, 1, 1)
forward = FlattenLayer(maxpool) # (None, 100) #(None, 50400)
hid = DenseLayer(forward, num_units=args.hiddenDim, nonlinearity=sigmoid)
network = DenseLayer(hid, num_units=2, nonlinearity=softmax)
prediction = get_output(network)
loss = T.mean(binary_crossentropy(prediction, target_var))
lambda_val = 0.5 * 1e-4
layers = {conv2d: lambda_val, hid: lambda_val, network: lambda_val}
penalty = regularize_layer_params_weighted(layers, l2)
loss = loss + penalty
params = get_all_params(network, trainable=True)
if args.optimizer == "sgd":
updates = sgd(loss, params, learning_rate=args.step)
elif args.optimizer == "adagrad":
updates = adagrad(loss, params, learning_rate=args.step)
elif args.optimizer == "adadelta":
updates = adadelta(loss, params, learning_rate=args.step)
elif args.optimizer == "nesterov":
updates = nesterov_momentum(loss, params, learning_rate=args.step)
elif args.optimizer == "rms":
updates = rmsprop(loss, params, learning_rate=args.step)
elif args.optimizer == "adam":
updates = adam(loss, params, learning_rate=args.step)
else:
raise "Need set optimizer correctly"
test_prediction = get_output(network, deterministic=True)
test_loss = T.mean(binary_crossentropy(test_prediction, target_var))
train_fn = theano.function([input_var, target_var], loss, updates=updates, allow_input_downcast=True)
test_acc = T.mean(binary_accuracy(test_prediction, target_var))
val_fn = theano.function([input_var, target_var], [test_loss, test_acc], allow_input_downcast=True)
return train_fn, val_fn
def marginal_likelihood(x, y, n_epochs=100, lrate=.1):
gp_params = np.array([2., 10.])
indep_noise = 0.3
#gp_params = np.array([.4966, 148.41])
#indep_noise = .0821
t_log_gp_params = theano.shared(np.log(gp_params))
t_log_indep_noise = theano.shared(np.log(indep_noise))
def print_params():
print t_log_gp_params.get_value()
print t_log_indep_noise.get_value()
print_params()
t_x = T.vector('x')
t_y = T.vector('y')
t_gp_params = T.exp(t_log_gp_params)
t_indep_noise = T.exp(t_log_indep_noise)
x_col = t_x.dimshuffle(0, 'x')
x_row = t_x.dimshuffle('x', 0)
K = t_gp_params[0] * T.exp(-t_gp_params[1] * T.sqr(x_col - x_row))
K = K + t_indep_noise * T.identity_like(K)
y_Kinv_y = t_y.dot(T.nlinalg.matrix_inverse(K)).dot(t_y)
#logdetK = T.log(T.nlinalg.det(K))
logdetK = logabsdet(K)
marginal_ll = -0.5 * (y_Kinv_y + logdetK)
loss = -marginal_ll
if batch_update:
loss_fn = theano.function([t_x, t_y], loss)
grad_loss_fn = theano.function(
[t_x, t_y], theano.grad(loss,
[t_log_gp_params, t_log_indep_noise]))
n_params = len(gp_params) + 1
def f_df(params):
a, b, c = params
t_log_gp_params.set_value([a, b])
t_log_indep_noise.set_value(c)
total_loss = 0
grad = np.zeros(n_params)
for each_x, each_y in izip(x, y):
total_loss += loss_fn(each_x, each_y)
grad += np.append(*grad_loss_fn(each_x, each_y))
return total_loss, grad
init_params = np.r_[gp_params, indep_noise]
opt = fmin_l_bfgs_b(
f_df,
init_params,
factr=1e3,
pgtol=1e-07,
disp=1,
)[0]
print opt
opt_gp_params = opt[:-1]
opt_indep_noise = opt[-1]
else:
# Stochastic optimization
updates = adagrad(loss, [t_log_gp_params, t_log_indep_noise],
learning_rate=lrate)
loss_fn = theano.function([t_x, t_y], loss, updates=updates)
grad_loss_fn = theano.function(
[t_x, t_y], theano.grad(loss,
[t_log_gp_params, t_log_indep_noise]))
for i in xrange(n_epochs):
count = 1
trace = []
sys.stderr.write('%4d ' % i)
for each_x, each_y in izip(x, y):
val = loss_fn(each_x, each_y)
trace.append(val)
count += 1
if count % 20 == 0:
sys.stderr.write('.')
print
print np.mean(trace), np.std(trace)
print_params()
opt_gp_params = t_log_gp_params.get_value()
opt_indep_noise = t_log_indep_noise.get_value()
return opt_gp_params, opt_indep_noise
def multi_task_classifier(args,
input_var,
target_var,
wordEmbeddings,
seqlen,
num_feats,
lambda_val=0.5 * 1e-4):
print("Building multi task model with 1D Convolution")
vocab_size = wordEmbeddings.shape[1]
wordDim = wordEmbeddings.shape[0]
kw = 2
num_filters = seqlen - kw + 1
stride = 1
filter_size = wordDim
pool_size = num_filters
input = InputLayer((None, seqlen, num_feats), input_var=input_var)
batchsize, _, _ = input.input_var.shape
#span
emb1 = EmbeddingLayer(input,
input_size=vocab_size,
output_size=wordDim,
W=wordEmbeddings.T)
reshape1 = ReshapeLayer(emb1, (batchsize, seqlen, num_feats * wordDim))
conv1d_1 = DimshuffleLayer(
Conv1DLayer(reshape1,
num_filters=num_filters,
filter_size=wordDim,
stride=1,
nonlinearity=tanh,
W=GlorotUniform()), (0, 2, 1))
maxpool_1 = MaxPool1DLayer(conv1d_1, pool_size=pool_size)
hid_1 = DenseLayer(maxpool_1,
num_units=args.hiddenDim,
nonlinearity=sigmoid)
network_1 = DenseLayer(hid_1, num_units=2, nonlinearity=softmax)
"""
#DocTimeRel
emb2 = EmbeddingLayer(input, input_size=vocab_size, output_size=wordDim, W=wordEmbeddings.T)
reshape2 = ReshapeLayer(emb2, (batchsize, seqlen, num_feats*wordDim))
conv1d_2 = DimshuffleLayer(Conv1DLayer(reshape2, num_filters=num_filters, filter_size=wordDim, stride=1,
nonlinearity=tanh,W=GlorotUniform()), (0,2,1))
maxpool_2 = MaxPool1DLayer(conv1d_2, pool_size=pool_size)
hid_2 = DenseLayer(maxpool_2, num_units=args.hiddenDim, nonlinearity=sigmoid)
network_2 = DenseLayer(hid_2, num_units=5, nonlinearity=softmax)
"""
#Type
emb3 = EmbeddingLayer(input,
input_size=vocab_size,
output_size=wordDim,
W=wordEmbeddings.T)
reshape3 = ReshapeLayer(emb3, (batchsize, seqlen, num_feats * wordDim))
conv1d_3 = DimshuffleLayer(
Conv1DLayer(reshape3,
num_filters=num_filters,
filter_size=wordDim,
stride=1,
nonlinearity=tanh,
W=GlorotUniform()), (0, 2, 1))
maxpool_3 = MaxPool1DLayer(conv1d_3, pool_size=pool_size)
hid_3 = DenseLayer(maxpool_3,
num_units=args.hiddenDim,
nonlinearity=sigmoid)
network_3 = DenseLayer(hid_3, num_units=4, nonlinearity=softmax)
#Degree
emb4 = EmbeddingLayer(input,
input_size=vocab_size,
output_size=wordDim,
W=wordEmbeddings.T)
reshape4 = ReshapeLayer(emb4, (batchsize, seqlen, num_feats * wordDim))
conv1d_4 = DimshuffleLayer(
Conv1DLayer(reshape4,
num_filters=num_filters,
filter_size=wordDim,
stride=1,
nonlinearity=tanh,
W=GlorotUniform()), (0, 2, 1))
maxpool_4 = MaxPool1DLayer(conv1d_4, pool_size=pool_size)
hid_4 = DenseLayer(maxpool_4,
num_units=args.hiddenDim,
nonlinearity=sigmoid)
network_4 = DenseLayer(hid_4, num_units=4, nonlinearity=softmax)
#Polarity
emb5 = EmbeddingLayer(input,
input_size=vocab_size,
output_size=wordDim,
W=wordEmbeddings.T)
reshape5 = ReshapeLayer(emb5, (batchsize, seqlen, num_feats * wordDim))
conv1d_5 = DimshuffleLayer(
Conv1DLayer(reshape5,
num_filters=num_filters,
filter_size=wordDim,
stride=1,
nonlinearity=tanh,
W=GlorotUniform()), (0, 2, 1))
maxpool_5 = MaxPool1DLayer(conv1d_5, pool_size=pool_size)
hid_5 = DenseLayer(maxpool_5,
num_units=args.hiddenDim,
nonlinearity=sigmoid)
network_5 = DenseLayer(hid_5, num_units=3, nonlinearity=softmax)
#ContextualModality
emb6 = EmbeddingLayer(input,
input_size=vocab_size,
output_size=wordDim,
W=wordEmbeddings.T)
reshape6 = ReshapeLayer(emb6, (batchsize, seqlen, num_feats * wordDim))
conv1d_6 = DimshuffleLayer(
Conv1DLayer(reshape6,
num_filters=num_filters,
filter_size=wordDim,
stride=1,
nonlinearity=tanh,
W=GlorotUniform()), (0, 2, 1))
maxpool_6 = MaxPool1DLayer(conv1d_6, pool_size=pool_size)
hid_6 = DenseLayer(maxpool_6,
num_units=args.hiddenDim,
nonlinearity=sigmoid)
network_6 = DenseLayer(hid_6, num_units=5, nonlinearity=softmax)
"""
#ContextualAspect
emb7 = EmbeddingLayer(input, input_size=vocab_size, output_size=wordDim, W=wordEmbeddings.T)
reshape7 = ReshapeLayer(emb7, (batchsize, seqlen, num_feats*wordDim))
conv1d_7 = DimshuffleLayer(Conv1DLayer(reshape7, num_filters=num_filters, filter_size=wordDim, stride=1,
nonlinearity=tanh,W=GlorotUniform()), (0,2,1))
maxpool_7 = MaxPool1DLayer(conv1d_7, pool_size=pool_size)
hid_7 = DenseLayer(maxpool_7, num_units=args.hiddenDim, nonlinearity=sigmoid)
network_7 = DenseLayer(hid_7, num_units=4, nonlinearity=softmax)
"""
"""
#Permanence
emb8 = EmbeddingLayer(input, input_size=vocab_size, output_size=wordDim, W=wordEmbeddings.T)
reshape8 = ReshapeLayer(emb8, (batchsize, seqlen, num_feats*wordDim))
conv1d_8 = DimshuffleLayer(Conv1DLayer(reshape8, num_filters=num_filters, filter_size=wordDim, stride=1,
nonlinearity=tanh,W=GlorotUniform()), (0,2,1))
maxpool_8 = MaxPool1DLayer(conv1d_8, pool_size=pool_size)
hid_8 = DenseLayer(maxpool_8, num_units=args.hiddenDim, nonlinearity=sigmoid)
network_8 = DenseLayer(hid_8, num_units=4, nonlinearity=softmax)
"""
# Is this important?
"""
network_1_out, network_2_out, network_3_out, network_4_out, \
network_5_out, network_6_out, network_7_out, network_8_out = \
get_output([network_1, network_2, network_3, network_4, network_5, network_6, network_7, network_8])
"""
network_1_out = get_output(network_1)
network_3_out = get_output(network_3)
network_4_out = get_output(network_4)
network_5_out = get_output(network_5)
network_6_out = get_output(network_6)
loss_1 = T.mean(binary_crossentropy(
network_1_out, target_var)) + regularize_layer_params_weighted(
{
emb1: lambda_val,
conv1d_1: lambda_val,
hid_1: lambda_val,
network_1: lambda_val
}, l2)
updates_1 = adagrad(loss_1,
get_all_params(network_1, trainable=True),
learning_rate=args.step)
train_fn_1 = theano.function([input_var, target_var],
loss_1,
updates=updates_1,
allow_input_downcast=True)
val_acc_1 = T.mean(
binary_accuracy(get_output(network_1, deterministic=True), target_var))
val_fn_1 = theano.function([input_var, target_var],
val_acc_1,
allow_input_downcast=True)
"""
loss_2 = T.mean(categorical_crossentropy(network_2_out,target_var)) + regularize_layer_params_weighted({emb2:lambda_val, conv1d_2:lambda_val,
hid_2:lambda_val, network_2:lambda_val} , l2)
updates_2 = adagrad(loss_2, get_all_params(network_2, trainable=True), learning_rate=args.step)
train_fn_2 = theano.function([input_var, target_var], loss_2, updates=updates_2, allow_input_downcast=True)
val_acc_2 = T.mean(categorical_accuracy(get_output(network_2, deterministic=True), target_var))
val_fn_2 = theano.function([input_var, target_var], val_acc_2, allow_input_downcast=True)
"""
loss_3 = T.mean(categorical_crossentropy(
network_3_out, target_var)) + regularize_layer_params_weighted(
{
emb3: lambda_val,
conv1d_3: lambda_val,
hid_3: lambda_val,
network_3: lambda_val
}, l2)
updates_3 = adagrad(loss_3,
get_all_params(network_3, trainable=True),
learning_rate=args.step)
train_fn_3 = theano.function([input_var, target_var],
loss_3,
updates=updates_3,
allow_input_downcast=True)
val_acc_3 = T.mean(
categorical_accuracy(get_output(network_3, deterministic=True),
target_var))
val_fn_3 = theano.function([input_var, target_var],
val_acc_3,
allow_input_downcast=True)
loss_4 = T.mean(categorical_crossentropy(
network_4_out, target_var)) + regularize_layer_params_weighted(
{
emb4: lambda_val,
conv1d_4: lambda_val,
hid_4: lambda_val,
network_4: lambda_val
}, l2)
updates_4 = adagrad(loss_4,
get_all_params(network_4, trainable=True),
learning_rate=args.step)
train_fn_4 = theano.function([input_var, target_var],
loss_4,
updates=updates_4,
allow_input_downcast=True)
val_acc_4 = T.mean(
categorical_accuracy(get_output(network_4, deterministic=True),
target_var))
val_fn_4 = theano.function([input_var, target_var],
val_acc_4,
allow_input_downcast=True)
loss_5 = T.mean(categorical_crossentropy(
network_5_out, target_var)) + regularize_layer_params_weighted(
{
emb5: lambda_val,
conv1d_5: lambda_val,
hid_5: lambda_val,
network_5: lambda_val
}, l2)
updates_5 = adagrad(loss_5,
get_all_params(network_5, trainable=True),
learning_rate=args.step)
train_fn_5 = theano.function([input_var, target_var],
loss_5,
updates=updates_5,
allow_input_downcast=True)
val_acc_5 = T.mean(
categorical_accuracy(get_output(network_5, deterministic=True),
target_var))
val_fn_5 = theano.function([input_var, target_var],
val_acc_5,
allow_input_downcast=True)
loss_6 = T.mean(categorical_crossentropy(
network_6_out, target_var)) + regularize_layer_params_weighted(
{
emb6: lambda_val,
conv1d_6: lambda_val,
hid_6: lambda_val,
network_6: lambda_val
}, l2)
updates_6 = adagrad(loss_6,
get_all_params(network_6, trainable=True),
learning_rate=args.step)
train_fn_6 = theano.function([input_var, target_var],
loss_6,
updates=updates_6,
allow_input_downcast=True)
val_acc_6 = T.mean(
categorical_accuracy(get_output(network_6, deterministic=True),
target_var))
val_fn_6 = theano.function([input_var, target_var],
val_acc_6,
allow_input_downcast=True)
"""
loss_7 = T.mean(categorical_crossentropy(network_7_out,target_var)) + regularize_layer_params_weighted({emb7:lambda_val, conv1d_7:lambda_val,
hid_7:lambda_val, network_7:lambda_val} , l2)
updates_7 = adagrad(loss_7, get_all_params(network_7, trainable=True), learning_rate=args.step)
train_fn_7 = theano.function([input_var, target_var], loss_7, updates=updates_7, allow_input_downcast=True)
val_acc_7 = T.mean(categorical_accuracy(get_output(network_7, deterministic=True), target_var))
val_fn_7 = theano.function([input_var, target_var], val_acc_7, allow_input_downcast=True)
loss_8 = T.mean(categorical_crossentropy(network_8_out,target_var)) + regularize_layer_params_weighted({emb8:lambda_val, conv1d_8:lambda_val,
hid_8:lambda_val, network_8:lambda_val} , l2)
updates_8 = adagrad(loss_8, get_all_params(network_8, trainable=True), learning_rate=args.step)
train_fn_8 = theano.function([input_var, target_var], loss_8, updates=updates_8, allow_input_downcast=True)
val_acc_8 = T.mean(categorical_accuracy(get_output(network_8, deterministic=True), target_var))
val_fn_8 = theano.function([input_var, target_var], val_acc_8, allow_input_downcast=True)
"""
"""
return train_fn_1, val_fn_1, network_1, train_fn_2, val_fn_2, network_2, train_fn_3, val_fn_3, \
network_3, train_fn_4, val_fn_4, network_4, train_fn_5, val_fn_5, network_5, \
train_fn_6, val_fn_6, network_6, train_fn_7, val_fn_7, network_7, train_fn_8, val_fn_8, network_8
"""
return train_fn_1, val_fn_1, network_1, train_fn_3, val_fn_3, \
network_3, train_fn_4, val_fn_4, network_4, train_fn_5, val_fn_5, network_5, \
train_fn_6, val_fn_6, network_6
def update_params(self, loss, all_params):
return adagrad(loss, all_params, self.learning_rate)
def build_network_2dconv(
args, input1_var, input1_mask_var, input2_var, intut2_mask_var, target_var, wordEmbeddings, maxlen=36
):
print ("Building model with 2D Convolution")
vocab_size = wordEmbeddings.shape[1]
wordDim = wordEmbeddings.shape[0]
num_filters = 100
stride = 1
# CNN_sentence config
filter_size = (3, wordDim)
pool_size = (maxlen - 3 + 1, 1)
# two conv pool layer
# filter_size=(10, 100)
# pool_size=(4,4)
input_1 = InputLayer((None, maxlen), input_var=input1_var)
batchsize, seqlen = input_1.input_var.shape
# input_1_mask = InputLayer((None, maxlen),input_var=input1_mask_var)
emb_1 = EmbeddingLayer(input_1, input_size=vocab_size, output_size=wordDim, W=wordEmbeddings.T)
emb_1.params[emb_1.W].remove("trainable") # (batchsize, maxlen, wordDim)
reshape_1 = ReshapeLayer(emb_1, (batchsize, 1, maxlen, wordDim))
conv2d_1 = Conv2DLayer(
reshape_1,
num_filters=num_filters,
filter_size=(filter_size),
stride=stride,
nonlinearity=rectify,
W=GlorotUniform(),
) # (None, 100, 34, 1)
maxpool_1 = MaxPool2DLayer(conv2d_1, pool_size=pool_size) # (None, 100, 1, 1)
"""
filter_size_2=(4, 10)
pool_size_2=(2,2)
conv2d_1 = Conv2DLayer(maxpool_1, num_filters=num_filters, filter_size=filter_size_2, stride=stride,
nonlinearity=rectify,W=GlorotUniform()) #(None, 100, 34, 1)
maxpool_1 = MaxPool2DLayer(conv2d_1, pool_size=pool_size_2) #(None, 100, 1, 1) (None, 100, 1, 20)
"""
forward_1 = FlattenLayer(maxpool_1) # (None, 100) #(None, 50400)
input_2 = InputLayer((None, maxlen), input_var=input2_var)
# input_2_mask = InputLayer((None, maxlen),input_var=input2_mask_var)
emb_2 = EmbeddingLayer(input_2, input_size=vocab_size, output_size=wordDim, W=wordEmbeddings.T)
emb_2.params[emb_2.W].remove("trainable")
reshape_2 = ReshapeLayer(emb_2, (batchsize, 1, maxlen, wordDim))
conv2d_2 = Conv2DLayer(
reshape_2,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
nonlinearity=rectify,
W=GlorotUniform(),
) # (None, 100, 34, 1)
maxpool_2 = MaxPool2DLayer(conv2d_2, pool_size=pool_size) # (None, 100, 1, 1)
"""
conv2d_2 = Conv2DLayer(maxpool_2, num_filters=num_filters, filter_size=filter_size_2, stride=stride,
nonlinearity=rectify,W=GlorotUniform()) #(None, 100, 34, 1)
maxpool_2 = MaxPool2DLayer(conv2d_2, pool_size=pool_size_2) #(None, 100, 1, 1)
"""
forward_2 = FlattenLayer(maxpool_2) # (None, 100)
# elementwisemerge need fix the sequence length
mul = ElemwiseMergeLayer([forward_1, forward_2], merge_function=T.mul)
sub = AbsSubLayer([forward_1, forward_2], merge_function=T.sub)
concat = ConcatLayer([mul, sub])
concat = ConcatLayer([forward_1, forward_2])
hid = DenseLayer(concat, num_units=args.hiddenDim, nonlinearity=sigmoid)
if args.task == "sts":
network = DenseLayer(hid, num_units=5, nonlinearity=softmax)
elif args.task == "ent":
network = DenseLayer(hid, num_units=3, nonlinearity=softmax)
# prediction = get_output(network, {input_1:input1_var, input_2:input2_var})
prediction = get_output(network)
loss = T.mean(categorical_crossentropy(prediction, target_var))
lambda_val = 0.5 * 1e-4
layers = {conv2d_1: lambda_val, hid: lambda_val, network: lambda_val}
penalty = regularize_layer_params_weighted(layers, l2)
loss = loss + penalty
params = get_all_params(network, trainable=True)
if args.optimizer == "sgd":
updates = sgd(loss, params, learning_rate=args.step)
elif args.optimizer == "adagrad":
updates = adagrad(loss, params, learning_rate=args.step)
elif args.optimizer == "adadelta":
updates = adadelta(loss, params, learning_rate=args.step)
elif args.optimizer == "nesterov":
updates = nesterov_momentum(loss, params, learning_rate=args.step)
elif args.optimizer == "rms":
updates = rmsprop(loss, params, learning_rate=args.step)
elif args.optimizer == "adam":
updates = adam(loss, params, learning_rate=args.step)
else:
raise "Need set optimizer correctly"
# test_prediction = get_output(network, {input_1:input1_var, input_2:input2_var}, deterministic=True)
test_prediction = get_output(network, deterministic=True)
test_loss = T.mean(categorical_crossentropy(test_prediction, target_var))
"""
train_fn = theano.function([input1_var, input1_mask_var, input2_var, intut2_mask_var, target_var],
loss, updates=updates, allow_input_downcast=True)
"""
train_fn = theano.function([input1_var, input2_var, target_var], loss, updates=updates, allow_input_downcast=True)
if args.task == "sts":
"""
val_fn = theano.function([input1_var, input1_mask_var, input2_var, intut2_mask_var, target_var],
[test_loss, test_prediction], allow_input_downcast=True)
"""
val_fn = theano.function(
[input1_var, input2_var, target_var], [test_loss, test_prediction], allow_input_downcast=True
)
elif args.task == "ent":
# test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var), dtype=theano.config.floatX)
test_acc = T.mean(categorical_accuracy(test_prediction, target_var))
"""
val_fn = theano.function([input1_var, input1_mask_var, input2_var, intut2_mask_var, target_var],
[test_loss, test_acc], allow_input_downcast=True)
"""
val_fn = theano.function([input1_var, input2_var, target_var], [test_loss, test_acc], allow_input_downcast=True)
return train_fn, val_fn
def event_span_classifier(args, input_var, target_var, wordEmbeddings, seqlen, num_feats):
print("Building model with 1D Convolution")
vocab_size = wordEmbeddings.shape[1]
wordDim = wordEmbeddings.shape[0]
kw = 2
num_filters = seqlen-kw+1
stride = 1
#important context words as channels
#CNN_sentence config
filter_size=wordDim
pool_size=seqlen-filter_size+1
input = InputLayer((None, seqlen, num_feats),input_var=input_var)
batchsize, _, _ = input.input_var.shape
emb = EmbeddingLayer(input, input_size=vocab_size, output_size=wordDim, W=wordEmbeddings.T)
#emb.params[emb.W].remove('trainable') #(batchsize, seqlen, wordDim)
#print get_output_shape(emb)
reshape = ReshapeLayer(emb, (batchsize, seqlen, num_feats*wordDim))
#print get_output_shape(reshape)
conv1d = Conv1DLayer(reshape, num_filters=num_filters, filter_size=wordDim, stride=1,
nonlinearity=tanh,W=GlorotUniform()) #nOutputFrame = num_flters,
#nOutputFrameSize = (num_feats*wordDim-filter_size)/stride +1
#print get_output_shape(conv1d)
conv1d = DimshuffleLayer(conv1d, (0,2,1))
#print get_output_shape(conv1d)
pool_size=num_filters
maxpool = MaxPool1DLayer(conv1d, pool_size=pool_size)
#print get_output_shape(maxpool)
#forward = FlattenLayer(maxpool)
#print get_output_shape(forward)
hid = DenseLayer(maxpool, num_units=args.hiddenDim, nonlinearity=sigmoid)
network = DenseLayer(hid, num_units=2, nonlinearity=softmax)
prediction = get_output(network)
loss = T.mean(binary_crossentropy(prediction,target_var))
lambda_val = 0.5 * 1e-4
layers = {emb:lambda_val, conv1d:lambda_val, hid:lambda_val, network:lambda_val}
penalty = regularize_layer_params_weighted(layers, l2)
loss = loss + penalty
params = get_all_params(network, trainable=True)
if args.optimizer == "sgd":
updates = sgd(loss, params, learning_rate=args.step)
elif args.optimizer == "adagrad":
updates = adagrad(loss, params, learning_rate=args.step)
elif args.optimizer == "adadelta":
updates = adadelta(loss, params, learning_rate=args.step)
elif args.optimizer == "nesterov":
updates = nesterov_momentum(loss, params, learning_rate=args.step)
elif args.optimizer == "rms":
updates = rmsprop(loss, params, learning_rate=args.step)
elif args.optimizer == "adam":
updates = adam(loss, params, learning_rate=args.step)
else:
raise "Need set optimizer correctly"
test_prediction = get_output(network, deterministic=True)
test_loss = T.mean(binary_crossentropy(test_prediction,target_var))
train_fn = theano.function([input_var, target_var],
loss, updates=updates, allow_input_downcast=True)
test_acc = T.mean(binary_accuracy(test_prediction, target_var))
val_fn = theano.function([input_var, target_var], [test_loss, test_acc], allow_input_downcast=True)
return train_fn, val_fn, network
def logisticRegression(X, Y, l, **options):
#%% imports
from theano import function
from theano import shared
import numpy as np
from numpy import random as rng
import matplotlib.pyplot as plt
from theano import tensor as Tn
from lasagne.updates import adagrad
if options.get('plotFigure'):
plotFigure = options.get('plotFigure')
else:
plotFigure = False
if options.get('verbose'):
verbose = options.get('verbose')
else:
verbose = False
#%% load data
l = np.array(l).astype(float)
numObservations = len(Y)
numFeatures = len(X) / numObservations
Y = np.squeeze(np.array(Y).astype('int'))
X = np.reshape(np.array(X).astype('float'), (numObservations, numFeatures),
order='F')
# remove any nans
mskOutNans = (np.sum(np.isnan(X), axis=1) + np.squeeze(np.isnan(Y))) < 1
X = X[mskOutNans, :]
Y = Y[mskOutNans]
numObservations, numFeatures = X.shape
Data = (X, Y)
# data tuple
#%% declaration of sympolic variables and initializations
# Declare theano symbolic input/ output variables
x = Tn.matrix('x')
# sympolic feature variable
y = Tn.vector('y')
# sympolic label variable
lambda_l2 = Tn.dscalar('lambda_l2')
# sympolic l2-regularization parameter variable
lambda_l1 = Tn.dscalar('lambda_l1')
# sympolic l1-regularization parameter variable
# Declare and initialize theano shared optimization variables
w = shared(rng.randn(numFeatures), name='w')
# initialize the weight vector (w) randomly
b = shared(np.random.randn(), name='b')
# initialize the bias variable (b) randomly
clps = shared(0.5 * np.random.rand(), name='clps')
# initialize the lapse rate variable (lps) randomly between 0 and 1
#%% functions expressions and compilations
# function sympolic expressions
XW = Tn.dot(x, w)
L_Exp = Tn.shape(y)
lps = 0.5 / (1. + Tn.exp(-10. * clps))
prob1Expression = lps + (1 - 2 * lps) / (1.0 + Tn.exp(-(XW + b)))
# expression of logistic function (prob of 1)
prob0Expression = 1.0 - prob1Expression
# expression of logistic function (prob of 0)
predictExpression = prob1Expression > 0.5
logLikelihood_Exp = (y * Tn.log(prob1Expression) +
(1.0 - y) * Tn.log(prob0Expression)).sum()
# loglikelihood expression
costExpression = -logLikelihood_Exp / (L_Exp[0]) + lambda_l2 * (
w**2).sum() + lambda_l1 * abs(w).sum()
# mean cost across all samples with the regularization
perClassErExpression = abs(predictExpression -
y).sum() / Tn.shape(y)[0] * 100
# percent classification error expression
# compiling function expressions for speed
prob1Fn = function(inputs=[x], outputs=prob1Expression)
prob0Fn = function(inputs=[x], outputs=prob0Expression)
predictFn = function(inputs=[x], outputs=predictExpression)
costFn = function(inputs=[x, y, lambda_l2, lambda_l1],
outputs=costExpression)
perClassErFn = function(inputs=[x, y], outputs=perClassErExpression)
# cost gradient with respect to all parameters
grad_w, grad_b, grad_clps = Tn.grad(costExpression, [w, b, clps])
updates = adagrad([grad_w, grad_b, grad_clps], [w, b, clps],
learning_rate=0.2,
epsilon=0.0001)
# training function
trainFn = function(
inputs=[x, y, lambda_l2, lambda_l1],
outputs=[costExpression, perClassErExpression, logLikelihood_Exp],
updates=updates
#updates = ((w, w - learnRate*grad_w), (b, b - learnRate*grad_b), (lps, Tn.clip((lps - learnRate*grad_lps) , 0.0, 0.4999999)))
)
#%% Training the model
maxIter = 10000
numRepetitions = 4
w_0 = []
b_0 = []
clps_0 = []
minCost = np.inf
rbest = 0
for r in range(numRepetitions):
w_0.append(rng.randn(numFeatures))
b_0.append(np.random.rand())
clps_0.append(np.random.randn())
w.set_value(w_0[r])
b.set_value(b_0[r])
clps.set_value(clps_0[r])
b_i = [b.get_value()]
lps_i = [lps.eval()]
cost = []
perClassEr = []
lklhood_i = []
for i in range(int(maxIter)):
Er1, Er2, lklhood = trainFn(Data[0], Data[1], l[0], l[1])
#lps.set_value(np.clip(lps.get_value(), 0., 0.5)) # enforce constraint
lklhood_i.append(lklhood)
cost.append(Er1)
perClassEr.append(Er2)
b_i.append(b.get_value())
lps_i.append(lps.eval())
if i > 500:
if abs(cost[i - 100] - cost[i]) < (10.0**-6):
break
if verbose:
print 'iteration %d , objective value %.5f, initial conditions %d out of %d' % (
i + 1, cost[i], r + 1, numRepetitions)
if cost[-1] < minCost:
rbest = r
minCost = cost[-1]
costbest = cost
perClassErbest = perClassEr
wbest = w.get_value()
clpsbest = clps.get_value()
bbest = b.get_value()
bbest_i = b_i
lpsbest_i = lps_i
lklhoodBest_i = lklhood_i
w_0 = w_0[rbest]
cost = costbest
perClassEr = perClassErbest
w.set_value(wbest)
clps.set_value(clpsbest)
b.set_value(bbest)
b_i = bbest_i
lps_i = lpsbest_i
lklhood_i = lklhoodBest_i
#%% plot results
#%%
msk1 = Data[1] > 0
msk0 = Data[1] < 1
Y1 = Data[1][msk1]
Y0 = Data[1][msk0]
Yhat = predictFn(Data[0])
Yhat1 = Yhat[msk1]
Yhat0 = Yhat[msk0]
prob1 = prob1Fn(Data[0])
prob1_1 = prob1[msk1]
prob1_0 = prob1[msk0]
if plotFigure:
plt.figure('cost')
plt.subplot(3, 1, 1)
plt.plot(cost)
plt.xlabel('iteration')
plt.ylabel('cross-entropy loss')
plt.subplot(3, 1, 2)
plt.plot(perClassEr)
plt.ylim(0, 100)
plt.xlabel('iteration')
plt.ylabel('classification error (%)')
plt.subplot(3, 1, 3)
plt.plot(lklhood_i)
plt.xlabel('iteration')
plt.ylabel('log likelihood')
plt.figure('prediction')
plt.plot(np.arange(1, len(Y0) + 1), Yhat0, 'ro', label='prediction')
plt.plot(np.arange(1,
len(Y0) + 1),
prob1_0,
'b.',
label='likelihood of success')
plt.plot(np.arange(len(Y0) + 1, len(Yhat) + 1), Yhat1, 'ro')
plt.plot(np.arange(len(Y0) + 1, len(Yhat) + 1), prob1_1, 'b.')
plt.plot(np.arange(1, len(Y0) + 1), Y0, 'k.', label='data')
plt.plot(np.arange(len(Y0) + 1, len(Yhat) + 1), Y1, 'k.')
plt.ylim(-0.1, 1.1)
plt.xticks([0, 1])
plt.xlabel('observation')
plt.ylabel('class label')
plt.legend()
plt.figure('weights')
plt.subplot(3, 1, 1)
plt.plot(w_0, 'g')
plt.plot(w.get_value(), 'r')
plt.xlabel('feature number')
plt.ylabel('feature weight')
plt.legend(('before optimization', 'after optimization'))
plt.subplot(3, 1, 2)
plt.plot(b_i)
plt.xlabel('iteration')
plt.ylabel('bias')
plt.subplot(3, 1, 3)
plt.plot(lps_i)
plt.ylim(0., 1.)
plt.xlabel('iteration')
plt.ylabel('lapse rate')
#%% save optimization parameters to class
class optParamsClass:
cost_per_iter = np.inf
perClassEr_per_iter = 100.
loglikelihood = -np.inf
prediction = []
likelihood_trial = []
predictFn = []
perClassErFn = []
probSucessFn = []
def description(self):
return 'object contains optimization parameters'
optParams = optParamsClass()
optParams.cost_per_iter = np.array(cost)
optParams.perClassEr_per_iter = np.array(perClassEr)
optParams.loglikelihood = lklhood_i
optParams.prediction = Yhat
optParams.likelihood_trial = prob1
optParams.predictFn = predictFn
optParams.perClassErFn = perClassErFn
optParams.probSucessFn = prob1Fn
#%% return parameters
return w.get_value(), b.get_value(), lps.eval(
), perClassEr[-1], cost[-1], optParams
