def create_encoder_decoder_func(layers, apply_updates=False):
X = T.fmatrix('X')
X_batch = T.fmatrix('X_batch')
X_hat = get_output(layers['l_decoder_out'], X, deterministic=False)
# reconstruction loss
encoder_decoder_loss = T.mean(
T.mean(T.sqr(X - X_hat), axis=1)
)
if apply_updates:
# all layers that participate in the forward pass should be updated
encoder_decoder_params = get_all_params(
layers['l_decoder_out'], trainable=True)
encoder_decoder_updates = nesterov_momentum(
encoder_decoder_loss, encoder_decoder_params, 0.01, 0.9)
else:
encoder_decoder_updates = None
encoder_decoder_func = theano.function(
inputs=[theano.In(X_batch)],
outputs=encoder_decoder_loss,
updates=encoder_decoder_updates,
givens={
X: X_batch,
},
)
return encoder_decoder_func
def create_updates(loss, network, opt, learning_rate, momentum, beta1, beta2):
params = lasagne.layers.get_all_params(network, trainable=True)
grads = theano.grad(loss, params)
# if max_norm:
# names = ['crf.U', 'crf.W_h', 'crf.W_c', 'crf.b']
# constraints = [grad for param, grad in zip(params, grads) if param.name in names]
# assert len(constraints) == 4
# scaled_grads = total_norm_constraint(constraints, max_norm=max_norm)
# counter = 0
# for i in xrange(len(params)):
# param = params[i]
# if param.name in names:
# grads[i] = scaled_grads[counter]
# counter += 1
# assert counter == 4
if opt == 'adam':
updates = adam(grads,
params=params,
learning_rate=learning_rate,
beta1=beta1,
beta2=beta2)
elif opt == 'momentum':
updates = nesterov_momentum(grads,
params=params,
learning_rate=learning_rate,
momentum=momentum)
else:
raise ValueError('unkown optimization algorithm: %s' % opt)
return updates
def __init__(self, C, lr):
self.C = C
self.X = T.ftensor4()
self.Y = T.fmatrix()
self.net = self._forward()
params = layers.get_all_params(self.net['flatten'], trainable=True)
netout = layers.get_output(self.net['out'])
flattenout = layers.get_output(self.net['flatten'])
reg = regularization.regularize_network_params(self.net['flatten'],
regularization.l2)
reg /= layers.helper.count_params(self.net['flatten'])
self.flattenfn = theano.function([self.X],
flattenout,
allow_input_downcast=True)
self.predictfn = theano.function([self.X],
netout,
allow_input_downcast=True)
accrarcy = myUtils.basic.accuracy(netout, self.Y)
self.scorefn = theano.function([self.X, self.Y],
accrarcy,
allow_input_downcast=True)
self.sharedBeta = self.net['out'].get_params()[0]
crossentropy = objectives.categorical_crossentropy(netout, self.Y)
cost = T.mean(crossentropy) + C * reg
updatesDict = updates.nesterov_momentum(cost, params, lr, 0.9)
# 训练随机参数
self.trainfn = theano.function([self.X, self.Y], [cost, accrarcy],
updates=updatesDict,
allow_input_downcast=True)
def __build_loss_train__fn__(self):
# create loss function
prediction = layers.get_output(self.net)
loss = objectives.categorical_crossentropy(prediction,
self.__target_var__)
loss = loss.mean() + 1e-4 * regularization.regularize_network_params(
self.net, regularization.l2)
val_acc = T.mean(T.eq(T.argmax(prediction, axis=1),
self.__target_var__),
dtype=theano.config.floatX)
# create parameter update expressions
params = layers.get_all_params(self.net, trainable=True)
self.eta = theano.shared(sp.array(sp.float32(0.05), dtype=sp.float32))
update_rule = updates.nesterov_momentum(loss,
params,
learning_rate=self.eta,
momentum=0.9)
# compile training function that updates parameters and returns training loss
self.__train_fn__ = theano.function(
[self.__input_var__, self.__target_var__],
loss,
updates=update_rule)
self.__predict_fn__ = theano.function(
[self.__input_var__],
layers.get_output(self.net, deterministic=True))
self.__val_fn__ = theano.function(
[self.__input_var__, self.__target_var__], [loss, val_acc])
def build_network(nkwargs):
opt_kwargs = {k: nkwargs[k] for k in ['learning_rate', 'momentum']}
input_var = T.tensor4('input_var')
target_var = T.tensor4('target_var')
network, hid_layer = build_denoising_convae(input_var, nkwargs)
#get outputs
prediction = get_output(network, deterministic=False)
hid_layer_output = get_output(hid_layer, deterministic=True)
#make losses
loss = squared_error(prediction, target_var).mean()
test_prediction = get_output(network, deterministic=True)
test_loss = squared_error(test_prediction, target_var).mean()
test_acc = test_loss
#set up updates
params = get_all_params(network, trainable=True)
updates = nesterov_momentum(loss, params, **opt_kwargs)
#make fns
train_fn = function([input_var, target_var], loss, updates=updates)
val_fn = function([input_var, target_var], [test_loss, test_acc])
pred_fn = function([input_var], test_prediction)
hlayer_fn = function([input_var], hid_layer_output)
return train_fn, val_fn, pred_fn, hlayer_fn, network
def __init__(self, istrained, name=None, args=None):
self.istrained = istrained
self.X = T.tensor4('X')
self.y = T.ivector('y')
self.outprob = build_model(self.X)
if self.istrained:
params = cPickle.load(open(dataset_path + 'plain_cnn.pkl', 'r'))
layers.set_all_param_values(self.outprob, params)
self.yFullProb = layers.get_output(self.outprob, deterministic=True)
self.predfn = makeFunc([self.X, ], [self.yFullProb, ], None)
else:
self.lr, self.C, self.momentum = args
self.params = layers.get_all_params(self.outprob, trainable=True)
reg = regularization.regularize_network_params(self.outprob, regularization.l2)
reg /= layers.helper.count_params(self.outprob)
# 训练集
self.yDropProb = layers.get_output(self.outprob)
trCrossentropy = objectives.categorical_crossentropy(self.yDropProb, self.y)
self.trCost = trCrossentropy.mean() + self.C * reg
# 验证、测试集
self.yFullProb = layers.get_output(self.outprob, deterministic=True)
vateCrossentropy = objectives.categorical_crossentropy(self.yFullProb, self.y)
self.vateCost = vateCrossentropy.mean() + self.C * reg
# 训练函数，输入训练集，输出训练损失和误差
updatesDict = updates.nesterov_momentum(self.trCost, self.params, self.lr, self.momentum)
self.trainfn = makeFunc([self.X, self.y], [self.trCost, self.yDropProb], updatesDict)
# 验证或测试函数，输入验证或测试集，输出损失和误差，不进行更新
self.vatefn = makeFunc([self.X, self.y], [self.vateCost, self.yFullProb], None)
def __init__(self, lr, C, momentum):
self.lr = lr
self.C = C
self.momentum = momentum
self.X = T.tensor4('X')
self.y = T.ivector('y')
self.network = self._build()
self.params = layers.get_all_params(self.network, trainable=True)
reg = regularization.regularize_network_params(self.network, regularization.l2)
reg /= layers.helper.count_params(self.network)
# 训练集
yDropProb = layers.get_output(self.network)
self.trEqs = myUtils.basic.eqs(yDropProb, self.y)
trCrossentropy = objectives.categorical_crossentropy(yDropProb, self.y)
self.trCost = trCrossentropy.mean() + C * reg
# 验证、测试集
yFullProb = layers.get_output(self.network, deterministic=True)
self.vateEqs = myUtils.basic.eqs(yFullProb, self.y)
vateCrossentropy = objectives.categorical_crossentropy(yFullProb, self.y)
self.vateCost = vateCrossentropy.mean() + C * reg
self.yPred = yFullProb
# 训练函数，输入训练集，输出训练损失和误差
updatesDict = updates.nesterov_momentum(self.trCost, self.params, lr, momentum)
self.trainfn = myUtils.basic.makeFunc([self.X, self.y], [self.trCost, self.trEqs], updatesDict)
# 验证或测试函数，输入验证或测试集，输出损失和误差，不进行更新
self.vatefn = myUtils.basic.makeFunc([self.X, self.y], [self.vateCost, self.vateEqs], None)
def create_encoder_decoder_func(layers, apply_updates=False):
X = T.fmatrix('X')
X_batch = T.fmatrix('X_batch')
X_hat = get_output(layers['l_decoder_out'], X, deterministic=False)
# reconstruction loss
encoder_decoder_loss = T.mean(T.mean(T.sqr(X - X_hat), axis=1))
if apply_updates:
# all layers that participate in the forward pass should be updated
encoder_decoder_params = get_all_params(layers['l_decoder_out'],
trainable=True)
encoder_decoder_updates = nesterov_momentum(encoder_decoder_loss,
encoder_decoder_params,
0.01, 0.9)
else:
encoder_decoder_updates = None
encoder_decoder_func = theano.function(
inputs=[theano.In(X_batch)],
outputs=encoder_decoder_loss,
updates=encoder_decoder_updates,
givens={
X: X_batch,
},
)
return encoder_decoder_func
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 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 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 __init__(self, conf):
self.conf = conf
if self.conf.act == "linear":
self.conf.act = linear
elif self.conf.act == "sigmoid":
self.conf.act = sigmoid
elif self.conf.act == "relu":
self.conf.act = rectify
elif self.conf.act == "tanh":
self.conf.act = tanh
else:
raise ValueError("Unknown activation function", self.conf.act)
input_var_first = T.matrix('inputs1')
input_var_second = T.matrix('inputs2')
target_var = T.matrix('targets')
# create network
self.autoencoder, encoder_first, encoder_second = self.__create_toplogy__(
input_var_first, input_var_second)
self.out = get_output(self.autoencoder)
loss = squared_error(self.out, target_var)
loss = loss.mean()
params = get_all_params(self.autoencoder, trainable=True)
updates = nesterov_momentum(loss,
params,
learning_rate=self.conf.lr,
momentum=self.conf.momentum)
# training function
self.train_fn = theano.function(
[input_var_first, input_var_second, target_var],
loss,
updates=updates)
# fuction to reconstruct
test_reconstruction = get_output(self.autoencoder, deterministic=True)
self.reconstruction_fn = theano.function(
[input_var_first, input_var_second], test_reconstruction)
# encoding function
test_encode = get_output([encoder_first, encoder_second],
deterministic=True)
self.encoding_fn = theano.function([input_var_first, input_var_second],
test_encode)
# utils
blas = lambda name, ndarray: scipy.linalg.get_blas_funcs(
(name, ), (ndarray, ))[0]
self.blas_nrm2 = blas('nrm2', np.array([], dtype=float))
self.blas_scal = blas('scal', np.array([], dtype=float))
# load weights if necessary
if self.conf.load_model is not None:
self.load_model()
def set_update(self):
params = get_all_params(self.model, trainable=True)
self.lr_schedule = {
0: 0.01,
6: 0.001,
NUM_EPOCHS-2: 0.0001
}
self.lr = theano.shared(np.float32(self.lr_schedule[0]))
self.updates = nesterov_momentum(self.train_loss, params,
learning_rate=self.lr, momentum=0.9)
def create_discriminator_func(layers, apply_updates=False):
X = T.fmatrix('X')
pz = T.fmatrix('pz')
X_batch = T.fmatrix('X_batch')
pz_batch = T.fmatrix('pz_batch')
# the discriminator receives samples from q(z|x) and p(z)
# and should predict to which distribution each sample belongs
discriminator_outputs = get_output(
layers['l_discriminator_out'],
inputs={
layers['l_prior_in']: pz,
layers['l_encoder_in']: X,
},
deterministic=False,
)
# label samples from q(z|x) as 1 and samples from p(z) as 0
discriminator_targets = T.vertical_stack(
T.ones((X_batch.shape[0], 1)),
T.zeros((pz_batch.shape[0], 1))
)
discriminator_loss = T.mean(
T.nnet.binary_crossentropy(
discriminator_outputs,
discriminator_targets,
)
)
if apply_updates:
# only layers that are part of the discriminator should be updated
discriminator_params = get_all_params(
layers['l_discriminator_out'], trainable=True, discriminator=True)
discriminator_updates = nesterov_momentum(
discriminator_loss, discriminator_params, 0.1, 0.0)
else:
discriminator_updates = None
discriminator_func = theano.function(
inputs=[
theano.In(X_batch),
theano.In(pz_batch),
],
outputs=discriminator_loss,
updates=discriminator_updates,
givens={
X: X_batch,
pz: pz_batch,
},
)
return discriminator_func
def trainModel(self, tr_X, va_X, tr_y, va_y, batchSize=64, maxIter=300, verbose=True,
start=10, period=2, threshold=10, earlyStopTol=2, totalStopTol=2):
trainfn = self.trainfn
validatefn = self.vatefn
lr = self.lr
earlyStop = myUtils.basic.earlyStopGen(start, period, threshold, earlyStopTol)
earlyStop.next() # 初始化生成器
totalStopCount = 0
for epoch in xrange(maxIter): # every epoch
# In each epoch, we do a full pass over the training data:
trAllPred = None
trRandy = None
trCostSum = 0.
startTime = time.time()
for batch in myUtils.basic.miniBatchGen(tr_X, tr_y, batchSize, shuffle=True):
Xb, yb = batch
trCost, trPred = trainfn(Xb, yb)
trCostSum += trCost
trAllPred = np.concatenate((trAllPred, trPred), axis=0) \
if trAllPred is not None else trPred
trRandy = np.concatenate((trRandy, yb)) if trRandy is not None else yb
trIter = len(tr_X) // batchSize
if len(tr_X) % batchSize != 0: trIter += 1
trCostMean = trCostSum / trIter
trAcc = myUtils.basic.accuracy(trAllPred, trRandy)
trP, trR = myUtils.basic.precision_recall(trAllPred, trRandy)
# And a full pass over the validation data:
vaAllPred = None
vaCostSum = 0.
for batch in myUtils.basic.miniBatchGen(va_X, va_y, batchSize, shuffle=False):
Xb, yb = batch
vaCost, vaPred = validatefn(Xb, yb)
vaCostSum += vaCost
vaAllPred = np.concatenate((vaAllPred, vaPred), axis=0) \
if vaAllPred is not None else vaPred
vaIter = len(va_X) // batchSize
if len(va_X) % batchSize != 0: vaIter += 1
vaCostMean = vaCostSum / vaIter
vaAcc = myUtils.basic.accuracy(vaAllPred, va_y)
vaP, vaR = myUtils.basic.precision_recall(vaAllPred, va_y)
if verbose:
print 'epoch ', epoch, ' time: %.3f' % (time.time() - startTime),
print 'trcost: %.5f tracc: %.5f trp: %.5f trr: %.5f' % (trCostMean, trAcc, trP, trR),
print 'vacost: %.5f vaacc: %.5f vap: %.5f var: %.5f' % (vaCostMean, vaAcc, vaP, vaR)
# Then we decide whether to early stop:
if earlyStop.send((trCostMean, vaCostMean)):
lr /= 10 # 如果一次早停止发生，则学习率降低继续迭代
updatesDict = updates.nesterov_momentum(self.trCost, self.params, lr, self.momentum)
trainfn = myUtils.basic.makeFunc([self.X, self.y], [self.trCost, self.yDropProb], updatesDict)
totalStopCount += 1
if totalStopCount > totalStopTol: # 如果学习率降低仍然发生早停止，则退出迭代
print 'stop'
break
if verbose: print 'learning rate decreases to ', lr
def build_train_fn(self):
prediction = get_output(self.network, deterministic=False)
loss = squared_error(prediction, self.target_var)
loss = loss.mean()
params = get_all_params(self.network, trainable=True)
updates = nesterov_momentum(loss, params, learning_rate=self.learning_rate, momentum=self.momentum)
self.train_fn = theano.function([self.input_var, self.target_var], loss, updates=updates)
def test_stochastic_layer_network():
learning_rate = 0.1
momentum = 0.9
num_epoch = 1000
input = T.fmatrix('input')
output = T.fmatrix('output')
print 'FF-Layer: (Batch_size, n_features)'
print 'Building stochastic layer model'
l_in = L.InputLayer(shape=(1, 10), input_var=input)
l_2 = L.DenseLayer(l_in,
num_units=10,
nonlinearity=lasagne.nonlinearities.softmax,
W=lasagne.init.Constant(0.))
print 'Input Layer shape: ', L.get_output_shape(l_in)
print 'Dense Layer shape: ', L.get_output_shape(l_2)
l_stochastic_layer = StochasticLayer(l_2, estimator='ST')
print 'Stochastic Layer shape: ', L.get_output_shape(l_stochastic_layer)
l_out = L.DenseLayer(l_stochastic_layer,
num_units=10,
b=lasagne.init.Constant(0.))
print 'Final Dense Layer shape: ', L.get_output_shape(l_out)
network_output = L.get_output(l_out)
print 'Building loss function...'
loss = lasagne.objectives.squared_error(network_output, output)
loss = loss.mean()
params = L.get_all_params(l_out, trainable=True)
updates = nesterov_momentum(loss, params, learning_rate, momentum)
train = theano.function([input, output],
loss,
updates=updates,
allow_input_downcast=True)
output_fn = theano.function([input],
network_output,
allow_input_downcast=True)
test_X = np.ones((1, 10))
test_Y = np.ones((1, 10))
losses = []
mean_losses = []
for epoch in range(num_epoch):
print 'Epoch number: ', epoch
losses.append(train(test_X, test_Y))
print 'epoch {} mean loss {}'.format(epoch, np.mean(losses))
print 'Current Output: ', output_fn(test_X)
mean_losses.append(np.mean(losses))
plt.title("Mean loss")
plt.xlabel("Training examples")
plt.ylabel("Loss")
plt.plot(mean_losses, label="train")
plt.grid()
plt.legend()
plt.draw()
def contrastive_loss_iter(embedder, update_params={}):
# assume it's the 1d version
X_pairs = {
'te1':T.tensor3(),
'te2':T.tensor3(),
}
y = T.ivector() # basically class labels
final_emb_layer = embedder[-1]
all_layers = ll.get_all_layers(embedder)
imwrite_architecture(all_layers, './layer_rep.png')
# assume we get a list of predictions (e.g. for jet architecture, but should work w/just one pred)
# another assumption (which must hold when the network is being made)
# the last prediction layer is a) the end of the network and b) what we ultimately care about
# however the other prediction layers will be incorporated into the training loss
predicted_embeds_train = {k:ll.get_output(embedder, X)[-1] for k, X in X_pairs.items()}
predicted_embeds_valid = {k:ll.get_output(final_emb_layer, X, deterministic=True) for k, X in X_pairs.items()}
margin = 1
# if distance is 0 that's bad
distance = lambda pred: (pred['te1'] - pred['te2'] + 1e-7).norm(2, axis=1)
contrastive_loss = lambda pred: T.mean(y*(distance(pred)) + (1 - y)*(margin - distance(pred)).clip(0,np.inf))
failed_matches = lambda pred: T.switch(T.eq(T.sum(y),0), 0, T.sum((y*distance(pred)) > margin) / T.sum(y))
failed_nonmatches = lambda pred: T.switch(T.eq(T.sum(1-y),0), 0, T.sum((1-y*distance(pred)) < margin) / T.sum(1-y))
failed_pairs = lambda pred: 0.5*failed_matches(pred) + 0.5*failed_nonmatches(pred)
decay = 0.001
reg = regularize_network_params(final_emb_layer, l2) * decay
losses_reg = lambda pred: contrastive_loss(pred) + reg
loss_train = losses_reg(predicted_embeds_train)
loss_train.name = 'CL' # for the names
#all_params = list(chain(*[ll.get_all_params(pred) for pred in embedder]))
all_params = ll.get_all_params(embedder, trainable=True) # this should work with multiple 'roots'
grads = T.grad(loss_train, all_params, add_names=True)
updates = nesterov_momentum(grads, all_params, update_params['l_r'], momentum=update_params['momentum'])
print("Compiling network for training")
tic = time.time()
train_iter = theano.function([X_pairs['te1'], X_pairs['te2'], y], [loss_train] + grads, updates=updates)
toc = time.time() - tic
print("Took %0.2f seconds" % toc)
#theano.printing.pydotprint(loss, outfile='./loss_graph.png',var_with_name_simple=True)
print("Compiling network for validation")
tic = time.time()
valid_iter = theano.function([X_pairs['te1'], X_pairs['te2'], y], [
contrastive_loss(predicted_embeds_valid),
losses_reg(predicted_embeds_valid),
failed_pairs(predicted_embeds_valid)])
toc = time.time() - tic
print("Took %0.2f seconds" % toc)
return {'train':train_iter, 'valid':valid_iter, 'gradnames':[g.name for g in grads]}
def create_generator2_func(layers, apply_updates=False):
X = T.fmatrix('X')
X_batch = T.fmatrix('X_batch')
# no need to pass an input to l_prior_in here
generator_outputs = get_output(layers['l_encoder_out2'],
X,
deterministic=False)
# so pass the output of the generator as the output of the concat layer
discriminator_outputs = get_output(layers['l_discriminator_out2'],
inputs={
layers['l_prior_encoder_concat2']:
generator_outputs,
},
deterministic=False)
# the discriminator learns to predict 1 for q(z|x),
# so the generator should fool it into predicting 0
generator_targets = T.zeros_like(X_batch.shape[0])
# so the generator needs to push the discriminator's output to 0
generator_loss = T.mean(
T.nnet.binary_crossentropy(
discriminator_outputs,
generator_targets,
))
if apply_updates:
# only layers that are part of the generator (i.e., encoder)
# should be updated
generator_params = get_all_params(layers['l_discriminator_out2'],
trainable=True,
generator2=True)
generator_updates = nesterov_momentum(generator_loss, generator_params,
0.1, 0.0)
else:
generator_updates = None
generator_func = theano.function(
inputs=[
theano.In(X_batch),
],
outputs=generator_loss,
updates=generator_updates,
givens={
X: X_batch,
},
)
return generator_func
def create_generator_func(layers, apply_updates=False):
X = T.fmatrix('X')
X_batch = T.fmatrix('X_batch')
# no need to pass an input to l_prior_in here
generator_outputs = get_output(
layers['l_encoder_out'], X, deterministic=False)
# so pass the output of the generator as the output of the concat layer
discriminator_outputs = get_output(
layers['l_discriminator_out'],
inputs={
layers['l_prior_encoder_concat']: generator_outputs,
},
deterministic=False
)
# the discriminator learns to predict 1 for q(z|x),
# so the generator should fool it into predicting 0
generator_targets = T.zeros_like(X_batch.shape[0])
# so the generator needs to push the discriminator's output to 0
generator_loss = T.mean(
T.nnet.binary_crossentropy(
discriminator_outputs,
generator_targets,
)
)
if apply_updates:
# only layers that are part of the generator (i.e., encoder)
# should be updated
generator_params = get_all_params(
layers['l_discriminator_out'], trainable=True, generator=True)
generator_updates = nesterov_momentum(
generator_loss, generator_params, 0.1, 0.0)
else:
generator_updates = None
generator_func = theano.function(
inputs=[
theano.In(X_batch),
],
outputs=generator_loss,
updates=generator_updates,
givens={
X: X_batch,
},
)
return generator_func
def define_updates(output_layer, X, Y):
output_train = lasagne.layers.get_output(output_layer)
output_test = lasagne.layers.get_output(output_layer, deterministic=True)
# set up the loss that we aim to minimize when using cat cross entropy our Y should be ints not one-hot
loss = lasagne.objectives.categorical_crossentropy(
T.clip(output_train, 0.000001, 0.999999), Y)
loss = loss.mean()
acc = T.mean(T.eq(T.argmax(output_train, axis=1), Y),
dtype=theano.config.floatX)
# if using ResNet use L2 regularization
all_layers = lasagne.layers.get_all_layers(output_layer)
l2_penalty = lasagne.regularization.regularize_layer_params(
all_layers, lasagne.regularization.l2) * P.L2_LAMBDA
loss = loss + l2_penalty
# set up loss functions for validation dataset
test_loss = lasagne.objectives.categorical_crossentropy(
T.clip(output_test, 0.000001, 0.999999), Y)
test_loss = test_loss.mean()
test_loss = test_loss + l2_penalty
test_acc = T.mean(T.eq(T.argmax(output_test, axis=1), Y),
dtype=theano.config.floatX)
# get parameters from network and set up sgd with nesterov momentum to update parameters, l_r is shared var so it can be changed
l_r = theano.shared(np.array(LR_SCHEDULE[0], dtype=theano.config.floatX))
params = lasagne.layers.get_all_params(output_layer, trainable=True)
updates = nesterov_momentum(loss,
params,
learning_rate=l_r,
momentum=P.MOMENTUM)
#updates = adam(loss, params, learning_rate=l_r)
prediction_binary = T.argmax(output_train, axis=1)
test_prediction_binary = T.argmax(output_test, axis=1)
# set up training and prediction functions
train_fn = theano.function(
inputs=[X, Y],
outputs=[loss, l2_penalty, acc, prediction_binary, output_train[:, 1]],
updates=updates)
valid_fn = theano.function(inputs=[X, Y],
outputs=[
test_loss, l2_penalty, test_acc,
test_prediction_binary, output_test[:, 1]
])
return train_fn, valid_fn, l_r
def trainModel(self, tr_X, va_X, tr_y, va_y, batchSize=128, maxIter=100, verbose=True,
start=5, period=2, threshold=10, earlyStopTol=2, totalStopTol=2):
trainfn = self.trainfn
validatefn = self.vatefn
lr = self.lr
earlyStop = myUtils.basic.earlyStopGen(start, period, threshold, earlyStopTol)
earlyStop.next() # 初始化生成器
totalStopCount = 0
for epoch in xrange(maxIter): # every epoch
# In each epoch, we do a full pass over the training data:
trEqCount = 0
trCostSum = 0.
startTime = time.time()
for batch in myUtils.basic.miniBatchGen(tr_X, tr_y, batchSize, shuffle=True):
Xb, yb = batch
trCost, trEqs = trainfn(Xb, yb)
trCostSum += trCost
trEqCount += trEqs
trIter = len(tr_X) // batchSize
if len(tr_X) % batchSize != 0: trIter += 1
trCostMean = trCostSum / trIter
trAccuracy = float(trEqCount) / len(tr_X)
# And a full pass over the validation data:
vaEqCount = 0
vaCostSum = 0.
for batch in myUtils.basic.miniBatchGen(va_X, va_y, batchSize, shuffle=False):
Xb, yb = batch
vaCost, vaEqs = validatefn(Xb, yb)
vaCostSum += vaCost
vaEqCount += vaEqs
vaIter = len(va_X) // batchSize
if len(va_X) % batchSize != 0: vaIter += 1
vaCostMean = vaCostSum / vaIter
vaAccuracy = float(vaEqCount) / len(va_X)
if verbose:
print 'epoch ', epoch, ' time: %.3f' % (time.time() - startTime),
print 'trcost: %.5f tracc: %.5f' % (trCostMean, trAccuracy),
print 'vacost: %.5f vaacc: %.5f' % (vaCostMean, vaAccuracy)
# Then we decide whether to early stop:
if earlyStop.send((trCostMean, vaCostMean)):
lr /= 10 # 如果一次早停止发生，则学习率降低继续迭代
updatesDict = updates.nesterov_momentum(self.trCost, self.params, lr, self.momentum)
trainfn = myUtils.basic.makeFunc([self.X, self.y], [self.trCost, self.trEqs], updatesDict)
totalStopCount += 1
if totalStopCount > totalStopTol: # 如果学习率降低仍然发生早停止，则退出迭代
print 'stop'
break
if verbose: print 'learning rate decreases to ', lr
def __init__(self, conf):
self.conf = conf
if self.conf.act == "linear":
self.conf.act = linear
elif self.conf.act == "sigmoid":
self.conf.act = sigmoid
elif self.conf.act == "relu":
self.conf.act = rectify
elif self.conf.act == "tanh":
self.conf.act = tanh
else:
raise ValueError("Unknown activation function", self.conf.act)
input_var_first = T.matrix('inputs1')
input_var_second = T.matrix('inputs2')
target_var = T.matrix('targets')
# create network
self.autoencoder, encoder_first, encoder_second = self.__create_toplogy__(input_var_first, input_var_second)
self.out = get_output(self.autoencoder)
loss = squared_error(self.out, target_var)
loss = loss.mean()
params = get_all_params(self.autoencoder, trainable=True)
updates = nesterov_momentum(loss, params, learning_rate=self.conf.lr, momentum=self.conf.momentum)
# training function
self.train_fn = theano.function([input_var_first, input_var_second, target_var], loss, updates=updates)
# fuction to reconstruct
test_reconstruction = get_output(self.autoencoder, deterministic=True)
self.reconstruction_fn = theano.function([input_var_first, input_var_second], test_reconstruction)
# encoding function
test_encode = get_output([encoder_first, encoder_second], deterministic=True)
self.encoding_fn = theano.function([input_var_first, input_var_second], test_encode)
# utils
blas = lambda name, ndarray: scipy.linalg.get_blas_funcs((name,), (ndarray,))[0]
self.blas_nrm2 = blas('nrm2', np.array([], dtype=float))
self.blas_scal = blas('scal', np.array([], dtype=float))
# load weights if necessary
if self.conf.load_model is not None:
self.load_model()
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 net_updates(net, loss, lr):
# Get all trainable parameters (weights) of our net
params = l.get_all_params(net, trainable=True)
# We use the adam update, other options are available
if cfg.OPTIMIZER == 'adam':
param_updates = updates.adam(loss, params, learning_rate=lr, beta1=0.9)
elif cfg.OPTIMIZER == 'nesterov':
param_updates = updates.nesterov_momentum(loss,
params,
learning_rate=lr,
momentum=0.9)
elif cfg.OPTIMIZER == 'sgd':
param_updates = updates.sgd(loss, params, learning_rate=lr)
return param_updates
def __build_loss_train__fn__(self):
# create loss function
prediction = layers.get_output(self.net)
loss = objectives.categorical_crossentropy(prediction, self.__target_var__)
loss = loss.mean() + 1e-4 * regularization.regularize_network_params(self.net, regularization.l2)
val_acc = T.mean(T.eq(T.argmax(prediction, axis=1), self.__target_var__),dtype=theano.config.floatX)
# create parameter update expressions
params = layers.get_all_params(self.net, trainable=True)
self.eta = theano.shared(sp.array(sp.float32(0.05), dtype=sp.float32))
update_rule = updates.nesterov_momentum(loss, params, learning_rate=self.eta,
momentum=0.9)
# compile training function that updates parameters and returns training loss
self.__train_fn__ = theano.function([self.__input_var__,self.__target_var__], loss, updates=update_rule)
self.__predict_fn__ = theano.function([self.__input_var__], layers.get_output(self.net,deterministic=True))
self.__val_fn__ = theano.function([self.__input_var__,self.__target_var__], [loss,val_acc])
def similarity_iter(output_layer, match_layer, update_params, match_layer_w=0):
X1 = T.tensor4()
X2 = T.tensor4()
y = T.ivector()
# find the input layers
# TODO this better
all_layers = ll.get_all_layers(match_layer)
# make image of all layers
imwrite_architecture(all_layers, './layer_rep.png')
input_1 = filter(lambda x: x.name == 'input1', all_layers)[0]
input_2 = filter(lambda x: x.name == 'input2', all_layers)[0]
descriptors_train, match_prob_train = ll.get_output([output_layer, match_layer], {input_1: X1, input_2: X2})
descriptors_eval, match_prob_eval = ll.get_output([output_layer, match_layer], {input_1: X1, input_2: X2}, deterministic=True)
#descriptor_shape = ll.get_output_shape(output_layer, {input_1: X1, input_2: X2})
#print("Network output shape: %r" % (descriptor_shape,))
# distance minimization
distance = lambda x: (x[:,0,:] - x[:,1,:] + 1e-7).norm(2, axis=1)
#distance_eval = (descriptors_eval[:,0,:] - descriptors_eval[:,1,:] + 1e-7).norm(2, axis=1)
# 9/21 squaring the loss seems to prevent it from getting to 0.5 really quickly (i.e. w/in 3 epochs)
# let's see if it will learn something good
margin = 1
decay = 0
reg = regularize_network_params(match_layer, l2) * decay
loss = lambda x, z: ((1-match_layer_w)*T.mean(y*(distance(x)) + (1 - y)*(T.maximum(0, margin - distance(x))))/2 # constrastive loss
+ match_layer_w*T.mean(binary_crossentropy(z.T + 1e-7,y))) # matching loss
loss_reg = lambda x, z: (loss(x,z) + reg)
# this loss doesn't work since it just pushes all the descriptors near each other and then predicts 0 all the time for tha matching
#jason_loss = lambda x, z: T.mean(distance(x)*y + (1-y)*binary_crossentropy(z.T + 1e-7,y))
#loss_eval = T.mean(y*(distance_eval**2) + (1 - y)*(T.maximum(0, 1 - distance_eval)**2))
all_params = ll.get_all_params(match_layer) # unsure how I would do this if there were truly two trainable branches...
loss_train = loss_reg(descriptors_train, match_prob_train)
loss_train.name = 'combined_loss' # for the names
grads = T.grad(loss_train, all_params, add_names=True)
#updates = adam(grads, all_params, **update_params)
updates = nesterov_momentum(grads, all_params, **update_params)
train_iter = theano.function([X1, X2, y], [loss_train, loss(descriptors_train, match_prob_train)] + grads, updates=updates)
#theano.printing.pydotprint(loss, outfile='./loss_graph.png',var_with_name_simple=True)
valid_iter = theano.function([X1, X2, y], loss(descriptors_eval, match_prob_eval))
return {'train':train_iter, 'valid':valid_iter, 'gradnames':[g.name for g in grads]}
def get_train_fn(self, last_only=False):
input_var = self.net['input'].input_var
target_var = T.ivector('targets')
prediction = lasagne.layers.get_output(self.output_layer)
loss = categorical_crossentropy(prediction, target_var)
loss = loss.mean()
error = T.mean(T.neq(T.argmax(prediction, axis=1), target_var),
dtype=theano.config.floatX)
regularization = self.regularizer_amount * regularize_network_params(
self.output_layer, l2)
if last_only:
all_params = self.output_layer.get_params(trainable=True)
else:
all_params = lasagne.layers.get_all_params(self.output_layer,
trainable=True)
updates = nesterov_momentum(loss + regularization,
all_params,
learning_rate=self.lr)
return theano.function([input_var, target_var], (loss, error),
updates=updates)
def create_train_func(layers):
Xa, Xb = T.tensor4('Xa'), T.tensor4('Xb')
Xa_batch, Xb_batch = T.tensor4('Xa_batch'), T.tensor4('Xb_batch')
Tp = get_output(
layers['trans'],
inputs={
layers['inputa']: Xa,
layers['inputb']: Xb,
},
deterministic=False,
)
# transforms: ground-truth, predicted
Tg = T.fmatrix('Tg')
Tg_batch = T.fmatrix('Tg_batch')
theta_gt = Tg.reshape((-1, 2, 3))
theta_pr = Tp.reshape((-1, 2, 3))
# grids: ground-truth, predicted
Gg = T.dot(theta_gt, _meshgrid(20, 20))
Gp = T.dot(theta_pr, _meshgrid(20, 20))
train_loss = T.mean(T.sqr(Gg - Gp))
params = get_all_params(layers['trans'], trainable=True)
updates = nesterov_momentum(train_loss, params, 1e-3, 0.9)
corr_func = theano.function(
inputs=[theano.In(Xa_batch),
theano.In(Xb_batch),
theano.In(Tg_batch)],
outputs=[Tp, train_loss],
updates=updates,
givens={
Xa: Xa_batch,
Xb: Xb_batch, # Ia, Ib
Tg: Tg_batch, # transform Ia --> Ib
})
return corr_func
def create_updates(loss, network, opt, learning_rate, momentum, beta1, beta2):
params = lasagne.layers.get_all_params(network, trainable=True)
grads = theano.grad(loss, params)
# if max_norm:
# names = ['crf.U', 'crf.W_h', 'crf.W_c', 'crf.b']
# constraints = [grad for param, grad in zip(params, grads) if param.name in names]
# assert len(constraints) == 4
# scaled_grads = total_norm_constraint(constraints, max_norm=max_norm)
# counter = 0
# for i in xrange(len(params)):
# param = params[i]
# if param.name in names:
# grads[i] = scaled_grads[counter]
# counter += 1
# assert counter == 4
if opt == 'adam':
updates = adam(grads, params=params, learning_rate=learning_rate, beta1=beta1, beta2=beta2)
elif opt == 'momentum':
updates = nesterov_momentum(grads, params=params, learning_rate=learning_rate, momentum=momentum)
else:
raise ValueError('unkown optimization algorithm: %s' % opt)
return updates
def build_train_func(self, lr=0.01, mntm=0.9):
y_hat = get_output(self.layers['out'], self.x, deterministic=False)
train_loss = T.mean(
T.nnet.categorical_crossentropy(y_hat, self.y)
)
train_acc = T.mean(
T.eq(y_hat.argmax(axis=1), self.y)
)
all_params = get_all_params(self.layers['out'], trainable=True)
updates = nesterov_momentum(
train_loss, all_params, lr, mntm)
train_func = theano.function(
inputs=[theano.In(self.x_batch), theano.In(self.y_batch)],
outputs=[train_loss, train_acc],
updates=updates,
givens={
self.x: self.x_batch,
self.y: self.y_batch,
},
)
return train_func
def define_updates(output_layer, X, Y):
output_train = lasagne.layers.get_output(output_layer)
output_test = lasagne.layers.get_output(output_layer, deterministic=True)
# set up the loss that we aim to minimize when using cat cross entropy our Y should be ints not one-hot
loss = lasagne.objectives.categorical_crossentropy(T.clip(output_train,0.000001,0.999999), Y)
loss = loss.mean()
acc = T.mean(T.eq(T.argmax(output_train, axis=1), Y), dtype=theano.config.floatX)
# if using ResNet use L2 regularization
all_layers = lasagne.layers.get_all_layers(output_layer)
l2_penalty = lasagne.regularization.regularize_layer_params(all_layers, lasagne.regularization.l2) * P.L2_LAMBDA
loss = loss + l2_penalty
# set up loss functions for validation dataset
test_loss = lasagne.objectives.categorical_crossentropy(T.clip(output_test,0.000001,0.999999), Y)
test_loss = test_loss.mean()
test_loss = test_loss + l2_penalty
test_acc = T.mean(T.eq(T.argmax(output_test, axis=1), Y), dtype=theano.config.floatX)
# get parameters from network and set up sgd with nesterov momentum to update parameters, l_r is shared var so it can be changed
l_r = theano.shared(np.array(LR_SCHEDULE[0], dtype=theano.config.floatX))
params = lasagne.layers.get_all_params(output_layer, trainable=True)
updates = nesterov_momentum(loss, params, learning_rate=l_r, momentum=P.MOMENTUM)
#updates = adam(loss, params, learning_rate=l_r)
prediction_binary = T.argmax(output_train, axis=1)
test_prediction_binary = T.argmax(output_test, axis=1)
# set up training and prediction functions
train_fn = theano.function(inputs=[X,Y], outputs=[loss, l2_penalty, acc, prediction_binary, output_train[:,1]], updates=updates)
valid_fn = theano.function(inputs=[X,Y], outputs=[test_loss, l2_penalty, test_acc, test_prediction_binary, output_test[:,1]])
return train_fn, valid_fn, l_r
def __init__(self, lr, C, momentum):
self.lr = lr
self.C = C
self.momentum = momentum
self.X = T.tensor4('X')
self.y = T.ivector('y')
self.network = build_model(self.X)
self.prob = self.network['prob']
feat = self.network['pool5/7x7_s1']
featout = layers.get_output(feat, deterministic=True)
self.params = layers.get_all_params(self.prob, trainable=True)
reg = regularization.regularize_network_params(self.prob,
regularization.l2)
reg /= layers.helper.count_params(self.prob)
# 训练集
self.yDropProb = layers.get_output(self.prob)
trCrossentropy = objectives.categorical_crossentropy(
self.yDropProb, self.y)
self.trCost = trCrossentropy.mean() + C * reg
# 验证、测试集
self.yFullProb = layers.get_output(self.prob, deterministic=True)
vateCrossentropy = objectives.categorical_crossentropy(
self.yFullProb, self.y)
self.vateCost = vateCrossentropy.mean() + C * reg
# 训练函数，输入训练集，输出训练损失和误差
updatesDict = updates.nesterov_momentum(self.trCost, self.params, lr,
momentum)
self.trainfn = myUtils.basic.makeFunc([self.X, self.y],
[self.trCost, self.yDropProb],
updatesDict)
# 验证或测试函数，输入验证或测试集，输出损失和误差，不进行更新
self.vatefn = myUtils.basic.makeFunc([self.X, self.y],
[self.vateCost, self.yFullProb],
None)
# 输出特征
self.featfn = myUtils.basic.makeFunc([self.X], [featout], None)
def train(files, batch_size, emb_dim_size, save_dir, load_dir, skip_window,
num_skips):
learning_rate = 0.1
momentum = 0.9
num_epochs = 1000
dataset = Dataset(files,
load_dir=load_dir,
skip_window=skip_window,
num_skips=num_skips)
data_stream = dataset.data_stream
if save_dir:
dataset.save_dictionary(save_dir)
dictionary = dataset.dictionary
reverse_dictionary = dict((v, k) for k, v in dictionary.iteritems())
print 'Dictionary size: ', len(dictionary)
query_input = T.ivector('query')
context_target = T.ivector('context')
word2vec = Word2VecDiscrete(batch_size=batch_size,
context_vocab_size=dataset.vocab_size,
query_vocab_size=dataset.vocab_size,
emb_dim_size=emb_dim_size)
word2vec.build_model(query_input)
prediction = word2vec.get_output()
loss = lasagne.objectives.categorical_crossentropy(prediction,
context_target)
loss = loss.mean()
params = word2vec.get_all_params()
updates = nesterov_momentum(loss, params, learning_rate, momentum)
train = theano.function([query_input, context_target],
loss,
updates=updates)
losses = []
mean_losses = []
start = time.time()
for epoch in range(num_epochs):
mean_loss = 0
for i, batch in enumerate(data_stream.get_batches(batch_size)):
queries, contexts = batch
losses.append(train(queries, contexts))
if save_dir and i % 10000 == 0:
word2vec.save(save_dir)
mean_loss = np.mean(losses)
mean_losses.append(mean_loss)
if epoch % 1 == 0:
print('epoch {} mean loss {}'.format(epoch, mean_loss))
#print 'Embedding for king is: ', word2vec.embed([dictionary['king']])
if save_dir:
word2vec.save(save_dir)
end = time.time()
print("Time: ", end - start)
print 'Top similar words: '
results = [
(word,
spatial.distance.euclidean(word2vec.embed([dictionary['king']]),
word2vec.embed([dictionary[word]])))
for (word, _) in dictionary.iteritems()
]
results.sort(key=operator.itemgetter(1))
out = [r[0] for r in results]
print 'closest to {} : {}'.format('king', out)
print 'Top similar words: '
results = [
(word,
spatial.distance.euclidean(word2vec.embed([dictionary['queen']]),
word2vec.embed([dictionary[word]])))
for (word, _) in dictionary.iteritems()
]
results.sort(key=operator.itemgetter(1))
out = [r[0] for r in results]
print 'closest to {} : {}'.format('queen', out)
plt.title("Mean loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.plot(mean_losses, label="train")
plt.grid()
plt.legend()
plt.draw()
plt.show()
def main():
# load the training and validation data sets
train_X, test_X, train_y, test_y = load_data_cv('data/train.csv')
X = T.ftensor4()
Y = T.fmatrix()
# set up theano functions to generate output by feeding data through network
output_layer = lasagne_model()
output_train = lasagne.layers.get_output(output_layer, X)
output_valid = lasagne.layers.get_output(output_layer, X, deterministic=True)
# set up the loss that we aim to minimize
loss_train = T.mean(T.nnet.categorical_crossentropy(output_train, Y))
loss_valid = T.mean(T.nnet.categorical_crossentropy(output_valid, Y))
# prediction functions for classifications
pred = T.argmax(output_train, axis=1)
pred_valid = T.argmax(output_valid, axis=1)
# get parameters from network and set up sgd with nesterov momentum to update parameters
params = lasagne.layers.get_all_params(output_layer)
updates = nesterov_momentum(loss_train, params, learning_rate=0.003, momentum=0.9)
# set up training and prediction functions
train = theano.function(inputs=[X, Y], outputs=loss_train, updates=updates, allow_input_downcast=True)
valid = theano.function(inputs=[X, Y], outputs=loss_valid, allow_input_downcast=True)
predict_valid = theano.function(inputs=[X], outputs=pred_valid, allow_input_downcast=True)
# loop over training functions for however many iterations, print information while training
train_eval = []
valid_eval = []
valid_acc = []
try:
for i in range(45):
train_loss = batch_iterator(train_X, train_y, BATCHSIZE, train)
train_eval.append(train_loss)
valid_loss = valid(test_X, test_y)
valid_eval.append(valid_loss)
acc = np.mean(np.argmax(test_y, axis=1) == predict_valid(test_X))
valid_acc.append(acc)
print 'iter:', i, '| Tloss:', train_loss, '| Vloss:', valid_loss, '| valid acc:', acc
except KeyboardInterrupt:
pass
# save weights
all_params = helper.get_all_param_values(output_layer)
f = gzip.open('data/weights.pklz', 'wb')
pickle.dump(all_params, f)
f.close()
# plot loss and accuracy
train_eval = np.array(train_eval)
valid_eval = np.array(valid_eval)
valid_acc = np.array(valid_acc)
sns.set_style("whitegrid")
pyplot.plot(train_eval, linewidth = 3, label = 'train loss')
pyplot.plot(valid_eval, linewidth = 3, label = 'valid loss')
pyplot.legend(loc = 2)
pyplot.twinx()
pyplot.plot(valid_acc, linewidth = 3, label = 'valid accuracy', color = 'r')
pyplot.grid()
pyplot.ylim([.9,1])
pyplot.legend(loc = 1)
pyplot.savefig('data/training_plot.png')
def main():
# load the training and validation data sets
# labels=int(0.7*speech.all_count)
global valid_best
data=speech.data_dict
labels=data.keys()
# assert (PIXELS,PIXELS)==speechSize
train_X=np.zeros([num_speechs,1,speechSize[0],speechSize[1]])
train_y=np.zeros([num_speechs,len(labels)])
i=0
for label in (data.keys()):
for im in data[label]:
train_X[i,0]=im
train_y[i]=label_binarize([label],labels)[0]
i+=1
if i>=num_speechs:
break
if i%500==0:
print 'idx of speechs:',i
if i>=num_speechs:
break
zipp=zip(train_X,train_y)
random.shuffle(zipp)
xx=np.array([one[0] for one in zipp])
yy=np.array([one[1] for one in zipp])
del train_X,train_y
train_X=xx[:size]
train_y=yy[:size]
valid_X=xx[size:]
valid_y=yy[size:]
del xx,yy
print 'Shuffle finish. Begin to build model.'
X = T.tensor4()
Y = T.matrix()
# set up theano functions to generate output by feeding data through network
output_layer = lasagne_model()
output_train = lasagne.layers.get_output(output_layer, X)
output_valid = lasagne.layers.get_output(output_layer, X, deterministic=True)
# set up the loss that we aim to minimize
loss_train = T.mean(T.nnet.categorical_crossentropy(output_train, Y))
loss_valid = T.mean(T.nnet.categorical_crossentropy(output_valid, Y))
# prediction functions for classifications
pred = T.argmax(output_train, axis=1)
pred_valid = T.argmax(output_valid, axis=1)
# get parameters from network and set up sgd with nesterov momentum to update parameters
params = lasagne.layers.get_all_params(output_layer,trainable=True)
updates = nesterov_momentum(loss_train, params, learning_rate=0.003, momentum=0.9)
# updates =lasagne.updates.sgd(loss_train, params, learning_rate=0.01)
# set up training and prediction functions
train = theano.function(inputs=[X, Y], outputs=[loss_train,pred], updates=updates, allow_input_downcast=True)
valid = theano.function(inputs=[X, Y], outputs=[loss_valid,pred_valid], allow_input_downcast=True)
# predict_valid = theano.function(inputs=[X], outputs=[pred_valid], allow_input_downcast=True)
# loop over training functions for however many iterations, print information while training
train_eval = []
valid_eval = []
valid_acc = []
for i in range(450):
batch_total_number = len(train_X) / BATCHSIZE
for idx_batch in range (batch_total_number):
xx_batch = np.float32(train_X[idx_batch * BATCHSIZE:(idx_batch + 1) * BATCHSIZE])
yy_batch = np.float32(train_y[idx_batch * BATCHSIZE:(idx_batch + 1) * BATCHSIZE])
train_loss ,pred = train(xx_batch,yy_batch)
count=np.count_nonzero(np.int32(pred ==np.argmax(yy_batch,axis=1)))
print i,idx_batch,'| Tloss:', train_loss,'| Count:',count,'| Acc:',float(count)/(BATCHSIZE)
print pred
print np.argmax(yy_batch,axis=1)
print "time:",time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
if 1 and idx_batch%15==0:
acc=0
valid_batch_number=len(valid_X)/BATCHSIZE
for j in tqdm(range(valid_batch_number)):
x_batch = np.float32(valid_X[j* BATCHSIZE:(j+ 1) * BATCHSIZE])
y_batch = np.float32(valid_y[j* BATCHSIZE:(j+ 1) * BATCHSIZE])
print len(x_batch),len(y_batch)
valid_loss,pred = valid(x_batch,y_batch)
# pred = predict_valid(x_batch)[0]
acc += np.count_nonzero(np.int32(pred ==np.argmax(y_batch,axis=1)))
acc=float(acc)/(valid_batch_number*BATCHSIZE)
print 'iter:', i,idx_batch, '| Vloss:',valid_loss,'|Acc:',acc
if acc>valid_best:
print 'new valid_best:',valid_best,'-->',acc
valid_best=acc
all_params = helper.get_all_param_values(output_layer)
f = gzip.open('speech_params/validbest_cnn_allbatchnorm_{}_{}_{}.pklz'.format(i,valid_best,time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())), 'wb')
pickle.dump(all_params, f)
f.close()
# save weights
if i%5:
all_params = helper.get_all_param_values(output_layer)
f = gzip.open('speech_params/validbest_cnn_allbatchnorm_{}_{}_{}.pklz'.format(i,acc,time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())), 'wb')
pickle.dump(all_params, f)
f.close()
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
def main():
# load model and parameters
output_layer = lasagne_model()
f = gzip.open('data/weights.pklz', 'rb')
all_params = pickle.load(f)
f.close()
X = T.ftensor4()
Y = T.fmatrix()
# set up theano functions to generate output by feeding data through network
output_layer = lasagne_model()
output_train = lasagne.layers.get_output(output_layer, X)
output_valid = lasagne.layers.get_output(output_layer,
X,
deterministic=True)
# set up the loss that we aim to minimize
loss_train = T.mean(T.nnet.categorical_crossentropy(output_train, Y))
loss_valid = T.mean(T.nnet.categorical_crossentropy(output_valid, Y))
# prediction functions for classifications
pred = T.argmax(output_train, axis=1)
pred_valid = T.argmax(output_valid, axis=1)
# get parameters from network and set up sgd with nesterov momentum to update parameters
helper.set_all_param_values(output_layer, all_params)
params = lasagne.layers.get_all_params(output_layer)
updates = nesterov_momentum(loss_train,
params,
learning_rate=0.0001,
momentum=0.9)
# set up training and prediction functions
train = theano.function(inputs=[X, Y],
outputs=loss_train,
updates=updates,
allow_input_downcast=True)
valid = theano.function(inputs=[X, Y],
outputs=loss_valid,
allow_input_downcast=True)
predict_valid = theano.function(inputs=[X],
outputs=pred_valid,
allow_input_downcast=True)
# fine tune network
train_X, test_X, train_y, test_y = load_data_cv('data/train.csv')
train_eval = []
valid_eval = []
valid_acc = []
try:
for i in range(5):
train_loss = batch_iterator_no_aug(train_X, train_y, BATCHSIZE,
train)
train_eval.append(train_loss)
valid_loss = valid(test_X, test_y)
valid_eval.append(valid_loss)
acc = np.mean(np.argmax(test_y, axis=1) == predict_valid(test_X))
valid_acc.append(acc)
print 'iter:', i, '| Tloss:', train_loss, '| Vloss:', valid_loss, '| valid acc:', acc
except KeyboardInterrupt:
pass
# after training create output for kaggle
testing_inputs = load_test_data('data/test.csv')
predictions = []
for j in range((testing_inputs.shape[0] + BATCHSIZE - 1) // BATCHSIZE):
sl = slice(j * BATCHSIZE, (j + 1) * BATCHSIZE)
X_batch = testing_inputs[sl]
predictions.extend(predict_valid(X_batch))
out = pd.read_csv('data/convnet_preds.csv')
out['Label'] = predictions
out.to_csv('preds/convnet_preds.csv', index=False)
####################### UPDATES #########################
#we use dynamic learning rates which change after some epochs
lr_dynamic = T.scalar(name='learning_rate')
#get all trainable parameters (weights) of our net
params = l.get_all_params(NET, trainable=True)
#we use the adam update
if OPTIMIZER == 'adam':
param_updates = updates.adam(loss,
params,
learning_rate=lr_dynamic,
beta1=0.5)
elif OPTIMIZER == 'nesterov':
param_updates = updates.nesterov_momentum(loss,
params,
learning_rate=lr_dynamic,
momentum=0.9)
#################### TRAIN FUNCTION ######################
#the theano train functions takes images and class targets as input
print "COMPILING THEANO TRAIN FUNCTION...",
start = time.time()
train_net = theano.function(
[l.get_all_layers(NET)[0].input_var, targets, lr_dynamic],
loss,
updates=param_updates)
print "DONE! (", int(time.time() - start), "s )"
################# PREDICTION FUNCTION ####################
#we need the prediction function to calculate the validation accuracy
#this way we can test the net during/after training
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
output_test = lasagne.layers.get_output(output_layer, deterministic=True)
# set up the loss that we aim to minimize, when using cat cross entropy our Y should be ints not one-hot
loss = lasagne.objectives.categorical_crossentropy(output_train, Y)
loss = loss.mean()
# set up loss functions for validation dataset
valid_loss = lasagne.objectives.categorical_crossentropy(output_test, Y)
valid_loss = valid_loss.mean()
valid_acc = T.mean(T.eq(T.argmax(output_test, axis=1), Y), dtype=theano.config.floatX)
# get parameters from network and set up sgd with nesterov momentum to update parameters
l_r = theano.shared(np.array(LR_SCHEDULE[0], dtype=theano.config.floatX))
params = lasagne.layers.get_all_params(output_layer, trainable=True)
updates = nesterov_momentum(loss, params, learning_rate=l_r)
# set up training and prediction functions
train_fn = theano.function(inputs=[X,Y], outputs=loss, updates=updates)
valid_fn = theano.function(inputs=[X,Y], outputs=[valid_loss, valid_acc])
# set up prediction function
predict_proba = theano.function(inputs=[X], outputs=output_test)
'''
load training data and start training
'''
encoder = LabelEncoder()
# load data
X_train = np.load('data/cache/X_train_%d_f32_clean.npy'%PIXELS)
def build_finetune_functions(self, datasets, batch_size, learning_rate):
"""Generates a function `train_function` that implements one step of
finetuning, a function `valid_score` that computes the cost on the validation set
and a function 'test_score' that computes the reconstruction cost on the test set.
:type datasets: list of pairs of theano.tensor.TensorType
:param datasets: It is a list that contain all the datasets;
the has to contain three pairs, `train`,
`valid`, `test` in this order, where each pair
is formed of two Theano variables, one for the
datapoints, the other for the labels
:type batch_size: int
:param batch_size: size of a minibatch
:type learning_rate: float (usually a shared variable so it can be updated)
:param learning_rate: learning rate used during finetune stage
"""
(train_set_x, train_set_y) = datasets[0]
(valid_set_x, valid_set_y) = datasets[1]
(test_set_x, test_set_y) = datasets[2]
index = T.lscalar('index') # index to a [mini]batch
updates = nesterov_momentum(self.fine_tune_cost, self.params, learning_rate=learning_rate, momentum=0.9)
train_function = theano.function(
inputs=[index],
outputs=self.fine_tune_cost,
updates=updates,
givens={
self.x: train_set_x[index * batch_size: (index + 1) * batch_size]
},
name='train')
valid_score_i = theano.function(
[index],
outputs=self.test_cost,
givens={
self.x: valid_set_x[index * batch_size: (index + 1) * batch_size],
},
name='valid'
)
test_score_i = theano.function(
[index],
outputs=self.test_cost,
givens={
self.x: test_set_x[index * batch_size: (index + 1) * batch_size],
},
name='test'
)
# Create a function that scans the entire validation set
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] // batch_size
def valid_score():
return [valid_score_i(i) for i in range(n_valid_batches)]
# Create a function that scans the entire test set
n_test_batches = test_set_x.get_value(borrow=True).shape[0] // batch_size
def test_score():
return [test_score_i(i) for i in range(n_test_batches)]
return train_function, valid_score, test_score
def get_model():
# Prepare Theano variables for inputs and targets
input_var = T.tensor4('inputs')
target_var = T.matrix('targets')
# input layer with unspecified batch size
layer_0 = InputLayer(shape=(None, 30, 64, 64), input_var=input_var)
# Z-score?
# Convolution then batchNormalisation then activation layer, twice, then zero padding layer followed by a dropout layer
layer_1 = batch_norm(
Conv2DLayer(layer_0,
64, (3, 3),
pad='same',
nonlinearity=leaky_rectify))
layer_2 = batch_norm(
Conv2DLayer(layer_1,
64, (3, 3),
pad='valid',
nonlinearity=leaky_rectify))
layer_3 = MaxPool2DLayer(layer_2,
pool_size=(2, 2),
stride=(2, 2),
pad=(1, 1))
layer_4 = DropoutLayer(layer_3, p=0.25)
# Convolution then batchNormalisation then activation layer, twice, then zero padding layer followed by a dropout layer
layer_5 = batch_norm(
Conv2DLayer(layer_4,
96, (3, 3),
pad='same',
nonlinearity=leaky_rectify))
layer_6 = batch_norm(
Conv2DLayer(layer_5,
96, (3, 3),
pad='valid',
nonlinearity=leaky_rectify))
layer_7 = MaxPool2DLayer(layer_6,
pool_size=(2, 2),
stride=(2, 2),
pad=(1, 1))
layer_8 = DropoutLayer(layer_7, p=0.25)
# Convolution then batchNormalisation then activation layer, twice, then zero padding layer followed by a dropout layer
layer_9 = batch_norm(
Conv2DLayer(layer_8,
128, (3, 3),
pad='same',
nonlinearity=leaky_rectify))
layer_10 = batch_norm(
Conv2DLayer(layer_9,
128, (3, 3),
pad='valid',
nonlinearity=leaky_rectify))
layer_11 = MaxPool2DLayer(layer_10,
pool_size=(2, 2),
stride=(2, 2),
pad=(1, 1))
layer_12 = DropoutLayer(layer_11, p=0.25)
# Last layers
layer_13 = FlattenLayer(layer_12)
layer_14 = DenseLayer(layer_13, 1024, nonlinearity=leaky_rectify)
layer_15 = DropoutLayer(layer_14, p=0.5)
layer_16 = DenseLayer(layer_15, 600, nonlinearity=softmax)
# Loss
prediction = get_output(layer_16)
loss = squared_error(prediction, target_var)
loss = loss.mean() + regularize_layer_params(layer_14, l2)
#Updates : Stochastic Gradient Descent (SGD) with Nesterov momentum
params = get_all_params(layer_16, trainable=True)
updates = nesterov_momentum(loss, params, learning_rate=0.01, momentum=0.9)
# Create a loss expression for validation/testing. The crucial difference
# here is that we do a deterministic forward pass through the network, disabling dropout layers.
test_prediction = get_output(layer_16, deterministic=True)
test_loss = squared_error(test_prediction, target_var)
test_loss = test_loss.mean()
# Compile a function performing a training step on a mini-batch (by giving
# the updates dictionary) and returning the corresponding training loss:
train_fn = theano.function([input_var, target_var], loss, updates=updates)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input_var, target_var], test_loss)
# Compule a third function computing the prediction
predict_fn = theano.function([input_var], test_prediction)
return [layer_16, train_fn, val_fn, predict_fn]
def get_model():
# Prepare Theano variables for inputs and targets
input_var = T.tensor4('inputs')
target_var = T.matrix('targets')
# input layer with unspecified batch size
layer_both_0 = InputLayer(shape=(None, 30, 64, 64), input_var=input_var)
# Z-score?
# Convolution then batchNormalisation then activation layer, twice, then zero padding layer followed by a dropout layer
layer_both_1 = batch_norm(Conv2DLayer(layer_both_0, 64, (3, 3), pad='same', nonlinearity=leaky_rectify))
layer_both_2 = batch_norm(Conv2DLayer(layer_both_1, 64, (3, 3), pad='valid', nonlinearity=leaky_rectify))
layer_both_3 = MaxPool2DLayer(layer_both_2, pool_size=(2, 2), stride=(2, 2), pad=(1, 1))
layer_both_4 = DropoutLayer(layer_both_3, p=0.25)
# Systole : Convolution then batchNormalisation then activation layer, twice, then zero padding layer followed by a dropout layer
layer_systole_0 = batch_norm(Conv2DLayer(layer_both_4, 96, (3, 3), pad='same',nonlinearity=leaky_rectify))
layer_systole_1 = batch_norm(Conv2DLayer(layer_systole_0, 96, (3, 3), pad='valid',nonlinearity=leaky_rectify))
layer_systole_2 = MaxPool2DLayer(layer_systole_1, pool_size=(2, 2), stride=(2, 2), pad=(1, 1))
layer_systole_3 = DropoutLayer(layer_systole_2, p=0.25)
# Diastole : Convolution then batchNormalisation then activation layer, twice, then zero padding layer followed by a dropout layer
layer_diastole_0 = batch_norm(Conv2DLayer(layer_both_4, 96, (3, 3), pad='same',nonlinearity=leaky_rectify))
layer_diastole_1 = batch_norm(Conv2DLayer(layer_diastole_0, 96, (3, 3), pad='valid',nonlinearity=leaky_rectify))
layer_diastole_2 = MaxPool2DLayer(layer_diastole_1, pool_size=(2, 2), stride=(2, 2), pad=(1, 1))
layer_diastole_3 = DropoutLayer(layer_diastole_2, p=0.25)
# Systole : Convolution then batchNormalisation then activation layer, twice, then zero padding layer followed by a dropout layer
layer_systole_4 = batch_norm(Conv2DLayer(layer_systole_3, 128, (3, 3), pad='same',nonlinearity=leaky_rectify))
layer_systole_5 = batch_norm(Conv2DLayer(layer_systole_4, 128, (3, 3), pad='valid',nonlinearity=leaky_rectify))
layer_systole_6 = MaxPool2DLayer(layer_systole_5, pool_size=(2, 2), stride=(2, 2), pad=(1, 1))
layer_systole_7 = DropoutLayer(layer_systole_6, p=0.25)
# Diastole : Convolution then batchNormalisation then activation layer, twice, then zero padding layer followed by a dropout layer
layer_diastole_4 = batch_norm(Conv2DLayer(layer_diastole_3, 128, (3, 3), pad='same',nonlinearity=leaky_rectify))
layer_diastole_5 = batch_norm(Conv2DLayer(layer_diastole_4, 128, (3, 3), pad='valid',nonlinearity=leaky_rectify))
layer_diastole_6 = MaxPool2DLayer(layer_diastole_5, pool_size=(2, 2), stride=(2, 2), pad=(1, 1))
layer_diastole_7 = DropoutLayer(layer_diastole_6, p=0.25)
# Systole : Last layers
layer_systole_8 = FlattenLayer(layer_systole_7)
layer_systole_9 = DenseLayer(layer_systole_8, 1024, nonlinearity=leaky_rectify)
layer_systole_10 = DropoutLayer(layer_systole_9, p=0.5)
layer_systole_11 = DenseLayer(layer_systole_10, 600, nonlinearity=softmax)
# Diastole : Last layers
layer_diastole_8 = FlattenLayer(layer_diastole_7)
layer_diastole_9 = DenseLayer(layer_diastole_8, 1024, nonlinearity=leaky_rectify)
layer_diastole_10 = DropoutLayer(layer_diastole_9, p=0.5)
layer_diastole_11 = DenseLayer(layer_diastole_10, 600, nonlinearity=softmax)
# Merge
layer_both_5 = ConcatLayer([layer_systole_11, layer_diastole_11])
# Loss
prediction = get_output(layer_both_5)
loss = squared_error(prediction, target_var)
loss = loss.mean() + regularize_layer_params(layer_systole_9, l2) + regularize_layer_params(layer_diastole_9, l2)
#Updates : Stochastic Gradient Descent (SGD) with Nesterov momentum
params = get_all_params(layer_both_5, trainable=True)
updates = nesterov_momentum(loss, params, learning_rate=0.01, momentum=0.9)
# Create a loss expression for validation/testing. The crucial difference
# here is that we do a deterministic forward pass through the network, disabling dropout layers.
test_prediction = get_output(layer_both_5, deterministic=True)
test_loss = squared_error(test_prediction, target_var)
test_loss = test_loss.mean()
# Compile a function performing a training step on a mini-batch (by giving
# the updates dictionary) and returning the corresponding training loss:
train_fn = theano.function([input_var, target_var], loss, updates=updates)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input_var, target_var], test_loss)
# Compule a third function computing the prediction
predict_fn = theano.function([input_var], test_prediction)
return [layer_both_5, train_fn, val_fn, predict_fn]
def main():
# load the training and validation data sets
labels=int(0.7*image.all_count)
data=image.ddd
train_X=np.zeros([size,1,PIXELS,PIXELS])
train_y=np.zeros([size,labels])
for i in range(size):
label=random.sample(range(labels),1)[0]
train_X[i,0]=0.01*random.sample(data[label],1)[0]
train_y[i]=label_binarize([label],range(labels))[0]
X = T.tensor4()
Y = T.matrix()
# set up theano functions to generate output by feeding data through network
output_layer = lasagne_model()
output_train = lasagne.layers.get_output(output_layer, X)
output_valid = lasagne.layers.get_output(output_layer, X, deterministic=True)
# set up the loss that we aim to minimize
loss_train = T.mean(T.nnet.categorical_crossentropy(output_train, Y))
loss_valid = T.mean(T.nnet.categorical_crossentropy(output_valid, Y))
# prediction functions for classifications
pred = T.argmax(output_train, axis=1)
pred_valid = T.argmax(output_valid, axis=1)
# get parameters from network and set up sgd with nesterov momentum to update parameters
params = lasagne.layers.get_all_params(output_layer)
updates = nesterov_momentum(loss_train, params, learning_rate=0.03, momentum=0.9)
# updates =lasagne.updates.adagrad(loss_train, params, learning_rate=0.003)
# set up training and prediction functions
train = theano.function(inputs=[X, Y], outputs=[loss_train,pred_valid], updates=updates, allow_input_downcast=True)
valid = theano.function(inputs=[X, Y], outputs=loss_valid, allow_input_downcast=True)
predict_valid = theano.function(inputs=[X], outputs=pred_valid, allow_input_downcast=True)
# loop over training functions for however many iterations, print information while training
train_eval = []
valid_eval = []
valid_acc = []
for i in range(450):
batch_total_number = len(train_X) / BATCHSIZE
for idx_batch in range (batch_total_number):
xx_batch = np.float32(train_X[idx_batch * BATCHSIZE:(idx_batch + 1) * BATCHSIZE])
yy_batch = np.float32(train_y[idx_batch * BATCHSIZE:(idx_batch + 1) * BATCHSIZE])
train_loss ,pred = train(xx_batch,yy_batch)
print i,idx_batch,'| Tloss:', train_loss,'| Count:',np.count_nonzero(np.int32(pred ==np.argmax(yy_batch,axis=1)))
print pred
print np.argmax(yy_batch,axis=1)
acc=0
for j in range(batch_total_number):
x_batch = np.float32(train_X[idx_batch * BATCHSIZE:(idx_batch + 1) * BATCHSIZE])
y_batch = np.float32(train_y[idx_batch * BATCHSIZE:(idx_batch + 1) * BATCHSIZE])
pred = predict_valid(x_batch)
acc += np.count_nonzero(np.int32(pred ==np.argmax(y_batch,axis=1)))
acc=float(acc)/(BATCHSIZE*batch_total_number)
print 'iter:', i,idx_batch, '| Tloss:', train_loss,'|Acc:',acc
# save weights
all_params = helper.get_all_param_values(output_layer)
f = gzip.open('params/weights.pklz', 'wb')
pickle.dump(all_params, f)
f.close()
def main():
parser = argparse.ArgumentParser(description='Tuning with bi-directional LSTM-CNN-CRF')
parser.add_argument('--num_epochs', type=int, default=1000, help='Number of training epochs')
parser.add_argument('--batch_size', type=int, default=10, help='Number of sentences in each batch')
parser.add_argument('--num_units', type=int, default=100, help='Number of hidden units in LSTM')
parser.add_argument('--num_filters', type=int, default=20, help='Number of filters in CNN')
parser.add_argument('--learning_rate', type=float, default=0.1, help='Learning rate')
parser.add_argument('--decay_rate', type=float, default=0.1, help='Decay rate of learning rate')
parser.add_argument('--grad_clipping', type=float, default=0, help='Gradient clipping')
parser.add_argument('--gamma', type=float, default=1e-6, help='weight for regularization')
parser.add_argument('--delta', type=float, default=0.0, help='weight for expectation-linear regularization')
parser.add_argument('--regular', choices=['none', 'l2'], help='regularization for training', required=True)
parser.add_argument('--dropout', choices=['std', 'recurrent'], help='dropout patten')
parser.add_argument('--schedule', nargs='+', type=int, help='schedule for learning rate decay')
parser.add_argument('--output_prediction', action='store_true', help='Output predictions to temp files')
parser.add_argument('--train') # "data/POS-penn/wsj/split1/wsj1.train.original"
parser.add_argument('--dev') # "data/POS-penn/wsj/split1/wsj1.dev.original"
parser.add_argument('--test') # "data/POS-penn/wsj/split1/wsj1.test.original"
args = parser.parse_args()
logger = get_logger("Sequence Labeling")
train_path = args.train
dev_path = args.dev
test_path = args.test
num_epochs = args.num_epochs
batch_size = args.batch_size
num_units = args.num_units
num_filters = args.num_filters
regular = args.regular
grad_clipping = args.grad_clipping
gamma = args.gamma
delta = args.delta
learning_rate = args.learning_rate
momentum = 0.9
decay_rate = args.decay_rate
schedule = args.schedule
output_predict = args.output_prediction
dropout = args.dropout
p = 0.5
logger.info("Creating Alphabets")
word_alphabet, char_alphabet, pos_alphabet, type_alphabet = data_utils.create_alphabets("data/alphabets/",
[train_path, dev_path,
test_path],
40000)
logger.info("Word Alphabet Size: %d" % word_alphabet.size())
logger.info("Character Alphabet Size: %d" % char_alphabet.size())
logger.info("POS Alphabet Size: %d" % pos_alphabet.size())
num_labels = pos_alphabet.size() - 1
logger.info("Reading Data")
data_train = data_utils.read_data(train_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet)
data_dev = data_utils.read_data(dev_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet)
data_test = data_utils.read_data(test_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet)
num_data = sum([len(bucket) for bucket in data_train])
logger.info("constructing network...")
# create variables
target_var = T.imatrix(name='targets')
mask_var = T.matrix(name='masks', dtype=theano.config.floatX)
mask_nr_var = T.matrix(name='masks_nr', dtype=theano.config.floatX)
word_var = T.imatrix(name='inputs')
char_var = T.itensor3(name='char-inputs')
network = build_network(word_var, char_var, mask_var, word_alphabet, char_alphabet, dropout, num_units, num_labels,
grad_clipping, num_filters, p)
logger.info("Network structure: hidden=%d, filter=%d, dropout=%s" % (num_units, num_filters, dropout))
# compute loss
num_tokens = mask_var.sum(dtype=theano.config.floatX)
num_tokens_nr = mask_nr_var.sum(dtype=theano.config.floatX)
# get outpout of bi-lstm-cnn-crf shape [batch, length, num_labels, num_labels]
energies_train = lasagne.layers.get_output(network)
energies_train_det = lasagne.layers.get_output(network, deterministic=True)
energies_eval = lasagne.layers.get_output(network, deterministic=True)
loss_train_org = chain_crf_loss(energies_train, target_var, mask_var).mean()
energy_shape = energies_train.shape
# [batch, length, num_labels, num_labels] --> [batch*length, num_labels*num_labels]
energies = T.reshape(energies_train, (energy_shape[0] * energy_shape[1], energy_shape[2] * energy_shape[3]))
energies = nonlinearities.softmax(energies)
energies_det = T.reshape(energies_train_det, (energy_shape[0] * energy_shape[1], energy_shape[2] * energy_shape[3]))
energies_det = nonlinearities.softmax(energies_det)
# [batch*length, num_labels*num_labels] --> [batch, length*num_labels*num_labels]
energies = T.reshape(energies, (energy_shape[0], energy_shape[1] * energy_shape[2] * energy_shape[3]))
energies_det = T.reshape(energies_det, (energy_shape[0], energy_shape[1] * energy_shape[2] * energy_shape[3]))
loss_train_expect_linear = lasagne.objectives.squared_error(energies, energies_det)
loss_train_expect_linear = loss_train_expect_linear.sum(axis=1)
loss_train_expect_linear = loss_train_expect_linear.mean()
loss_train = loss_train_org + delta * loss_train_expect_linear
# l2 regularization?
if regular == 'l2':
l2_penalty = lasagne.regularization.regularize_network_params(network, lasagne.regularization.l2)
loss_train = loss_train + gamma * l2_penalty
_, corr_train = chain_crf_accuracy(energies_train, target_var)
corr_nr_train = (corr_train * mask_nr_var).sum(dtype=theano.config.floatX)
corr_train = (corr_train * mask_var).sum(dtype=theano.config.floatX)
prediction_eval, corr_eval = chain_crf_accuracy(energies_eval, target_var)
corr_nr_eval = (corr_eval * mask_nr_var).sum(dtype=theano.config.floatX)
corr_eval = (corr_eval * mask_var).sum(dtype=theano.config.floatX)
params = lasagne.layers.get_all_params(network, trainable=True)
updates = nesterov_momentum(loss_train, params=params, learning_rate=learning_rate, momentum=momentum)
# Compile a function performing a training step on a mini-batch
train_fn = theano.function([word_var, char_var, target_var, mask_var, mask_nr_var],
[loss_train, loss_train_org, loss_train_expect_linear,
corr_train, corr_nr_train, num_tokens, num_tokens_nr], updates=updates)
# Compile a second function evaluating the loss and accuracy of network
eval_fn = theano.function([word_var, char_var, target_var, mask_var, mask_nr_var],
[corr_eval, corr_nr_eval, num_tokens, num_tokens_nr, prediction_eval])
# Finally, launch the training loop.
logger.info(
"Start training: regularization: %s(%f), dropout: %s, delta: %.2f (#training data: %d, batch size: %d, clip: %.1f)..." \
% (regular, (0.0 if regular == 'none' else gamma), dropout, delta, num_data, batch_size, grad_clipping))
num_batches = num_data / batch_size + 1
dev_correct = 0.0
dev_correct_nr = 0.0
best_epoch = 0
test_correct = 0.0
test_correct_nr = 0.0
test_total = 0
test_total_nr = 0
test_inst = 0
lr = learning_rate
for epoch in range(1, num_epochs + 1):
print 'Epoch %d (learning rate=%.4f, decay rate=%.4f): ' % (epoch, lr, decay_rate)
train_err = 0.0
train_err_org = 0.0
train_err_linear = 0.0
train_corr = 0.0
train_corr_nr = 0.0
train_total = 0
train_total_nr = 0
train_inst = 0
start_time = time.time()
num_back = 0
for batch in xrange(1, num_batches + 1):
wids, cids, pids, _, _, masks = data_utils.get_batch(data_train, batch_size)
masks_nr = np.copy(masks)
masks_nr[:, 0] = 0
err, err_org, err_linear, corr, corr_nr, num, num_nr = train_fn(wids, cids, pids, masks, masks_nr)
train_err += err * wids.shape[0]
train_err_org += err_org * wids.shape[0]
train_err_linear += err_linear * wids.shape[0]
train_corr += corr
train_corr_nr += corr_nr
train_total += num
train_total_nr += num_nr
train_inst += wids.shape[0]
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
# update log
sys.stdout.write("\b" * num_back)
log_info = 'train: %d/%d loss: %.4f, loss_org: %.4f, loss_linear: %.4f, acc: %.2f%%, acc(no root): %.2f%%, time left (estimated): %.2fs' % (
batch, num_batches, train_err / train_inst, train_err_org / train_inst, train_err_linear / train_inst,
train_corr * 100 / train_total, train_corr_nr * 100 / train_total_nr, time_left)
sys.stdout.write(log_info)
num_back = len(log_info)
# update training log after each epoch
assert train_inst == num_batches * batch_size
assert train_total == train_total_nr + train_inst
sys.stdout.write("\b" * num_back)
print 'train: %d/%d loss: %.4f, loss_org: %.4f, loss_linear: %.4f, acc: %.2f%%, acc(no root): %.2f%%, time: %.2fs' % (
train_inst, train_inst, train_err / train_inst, train_err_org / train_inst, train_err_linear / train_inst,
train_corr * 100 / train_total, train_corr_nr * 100 / train_total_nr, time.time() - start_time)
# evaluate performance on dev data
dev_corr = 0.0
dev_corr_nr = 0.0
dev_total = 0
dev_total_nr = 0
dev_inst = 0
for batch in data_utils.iterate_batch(data_dev, batch_size):
wids, cids, pids, _, _, masks = batch
masks_nr = np.copy(masks)
masks_nr[:, 0] = 0
corr, corr_nr, num, num_nr, predictions = eval_fn(wids, cids, pids, masks, masks_nr)
dev_corr += corr
dev_corr_nr += corr_nr
dev_total += num
dev_total_nr += num_nr
dev_inst += wids.shape[0]
assert dev_total == dev_total_nr + dev_inst
print 'dev corr: %d, total: %d, acc: %.2f%%, no root corr: %d, total: %d, acc: %.2f%%' % (
dev_corr, dev_total, dev_corr * 100 / dev_total, dev_corr_nr, dev_total_nr, dev_corr_nr * 100 / dev_total_nr)
if dev_correct_nr < dev_corr_nr:
dev_correct = dev_corr
dev_correct_nr = dev_corr_nr
best_epoch = epoch
# evaluate on test data when better performance detected
test_corr = 0.0
test_corr_nr = 0.0
test_total = 0
test_total_nr = 0
test_inst = 0
for batch in data_utils.iterate_batch(data_test, batch_size):
wids, cids, pids, _, _, masks = batch
masks_nr = np.copy(masks)
masks_nr[:, 0] = 0
corr, corr_nr, num, num_nr, predictions = eval_fn(wids, cids, pids, masks, masks_nr)
test_corr += corr
test_corr_nr += corr_nr
test_total += num
test_total_nr += num_nr
test_inst += wids.shape[0]
assert test_total + test_total_nr + test_inst
test_correct = test_corr
test_correct_nr = test_corr_nr
print "best dev corr: %d, total: %d, acc: %.2f%%, no root corr: %d, total: %d, acc: %.2f%% (epoch: %d)" % (
dev_correct, dev_total, dev_correct * 100 / dev_total,
dev_correct_nr, dev_total_nr, dev_correct_nr * 100 / dev_total_nr, best_epoch)
print "best test corr: %d, total: %d, acc: %.2f%%, no root corr: %d, total: %d, acc: %.2f%% (epoch: %d)" % (
test_correct, test_total, test_correct * 100 / test_total,
test_correct_nr, test_total_nr, test_correct_nr * 100 / test_total_nr, best_epoch)
if epoch in schedule:
lr = lr * decay_rate
updates = nesterov_momentum(loss_train, params=params, learning_rate=lr, momentum=momentum)
train_fn = theano.function([word_var, char_var, target_var, mask_var, mask_nr_var],
[loss_train, loss_train_org, loss_train_expect_linear,
corr_train, corr_nr_train, num_tokens, num_tokens_nr], updates=updates)
def train_setup():
# Prepare Theano variables for inputs and targets
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')
# Create neural network model (depending on first command line parameter)
print("Building model and compiling functions...")
print( " with input dimension {0},{1},{2}".format( config.image_height, \
config.image_width, \
config. image_channel ) )
network = cnn_archi( input_var, \
config.image_channel,\
config.image_height, config.image_width,\
config.output_length )
print('Number of parameters : {0}'.format(count_params(network)))
if (config.init_model is not None):
with np.load(config.init_model) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
set_all_param_values(network, param_values)
# Create a loss expression for training, i.e., a scalar objective we want
# to minimize (for our multi-class problem, it is the cross-entropy loss):
prediction = lasagne.layers.get_output(network)
ent_loss = categorical_crossentropy(prediction, target_var)
ent_loss = ent_loss.mean()
l1_regu = config.l1_regu * regularize_network_params(network, l1)
l2_regu = config.l2_regu * regularize_network_params(network, l2)
loss = ent_loss + l1_regu + l2_regu
# We could add some weight decay as well here, see lasagne.regularization.
# Create update expressions for training, i.e., how to modify the
# parameters at each training step. Here, we'll use Stochastic Gradient
# Descent (SGD) with Nesterov momentum, but Lasagne offers plenty more.
params = get_all_params(network, trainable=True)
#grads = T.grad( loss, params )
#scaled_grads = norm_constraint( grads, 5. )
updates = nesterov_momentum(loss, params, \
learning_rate=config.learning_rate, \
momentum=config.momentum )
#updates = rmsprop( loss , params, learning_rate = config.learning_rate )
for param in get_all_params(network, regularizable=True):
norm_axis = None
if param.ndim == 1:
norm_axis = [0]
updates[param] = norm_constraint( updates[param], \
5. * compute_norms( param.get_value() ).mean(),
norm_axes = norm_axis )
#Create a loss expression for validation/testing. The crucial difference
# here is that we do a deterministic forward pass through the network,
# disabling dropout layers.
test_prediction = get_output(network, deterministic=True)
test_classes = T.argmax(test_prediction, axis=1)
test_loss = categorical_crossentropy(test_prediction, target_var)
test_loss = test_loss.mean()
# As a bonus, also create an expression for the classification accuracy:
test_acc = T.eq(test_classes, target_var)
# Compile a function performing a training step on a mini-batch (by giving
# the updates dictionary) and returning the corresponding training loss:
train_fn = theano.function([input_var,target_var], \
ent_loss,\
updates=updates, \
allow_input_downcast=True)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input_var, target_var], \
[test_loss, test_prediction, test_acc], \
allow_input_downcast=True )
return network, 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 _prepare(self, X, y, X_valid=None, y_valid=None, sample_weight=None,
whole_dataset_in_device=True):
self._stats = []
self._class_label_encoder = LabelEncoder()
if self.is_classification is True:
self._class_label_encoder.fit(y)
self.classes_ = self._class_label_encoder.classes_
y = self._class_label_encoder.transform(y).astype(y.dtype)
self.y_train_transformed = y
if y_valid is not None:
y_valid_transformed = self._class_label_encoder.transform(
y_valid).astype(y_valid.dtype)
self._l_x_in = layers.InputLayer(shape=(None, X.shape[1]))
batch_index, X_batch, y_batch, batch_slice = get_theano_batch_variables(
self.batch_size, y_softmax=self.is_classification)
if sample_weight is not None:
t_sample_weight = T.vector('sample_weight')
sample_weight = sample_weight.astype(theano.config.floatX)
else:
t_sample_weight = T.scalar('sample_weight')
if self.is_classification is True:
y_dim = len(set(y.flatten().tolist()))
else:
y_dim = y.shape[1]
self._prediction_layer = self._build_model(y_dim)
self._layers = layers.get_all_layers(self._prediction_layer)
self._build_prediction_functions(X_batch, self._prediction_layer)
if self.input_noise_function is None:
output = layers.get_output(self._prediction_layer, X_batch)
else:
X_batch_noisy = self.input_noise_function(X_batch)
output = layers.get_output(self._prediction_layer, X_batch_noisy)
if self.is_classification:
loss = -T.mean(t_sample_weight * T.log(output)
[T.arange(y_batch.shape[0]), y_batch])
else:
loss = T.mean(
t_sample_weight * T.sum((output - y_batch) ** 2, axis=1))
loss_unreg = loss
all_params = layers.get_all_params(self._prediction_layer)
if self._output_softener_coefs is not None:
all_params.append(self._output_softener_coefs)
W_params = layers.get_all_param_values(
self._prediction_layer, regularizable=True)
# regularization
if self.L1_factor is not None:
for L1_factor_layer, W in zip(self.L1_factor, W_params):
loss = loss + L1_factor_layer * T.sum(abs(W))
if self.L2_factor is not None:
for L2_factor_layer, W in zip(self.L2_factor, W_params):
loss = loss + L2_factor_layer * T.sum(W**2)
if self.optimization_method == 'nesterov_momentum':
gradient_updates = updates.nesterov_momentum(loss, all_params, learning_rate=self.learning_rate,
momentum=self.momentum)
elif self.optimization_method == 'adadelta':
# don't need momentum there
gradient_updates = updates.adadelta(
loss, all_params, learning_rate=self.learning_rate)
elif self.optimization_method == 'adam':
gradient_updates = updates.Adam(
loss, all_params, learning_rate=self.learning_rate)
elif self.optimization_method == 'momentum':
gradient_updates = updates.momentum(
loss, all_params, learning_rate=self.learning_rate,
momentum=self.momentum
)
elif self.optimization_method == 'adagrad':
gradient_updates = updates.adadelta(
loss, all_params, learning_rate=self.learning_rate)
elif self.optimization_method == 'rmsprop':
gradient_updates = updates.adadelta(
loss, all_params, learning_rate=self.learning_rate)
elif self.optimization_method == 'sgd':
gradient_updates = updates.sgd(
loss, all_params, learning_rate=self.learning_rate,
)
else:
raise Exception("wrong optimization method")
nb_batches = X.shape[0] // self.batch_size
if (X.shape[0] % self.batch_size) != 0:
nb_batches += 1
X = X.astype(theano.config.floatX)
if self.is_classification == True:
y = y.astype(np.int32)
else:
y = y.astype(theano.config.floatX)
if whole_dataset_in_device == True:
X_shared = theano.shared(X, borrow=True)
y_shared = theano.shared(y, borrow=True)
givens = {
X_batch: X_shared[batch_slice],
y_batch: y_shared[batch_slice]
}
if sample_weight is not None:
sample_weight_shared = theano.shared(
sample_weight, borrow=True)
givens[t_sample_weight] = sample_weight_shared[batch_slice]
else:
givens[t_sample_weight] = T.as_tensor_variable(
np.array(1., dtype=theano.config.floatX))
iter_update_batch = theano.function(
[batch_index], loss,
updates=gradient_updates,
givens=givens,
)
else:
if sample_weight is None:
iter_update_gradients = theano.function(
[X_batch, y_batch],
loss,
updates=gradient_updates,
givens={t_sample_weight: T.as_tensor_variable(
np.array(1., dtype=theano.config.floatX))},
)
def iter_update_batch(batch_index):
sl = slice(batch_index * self.batch_size,
(batch_index + 1) * self.batch_size)
return iter_update_gradients(X[sl], y[sl])
else:
iter_update_gradients = theano.function(
[X_batch, y_batch, t_sample_weight],
loss,
updates=gradient_updates
)
def iter_update_batch(batch_index):
sl = slice(batch_index * self.batch_size,
(batch_index + 1) * self.batch_size)
return iter_update_gradients(X[sl], y[sl], sample_weight[sl])
self._iter_update_batch = iter_update_batch
self._get_loss = theano.function(
[X_batch, y_batch, t_sample_weight], loss_unreg, allow_input_downcast=True)
def iter_update(epoch):
losses = []
#self.learning_rate.set_value(self.learning_rate.get_value() * np.array(0.99, dtype=theano.config.floatX))
for i in xrange(nb_batches):
losses.append(self._iter_update_batch(i))
# max norm
if self.max_norm is not None:
for max_norm_layer, layer in zip(self.max_norm, self._layers):
layer.W = updates.norm_constraint(
layer.W, self.max_norm)
losses = np.array(losses)
d = OrderedDict()
d["epoch"] = epoch
#d["loss_train_std"] = losses.std()
#d["loss_train"] = losses.mean()
d["loss_train"] = self._get_loss(
self.X_train, self.y_train_transformed, 1.)
d["accuracy_train"] = (
self.predict(self.X_train) == self.y_train).mean()
if X_valid is not None and y_valid is not None:
d["loss_valid"] = self._get_loss(
X_valid, y_valid_transformed, 1.)
if self.is_classification == True:
d["accuracy_valid"] = (
self.predict(X_valid) == y_valid).mean()
if self.verbose > 0:
if (epoch % self.report_each) == 0:
print(tabulate([d], headers="keys"))
self._stats.append(d)
return d
def quitter(update_status):
cur_epoch = len(self._stats) - 1
if self.patience_nb_epochs > 0:
# patience heuristic (for early stopping)
cur_patience_stat = update_status[self.patience_stat]
if self.cur_best_patience_stat is None:
self.cur_best_patience_stat = cur_patience_stat
first_time = True
else:
first_time = False
thresh = self.patience_progression_rate_threshold
if cur_patience_stat < self.cur_best_patience_stat * thresh or first_time:
if self.verbose >= 2:
fmt = "--Early stopping-- good we have a new best value : {0}={1}, last best : epoch {2}, value={3}"
print(fmt.format(self.patience_stat, cur_patience_stat,
self.cur_best_epoch, self.cur_best_patience_stat))
self.cur_best_epoch = cur_epoch
self.cur_best_patience_stat = cur_patience_stat
if hasattr(self, "set_state") and hasattr(self, "get_state"):
self.cur_best_model = self.get_state()
else:
self.cur_best_model = pickle.dumps(
self.__dict__, protocol=pickle.HIGHEST_PROTOCOL)
if (cur_epoch - self.cur_best_epoch) >= self.patience_nb_epochs:
finish = True
if hasattr(self, "set_state") and hasattr(self, "get_state"):
self.set_state(self.cur_best_model)
else:
self.__dict__.update(pickle.loads(self.cur_best_model))
self._stats = self._stats[0:self.cur_best_epoch + 1]
if self.verbose >= 2:
print("out of patience...take the model at epoch {0} and quit".format(
self.cur_best_epoch + 1))
else:
finish = False
return finish
else:
return False
def monitor(update_status):
pass
def observer(monitor_output):
pass
return (iter_update, quitter, monitor, observer)
def main():
# load the training and validation data sets
# labels=int(0.7*speech.all_count)
global valid_best
X = T.tensor4()
Y = T.matrix()
# set up theano functions to generate output by feeding data through network
output_layer,hidden_layer = lasagne_model()
output_train = lasagne.layers.get_output(output_layer, X)
output_valid = lasagne.layers.get_output(output_layer, X, deterministic=True)
# set up the loss that we aim to minimize
loss_train = T.mean(T.nnet.categorical_crossentropy(output_train, Y))
loss_valid = T.mean(T.nnet.categorical_crossentropy(output_valid, Y))
# prediction functions for classifications
pred = T.argmax(output_train, axis=1)
pred_valid = T.argmax(output_valid, axis=1)
# get parameters from network and set up sgd with nesterov momentum to update parameters
params = lasagne.layers.get_all_params(output_layer,trainable=True)
print params
updates = nesterov_momentum(loss_train, params, learning_rate=0.2, momentum=0.9)
# updates =lasagne.updates.sgd(loss_train, params, learning_rate=0.1)
# set up training and prediction functions
train = theano.function(inputs=[X, Y], outputs=[loss_train,pred], updates=updates, allow_input_downcast=True)
valid = theano.function(inputs=[X, Y], outputs=[loss_valid,pred_valid], allow_input_downcast=True)
# predict_valid = theano.function(inputs=[X], outputs=[pred_valid], allow_input_downcast=True)
# loop over training functions for however many iterations, print information while training
train_eval = []
valid_eval = []
valid_acc = []
if 0:
# pre_params = 'speech_params/validbest_cnn_spkall_22_0.95458984375_2017-06-24 00:50:39.pklz'
pre_params='speech_params/validbest_cnn_fisher_0_idxbatch20000_0.65625_2017-08-17 16:14:04.pklz' # 1 shot: 99.0 2000多次
load_params = pickle.load(gzip.open(pre_params))
# load_params=load_params[:-2]
lasagne.layers.set_all_param_values(output_layer,load_params)
print 'load params: ', pre_params,'\n'
for i in range(450):
train_list,valid_list=get_data(train_files,num_speechs,size,valid_size)
batch_total_number = size / BATCHSIZE
if 1:
valid_list_limit=3000
valid_list=valid_list[:valid_list_limit]
print 'batch_total_number:',batch_total_number
train_acc_aver=0.0
for idx_batch in range (batch_total_number):
batch_list = train_list[idx_batch * BATCHSIZE:(idx_batch + 1) * BATCHSIZE]
xx_batch=np.zeros((BATCHSIZE,1,speechSize[0],speechSize[1]))
yy_batch=np.zeros((BATCHSIZE,num_labels_train))
for ind,one_name in enumerate(batch_list):
aim_file_name,aim_idx=one_name.split('.')
aim_file_name+='.npy'
aim_file_name_full=train_load_path+aim_file_name
xx_batch[ind,0]=np.load(aim_file_name_full)[int(aim_idx)]
yy_batch[ind]=label_binarize([train_files.index(aim_file_name)],range(num_labels_train))[0]
train_loss ,pred = train(xx_batch,yy_batch)
count=np.count_nonzero(np.int32(pred ==np.argmax(yy_batch,axis=1)))
train_acc = float(count) / (BATCHSIZE)
print i,idx_batch,'| Tloss:', train_loss,'| Count:',count,'| Acc:',train_acc
print pred
print np.argmax(yy_batch,axis=1)
print "time:",time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
train_acc_aver += train_acc
# raise EOFError
if train_acc>0.9 and idx_batch%1000==0 and idx_batch>=0:
acc=0
valid_batch_number=len(valid_list)/BATCHSIZE
for j in tqdm(range(valid_batch_number)):
batch_list = valid_list[j * BATCHSIZE:(j + 1) * BATCHSIZE]
x_batch=np.zeros((BATCHSIZE,1,speechSize[0],speechSize[1]))
y_batch=np.zeros((BATCHSIZE,num_labels_train))
for ind,one_name in enumerate(batch_list):
aim_file_name,aim_idx=one_name.split('.')
aim_file_name+='.npy'
aim_file_name_full=train_load_path+aim_file_name
x_batch[ind,0]=np.load(aim_file_name_full)[int(aim_idx)]
y_batch[ind]=label_binarize([train_files.index(aim_file_name)],range(num_labels_train))[0]
valid_loss,pred = valid(x_batch,y_batch)
acc += np.count_nonzero(np.int32(pred ==np.argmax(y_batch,axis=1)))
acc=float(acc)/(valid_batch_number*BATCHSIZE)
print 'iter:', i,idx_batch, '| Vloss:',valid_loss,'|Acc:',acc
if acc>valid_best:
print 'new valid_best:',valid_best,'-->',acc
valid_best=acc
all_params = helper.get_all_param_values(output_layer)
f = gzip.open('speech_params/validbest_cnn_fisher_{}_validacc{}_{}.pklz'.format(i,valid_best,time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())), 'wb')
pickle.dump(all_params, f)
f.close()
if idx_batch%3000==0 and idx_batch>0:
all_params = helper.get_all_param_values(output_layer)
f = gzip.open('speech_params/validbest_cnn_fisher_{}_idxbatch{}_{}.pklz'.format(i,idx_batch,time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())), 'wb')
pickle.dump(all_params, f)
f.close()
# save weights
if i%1==0:
all_params = helper.get_all_param_values(output_layer)
f = gzip.open('speech_params/validbest_cnn_fisher_averacc{}_{}_{}.pklz'.format(i,train_acc_aver/batch_total_number,time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())), 'wb')
pickle.dump(all_params, f)
f.close()
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
all_layers = lasagne.layers.get_all_layers(output_layer)
l2_penalty = lasagne.regularization.regularize_layer_params(
all_layers, lasagne.regularization.l2) * 0.0001
loss = loss + l2_penalty
# set up loss functions for validation dataset
test_loss = lasagne.objectives.categorical_crossentropy(output_test, Y)
test_loss = test_loss.mean()
test_acc = T.mean(T.eq(T.argmax(output_test, axis=1), Y),
dtype=theano.config.floatX)
# get parameters from network and set up sgd with nesterov momentum to update parameters, l_r is shared var so it can be changed
l_r = theano.shared(np.array(LR_SCHEDULE[0], dtype=theano.config.floatX))
params = lasagne.layers.get_all_params(output_layer, trainable=True)
updates = nesterov_momentum(loss, params, learning_rate=l_r, momentum=0.9)
#updates = adam(loss, params, learning_rate=l_r)
# set up training and prediction functions
train_fn = theano.function(inputs=[X, Y], outputs=loss, updates=updates)
valid_fn = theano.function(inputs=[X, Y], outputs=[test_loss, test_acc])
'''
load training data and start training
'''
# load the training and validation data sets
train_X, test_X, train_y, test_y = load_pickle_data_cv()
print 'Train shape:', train_X.shape, 'Test shape:', test_X.shape
print 'Train y shape:', train_y.shape, 'Test y shape:', test_y.shape
print np.amax(train_X), np.amin(train_X), np.mean(train_X)
def main():
# load model and parameters
output_layer = lasagne_model()
f = gzip.open('data/weights.pklz', 'rb')
all_params = pickle.load(f)
f.close()
X = T.ftensor4()
Y = T.fmatrix()
# set up theano functions to generate output by feeding data through network
output_layer = lasagne_model()
output_train = lasagne.layers.get_output(output_layer, X)
output_valid = lasagne.layers.get_output(output_layer, X, deterministic=True)
# set up the loss that we aim to minimize
loss_train = T.mean(T.nnet.categorical_crossentropy(output_train, Y))
loss_valid = T.mean(T.nnet.categorical_crossentropy(output_valid, Y))
# prediction functions for classifications
pred = T.argmax(output_train, axis=1)
pred_valid = T.argmax(output_valid, axis=1)
# get parameters from network and set up sgd with nesterov momentum to update parameters
helper.set_all_param_values(output_layer, all_params)
params = lasagne.layers.get_all_params(output_layer)
updates = nesterov_momentum(loss_train, params, learning_rate=0.0001, momentum=0.9)
# set up training and prediction functions
train = theano.function(inputs=[X, Y], outputs=loss_train, updates=updates, allow_input_downcast=True)
valid = theano.function(inputs=[X, Y], outputs=loss_valid, allow_input_downcast=True)
predict_valid = theano.function(inputs=[X], outputs=pred_valid, allow_input_downcast=True)
# fine tune network
train_X, test_X, train_y, test_y = load_data_cv('data/train.csv')
train_eval = []
valid_eval = []
valid_acc = []
try:
for i in range(5):
train_loss = batch_iterator_no_aug(train_X, train_y, BATCHSIZE, train)
train_eval.append(train_loss)
valid_loss = valid(test_X, test_y)
valid_eval.append(valid_loss)
acc = np.mean(np.argmax(test_y, axis=1) == predict_valid(test_X))
valid_acc.append(acc)
print 'iter:', i, '| Tloss:', train_loss, '| Vloss:', valid_loss, '| valid acc:', acc
except KeyboardInterrupt:
pass
# after training create output for kaggle
testing_inputs = load_test_data('data/test.csv')
predictions = []
for j in range((testing_inputs.shape[0] + BATCHSIZE -1) // BATCHSIZE):
sl = slice(j * BATCHSIZE, (j + 1) * BATCHSIZE)
X_batch = testing_inputs[sl]
predictions.extend(predict_valid(X_batch))
out = pd.read_csv('data/convnet_preds.csv')
out['Label'] = predictions
out.to_csv('preds/convnet_preds.csv', index = False)
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 word2vec( files=[], directories=[], skip=[], save_dir=None, num_epochs=5, unigram_dictionary=None, noise_ratio=15, kernel=[1,2,3,4,5,5,4,3,2,1], t = 1.0e-5, batch_size = 1000, # Number of *signal* examples per batch num_embedding_dimensions=500, word_embedding_init=Normal(), context_embedding_init=Normal(), learning_rate=0.1, momentum=0.9, num_processes=3, load_dictionary_dir=None, min_frequency=10, macrobatch_size = 100000, max_queue_size=0, verbose=True ): ''' Helper function that handles all concerns involved in training A word2vec model using the approach of Mikolov et al. It surfaces all of the options. For customizations going beyond simply tweeking existing options and hyperparameters, substitute this function by writing your own training routine using the provided classes. This function would be a starting point for you. ''' # Make a Word2VecMinibatcher, pass through parameters sent by caller reader = DatasetReader( files=files, directories=directories, skip=skip, noise_ratio=noise_ratio, t=t, num_processes=num_processes, unigram_dictionary=unigram_dictionary, kernel=kernel, max_queue_size=max_queue_size, macrobatch_size=macrobatch_size, verbose=verbose ) # Prepare the minibatch generator # (this produces the counter_sampler stats) if load_dictionary_dir is None and unigram_dictionary is None: if verbose: print 'preparing dictionaries...' reader.prepare(save_dir=save_dir) # If min_frequency was specified, prune the dictionaries if min_frequency is not None: if verbose: print 'prunning dictionaries...' reader.prune(min_frequency) # Make a symbolic minibatcher minibatcher = NoiseContrastiveTheanoMinibatcher( batch_size=batch_size, noise_ratio=noise_ratio, dtype="int32", num_dims=2 ) # Make a Word2VecEmbedder object, feed it the combined input. # Note that the full batch includes noise examples and signal_examples # so is larger than batch_size, which is the number of signal_examples # only per batch. full_batch_size = batch_size * (1 + noise_ratio) embedder = Word2VecEmbedder( input_var=minibatcher.get_batch(), batch_size=full_batch_size, vocabulary_size=reader.get_vocab_size(), num_embedding_dimensions=num_embedding_dimensions, word_embedding_init=word_embedding_init, context_embedding_init=context_embedding_init ) # Architectue is ready. Make the loss function, and use it to create # the parameter updates responsible for learning loss = get_noise_contrastive_loss(embedder.get_output(), batch_size) updates = nesterov_momentum( loss, embedder.get_params(), learning_rate, momentum ) # Include minibatcher updates, which cause the symbolic batch to move # through the dataset like a sliding window updates.update(minibatcher.get_updates()) # Use the loss function and the updates to compile a training function. # Note that it takes no inputs because the dataset is fully loaded using # theano shared variables train = function([], loss, updates=updates) # Iterate through the dataset, training the embeddings for epoch in range(num_epochs): if verbose: print 'starting epoch %d' % epoch macrobatches = reader.generate_dataset_serial() macrobatch_num = 0 for signal_macrobatch, noise_macrobatch in macrobatches: macrobatch_num += 1 if verbose: print 'running macrobatch %d' % (macrobatch_num - 1) minibatcher.load_dataset(signal_macrobatch, noise_macrobatch) losses = [] for batch_num in range(minibatcher.get_num_batches()): if verbose: print 'running minibatch', batch_num losses.append(train()) if verbose: print '\taverage loss: %f' % np.mean(losses) # Save the model (the embeddings) if save_dir was provided if save_dir is not None: embedder.save(save_dir) # Return the trained embedder and the dictionary mapping tokens # to ids return embedder, reader
# load datasets
X_train_fc7 = theano.shared(np.load('/root/proj/MIT_dumped/X_train_fc7.npy').astype(theano.config.floatX))
X_test_fc7 = theano.shared(np.load('/root/proj/MIT_dumped/X_test_fc7.npy').astype(theano.config.floatX))
all_params = layers.get_all_params(output)
objective = objectives.Objective(output,loss_function=objectives.multinomial_nll)
loss_train = objective.get_loss([X_batch_one, X_batch_two], target=y_batch)
LEARNING_RATE =0.0122
MOMENTUM=0.9
REG = .0009
reg_loss = regularization.l2(output) * REG
total_loss = loss_train + reg_loss
upds = updates.nesterov_momentum(total_loss, all_params, LEARNING_RATE, MOMENTUM)
pred = T.argmax(
output.get_output([X_batch_one, X_batch_two], deterministic=True), axis=1)
accuracy = T.mean(T.eq(pred, y_batch), dtype=theano.config.floatX)
print "begin compiling"
givens = {X_batch_one: X_train_fc6[batch_index*batch_size:(batch_index+1)*batch_size],
X_batch_two: X_train_fc7[batch_index*batch_size:(batch_index+1)*batch_size],
y_batch: y_train[batch_index*batch_size:(batch_index+1)*batch_size]}
train = theano.function([batch_index], loss_train, updates=upds, givens=givens)
test = theano.function([], accuracy, givens={X_batch_one:X_test_fc6, X_batch_two:X_test_fc7, y_batch:y_test})
num_epochs = 1000
for epoch in range(num_epochs):
print "epoch %s" % epoch
for batch in range(total/batch_size):
loss = train(batch)
def update(all_grads, all_params, learning_rate):
return nesterov_momentum(all_grads,
all_params,
learning_rate,
momentum=m)
