def build_loss(self):
net = self.net['out']
prediction = lasagne.layers.get_output(net)
prediction = T.clip(prediction, 1e-9, 1 - 1e-9)
loss = lasagne.objectives.categorical_crossentropy(prediction, self.target_var)
loss = loss.mean() + self.lambda2 * regularization.regularize_network_params(net, regularization.l2)
params = lasagne.layers.get_all_params(net, trainable=True)
updates = lasagne.updates.nesterov_momentum(
loss, params, learning_rate=self.learning_rate,momentum = 0.9)
test_prediction = lasagne.layers.get_output(net, deterministic=True)
test_prediction = T.clip(test_prediction, 1e-9, 1 - 1e-9)
test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,
self.target_var)
test_loss = test_loss.mean() + self.lambda2 * regularization.regularize_network_params(net,
regularization.l2)
test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), self.target_var),
dtype=theano.config.floatX)
train_fn = theano.function([self.input_var, self.target_var], loss, updates=updates)
pred_fn = theano.function([self.input_var], lasagne.layers.get_output(net,deterministic=True))
val_fn = theano.function([self.input_var, self.target_var], [test_loss, test_acc])
return train_fn,val_fn,pred_fn
def my_loss(model, predictions, targets, regularization, params):
predictions = predictions[0][0][
params['left_border']:-params['right_border']]
targets = targets[0][params['left_border']:-params['right_border']]
loss = tensor.abs_(tensor.log((targets * predictions).sum() / targets.sum())) +\
tensor.abs_(tensor.log(((1-targets) * (1-predictions)).sum() / (1-targets).sum()))
reg_loss_l1 = regularize_network_params(model, l1) * 1e-4
reg_loss_l2 = regularize_network_params(model, l2)
if regularization:
return loss + reg_loss_l1 # + reg_loss_l2
else:
return loss
def build_loss(self, env, agent, replay_seq_len):
# get agent's Qvalues obtained via experience replay
_, _, _, _, qvalues_seq = agent.get_sessions(
env,
# initial_hidden = env.preceding_agent_memories,
session_length=replay_seq_len,
batch_size=env.batch_size,
optimize_experience_replay=True,
)
scaled_reward_seq = env.rewards
elwise_mse_loss = qlearning_n_step.get_elementwise_objective(qvalues_seq,
env.actions[0],
scaled_reward_seq,
env.is_alive,
gamma_or_gammas=self.gamma,
n_steps=self.n_steps)
# compute mean over "alive" fragments
mse_loss = elwise_mse_loss.sum() / env.is_alive.sum()
# regularize network weights
reg_l2 = regularize_network_params(agent.state_variables.keys(), l2) * 10 ** -5
return mse_loss + reg_l2
def make_training_functions(network, encode_layer, input_var, aug_var,
target_var, stack_params, weight_decay):
output = lasagne.layers.get_output(network, deterministic=True)
loss = lasagne.objectives.squared_error(output, target_var).mean() + \
weight_decay * regularization.regularize_network_params(
layer = network, penalty = regularization.l2, tags={'regularizable' : True})
params = layers.get_all_params(network, trainable=True)
updates = lasagne.updates.nesterov_momentum(loss,
params,
learning_rate=0.0001,
momentum=0.95)
stack_updates = lasagne.updates.nesterov_momentum(loss,
stack_params,
learning_rate=0.0001,
momentum=0.95)
encode = lasagne.layers.get_output(encode_layer, deterministic=True)
val_fn = theano.function([input_var, aug_var, target_var],
[loss, encode, output])
train_fn = theano.function([input_var, aug_var, target_var],
loss,
updates=updates)
stack_train_fn = theano.function([input_var, aug_var, target_var],
loss,
updates=stack_updates)
return val_fn, train_fn, stack_train_fn
def build_loss(self, env):
_, _, _, _, qvalues_seq = self.agent.get_sessions(
env,
session_length=self.replay_seq_len,
batch_size=self.replay_batch_size,
optimize_experience_replay=True,
# unroll_scan=,
)
scaled_reward_seq = env.rewards
elwise_mse_loss = qlearning_n_step.get_elementwise_objective(qvalues_seq,
env.actions[0],
scaled_reward_seq,
env.is_alive,
n_steps=self.n_steps,
gamma_or_gammas=self.gamma, )
mse_loss = elwise_mse_loss.sum() / env.is_alive.sum()
reg_l2 = regularize_network_params(self.resolver, l2) * 10 ** -4
loss = mse_loss + reg_l2
return loss
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 complieTrainFunction(self):
message = 'Compiling the Training Function'
self.logger.info(logMessage('+', message))
startTime = time.time()
trainPrediction = get_output(self.outputLayer,
deterministic = False,
batch_norm_update_averages=False,
batch_norm_use_averages=False)
# TODO. Chack wheather the flatten style of targetvar and output are same.
self.flattenedTargetVar = T.flatten(self.targetVar)
trainLoss = categorical_crossentropy(trainPrediction, self.flattenedTargetVar).mean()
weightNorm = regularize_network_params(self.outputLayer, lasagne.regularization.l2)
trainLoss += self.weightDecay * weightNorm
trainPredictionLabel = T.argmax(trainPrediction, axis = 1)
trainACC = T.mean(T.eq(trainPredictionLabel, self.flattenedTargetVar),
dtype = theano.config.floatX)
params = get_all_params(self.outputLayer, trainable = True)
update = self.optimizer(trainLoss, params, learning_rate = self.learningRate)
trainFunc = theano.function([self.inputVar, self.targetVar],
[trainLoss, trainACC],
updates = update)
message = 'Compiled the Training Function, spent {:.2f}s'.format(time.time()- startTime)
self.logger.info(logMessage('+', message))
return trainFunc
def build_loss(self, env):
_, _, _, _, qvalues_seq = self.agent.get_sessions(
env,
session_length=self.replay_seq_len,
batch_size=self.replay_batch_size,
optimize_experience_replay=True,
# unroll_scan=,
)
scaled_reward_seq = env.rewards
elwise_mse_loss = qlearning_n_step.get_elementwise_objective(
qvalues_seq,
env.actions[0],
scaled_reward_seq,
env.is_alive,
n_steps=self.n_steps,
gamma_or_gammas=self.gamma,
)
mse_loss = elwise_mse_loss.sum() / env.is_alive.sum()
reg_l2 = regularize_network_params(self.resolver, l2) * 10**-4
loss = mse_loss + reg_l2
return loss
def build_loss(self, env, agent, replay_seq_len):
# get agent's Qvalues obtained via experience replay
_, _, _, _, qvalues_seq = agent.get_sessions(
env,
# initial_hidden = env.preceding_agent_memories,
session_length=replay_seq_len,
batch_size=env.batch_size,
optimize_experience_replay=True,
)
scaled_reward_seq = env.rewards
elwise_mse_loss = qlearning_n_step.get_elementwise_objective(
qvalues_seq,
env.actions[0],
scaled_reward_seq,
env.is_alive,
gamma_or_gammas=self.gamma,
n_steps=self.n_steps)
# compute mean over "alive" fragments
mse_loss = elwise_mse_loss.sum() / env.is_alive.sum()
# regularize network weights
reg_l2 = regularize_network_params(agent.state_variables.keys(),
l2) * 10**-5
return mse_loss + reg_l2
def create_iter_funcs_train(l_out, lr, mntm, wd):
X = T.tensor4('X')
y = T.ivector('y')
X_batch = T.tensor4('X_batch')
y_batch = T.ivector('y_batch')
y_hat = layers.get_output(l_out, X, deterministic=False)
# softmax loss
train_loss = T.mean(T.nnet.categorical_crossentropy(y_hat, y))
# L2 regularization
train_loss += wd * regularize_network_params(l_out, l2)
train_acc = T.mean(T.eq(y_hat.argmax(axis=1), y))
all_params = layers.get_all_params(l_out, trainable=True)
updates = lasagne.updates.nesterov_momentum(train_loss, all_params, lr,
mntm)
train_iter = theano.function(
inputs=[theano.Param(X_batch),
theano.Param(y_batch)],
outputs=[train_loss, train_acc],
updates=updates,
givens={
X: X_batch,
y: y_batch,
},
)
return train_iter
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 define_updates(network, input_var, target_var, weight_var, learning_rate=0.01, momentum=0.9, l2_lambda=1e-5):
params = lasagne.layers.get_all_params(network, trainable=True)
out = lasagne.layers.get_output(network)
test_out = lasagne.layers.get_output(network, deterministic=True)
l2_loss = l2_lambda * regularize_network_params(network, l2)
train_metrics = _score_metrics(out, target_var, weight_var, l2_loss)
loss, acc, target_prediction, prediction = train_metrics
val_metrics = _score_metrics(test_out, target_var, weight_var, l2_loss)
t_loss, t_acc, t_target_prediction, t_prediction = val_metrics
updates = lasagne.updates.nesterov_momentum(
loss, params, learning_rate=learning_rate, momentum=momentum)
train_fn = theano.function([input_var, target_var, weight_var],[
loss, l2_loss, acc, target_prediction, prediction],
updates=updates)
val_fn = theano.function([input_var, target_var, weight_var], [
t_loss, l2_loss, t_acc, t_target_prediction, t_prediction])
return train_fn, val_fn
def compileValFunction(self):
message = 'Compiling the Validation Function'
self.logger.info(logMessage('+', message))
startTime = time.time()
valPrediction = get_output(self.outputLayer,
deterministic = True,
batch_norm_update_averages=False,
batch_norm_use_averages=False)
# TODO. Chack wheather the flatten style of targetvar and output are same.
self.flattenedTargetVar = T.flatten(self.targetVar)
valLoss = categorical_crossentropy(valPrediction, self.flattenedTargetVar).mean()
weightNorm = regularize_network_params(self.outputLayer, lasagne.regularization.l2)
valLoss += self.weightDecay * weightNorm
valPredictionLabel = T.argmax(valPrediction, axis = 1)
valACC = T.mean(T.eq(valPredictionLabel, self.flattenedTargetVar),
dtype = theano.config.floatX)
valFunc = theano.function([self.inputVar, self.targetVar],
[valLoss, valACC])
message = 'Compiled the Validation Function, spent {:.2f}s'.format(time.time()- startTime)
self.logger.info(logMessage('+', message))
return valFunc
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 create_iter_funcs_train(l_out, lr, mntm, wd):
X = T.tensor4('X')
y = T.ivector('y')
X_batch = T.tensor4('X_batch')
y_batch = T.ivector('y_batch')
y_hat = layers.get_output(l_out, X, deterministic=False)
# softmax loss
train_loss = T.mean(
T.nnet.categorical_crossentropy(y_hat, y))
# L2 regularization
train_loss += wd * regularize_network_params(l_out, l2)
train_acc = T.mean(
T.eq(y_hat.argmax(axis=1), y))
all_params = layers.get_all_params(l_out, trainable=True)
updates = lasagne.updates.nesterov_momentum(
train_loss, all_params, lr, mntm)
train_iter = theano.function(
inputs=[theano.Param(X_batch), theano.Param(y_batch)],
outputs=[train_loss, train_acc],
updates=updates,
givens={
X: X_batch,
y: y_batch,
},
)
return train_iter
def _create_loss(self, output_layer, predicted, target, error_threshold, proto_loss_multiplier=1.0):
# Regularization term
reg_term = self.reg_weight * regularize_network_params(output_layer, l2)
# Source-target penalty term:
# distance between source and target is subtracted from loss
# So less is learned from non-cognates
# Phonetic: binary crossentropy, multi-label classifcation
if self.output_encoding == "phonetic" or self.output_encoding == "embedding":
loss = T.sum(lasagne.objectives.binary_crossentropy(predicted, target)) / self.batch_size + reg_term
# Character: categorical crossentropy, single label classification
elif self.output_encoding == "character":
loss = T.sum(lasagne.objectives.categorical_crossentropy(predicted, target)) / self.batch_size + reg_term
# Multiply loss with cognacy prior:
# more should be learned from probable cognate examples
if self.cognacy_prior > 0.0:
target_prediction_error = T.sum(lasagne.objectives.squared_error(predicted, target)) / self.batch_size
# sigmoid(-error+mean_error_history)
# Cognacy prior is high for low error, but declines steeply
# when error above mean_error_history
cognacy_prior_factor = utility.sigmoid(-target_prediction_error + error_threshold)
loss *= cognacy_prior_factor
else:
cognacy_prior_factor = T.constant(1)
target_prediction_error = T.constant(0)
loss *= proto_loss_multiplier
return loss, cognacy_prior_factor, target_prediction_error, error_threshold
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 compile_train_predict(self, stochastic_train, stochastic_predict):
# symbolic functions to compute marginal posterior GP
input_vars = self.post_gp.data_variables
gp_hyperparams = self.post_gp.params
self.gp_hyperparams = gp_hyperparams
mu = self.post_gp.mean()
mu = mu.dimshuffle('x', 0) # make a row out of 1d vector (N to 1xN)
self.train_network = self.extend_network(mu, stochastic_train)
train_predict = lasagne.layers.get_output(self.train_network)
# Compute the exepcted prediction
#if stochastic_train and self.n_samples > 1:
# train_predict = train_predict.mean(axis=0, keepdims=True)
label = T.ivector('label')
# For expected loss
if stochastic_train:
label_rep = label.repeat(self.n_samples)
else:
label_rep = label
loss = categorical_crossentropy(train_predict, label_rep).mean()
# For expected prediction
#loss = categorical_crossentropy(train_predict, label).mean()
if self.regularize_weight > 0:
penalty = (self.regularize_weight *
regularize_network_params(self.train_network, l2))
loss += penalty
params = lasagne.layers.get_all_params(self.train_network,
trainable=True)
update_params = params
if self.update_gp:
update_params += gp_hyperparams
grad_loss = theano.grad(loss, update_params,
consider_constant=input_vars)
updates = self.optimizer(grad_loss, update_params,
**self.optimizer_kwargs)
self.train_fn = theano.function(input_vars + [label],
loss, updates=updates)
if stochastic_train == stochastic_predict:
self.test_network = self.train_network
self.copy_params = False
else:
self.test_network = self.extend_network(mu, stochastic_predict)
self.copy_params = True
# Set deterministic=True for dropout training if used.
test_predict = lasagne.layers.get_output(self.test_network,
deterministic=True)
if stochastic_predict and self.n_samples > 1:
test_predict = test_predict.mean(axis=0, keepdims=True)
self.predict_fn = theano.function(input_vars, test_predict)
def train_setup():
x = T.tensor3('input')
y = T.matrix('output')
encoding, decoding = cnn( x, config.input_length, config.output_length, \
config.encoding_length )
print 'Number of Parameters {0}'.format(count_params(decoding))
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(decoding, param_values)
# training tasks in sequence
prediction = get_output(decoding)
error = squared_error(y, prediction)
error = error.mean()
l1_norm = config.l1_weight * regularize_network_params(decoding, l1)
l2_norm = config.l2_weight * regularize_network_params(decoding, l2)
total_error = error + l1_norm + l2_norm
params = get_all_params(decoding, trainable=True)
updates = adadelta( total_error, params, config.learning_rate, \
config.rho, \
config.eps )
train_fn = function( [x, y], [error, l1_norm, l2_norm], \
updates = updates, \
allow_input_downcast = True )
val_prediction = get_output(decoding, deterministic=True)
val_error = squared_error(y, val_prediction)
val_error = val_error.mean()
val_fn = function([x, y], val_error, allow_input_downcast=True)
return encoding, decoding, train_fn, val_fn
def train_setup():
x = T.tensor3('input')
y = T.lvector('output')
network = cnn(x, config.input_length, 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(decoding, param_values)
# training tasks in sequence
prediction = get_output(network)
ent = categorical_crossentropy(prediction, y)
ent = ent.mean()
l1_norm = config.l1_weight * regularize_network_params(network, l1)
l2_norm = config.l2_weight * regularize_network_params(network, l2)
total_error = ent + l1_norm + l2_norm
params = get_all_params(network, trainable=True)
updates = adadelta( total_error, params, config.learning_rate, \
config.rho, \
config.eps )
train_fn = function( [x, y], [ent, l1_norm, l2_norm, prediction], \
updates = updates, \
allow_input_downcast = True )
val_prediction = get_output(network, deterministic=True)
val_ent = categorical_crossentropy(val_prediction, y)
val_ent = val_ent.mean()
val_fn = function([x, y], [val_ent, val_prediction],
allow_input_downcast=True)
return network, train_fn, val_fn
def get_functions(cfg):
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')
weights_var = T.vector('weights')
lr=theano.shared(np.float32(0.0000))
network = build_network(cfg,input_var)
prediction = lasagne.layers.get_output(network)
test_prediction = lasagne.layers.get_output(network, deterministic = True)
output_shape = lasagne.layers.get_output_shape(network)
l2_penalty = regularize_network_params(network, l2)
l1_penalty = regularize_network_params(network, l1)
cost = loss(prediction,target_var,weights_var) + 5*1e-6*(l1_penalty + l2_penalty)
params = lasagne.layers.get_all_params(network, trainable=True)
#print (len(params))
def save_params(path):
np.savez(path,params)
return
def load_params(path):
data = np.load(path)
param_values = [ x.get_value() for x in data['arr_0'] ]
# print (len(param_values))
lasagne.layers.set_all_param_values(network, param_values, trainable=True)
return
def set_lr(value):
lr.set_value(value)
return
optimiser= cfg['optimiser']
updates = get_updates(cost,params, optimiser ,lr)
def acc(yp,yt):
output = T.argmax(yp,axis=1)
return T.mean(T.eq(output, target_var))
accuracy = acc(prediction,target_var)
train_fn = theano.function([input_var, target_var,weights_var], cost, updates=updates)
val_fn = theano.function([input_var, target_var], accuracy)
train_predict_fn = theano.function([input_var], prediction)
test_predict_fn = theano.function([input_var], test_prediction)
return train_fn, test_predict_fn, train_predict_fn, save_params, load_params, output_shape, set_lr
def __init__(self,
steps = 1,
num_layers = 2,
num_units = 32,
eps = 1e-2,
recurrent = False,
nonlinearity = tanh,
):
self.steps = steps
self.X = T.fmatrix()
self.Y = T.fmatrix()
def network(l):
if recurrent:
l = ReshapeLayer(l,
shape = (-1, steps, 1))
l = LSTMLayer(l, num_units)
for k in range(num_layers):
l = DenseLayer(l,
num_units = num_units,
nonlinearity = nonlinearity)
l = DenseLayer(l,
num_units = 1,
nonlinearity = linear)
return l
self.network = network
l = InputLayer(input_var = self.X,
shape = (None, steps))
l = self.network(l)
self.l_ = l
self.x_ = get_output(self.l_)
self.f = theano.function([self.X],
self.x_,
allow_input_downcast=True)
l2_penalty = regularize_network_params(l,L2)
error = squared_error(self.x_, self.Y).mean()
loss = error + eps * l2_penalty
params = get_all_params(l)
updates = adam(loss,
params)
self.error = theano.function([self.X,self.Y],
error,
allow_input_downcast=True)
self.train = theano.function([self.X,self.Y],
loss,
updates=updates,
allow_input_downcast=True)
def set_network_trainer(input_data,
input_mask,
target_data,
target_mask,
network,
updater,
learning_rate,
grad_max_norm=10.,
# l2_lambda=1e-5,
load_updater_params=None):
# get network output data
predict_data = get_output(network, deterministic=False)
predict_idx = T.argmax(predict_data, axis=-1)
# get prediction cost
train_predict_cost = categorical_crossentropy(predictions=T.reshape(predict_data, (-1, predict_data.shape[-1])) + eps,
targets=T.flatten(target_data, 1))
train_predict_cost = train_predict_cost*T.flatten(target_mask, 1)
train_predict_cost = train_predict_cost.sum()/target_mask.sum()
# get regularizer cost
train_regularizer_cost = regularize_network_params(network, penalty=l2)
# get network parameters
network_params = get_all_params(network, trainable=True)
# get network gradients with clipping
network_grads = theano.grad(cost=train_predict_cost + train_regularizer_cost*l2_lambda,
wrt=network_params)
network_grads = theano.grad(cost=train_predict_cost,
wrt=network_params)
network_grads, network_grads_norm = total_norm_constraint(tensor_vars=network_grads,
max_norm=grad_max_norm,
return_norm=True)
# set updater
train_lr = theano.shared(lasagne.utils.floatX(learning_rate))
train_updates, trainer_params = updater(loss_or_grads=network_grads,
params=network_params,
learning_rate=train_lr,
load_params_dict=load_updater_params)
# get training (update) function
training_fn = theano.function(inputs=[input_data,
input_mask,
target_data,
target_mask],
outputs=[predict_data,
predict_idx,
train_predict_cost,
train_regularizer_cost],
network_grads_norm],
updates=train_updates, allow_input_downcast=True)
def build_treatment_model(self, n_vars, **kwargs):
input_vars = TT.matrix()
instrument_vars = TT.matrix()
targets = TT.vector()
inputs = layers.InputLayer((None, n_vars), input_vars)
inputs = layers.DropoutLayer(inputs, p=0.2)
dense_layer = layers.DenseLayer(inputs, 2 * kwargs['dense_size'], nonlinearity=nonlinearities.rectify)
dense_layer = layers.batch_norm(dense_layer)
dense_layer= layers.DropoutLayer(dense_layer, p=0.2)
for _ in xrange(kwargs['n_dense_layers'] - 1):
dense_layer = layers.DenseLayer(dense_layer, kwargs['dense_size'], nonlinearity=nonlinearities.rectify)
dense_layer = layers.batch_norm(dense_layer)
self.treatment_output = layers.DenseLayer(dense_layer, 1, nonlinearity=nonlinearities.linear)
init_params = layers.get_all_param_values(self.treatment_output)
prediction = layers.get_output(self.treatment_output, deterministic=False)
test_prediction = layers.get_output(self.treatment_output, deterministic=True)
l2_cost = regularization.regularize_network_params(self.treatment_output, regularization.l2)
loss = gmm_loss(prediction, targets, instrument_vars) + 1e-4 * l2_cost
params = layers.get_all_params(self.treatment_output, trainable=True)
param_updates = updates.adadelta(loss, params)
self._train_fn = theano.function(
[
input_vars,
targets,
instrument_vars,
],
loss,
updates=param_updates
)
self._loss_fn = theano.function(
[
input_vars,
targets,
instrument_vars,
],
loss,
)
self._output_fn = theano.function(
[
input_vars,
],
test_prediction,
)
return init_params
def test_regularize_network_params(self, layers):
from lasagne.regularization import regularize_network_params
l_1, l_2, l_3 = layers
penalty = Mock(return_value=0)
loss = regularize_network_params(l_3, penalty)
assert penalty.call_count == 2
penalty.assert_any_call(l_2.W)
penalty.assert_any_call(l_3.W)
def test_regularize_network_params(self, layers):
from lasagne.regularization import regularize_network_params
l_1, l_2, l_3 = layers
penalty = Mock(return_value=0)
loss = regularize_network_params(l_3, penalty)
assert penalty.call_count == 2
penalty.assert_any_call(l_2.W)
penalty.assert_any_call(l_3.W)
def build_instrument_model(self, n_vars, **kwargs):
targets = TT.vector()
instrument_vars = TT.matrix()
instruments = layers.InputLayer((None, n_vars), instrument_vars)
instruments = layers.DropoutLayer(instruments, p=0.2)
dense_layer = layers.DenseLayer(instruments,
kwargs['dense_size'],
nonlinearity=nonlinearities.tanh)
dense_layer = layers.DropoutLayer(dense_layer, p=0.2)
for _ in xrange(kwargs['n_dense_layers'] - 1):
dense_layer = layers.DenseLayer(dense_layer,
kwargs['dense_size'],
nonlinearity=nonlinearities.tanh)
dense_layer = layers.DropoutLayer(dense_layer, p=0.5)
self.instrument_output = layers.DenseLayer(
dense_layer, 1, nonlinearity=nonlinearities.linear)
init_params = layers.get_all_param_values(self.instrument_output)
prediction = layers.get_output(self.instrument_output,
deterministic=False)
test_prediction = layers.get_output(self.instrument_output,
deterministic=True)
# flexible here, endog variable can be categorical, continuous, etc.
l2_cost = regularization.regularize_network_params(
self.instrument_output, regularization.l2)
loss = objectives.squared_error(
prediction.flatten(), targets.flatten()).mean() + 1e-4 * l2_cost
loss_total = objectives.squared_error(prediction.flatten(),
targets.flatten()).mean()
params = layers.get_all_params(self.instrument_output, trainable=True)
param_updates = updates.adadelta(loss, params)
self._instrument_train_fn = theano.function([
targets,
instrument_vars,
],
loss,
updates=param_updates)
self._instrument_loss_fn = theano.function([
targets,
instrument_vars,
], loss_total)
self._instrument_output_fn = theano.function([instrument_vars],
test_prediction)
return init_params
def triplet_loss_iter(embedder, update_params={}):
X_triplets = {
'anchor':T.tensor4(),
'positive':T.tensor4(),
'negative':T.tensor4(),
} # each will be a batch of images
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_triplets.items()}
predicted_embeds_valid = {k:ll.get_output(final_emb_layer, X, deterministic=True) for k, X in X_triplets.items()}
# each output should be batch_size x embed_size
# should give us a vector of batch_size of distances btw anchor and positive
alpha = 0.2 # FaceNet alpha
triplet_pos = lambda pred: (pred['anchor'] - pred['positive']).norm(2,axis=1)
triplet_neg = lambda pred: (pred['anchor'] - pred['negative']).norm(2,axis=1)
triplet_distances = lambda pred: (triplet_pos(pred) - triplet_neg(pred) + alpha).clip(0, np.inf)
triplet_failed = lambda pred: T.mean(triplet_distances(pred) > alpha)
triplet_loss = lambda pred: T.sum(triplet_distances(pred))
decay = 0.001
reg = regularize_network_params(final_emb_layer, l2) * decay
losses_reg = lambda pred: triplet_loss(pred) + reg
loss_train = losses_reg(predicted_embeds_train)
loss_train.name = 'TL' # 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 = adam(grads, all_params)
#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_triplets['anchor'], X_triplets['positive'], X_triplets['negative']], [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_triplets['anchor'], X_triplets['positive'], X_triplets['negative']], [triplet_loss(predicted_embeds_valid),
losses_reg(predicted_embeds_valid),
triplet_failed(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 contrastive_loss_iter(embedder, update_params={}):
X_pairs = {
'img1':T.tensor4(),
'img2':T.tensor4(),
}
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['img1'] - pred['img2'] + 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 = 0.0001
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 = adam(grads, all_params)
#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['img1'], X_pairs['img2'], 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['img1'], X_pairs['img2'], 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 loss_iter(segmenter, update_params={}):
X = T.tensor4()
y = T.tensor4()
pixel_weights = T.tensor3()
final_pred_layer = segmenter[-1]
all_layers = ll.get_all_layers(segmenter)
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_masks_train = ll.get_output(segmenter, X)
predicted_mask_valid = ll.get_output(final_pred_layer, X, deterministic=True)
thresh = 0.5
accuracy = lambda pred: T.mean(T.eq(T.argmax(pred, axis=1), T.argmax(y, axis=1)))
true_pos = lambda pred: T.sum((pred[:,0,:,:] > thresh) * (y[:,0,:,:] > thresh))
false_pos = lambda pred: T.sum((pred[:,0,:,:] > thresh) - (y[:,0,:,:] > thresh))
precision = lambda pred: (true_pos(pred) / (true_pos(pred) + false_pos(pred)))
pixel_weights_1d = pixel_weights.flatten(ndim=1)
losses = lambda pred: T.mean(crossentropy_flat(pred + 1e-7, y + 1e-7) * pixel_weights_1d)
decay = 0.0001
reg = regularize_network_params(final_pred_layer, l2) * decay
losses_reg = lambda pred: losses(pred) + reg
loss_train = T.sum([losses_reg(mask) for mask in predicted_masks_train])
loss_train.name = 'CE' # for the names
#all_params = list(chain(*[ll.get_all_params(pred) for pred in segmenter]))
all_params = ll.get_all_params(segmenter, trainable=True) # this should work with multiple 'roots'
grads = T.grad(loss_train, all_params, add_names=True)
updates = adam(grads, all_params)
#updates = nesterov_momentum(grads, all_params, update_params['l_r'], momentum=update_params['momentum'])
acc_train = accuracy(predicted_masks_train[-1])
acc_valid = accuracy(predicted_mask_valid)
prec_train = precision(predicted_masks_train[-1])
prec_valid = precision(predicted_mask_valid)
print("Compiling network for training")
tic = time.time()
train_iter = theano.function([X, y, pixel_weights], [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, y, pixel_weights], [losses(predicted_mask_valid), losses_reg(predicted_mask_valid), prec_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 initialize(self):
self.prediction = lasagne.layers.get_output(self.network)
loss = lasagne.objectives.categorical_crossentropy(
self.prediction, self.target)
self.loss = loss.mean()
self.params = lasagne.layers.get_all_params(self.network,
trainable=True)
self.updates = lasagne.updates.nesterov_momentum(
self.loss,
self.params,
learning_rate=self.learning_rate,
momentum=0.9)
self.train_fn = theano.function([self.input, self.target],
loss,
updates=self.updates,
allow_input_downcast=True)
outputs = T.argmax(self.prediction, axis=1)
# self.predict_values = theano.function([self.input], self.prediction, allow_input_downcast=True)
self.predict_values = theano.function([self.input],
outputs,
allow_input_downcast=True)
self.test_prediction = lasagne.layers.get_output(self.network,
deterministic=True)
self.test_loss = lasagne.objectives.categorical_crossentropy(
self.test_prediction, self.target)
l1 = regularize_network_params(self.network, lasagne.regularization.l1)
l2 = regularize_network_params(self.network, lasagne.regularization.l2)
self.test_loss = self.test_loss.mean() + (l1 * 1e-4) + l2
self.test_acc = T.mean(T.eq(T.argmax(self.test_prediction, axis=1),
self.target),
dtype=theano.config.floatX)
self.val_fn = theano.function([self.input, self.target],
[self.test_loss, self.test_acc],
allow_input_downcast=True)
def _create_network(self):
logger.info("Building network ...")
net, input_var = self._build_network()
target_values = T.matrix('target_output')
actions = T.icol('actions')
# Create masks
# mask = theano.shared(np.zeros((self.batch_size, self.num_actions)).astype(np.int32))
mask = T.zeros_like(target_values)
mask = T.set_subtensor(
mask[T.arange(self.batch_size),
actions.reshape((-1, ))], 1)
# feed-forward path
network_output = lasagne.layers.get_output(net, input_var / 255.0)
# Add regularization penalty
loss = squared_error(network_output * mask, target_values).mean()
if self.weight_decay > 0.0:
loss += regularize_network_params(net, l2) * self.weight_decay
# Retrieve all parameters from the network
all_params = lasagne.layers.get_all_params(net, trainable=True)
# Compute updates for training
if self.clip_error:
grads = theano.gradient.grad(loss, all_params)
grads = [
lasagne.updates.norm_constraint(grad, self.clip_error,
range(grad.ndim))
for grad in grads
]
updates = self.optimizer(grads,
all_params,
learning_rate=self.learning_rate,
rho=self.decay_rate)
else:
updates = self.optimizer(loss,
all_params,
learning_rate=self.learning_rate,
rho=self.decay_rate)
# Theano functions for training and computing cost
logger.info("Compiling functions ...")
train = theano.function([input_var, target_values, actions],
[loss, network_output, target_values, mask],
updates=updates)
predict = theano.function([input_var], network_output)
return net, train, predict
def initialize(self):
self.prediction = lasagne.layers.get_output(self.network)
loss = lasagne.objectives.categorical_crossentropy(self.prediction, self.target)
self.loss = loss.mean()
self.params = lasagne.layers.get_all_params(self.network, trainable=True)
self.updates = lasagne.updates.nesterov_momentum(
self.loss, self.params, learning_rate=self.learning_rate, momentum=0.9)
self.train_fn = theano.function([self.input, self.target], loss, updates=self.updates,
allow_input_downcast=True)
self.predict_values = theano.function([self.input], T.argmax(self.prediction, axis=1),
allow_input_downcast=True)
self.test_prediction = lasagne.layers.get_output(self.network, deterministic=True)
self.test_loss = lasagne.objectives.categorical_crossentropy(self.test_prediction, self.target)
l1 = regularize_network_params(self.network, lasagne.regularization.l1)
l2 = regularize_network_params(self.network, lasagne.regularization.l2)
self.test_loss = self.test_loss.mean() + (l1 * 1e-4) + l2
self.test_acc = T.mean(T.eq(T.argmax(self.test_prediction, axis=1), self.target), dtype=theano.config.floatX)
self.val_fn = theano.function([self.input, self.target], [self.test_loss, self.test_acc],
allow_input_downcast=True)
def build_network(args, network):
X = T.tensor4('X')
Y = T.ivector('Y')
#physics weights
W = T.dvector('W')
#make sum to 1
#w = W / T.sum(W)
#network = build_layers(args)
'''write loss function equation'''
prediction = get_output(network, X)
loss = categorical_crossentropy(prediction, Y)
#multiply by weights
loss = T.dot(loss.T,W)
weightsl2 = regularize_network_params(network, l2)
loss += args['weight_decay'] * weightsl2
'''calculate test loss (cross entropy with no regularization) and accuracy'''
test_prediction = get_output(network, X, deterministic=True)
test_loss = categorical_crossentropy(test_prediction, Y)
test_loss = T.dot(test_loss.T,W)
'''classification percentage: we can change this based on false postive/false negative criteria'''
test_acc = categorical_accuracy(test_prediction,Y)
test_acc = T.dot(test_acc.T,W) / T.sum(W)
params = get_all_params(network, trainable=True)
updates = adam(loss, learning_rate=args['learning_rate'], params=params)
#updates = nesterov_momentum(loss, params, learning_rate=args['learning_rate'], momentum=args['momentum'])
'''train_fn -> takes in input,label pairs -> outputs loss '''
train_fn = theano.function([X, Y, W], loss, updates=updates)
'''val_fn -> takes in input,label pairs -> outputs non regularized loss and accuracy '''
val_fn = theano.function([X, Y, W], test_loss)
acc_fn = theano.function([X, Y, W], test_acc)
out_fn = theano.function([X], test_prediction)
score_fn = theano.function([X], test_prediction[:,1].T)
return {"net":network}, {'tr': train_fn,
'val': val_fn,
'test': val_fn,
'acc': acc_fn,
'out': out_fn, "score":score_fn}
def build_instrument_model(self, n_vars, **kwargs):
targets = TT.vector()
instrument_vars = TT.matrix()
instruments = layers.InputLayer((None, n_vars), instrument_vars)
instruments = layers.DropoutLayer(instruments, p=0.2)
dense_layer = layers.DenseLayer(instruments, kwargs['dense_size'], nonlinearity=nonlinearities.tanh)
dense_layer = layers.DropoutLayer(dense_layer, p=0.2)
for _ in xrange(kwargs['n_dense_layers'] - 1):
dense_layer = layers.DenseLayer(dense_layer, kwargs['dense_size'], nonlinearity=nonlinearities.tanh)
dense_layer = layers.DropoutLayer(dense_layer, p=0.5)
self.instrument_output = layers.DenseLayer(dense_layer, 1, nonlinearity=nonlinearities.linear)
init_params = layers.get_all_param_values(self.instrument_output)
prediction = layers.get_output(self.instrument_output, deterministic=False)
test_prediction = layers.get_output(self.instrument_output, deterministic=True)
# flexible here, endog variable can be categorical, continuous, etc.
l2_cost = regularization.regularize_network_params(self.instrument_output, regularization.l2)
loss = objectives.squared_error(prediction.flatten(), targets.flatten()).mean() + 1e-4 * l2_cost
loss_total = objectives.squared_error(prediction.flatten(), targets.flatten()).mean()
params = layers.get_all_params(self.instrument_output, trainable=True)
param_updates = updates.adadelta(loss, params)
self._instrument_train_fn = theano.function(
[
targets,
instrument_vars,
],
loss,
updates=param_updates
)
self._instrument_loss_fn = theano.function(
[
targets,
instrument_vars,
],
loss_total
)
self._instrument_output_fn = theano.function([instrument_vars], test_prediction)
return init_params
def build(layer_heads, params):
""""""
fns = {} # model methods
x = T.tensor4('input')
for target in params['targets']:
fns[target['name']] = {}
out_layer = layer_heads[target['name']]
y = T.matrix('target')
o = L.get_output(out_layer, inputs=x)
o_vl = L.get_output(out_layer, inputs=x, deterministic=True)
if 'class_weight' in params and params['class_weight']:
loss_fn = partial(weighted_cce, weights=params['class_weight'])
else:
loss_fn = obj.categorical_crossentropy
loss = loss_fn(o, y).mean()
loss_vl = loss_fn(o_vl, y).mean()
wd_l2 = reg.regularize_network_params(out_layer, reg.l2)
wd_l2 *= params['beta']
acc_vl = obj.categorical_accuracy(o_vl, y).mean()
updates_ = updates.adam(loss + wd_l2,
L.get_all_params(out_layer, trainable=True),
learning_rate=params['learning_rate'],
epsilon=params['epsilon'])
fns[target['name']]['train'] = theano.function(
[x, y], updates=updates_, allow_input_downcast=True)
fns[target['name']]['predict'] = theano.function(
[x], o_vl, allow_input_downcast=True)
fns[target['name']]['cost'] = theano.function(
[x, y], loss_vl, allow_input_downcast=True)
fns[target['name']]['acc'] = theano.function([x, y],
acc_vl,
allow_input_downcast=True)
fns[target['name']]['transform'] = theano.function(
[x],
L.get_output(L.get_all_layers(layer_heads[target['name']])[-2],
inputs=x,
deterministic=True),
allow_input_downcast=True)
return fns, layer_heads
def build_network(args, network):
X = T.tensor4('X')
Y = T.ivector('Y')
#physics weights
W = T.dvector('W')
#make sum to 1
#w = W / T.sum(W)
#network = build_layers(args)
'''write loss function equation'''
prediction = get_output(network, X)
loss = categorical_crossentropy(prediction, Y)
#multiply by weights
loss = T.dot(loss.T, W)
weightsl2 = regularize_network_params(network, l2)
loss += args['weight_decay'] * weightsl2
'''calculate test loss (cross entropy with no regularization) and accuracy'''
test_prediction = get_output(network, X, deterministic=True)
test_loss = categorical_crossentropy(test_prediction, Y)
test_loss = T.dot(test_loss.T, W)
'''classification percentage: we can change this based on false postive/false negative criteria'''
test_acc = categorical_accuracy(test_prediction, Y)
test_acc = T.dot(test_acc.T, W) / T.sum(W)
params = get_all_params(network, trainable=True)
updates = adam(loss, learning_rate=args['learning_rate'], params=params)
#updates = nesterov_momentum(loss, params, learning_rate=args['learning_rate'], momentum=args['momentum'])
'''train_fn -> takes in input,label pairs -> outputs loss '''
train_fn = theano.function([X, Y, W], loss, updates=updates)
'''val_fn -> takes in input,label pairs -> outputs non regularized loss and accuracy '''
val_fn = theano.function([X, Y, W], test_loss)
acc_fn = theano.function([X, Y, W], test_acc)
out_fn = theano.function([X], test_prediction)
score_fn = theano.function([X], test_prediction[:, 1].T)
return {
"net": network
}, {
'tr': train_fn,
'val': val_fn,
'test': val_fn,
'acc': acc_fn,
'out': out_fn,
"score": score_fn
}
def build_network(args, network):
X = T.tensor4('X')
#Y = T.tensor4('Y')
thresh = 1.0
#network = build_layers(args)
'''write loss function equation'''
prediction = get_output(network, X)
loss = squared_error(prediction, X).mean()
weightsl2 = regularize_network_params(network, l2).sum()
loss += args['weight_decay'] * weightsl2
'''calculate test loss (cross entropy with no regularization) and accuracy'''
test_prediction = get_output(network, X, deterministic=True)
test_loss = squared_error(test_prediction, X).sum()
'''classification percentage: we can change this based on false postive/false negative criteria'''
'''max reconstriuction error'''
test_acc = test_loss
test_score = T.sum(squared_error(test_prediction, X), axis=(1,2,3))
with T.autocast_float_as("float64"):
test_score = test_score / (T.prod(X.shape[1:]))
inds = test_score[test_score > thresh].nonzero()
test_score = T.set_subtensor(test_score[inds], 1)
#test_score = ifelse(T.gt(test_score,thresh), thresh,test_score )
test_score = 1 - test_score
params = get_all_params(network, trainable=True)
updates = adam(loss, learning_rate=args['learning_rate'], params=params)
#updates = nesterov_momentum(loss, params, learning_rate=args['learning_rate'], momentum=args['momentum'])
'''train_fn -> takes in input,label pairs -> outputs loss '''
train_fn = theano.function([X], loss, updates=updates)
'''val_fn -> takes in input,label pairs -> outputs non regularized loss and accuracy '''
val_fn = theano.function([X], test_loss)
acc_fn = theano.function([X], test_acc)
out_fn = theano.function([X], test_prediction)
score_fn = theano.function([X], test_score)
return {"net":network}, {'tr': train_fn,
'val': val_fn,
'acc': acc_fn,
'out': out_fn,
"score": score_fn}
def objective(layers, loss_function, target, aggregate=aggregate,
deterministic=False, get_output_kw=None):
if get_output_kw is None:
get_output_kw = {}
output_layer = layers[-1]
first_layer = layers[1]
network_output = lasagne.layers.get_output(
output_layer, deterministic=deterministic, **get_output_kw)
if not deterministic:
losses = loss_function(network_output, target) \
+ l2 * regularization.regularize_network_params(
output_layer, regularization.l2) \
+ l1 * regularization.regularize_layer_params(
first_layer, regularization.l1)
else:
losses = loss_function(network_output, target)
return aggregate(losses)
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 __init__(self, lambda1 = 1e-5, lambda2 = 1e-6):
self.input_var = T.tensor4('inputs')
self.target_var = T.matrix('targets')
self.are_net = build_ARE(self.input_var, ENCODE_SIZE)
self.reconstructed = lasagne.layers.get_output(self.are_net)
self.encode_layer, _ = get_layer_by_name(self.are_net, 'encode')
self.action_layer, _ = get_layer_by_name(self.are_net, 'action')
self.encoded_feature = lasagne.layers.get_output(self.encode_layer)
self.transformed_feature = lasagne.layers.get_output(self.action_layer)
self.XXT = T.dot(self.encoded_feature, self.encoded_feature.transpose())
self.l2_penalty = regularize_network_params(self.are_net,l2)
self.loss = lasagne.objectives.squared_error(self.reconstructed, self.target_var)
self.loss = 1000*self.loss.mean() - lambda1 * self.XXT.trace() + lambda2 * self.l2_penalty
self.params = lasagne.layers.get_all_params(self.are_net, trainable=True)
self.updates = lasagne.updates.adadelta(self.loss, self.params)
self.train_fn = theano.function([self.input_var, self.target_var], self.loss, updates=self.updates,on_unused_input='warn')
self.best_err = 999
self.action1_w = np.eye(ENCODE_SIZE, dtype = np.float32)
self.action1_b = np.zeros(ENCODE_SIZE, dtype = np.float32)
self.action2_w = np.eye(ENCODE_SIZE, dtype = np.float32)
self.action2_b = np.zeros(ENCODE_SIZE, dtype = np.float32)
def make_training_functions(network_layers, input_var, target_var, stack_params, weight_decay):
encode_layer, hidden_layer, smth_act_layer, network = network_layers;
output = lasagne.layers.get_output(network, deterministic = True);
loss = lasagne.objectives.squared_error(output, target_var).mean() + \
weight_decay * regularization.regularize_network_params(
layer = network, penalty = regularization.l2, tags={'regularizable' : True});
params = layers.get_all_params(network, trainable = True);
updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate = 0.00001, momentum = 0.95);
stack_updates = lasagne.updates.nesterov_momentum(loss, stack_params, learning_rate = 0.00001, momentum = 0.95);
encode = lasagne.layers.get_output(encode_layer, deterministic = True);
hidden = lasagne.layers.get_output(hidden_layer, deterministic = True);
smth_act = lasagne.layers.get_output(smth_act_layer, deterministic = True);
val_fn = theano.function([input_var, target_var], [loss, encode, hidden, smth_act, output]);
train_fn = theano.function([input_var, target_var], loss, updates = updates);
stack_train_fn = theano.function([input_var, target_var], loss, updates = stack_updates);
return val_fn, train_fn, stack_train_fn;
def loss_iter(segmenter, update_params={}):
X = T.tensor4()
y = T.tensor4()
pixel_weights = T.tensor3()
all_layers = ll.get_all_layers(segmenter)
imwrite_architecture(all_layers, './layer_rep.png')
predicted_mask_train = ll.get_output(segmenter, X)
predicted_mask_valid = ll.get_output(segmenter, X, deterministic=True)
accuracy = lambda pred: T.mean(T.eq(T.argmax(pred, axis=1), T.argmax(y, axis=1)))
pixel_weights_1d = pixel_weights.flatten(ndim=1)
losses = lambda pred: T.mean(crossentropy_flat(pred + 1e-7, y + 1e-7) * pixel_weights_1d)
decay = 0.0001
reg = regularize_network_params(segmenter, l2) * decay
losses_reg = lambda pred: losses(pred) + reg
loss_train = losses_reg(predicted_mask_train)
loss_train.name = 'combined_loss' # for the names
all_params = ll.get_all_params(segmenter)
grads = T.grad(loss_train, all_params, add_names=True)
#updates = adam(grads, all_params, **update_params)
updates = adam(grads, all_params, **update_params)
acc_train = accuracy(predicted_mask_train)
acc_valid = accuracy(predicted_mask_valid)
print("Compiling network for training")
tic = time.time()
train_iter = theano.function([X, y, pixel_weights], [loss_train, losses(predicted_mask_train), acc_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, y, pixel_weights], [losses(predicted_mask_valid), acc_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_network(self):
print("Building network ...")
net, input_var = self._build_network()
target_values = T.matrix('target_output')
maxQ_idx = target_values.argmax(1)
# Create masks
mask = theano.shared(
np.ones((BATCH_SIZE, self.actionsNum)).astype(np.int32))
maxQ_mask = theano.shared(
np.zeros((BATCH_SIZE, self.actionsNum)).astype(np.int32))
mask = T.set_subtensor(mask[np.arange(BATCH_SIZE), maxQ_idx], 0)
maxQ_mask = T.set_subtensor(maxQ_mask[np.arange(BATCH_SIZE), maxQ_idx],
1)
# lasagne.layers.get_output produces a variable for the output of the net
network_output = lasagne.layers.get_output(net)
new_target_values = target_values * maxQ_mask + network_output * mask
err = squared_error(network_output, new_target_values)
# Add regularization penalty
cost = err.mean() + regularize_network_params(net, l2) * DECAY
# Retrieve all parameters from the network
all_params = lasagne.layers.get_all_params(net, trainable=True)
# Compute SGD updates for training
updates = lasagne.updates.adadelta(cost, all_params)
# Theano functions for training and computing cost
print("Compiling functions ...")
train = theano.function(
[input_var, target_values],
[cost, new_target_values, network_output,
err.mean(1), maxQ_idx],
updates=updates)
predict = theano.function([input_var], lasagne.layers.get_output(net))
return net, train, predict
def __init__(self, lambda1 = 0, lambda2 = 0):
self.input_var = T.tensor4('inputs')
self.target_var = T.matrix('targets')
self.are_net = build_ARE(self.input_var, ENCODE_SIZE)
self.reconstructed = lasagne.layers.get_output(self.are_net)
self.encode_layer, _ = get_layer_by_name(self.are_net, 'encode')
self.action_layer, _ = get_layer_by_name(self.are_net, 'action')
self.encoded_feature = lasagne.layers.get_output(self.encode_layer)
self.transformed_feature = lasagne.layers.get_output(self.action_layer)
self.l1_penalty = regularize_network_params(self.are_net, l1)
self.loss = lasagne.objectives.squared_error(self.reconstructed, self.target_var)
self.XXT = T.dot(self.encoded_feature, self.encoded_feature.transpose()) + T.dot(self.transformed_feature, self.transformed_feature.transpose())
self.loss = self.loss.mean() + lambda1 * self.l1_penalty + lambda2 * self.XXT.trace()
self.loss = self.loss.mean() + lambda1 * self.l1_penalty
self.params = lasagne.layers.get_all_params(self.are_net, trainable=True)
self.l_r = theano.shared(np.array(0.01, dtype=theano.config.floatX))
self.updates = lasagne.updates.nesterov_momentum(
self.loss, self.params, learning_rate=self.l_r, momentum=0.90)
self.train_fn = theano.function([self.input_var, self.target_var], self.loss, updates=self.updates,on_unused_input='warn')
self.best_err = 999
self.action1_w = np.eye(ENCODE_SIZE, dtype = np.float32)
self.action1_b = np.zeros(ENCODE_SIZE, dtype = np.float32)
self.action2_w = np.eye(ENCODE_SIZE, dtype = np.float32)
self.action2_b = np.zeros(ENCODE_SIZE, dtype = np.float32)
def define_updates(network, input_var, target_var, weight_var):
params = lasagne.layers.get_all_params(network, trainable=True)
out = lasagne.layers.get_output(network)
test_out = lasagne.layers.get_output(network, deterministic=True)
l2_loss = P.L2_LAMBDA * regularize_network_params(network, l2)
train_metrics = score_metrics(out, target_var, weight_var, l2_loss)
loss, acc, dice_score, target_prediction, prediction, prediction_binary = train_metrics
val_metrics = score_metrics(test_out, target_var, weight_var, l2_loss)
t_loss, t_acc, t_dice_score, t_target_prediction, t_prediction, t_prediction_binary = train_metrics
l_r = theano.shared(np.array(P.LEARNING_RATE, dtype=theano.config.floatX))
if P.OPTIMIZATION == 'nesterov':
updates = lasagne.updates.nesterov_momentum(
loss, params, learning_rate=l_r, momentum=P.MOMENTUM)
if P.OPTIMIZATION == 'adam':
updates = lasagne.updates.adam(
loss, params, learning_rate=l_r)
logging.info("Defining train function")
train_fn = theano.function([input_var, target_var, weight_var],[
loss, l2_loss, acc, dice_score, target_prediction, prediction, prediction_binary],
updates=updates)
logging.info("Defining validation function")
val_fn = theano.function([input_var, target_var, weight_var], [
t_loss, l2_loss, t_acc, t_dice_score, t_target_prediction, t_prediction, t_prediction_binary])
return train_fn, val_fn, l_r
def _create_network(self):
logger.info("Building network ...")
net, input_var = self._build_network()
target_values = T.matrix('target_output')
actions = T.icol('actions')
# Create masks
# mask = theano.shared(np.zeros((self.batch_size, self.num_actions)).astype(np.int32))
mask = T.zeros_like(target_values)
mask = T.set_subtensor(mask[T.arange(self.batch_size), actions.reshape((-1,))], 1)
# feed-forward path
network_output = lasagne.layers.get_output(net, input_var / 255.0)
# Add regularization penalty
loss = squared_error(network_output * mask, target_values).mean()
if self.weight_decay > 0.0:
loss += regularize_network_params(net, l2) * self.weight_decay
# Retrieve all parameters from the network
all_params = lasagne.layers.get_all_params(net, trainable=True)
# Compute updates for training
if self.clip_error:
grads = theano.gradient.grad(loss, all_params)
grads = [lasagne.updates.norm_constraint(grad, self.clip_error, range(grad.ndim)) for grad in grads]
updates = self.optimizer(grads, all_params, learning_rate=self.learning_rate, rho=self.decay_rate)
else:
updates = self.optimizer(loss, all_params, learning_rate=self.learning_rate, rho=self.decay_rate)
# Theano functions for training and computing cost
logger.info("Compiling functions ...")
train = theano.function([input_var, target_values, actions], [loss, network_output, target_values, mask], updates=updates)
predict = theano.function([input_var], network_output)
return net, train, predict
def train(dataset, learn_step=0.005,
weight_decay=1e-4, num_epochs=500,
max_patience=100, data_augmentation={},
savepath=None, loadpath=None,
early_stop_class=None,
batch_size=None,
resume=False,
train_from_0_255=False):
#
# Prepare load/save directories
#
exp_name = 'unet_' + 'data_aug' if bool(data_augmentation) else ''
if savepath is None:
raise ValueError('A saving directory must be specified')
savepath = os.path.join(savepath, dataset, exp_name)
# loadpath = os.path.join(loadpath, dataset, exp_name)
print(savepath)
# print loadpath
if not os.path.exists(savepath):
os.makedirs(savepath)
else:
print('\033[93m The following folder already exists {}. '
'It will be overwritten in a few seconds...\033[0m'.format(
savepath))
print('Saving directory : ' + savepath)
with open(os.path.join(savepath, "config.txt"), "w") as f:
for key, value in locals().items():
f.write('{} = {}\n'.format(key, value))
#
# Define symbolic variables
#
input_var = T.tensor4('input_var')
target_var = T.ivector('target_var')
#
# Build dataset iterator
#
if batch_size is not None:
bs = batch_size
else:
bs = [10, 1, 1]
train_iter = IsbiEmStacksDataset(which_set='train',
batch_size=batch_size[0],
seq_per_subset=0,
seq_length=0,
data_augm_kwargs=data_augmentation,
return_one_hot=False,
return_01c=False,
overlap=0,
use_threads=True,
shuffle_at_each_epoch=True,
return_list=True,
return_0_255=False)
val_iter = IsbiEmStacksDataset(which_set='val',
batch_size=batch_size[1],
seq_per_subset=0,
seq_length=0,
return_one_hot=False,
return_01c=False,
use_threads=True,
shuffle_at_each_epoch=False,
return_list=True,
return_0_255=False)
test_iter = None
batch = train_iter.next()
input_dim = (np.shape(batch[0])[2], np.shape(batch[0])[3]) #(x,y) image shape
n_batches_train = train_iter.nbatches
n_batches_val = val_iter.nbatches
n_batches_test = test_iter.nbatches if test_iter is not None else 0
n_classes = train_iter.non_void_nclasses
void_labels = train_iter.void_labels
nb_in_channels = train_iter.data_shape[0]
print("Batch. train: %d, val %d, test %d" % (n_batches_train, n_batches_val, n_batches_test))
print("Nb of classes: %d" % (n_classes))
print("Nb. of input channels: %d" % (nb_in_channels))
#
# Build network
#
net = build_UNet(n_input_channels= nb_in_channels,# BATCH_SIZE = batch_size,
num_output_classes = n_classes, base_n_filters = 64, do_dropout=False,
input_dim = (None, None))
output_layer = net["output_flattened"]
#
# Define and compile theano functions
#
print("Defining and compiling training functions")
prediction = lasagne.layers.get_output(output_layer, input_var)
loss = crossentropy_metric(prediction, target_var, void_labels)
if weight_decay > 0:
weightsl2 = regularize_network_params(output_layer, lasagne.regularization.l2)
loss += weight_decay * weightsl2
params = lasagne.layers.get_all_params(output_layer, trainable=True)
updates = lasagne.updates.adam(loss, params, learning_rate=learn_step)
train_fn = theano.function([input_var, target_var], loss, updates=updates)
print("Defining and compiling test functions")
test_prediction = lasagne.layers.get_output(output_layer, input_var,deterministic=True)
test_loss = crossentropy_metric(test_prediction, target_var, void_labels)
test_acc = accuracy_metric(test_prediction, target_var, void_labels)
test_jacc = jaccard_metric(test_prediction, target_var, n_classes)
val_fn = theano.function([input_var, target_var], [test_loss, test_acc, test_jacc])
#
# Train
#
err_train = []
err_valid = []
acc_valid = []
jacc_valid = []
patience = 0
# Training main loop
print("Start training")
for epoch in range(num_epochs):
# Single epoch training and validation
start_time = time.time()
cost_train_tot = 0
# Train
print('Training steps ')
for i in range(n_batches_train):
print(i)
# Get minibatch
X_train_batch, L_train_batch = train_iter.next()
L_train_batch = np.reshape(L_train_batch, np.prod(L_train_batch.shape))
# Training step
cost_train = train_fn(X_train_batch, L_train_batch)
out_str = "cost %f" % (cost_train)
cost_train_tot += cost_train
err_train += [cost_train_tot/n_batches_train]
# Validation
cost_val_tot = 0
acc_val_tot = 0
jacc_val_tot = np.zeros((2, n_classes))
print('Validation steps')
for i in range(n_batches_val):
print(i)
# Get minibatch
X_val_batch, L_val_batch = val_iter.next()
L_val_batch = np.reshape(L_val_batch, np.prod(L_val_batch.shape))
# Validation step
cost_val, acc_val, jacc_val = val_fn(X_val_batch, L_val_batch)
acc_val_tot += acc_val
cost_val_tot += cost_val
jacc_val_tot += jacc_val
err_valid += [cost_val_tot/n_batches_val]
acc_valid += [acc_val_tot/n_batches_val]
jacc_perclass_valid = jacc_val_tot[0, :] / jacc_val_tot[1, :]
if early_stop_class == None:
jacc_valid += [np.mean(jacc_perclass_valid)]
else:
jacc_valid += [jacc_perclass_valid[early_stop_class]]
out_str = "EPOCH %i: Avg epoch training cost train %f, cost val %f" +\
", acc val %f, jacc val class 0 % f, jacc val class 1 %f, jacc val %f took %f s"
out_str = out_str % (epoch, err_train[epoch],
err_valid[epoch],
acc_valid[epoch],
jacc_perclass_valid[0],
jacc_perclass_valid[1],
jacc_valid[epoch],
time.time()-start_time)
print(out_str)
with open(os.path.join(savepath, "unet_output.log"), "a") as f:
f.write(out_str + "\n")
# Early stopping and saving stuff
if epoch == 0:
best_jacc_val = jacc_valid[epoch]
elif epoch > 1 and jacc_valid[epoch] > best_jacc_val:
best_jacc_val = jacc_valid[epoch]
patience = 0
np.savez(os.path.join(savepath, 'new_unet_model_best.npz'), *lasagne.layers.get_all_param_values(output_layer))
np.savez(os.path.join(savepath, 'unet_errors_best.npz'), err_valid, err_train, acc_valid, jacc_valid)
else:
patience += 1
np.savez(os.path.join(savepath, 'new_unet_model_last.npz'), *lasagne.layers.get_all_param_values(output_layer))
np.savez(os.path.join(savepath, 'unet_errors_last.npz'), err_valid, err_train, acc_valid, jacc_valid)
# Finish training if patience has expired or max nber of epochs
# reached
if patience == max_patience or epoch == num_epochs-1:
if test_iter is not None:
# Load best model weights
with np.load(os.path.join(savepath, 'new_unet_model_best.npz')) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
nlayers = len(lasagne.layers.get_all_params(output_layer))
lasagne.layers.set_all_param_values(output_layer, param_values[:nlayers])
# Test
cost_test_tot = 0
acc_test_tot = 0
jacc_test_tot = np.zeros((2, n_classes))
for i in range(n_batches_test):
# Get minibatch
X_test_batch, L_test_batch = test_iter.next()
L_test_batch = np.reshape(L_test_batch, np.prod(L_test_batch.shape))
# Test step
cost_test, acc_test, jacc_test = val_fn(X_test_batch, L_test_batch)
acc_test_tot += acc_test
cost_test_tot += cost_test
jacc_test_tot += jacc_test
err_test = cost_test_tot/n_batches_test
acc_test = acc_test_tot/n_batches_test
jacc_test_perclass = jacc_test_tot[0, :] / jacc_test_tot[1, :]
jacc_test = np.mean(jacc_test_perclass)
out_str = "FINAL MODEL: err test % f, acc test %f, " +\
"jacc test class 0 %f, jacc test class 1 %f, jacc test %f"
out_str = out_str % (err_test, acc_test, jacc_test_perclass[0],
jacc_test_perclass[1], jacc_test)
print(out_str)
if savepath != loadpath:
print('Copying model and other training files to {}'.format(loadpath))
copy_tree(savepath, loadpath)
# End
return
def train(cf):
###############
# load data #
###############
print('-' * 75)
print('Loading data')
#TODO ; prepare a public version of the data loader
train_iter, val_iter, test_iter = load_data(cf.dataset,
train_crop_size=cf.train_crop_size,
batch_size=cf.batch_size,
horizontal_flip=True,
)
n_classes = train_iter.get_n_classes()
void_labels = train_iter.get_void_labels()
print('Number of images : train : {}, val : {}, test : {}'.format(
train_iter.get_n_samples(), val_iter.get_n_samples(), test_iter.get_n_samples()))
###################
# Build model #
###################
# Build model and display summary
net = cf.net
net.summary()
# Restore
if hasattr(cf, 'pretrained_model'):
print('Using a pretrained model : {}'.format(cf.pretrained_model))
net.restore(cf.pretrained_model)
# Compile functions
print('Compilation starts at ' + str(datetime.now()).split('.')[0])
params = lasagne.layers.get_all_params(net.output_layer, trainable=True)
lr_shared = theano.shared(np.array(cf.learning_rate, dtype='float32'))
lr_decay = np.array(cf.lr_sched_decay, dtype='float32')
# Create loss and metrics
for key in ['train', 'valid']:
# LOSS
pred = get_output(net.output_layer, deterministic=key == 'valid',
batch_norm_update_averages=False, batch_norm_use_averages=False)
loss = crossentropy(pred, net.target_var, void_labels)
if cf.weight_decay:
weightsl2 = regularize_network_params(net.output_layer, lasagne.regularization.l2)
loss += cf.weight_decay * weightsl2
# METRICS
I, U, acc = theano_metrics(pred, net.target_var, n_classes, void_labels)
# COMPILE
start_time_compilation = time.time()
if key == 'train':
updates = cf.optimizer(loss, params, learning_rate=lr_shared)
train_fn = theano.function([net.input_var, net.target_var], [loss, I, U, acc], updates=updates)
else:
val_fn = theano.function([net.input_var, net.target_var], [loss, I, U, acc])
print('{} compilation took {:.3f} seconds'.format(key, time.time() - start_time_compilation))
###################
# Main loops #
###################
# metric's sauce
init_history = lambda: {'loss': [], 'jaccard': [], 'accuracy': []}
history = {'train': init_history(), 'val': init_history(), 'test': init_history()}
patience = 0
best_jacc_val = 0
best_epoch = 0
if hasattr(cf, 'pretrained_model'):
print('Validation score before training')
print batch_loop(val_iter, val_fn, 0, 'val', {'val': init_history()})
# Training main loop
print('-' * 30)
print('Training starts at ' + str(datetime.now()).split('.')[0])
print('-' * 30)
for epoch in range(cf.num_epochs):
# Train
start_time_train = time.time()
history = batch_loop(train_iter, train_fn, epoch, 'train', history)
# Validation
start_time_valid = time.time()
history = batch_loop(val_iter, val_fn, epoch, 'val', history)
# Print
out_str = \
'\r\x1b[2 Epoch {} took {}+{} sec. ' \
'loss = {:.5f} | jacc = {:.5f} | acc = {:.5f} || ' \
'loss = {:.5f} | jacc = {:.5f} | acc = {:.5f}'.format(
epoch, int(start_time_valid - start_time_train), int(time.time() - start_time_valid),
history['train']['loss'][-1], history['train']['jaccard'][-1], history['train']['accuracy'][-1],
history['val']['loss'][-1], history['val']['jaccard'][-1], history['val']['accuracy'][-1])
# Monitoring jaccard
if history['val']['jaccard'][-1] > best_jacc_val:
out_str += ' (BEST)'
best_jacc_val = history['val']['jaccard'][-1]
best_epoch = epoch
patience = 0
net.save(os.path.join(cf.savepath, 'model.npz'))
else:
patience += 1
print out_str
np.savez(os.path.join(cf.savepath, 'errors.npz'), metrics=history, best_epoch=best_epoch)
# Learning rate scheduler
lr_shared.set_value(lr_shared.get_value() * lr_decay)
# Finish training if patience has expired or max nber of epochs reached
if patience == cf.max_patience or epoch == cf.num_epochs - 1:
# Load best model weights
net.restore(os.path.join(cf.savepath, 'model.npz'))
# Test
print('Training ends\nTest')
if test_iter.get_n_samples() == 0:
print 'No test set'
else:
history = batch_loop(test_iter, val_fn, epoch, 'test', history)
print ('Average cost test = {:.5f} | jacc test = {:.5f} | acc_test = {:.5f} '.format(
history['test']['loss'][-1],
history['test']['jaccard'][-1],
history['test']['accuracy'][-1]))
np.savez(os.path.join(cf.savepath, 'errors.npz'), metrics=history, best_epoch=best_epoch)
# Exit
return
OUTPUT = open(progress_filename, 'w')
OUTPUT.write("NUM_PARAMS,"+str(lasagne.layers.count_params(cnn_model['output']))+'\n')
OUTPUT.write("EPOCH,RMSE,MSE\n")
OUTPUT.close()
#mulitply our training predictions and visualizations by 1.0
# this makes it so these numbers are part of the theano graph but not changed in value
# in this way, theano doesn't complain at me for unused variables
context_output_train = lasagne.layers.get_output(cnn_model['output'],deterministic=False)
train_prediction = context_output_train[0] * 1.0
visual_predictions_train = context_output_train[1] * 1.0
train_prediction = train_prediction.flatten()
train_loss = lasagne.objectives.squared_error(target_vals,train_prediction)
#get our loss and our cost
l2_loss = regularize_network_params(cnn_model['output'],l2)
train_cost = T.mean(train_loss) + l2_loss*l2_regularization_lambda
#then get our parameters and update from lasagne
params = lasagne.layers.get_all_params(cnn_model['output'], trainable=True)
updates = lasagne.updates.adam(train_cost, params, learning_rate=learning_rate)
#then get the outputs for the test and multiply them by 1.0 like above
context_output_test = lasagne.layers.get_output(cnn_model['output'],deterministic=True)
test_predicition = context_output_test[0] * 1.0
visual_predictions_test = context_output_test[1] * 1.0
test_predicition = test_predicition.flatten()
test_cost = lasagne.objectives.squared_error(target_vals,test_predicition)
#then define my theano functions for train and test
train_func = theano.function([input_atom,input_bonds,input_atom_index,\
def initialize_network(self):
"""
:description: this method initializes the network, updates, and theano functions for training and
retrieving q values. Here's an outline:
1. build the q network and target q network
2. initialize theano symbolic variables used for compiling functions
3. initialize the theano numeric variables used as input to functions
4. formulate the symbolic loss
5. formulate the symbolic updates
6. compile theano functions for training and for getting q_values
"""
batch_size, input_shape = self.batch_size, self.input_shape
lasagne.random.set_rng(self.rng)
# 1. build the q network and target q network
self.l_out = self.build_network(input_shape, self.num_actions, batch_size)
self.next_l_out = self.build_network(input_shape, self.num_actions, batch_size)
self.reset_target_network()
# 2. initialize theano symbolic variables used for compiling functions
states = T.tensor4('states')
actions = T.icol('actions')
rewards = T.col('rewards')
next_states = T.tensor4('next_states')
# terminals are used to indicate a terminal state in the episode and hence a mask over the future
# q values i.e., Q(s',a')
terminals = T.icol('terminals')
# 3. initialize the theano numeric variables used as input to functions
self.states_shape = (batch_size,) + (1,) + input_shape
self.states_shared = theano.shared(np.zeros(self.states_shape, dtype=theano.config.floatX))
self.next_states_shared = theano.shared(np.zeros(self.states_shape, dtype=theano.config.floatX))
self.rewards_shared = theano.shared(np.zeros((batch_size, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
self.actions_shared = theano.shared(np.zeros((batch_size, 1), dtype='int32'),
broadcastable=(False, True))
self.terminals_shared = theano.shared(np.zeros((batch_size, 1), dtype='int32'),
broadcastable=(False, True))
# 4. formulate the symbolic loss
q_vals = lasagne.layers.get_output(self.l_out, states)
next_q_vals = lasagne.layers.get_output(self.next_l_out, next_states)
target = (rewards +
(T.ones_like(terminals) - terminals) *
self.discount * T.max(next_q_vals, axis=1, keepdims=True))
# reshape((-1,)) == 'make a row vector', reshape((-1, 1) == 'make a column vector'
diff = target - q_vals[T.arange(batch_size), actions.reshape((-1,))].reshape((-1, 1))
# a lot of the deepmind work clips the td error at 1 so we do that here
# the problem is that gradient backpropagating through this minimum node
# will be zero if diff is larger then 1.0 (because changing params before
# the minimum does not impact the output of the minimum). To account for
# this we take the part of the td error (magnitude) greater than 1.0 and simply
# add it to the loss, which allows gradient to backprop but just linearly
# in the td error rather than quadratically
quadratic_part = T.minimum(abs(diff), 1.0)
linear_part = abs(diff) - quadratic_part
loss = 0.5 * quadratic_part ** 2 + linear_part
loss = T.mean(loss) + self.regularization * regularize_network_params(self.l_out, l2)
# 5. formulate the symbolic updates
params = lasagne.layers.helper.get_all_params(self.l_out)
updates = self.initialize_updates(self.update_rule, loss, params, self.learning_rate)
# 6. compile theano functions for training and for getting q_values
givens = {
states: self.states_shared,
next_states: self.next_states_shared,
rewards: self.rewards_shared,
actions: self.actions_shared,
terminals: self.terminals_shared
}
self._train = theano.function([], [loss, q_vals], updates=updates, givens=givens)
self._get_q_values = theano.function([], q_vals, givens={states: self.states_shared})
def test_space_invaders(game_title='SpaceInvaders-v0',
n_parallel_games=3,
replay_seq_len=2,
):
"""
:param game_title: name of atari game in Gym
:param n_parallel_games: how many games we run in parallel
:param replay_seq_len: how long is one replay session from a batch
"""
atari = gym.make(game_title)
atari.reset()
# Game Parameters
n_actions = atari.action_space.n
observation_shape = (None,) + atari.observation_space.shape
action_names = atari.get_action_meanings()
del atari
# ##### Agent observations
# image observation at current tick goes here
observation_layer = InputLayer(observation_shape, name="images input")
# reshape to [batch, color, x, y] to allow for convolutional layers to work correctly
observation_reshape = DimshuffleLayer(observation_layer, (0, 3, 1, 2))
# Agent memory states
window_size = 3
# prev state input
prev_window = InputLayer((None, window_size) + tuple(observation_reshape.output_shape[1:]),
name="previous window state")
# our window
window = WindowAugmentation(observation_reshape,
prev_window,
name="new window state")
memory_dict = {window: prev_window}
# ##### Neural network body
# you may use any other lasagne layers, including convolutions, batch_norms, maxout, etc
# pixel-wise maximum over the temporal window (to avoid flickering)
window_max = ExpressionLayer(window,
lambda a: a.max(axis=1),
output_shape=(None,) + window.output_shape[2:])
# a simple lasagne network (try replacing with any other lasagne network and see what works best)
nn = DenseLayer(window_max, num_units=50, name='dense0')
# Agent policy and action picking
q_eval = DenseLayer(nn,
num_units=n_actions,
nonlinearity=lasagne.nonlinearities.linear,
name="QEvaluator")
#fakes for a2c
policy_eval = DenseLayer(nn,
num_units=n_actions,
nonlinearity=lasagne.nonlinearities.softmax,
name="a2c action probas")
state_value_eval = DenseLayer(nn,
num_units=1,
nonlinearity=None,
name="a2c state values")
# resolver
resolver = ProbabilisticResolver(policy_eval, name="resolver")
# agent
agent = Agent(observation_layer,
memory_dict,
(q_eval,policy_eval,state_value_eval), resolver)
# Since it's a single lasagne network, one can get it's weights, output, etc
weights = lasagne.layers.get_all_params(resolver, trainable=True)
# Agent step function
print('compiling react')
applier_fun = agent.get_react_function()
# a nice pythonic interface
def step(observation, prev_memories='zeros', batch_size=n_parallel_games):
""" returns actions and new states given observation and prev state
Prev state in default setup should be [prev window,]"""
# default to zeros
if prev_memories == 'zeros':
prev_memories = [np.zeros((batch_size,) + tuple(mem.output_shape[1:]),
dtype='float32')
for mem in agent.agent_states]
res = applier_fun(np.array(observation), *prev_memories)
action = res[0]
memories = res[1:]
return action, memories
# # Create and manage a pool of atari sessions to play with
pool = GamePool(game_title, n_parallel_games)
observation_log, action_log, reward_log, _, _, _ = pool.interact(step, 50)
print(np.array(action_names)[np.array(action_log)[:3, :5]])
# # experience replay pool
# Create an environment with all default parameters
env = SessionPoolEnvironment(observations=observation_layer,
actions=resolver,
agent_memories=agent.agent_states)
def update_pool(env, pool, n_steps=100):
""" a function that creates new sessions and ads them into the pool
throwing the old ones away entirely for simplicity"""
preceding_memory_states = list(pool.prev_memory_states)
# get interaction sessions
observation_tensor, action_tensor, reward_tensor, _, is_alive_tensor, _ = pool.interact(step, n_steps=n_steps)
# load them into experience replay environment
env.load_sessions(observation_tensor, action_tensor, reward_tensor, is_alive_tensor, preceding_memory_states)
# load first sessions
update_pool(env, pool, replay_seq_len)
# A more sophisticated way of training is to store a large pool of sessions and train on random batches of them.
# ### Training via experience replay
# get agent's Q-values, policy, etc obtained via experience replay
_env_states, _observations, _memories, _imagined_actions, estimators = agent.get_sessions(
env,
session_length=replay_seq_len,
batch_size=env.batch_size,
optimize_experience_replay=True,
)
(q_values_sequence,policy_sequence,value_sequence) = estimators
# Evaluating loss function
scaled_reward_seq = env.rewards
# For SpaceInvaders, however, not scaling rewards is at least working
elwise_mse_loss = 0.
#1-step algos
for algo in qlearning,sarsa:
elwise_mse_loss += algo.get_elementwise_objective(q_values_sequence,
env.actions[0],
scaled_reward_seq,
env.is_alive,
gamma_or_gammas=0.99, )
#qlearning_n_step
for n in (1,3,replay_seq_len-1, replay_seq_len, replay_seq_len+1,None):
elwise_mse_loss += qlearning_n_step.get_elementwise_objective(q_values_sequence,
env.actions[0],
scaled_reward_seq,
env.is_alive,
gamma_or_gammas=0.99,
n_steps=n)
#a2c n_step
elwise_mse_loss += a2c_n_step.get_elementwise_objective(policy_sequence,
value_sequence[:,:,0],
env.actions[0],
scaled_reward_seq,
env.is_alive,
gamma_or_gammas=0.99,
n_steps=3)
# compute mean over "alive" fragments
mse_loss = elwise_mse_loss.sum() / env.is_alive.sum()
# regularize network weights
reg_l2 = regularize_network_params(resolver, l2) * 10 ** -4
loss = mse_loss + reg_l2
# Compute weight updates
updates = lasagne.updates.adadelta(loss, weights, learning_rate=0.01)
# mean session reward
mean_session_reward = env.rewards.sum(axis=1).mean()
# # Compile train and evaluation functions
print('compiling')
train_fun = theano.function([], [loss, mean_session_reward], updates=updates)
evaluation_fun = theano.function([], [loss, mse_loss, reg_l2, mean_session_reward])
print("I've compiled!")
# # Training loop
for epoch_counter in range(10):
update_pool(env, pool, replay_seq_len)
loss, avg_reward = train_fun()
full_loss, q_loss, l2_penalty, avg_reward_current = evaluation_fun()
print("epoch %i,loss %.5f, rewards: %.5f " % (
epoch_counter, full_loss, avg_reward_current))
print("rec %.3f reg %.3f" % (q_loss, l2_penalty))
def test_memory(game_title='SpaceInvaders-v0',
n_parallel_games=3,
replay_seq_len=2,
):
"""
:param game_title: name of atari game in Gym
:param n_parallel_games: how many games we run in parallel
:param replay_seq_len: how long is one replay session from a batch
"""
atari = gym.make(game_title)
atari.reset()
# Game Parameters
n_actions = atari.action_space.n
observation_shape = (None,) + atari.observation_space.shape
action_names = atari.get_action_meanings()
del atari
# ##### Agent observations
# image observation at current tick goes here
observation_layer = InputLayer(observation_shape, name="images input")
# reshape to [batch, color, x, y] to allow for convolutional layers to work correctly
observation_reshape = DimshuffleLayer(observation_layer, (0, 3, 1, 2))
# Agent memory states
memory_dict = OrderedDict([])
###Window
window_size = 3
# prev state input
prev_window = InputLayer((None, window_size) + tuple(observation_reshape.output_shape[1:]),
name="previous window state")
# our window
window = WindowAugmentation(observation_reshape,
prev_window,
name="new window state")
# pixel-wise maximum over the temporal window (to avoid flickering)
window_max = ExpressionLayer(window,
lambda a: a.max(axis=1),
output_shape=(None,) + window.output_shape[2:])
memory_dict[window] = prev_window
###Stack
#prev stack
stack_w,stack_h = 4, 5
stack_inputs = DenseLayer(observation_reshape,stack_w,name="prev_stack")
stack_controls = DenseLayer(observation_reshape,3,
nonlinearity=lasagne.nonlinearities.softmax,
name="prev_stack")
prev_stack = InputLayer((None,stack_h,stack_w),
name="previous stack state")
stack = StackAugmentation(stack_inputs,prev_stack, stack_controls)
memory_dict[stack] = prev_stack
stack_top = lasagne.layers.SliceLayer(stack,0,1)
###RNN preset
prev_rnn = InputLayer((None,16),
name="previous RNN state")
new_rnn = RNNCell(prev_rnn,observation_reshape)
memory_dict[new_rnn] = prev_rnn
###GRU preset
prev_gru = InputLayer((None,16),
name="previous GRUcell state")
new_gru = GRUCell(prev_gru,observation_reshape)
memory_dict[new_gru] = prev_gru
###GRUmemorylayer
prev_gru1 = InputLayer((None,15),
name="previous GRUcell state")
new_gru1 = GRUMemoryLayer(15,observation_reshape,prev_gru1)
memory_dict[new_gru1] = prev_gru1
#LSTM with peepholes
prev_lstm0_cell = InputLayer((None,13),
name="previous LSTMCell hidden state [with peepholes]")
prev_lstm0_out = InputLayer((None,13),
name="previous LSTMCell output state [with peepholes]")
new_lstm0_cell,new_lstm0_out = LSTMCell(prev_lstm0_cell,prev_lstm0_out,
input_or_inputs = observation_reshape,
peepholes=True,name="newLSTM1 [with peepholes]")
memory_dict[new_lstm0_cell] = prev_lstm0_cell
memory_dict[new_lstm0_out] = prev_lstm0_out
#LSTM without peepholes
prev_lstm1_cell = InputLayer((None,14),
name="previous LSTMCell hidden state [no peepholes]")
prev_lstm1_out = InputLayer((None,14),
name="previous LSTMCell output state [no peepholes]")
new_lstm1_cell,new_lstm1_out = LSTMCell(prev_lstm1_cell,prev_lstm1_out,
input_or_inputs = observation_reshape,
peepholes=False,name="newLSTM1 [no peepholes]")
memory_dict[new_lstm1_cell] = prev_lstm1_cell
memory_dict[new_lstm1_out] = prev_lstm1_out
##concat everything
for i in [flatten(window_max),stack_top,new_rnn,new_gru,new_gru1]:
print(i.output_shape)
all_memory = concat([flatten(window_max),stack_top,new_rnn,new_gru,new_gru1,new_lstm0_out,new_lstm1_out,])
# ##### Neural network body
# you may use any other lasagne layers, including convolutions, batch_norms, maxout, etc
# a simple lasagne network (try replacing with any other lasagne network and see what works best)
nn = DenseLayer(all_memory, num_units=50, name='dense0')
# Agent policy and action picking
q_eval = DenseLayer(nn,
num_units=n_actions,
nonlinearity=lasagne.nonlinearities.linear,
name="QEvaluator")
# resolver
resolver = EpsilonGreedyResolver(q_eval, epsilon=0.1, name="resolver")
# agent
agent = Agent(observation_layer,
memory_dict,
q_eval, resolver)
# Since it's a single lasagne network, one can get it's weights, output, etc
weights = lasagne.layers.get_all_params(resolver, trainable=True)
# Agent step function
print('compiling react')
applier_fun = agent.get_react_function()
# a nice pythonic interface
def step(observation, prev_memories='zeros', batch_size=n_parallel_games):
""" returns actions and new states given observation and prev state
Prev state in default setup should be [prev window,]"""
# default to zeros
if prev_memories == 'zeros':
prev_memories = [np.zeros((batch_size,) + tuple(mem.output_shape[1:]),
dtype='float32')
for mem in agent.agent_states]
res = applier_fun(np.array(observation), *prev_memories)
action = res[0]
memories = res[1:]
return action, memories
# # Create and manage a pool of atari sessions to play with
pool = GamePool(game_title, n_parallel_games)
observation_log, action_log, reward_log, _, _, _ = pool.interact(step, 50)
print(np.array(action_names)[np.array(action_log)[:3, :5]])
# # experience replay pool
# Create an environment with all default parameters
env = SessionPoolEnvironment(observations=observation_layer,
actions=resolver,
agent_memories=agent.agent_states)
def update_pool(env, pool, n_steps=100):
""" a function that creates new sessions and ads them into the pool
throwing the old ones away entirely for simplicity"""
preceding_memory_states = list(pool.prev_memory_states)
# get interaction sessions
observation_tensor, action_tensor, reward_tensor, _, is_alive_tensor, _ = pool.interact(step, n_steps=n_steps)
# load them into experience replay environment
env.load_sessions(observation_tensor, action_tensor, reward_tensor, is_alive_tensor, preceding_memory_states)
# load first sessions
update_pool(env, pool, replay_seq_len)
# A more sophisticated way of training is to store a large pool of sessions and train on random batches of them.
# ### Training via experience replay
# get agent's Q-values obtained via experience replay
_env_states, _observations, _memories, _imagined_actions, q_values_sequence = agent.get_sessions(
env,
session_length=replay_seq_len,
batch_size=env.batch_size,
optimize_experience_replay=True,
)
# Evaluating loss function
scaled_reward_seq = env.rewards
# For SpaceInvaders, however, not scaling rewards is at least working
elwise_mse_loss = qlearning.get_elementwise_objective(q_values_sequence,
env.actions[0],
scaled_reward_seq,
env.is_alive,
gamma_or_gammas=0.99, )
# compute mean over "alive" fragments
mse_loss = elwise_mse_loss.sum() / env.is_alive.sum()
# regularize network weights
reg_l2 = regularize_network_params(resolver, l2) * 10 ** -4
loss = mse_loss + reg_l2
# Compute weight updates
updates = lasagne.updates.adadelta(loss, weights, learning_rate=0.01)
# mean session reward
mean_session_reward = env.rewards.sum(axis=1).mean()
# # Compile train and evaluation functions
print('compiling')
train_fun = theano.function([], [loss, mean_session_reward], updates=updates)
evaluation_fun = theano.function([], [loss, mse_loss, reg_l2, mean_session_reward])
print("I've compiled!")
# # Training loop
for epoch_counter in range(10):
update_pool(env, pool, replay_seq_len)
loss, avg_reward = train_fun()
full_loss, q_loss, l2_penalty, avg_reward_current = evaluation_fun()
print("epoch %i,loss %.5f, rewards: %.5f " % (
epoch_counter, full_loss, avg_reward_current))
print("rec %.3f reg %.3f" % (q_loss, l2_penalty))
def test_reasoning_value_based(n_parallel_games=25,
algo = qlearning,
n_steps=1
):
"""
:param game_title: name of atari game in Gym
:param n_parallel_games: how many games we run in parallel
:param algo: training algorithm to use (module)
"""
# instantiate an experiment environment with default parameters
env = experiment.BooleanReasoningEnvironment()
# hidden neurons
n_hidden_neurons = 64
observation_size = (None,) + tuple(env.observation_shapes)
observation_layer = lasagne.layers.InputLayer(observation_size, name="observation_input")
prev_state_layer = lasagne.layers.InputLayer([None, n_hidden_neurons], name="prev_state_input")
# memory layer (this isn't the same as lasagne recurrent units)
rnn = RNNCell(prev_state_layer, observation_layer, name="rnn0")
# q_values (estimated using very simple neural network)
q_values = lasagne.layers.DenseLayer(rnn,
num_units=env.n_actions,
nonlinearity=lasagne.nonlinearities.linear,
name="QEvaluator")
# resolver uses epsilon - parameter which defines a probability of randomly taken action.
epsilon = theano.shared(np.float32(0.1), name="e-greedy.epsilon")
resolver = EpsilonGreedyResolver(q_values, epsilon=epsilon, name="resolver")
# packing this into agent
agent = Agent(observation_layer,
agent_states={rnn:prev_state_layer},
policy_estimators=q_values,
action_layers=resolver)
# Since it's a lasagne network, one can get it's weights, output, etc
weights = lasagne.layers.get_all_params(resolver,trainable=True)
# produce interaction sequences of length <= 10
(state_seq,), observation_seq, agent_state, action_seq, qvalues_seq = agent.get_sessions(
env,
session_length=10,
batch_size=env.batch_size,
)
hidden_seq = agent_state[rnn]
# get rewards for all actions
rewards_seq = env.get_reward_sequences(state_seq, action_seq)
# get indicator whether session is still active
is_alive_seq = env.get_whether_alive(observation_seq)
# gamma - delayed reward coefficient - what fraction of reward is retained if it is obtained one tick later
gamma = theano.shared(np.float32(0.99), name='q_learning_gamma')
squarred_Qerror = algo.get_elementwise_objective(
qvalues_seq,
action_seq,
rewards_seq,
is_alive_seq,
gamma_or_gammas=gamma)
# take sum over steps, average over sessions
mse_Qloss = squarred_Qerror.sum(axis=1).mean()
# impose l2 regularization on network weights
reg_l2 = regularize_network_params(resolver, l2) * 10**-3
loss = mse_Qloss + reg_l2
# compute weight updates
updates = lasagne.updates.adadelta(loss, weights, learning_rate=0.1)
# take sum over steps, average over sessions
mean_session_reward = rewards_seq.sum(axis=1).mean()
train_fun = theano.function([], [loss, mean_session_reward], updates=updates)
compute_mean_session_reward = theano.function([], mean_session_reward)
score_log = Metrics()
for epoch in range(5000):
# update resolver's epsilon (chance of random action instead of optimal one)
# epsilon decreases over time
current_epsilon = 0.05 + 0.95 * np.exp(-epoch / 2500.)
resolver.epsilon.set_value(np.float32(current_epsilon))
# train
env.generate_new_data_batch(n_parallel_games)
loss, avg_reward = train_fun()
# show current learning progress
if epoch % 100 == 0:
print(epoch),
# estimate reward for epsilon-greedy strategy
avg_reward_current = compute_mean_session_reward()
score_log["expected epsilon-greedy reward"][epoch] = avg_reward_current
# estimating the reward under assumption of greedy strategy
resolver.epsilon.set_value(0)
avg_reward_greedy = compute_mean_session_reward()
score_log["expected greedy reward"][epoch] = avg_reward_greedy
if avg_reward_greedy > 2:
print("converged")
break
else:
print("diverged")
raise ValueError("Algorithm diverged")
def build_update_functions(train_set_x, train_set_y,
valid_set_x, valid_set_y,
network,
y, X,
train_MASK, val_MASK,
batch_size=32,
l2_reg=.0001,
learning_rate=.005,
momentum=.9):
# build update functions
# extract tensor representing the network predictions
prediction = get_output(network)
################################################
##################old###########################
# # collect squared error
# loss_RMSE = squared_error(prediction, y)
# # compute the root mean squared error
# loss_RMSE = loss_RMSE.mean().sqrt()
###################New#########################
# Aggregate the element-wise error into a scalar value using a mask
# note that y should note contain NAN, replace them with 0 or -1. The value does not matter. It
# is not used to calculate the aggregated error and update of the network.
# MASK should be a matrix of size(y), with 0s in place of NaN values and 1s everywhere else.
# build tensor variable for mask
trainMASK = T.matrix('trainMASK')
# collect squared error
loss_RMSE = squared_error(prediction, y)
# Drop nan values and average over the remaining values
loss_RMSE = aggregate(loss_RMSE, weights=trainMASK, mode='normalized_sum')
# compute the square root
loss_RMSE = loss_RMSE.sqrt()
###############################################
# add l2 regularization
l2_penalty = regularize_network_params(network, l2)
loss = (1 - l2_reg) * loss_RMSE + l2_reg * l2_penalty
# get network params
params = get_all_params(network, trainable = True)
# # create update criterion
# print('nestrov')
# updates = nesterov_momentum( loss, params, learning_rate=.01, momentum=.9)
# print('AdaGrad')
# updates = adagrad(loss, params,learning_rate= 1e-2)
#
print('RMSPROP \n')
updates = rmsprop(loss, params, learning_rate=learning_rate)
# create validation/test loss expression
# the loss represents the loss for all the labels
test_prediction = get_output(network, deterministic=True)
################################################
##################old###########################
# # collect squared error
# test_loss = squared_error(test_prediction,y)
# # compute the root mean squared error
# test_loss = test_loss.mean().sqrt()
# # test_loss_withl2 = (1-l2_reg) * test_loss + l2_reg * l2_penalty
################################################
###################New#########################
# Aggregate the element-wise error into a scalar value using a mask
# note that y should note contain NAN, replace them with 0 or -1. The value does not matter. It
# is not used to calculate the aggregated error and update of the network.
# MASK should be a matrix of size(y), with 0s in place of NaN values and 1s everywhere else.
# build tensor variable for mask
valMASK = T.matrix('valMASK')
# collect squared error
test_loss = squared_error(test_prediction, y)
# Drop nan values and average over the remaining values
test_loss = aggregate(test_loss, weights=valMASK, mode='normalized_sum')
# compute the square root
test_loss = test_loss.sqrt()
################################################
# index for mini-batch slicing
index = T.lscalar()
# training function
train_set_x_size = train_set_x.get_value().shape[0]
val_set_x_size = valid_set_x.get_value().shape[0]
train_fn = theano.function(inputs=[index],
outputs=[loss, loss_RMSE],
updates=updates,
givens={X: train_set_x[
index * batch_size: T.minimum((index + 1) * batch_size, train_set_x_size)],
y: train_set_y[
index * batch_size: T.minimum((index + 1) * batch_size, train_set_x_size)],
trainMASK: train_MASK[index * batch_size: T.minimum((index + 1) * batch_size,
train_set_x_size)]})
# validation function
val_fn = theano.function(inputs=[index],
outputs=[test_loss, prediction],
givens={X: valid_set_x[
index * batch_size: T.minimum((index + 1) * batch_size, val_set_x_size)],
y: valid_set_y[
index * batch_size: T.minimum((index + 1) * batch_size, val_set_x_size)],
valMASK: val_MASK[
index * batch_size: T.minimum((index + 1) * batch_size, val_set_x_size)]})
return train_fn, val_fn
def train(model, batch_size = 200, learning_rate=0.1):
np.random.seed(5468)
net = model()
x = net['input'].input_var
y = T.ivector('y')
print("........ building model")
prediction = lasagne.layers.get_output(net['output'],x)
train_prediction = lasagne.layers.get_output(net['output'], x, deterministic=False)
test_prediction = lasagne.layers.get_output(net['output'], x, deterministic=True)
global_avg = lasagne.layers.get_output(net['global_avg'],x)
before_avg = lasagne.layers.get_output(net['conv7_1'],x)
lamda = 0.001
l2_penalty = regularize_network_params(net['output'], l2)
loss = lasagne.objectives.categorical_crossentropy(train_prediction, y)
loss_train = lasagne.objectives.aggregate(loss, mode='mean') + lamda*l2_penalty
test_loss = lasagne.objectives.categorical_crossentropy(test_prediction, y)
loss_test = lasagne.objectives.aggregate(test_loss, mode='mean')
params = lasagne.layers.get_all_params(net['output'], trainable=True)
lr_theano = T.fscalar()
updates = lasagne.updates.momentum(loss_train, params, momentum=np.float32(0.9), learning_rate=lr_theano)
# updates = gradient_descend_momentum(cost=loss_train, params=params, lr=lr_theano, m=np.float32(0.9))
lr_epochs = [200,250, 300]
y_pred = T.argmax(test_prediction, axis=1)
errors = T.mean(T.neq(y_pred, y))
test_prediction_fn = theano.function(inputs=[x], outputs=test_prediction)
index = T.iscalar()
# Load Dataset
train_x, train_y, test_x, test_y = load_cifar_whitened()
valid_x, valid_y = test_x, test_y
n_train_batches = train_x.get_value(borrow=True).shape[0] // batch_size
n_valid_batches = valid_x.get_value(borrow=True).shape[0] // batch_size
n_test_batches = test_x.get_value(borrow=True).shape[0] // batch_size
train_model = theano.function(inputs=[index, lr_theano], outputs=[loss_train], updates=updates,
givens={
x: train_x[index*batch_size:(index+1)*batch_size],
y: train_y[index*batch_size:(index+1)*batch_size]
})
validate_model = theano.function(inputs=[index], outputs=[errors],
givens={
x: valid_x[index*batch_size:(index+1)*batch_size],
y: valid_y[index*batch_size:(index+1)*batch_size]
})
test_model = theano.function(inputs=[index], outputs=[errors],
givens={
x: test_x[index*batch_size:(index+1)*batch_size],
y: test_y[index*batch_size:(index+1)*batch_size]
})
get_pred = theano.function(inputs=[index], outputs=[y_pred],
givens={
x: test_x[index*batch_size:(index+1)*batch_size]
})
global_avg_fn = theano.function(inputs=[index], outputs=[global_avg],
givens={
x: train_x[index*batch_size:(index+1)*batch_size]
})
before_avg_fn = theano.function(inputs=[index], outputs=[before_avg],
givens={
x: train_x[index*batch_size:(index+1)*batch_size]
})
print("........ training")
model_name = model.__name__
n_epochs=350
lr_epochs=[200, 250, 300]
verbose = True
lr = learning_rate
"""
Wrapper function for training and test THEANO model
:type train_model: Theano.function
:param train_model:
:type validate_model: Theano.function
:param validate_model:
:type test_model: Theano.function
:param test_model:
:type n_train_batches: int
:param n_train_batches: number of training batches
:type n_valid_batches: int
:param n_valid_batches: number of validation batches
:type n_test_batches: int
:param n_test_batches: number of testing batches
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type verbose: boolean
:param verbose: to print out epoch summary or not to
"""
# early-stopping parameters
patience = 100000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.9995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience // 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_validation_loss = np.inf
best_iter = 0
test_score = 0.
start_time = timeit.default_timer()
epoch = 0
best_epoch = 0
done_looping = False
curframe = inspect.currentframe()
calframe = inspect.getouterframes(curframe, 2)
while (epoch < n_epochs) and (not done_looping):
if epoch % 50 == 0 or epoch in lr_epochs:
save_model(net['output'], "{0}_{1}.pklz".format(model_name, epoch))
if epoch in lr_epochs:
lr *= 0.1
epoch = epoch + 1
for minibatch_index in range(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index
if (iter % 100 == 0) and verbose:
print('training @ iter = ', iter, file=sys.stderr)
cost_ij = train_model(minibatch_index, lr)
if (iter + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
validation_losses = [validate_model(i) for i
in range(n_valid_batches)]
this_validation_loss = np.mean(validation_losses)
if verbose:
print('epoch %i, loss %f, minibatch %i/%i, validation error %f %%' %
(epoch,
cost_ij[0],
minibatch_index + 1,
n_train_batches,
this_validation_loss * 100.), file=sys.stderr)
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
best_epoch = epoch
#improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
test_losses = [
test_model(i)
for i in range(n_test_batches)
]
test_score = np.mean(test_losses)
if verbose:
csvfile = open(model_name + '_results.csv', 'a')
resultswriter = csv.writer(csvfile)
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f %%') %
(epoch, minibatch_index + 1,
n_train_batches,
test_score * 100.), file=sys.stderr)
resultswriter.writerow([best_validation_loss, epoch, best_iter, n_train_batches, test_score, model_name, learning_rate])
csvfile.close()
if patience <= iter or (best_validation_loss == 0.0 and test_score == 0.0):
done_looping = True
break
end_time = timeit.default_timer()
# Retrieve the name of function who invokes train_nn() (caller's name)
# Print out summary
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i, '
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print(('The training process for function ' +
calframe[1][3] +
' ran for %.2fm' % ((end_time - start_time) / 60.)))
def train(dataset, learning_rate=0.0005,
weight_decay=0.001, num_epochs=500,
max_patience=25, data_augmentation={},
savepath=None, loadpath=None,
batch_size=None, resume=False):
if savepath is None:
raise ValueError('A saving directory must be specified')
if batch_size is None:
batch_size = [1024, 1024, 1]
# Model hyperparameters
n_filters = 64
filter_size = 25
depth = 8
block = 'bn_relu_conv'
# Hyperparameters for the dataset loader
smooth_or_raw = 'both' # use both input channels
shuffle_at_each_epoch = True
#
# Prepare load/save directories
#
exp_name = 'fcn1D'
exp_name += '_lrate=' + str(learning_rate)
exp_name += '_fil=' + str(n_filters)
exp_name += '_fsizes=' + str(filter_size)
exp_name += '_depth=' + str(depth)
exp_name += '_data=' + smooth_or_raw
exp_name += '_decay=' + str(weight_decay)
exp_name += '_pat=' + str(max_patience)
savepath = os.path.join(savepath, dataset, exp_name)
loadpath = os.path.join(loadpath, dataset, exp_name)
print('Savepath : ')
print(savepath)
print('Loadpath : ')
print(loadpath)
if not os.path.exists(savepath):
os.makedirs(savepath)
else:
print('\033[93m The following folder already exists {}. '
'It will be overwritten in a few seconds...\033[0m'.format(
savepath))
print('Saving directory : ' + savepath)
with open(os.path.join(savepath, "config.txt"), "w") as f:
for key, value in locals().items():
f.write('{} = {}\n'.format(key, value))
#
# Define symbolic variables
#
input_var = T.tensor3('input_var') # n_example*nb_in_channels*ray_size
target_var = T.ivector('target_var') # n_example*ray_size
# learning rate is defined below as a theano variable.
learn_step = theano.shared(np.array(learning_rate, dtype=theano.config.floatX))
#
# Build dataset iterator
#
if smooth_or_raw == 'both':
nb_in_channels = 2
use_threads = False
else:
nb_in_channels = 1
use_threads = True
train_iter = Cortical6LayersDataset(
which_set='train',
smooth_or_raw=smooth_or_raw,
batch_size=batch_size[0],
data_augm_kwargs=data_augmentation,
shuffle_at_each_epoch=True,
return_one_hot=False,
return_01c=False,
return_list=False,
use_threads=use_threads,
preload=True)
val_iter = Cortical6LayersDataset(
which_set='valid',
smooth_or_raw=smooth_or_raw,
batch_size=batch_size[1],
shuffle_at_each_epoch=True,
return_one_hot=False,
return_01c=False,
return_list=False,
use_threads=use_threads,
preload=True)
test_iter = None
n_batches_train = train_iter.nbatches
n_batches_val = val_iter.nbatches
n_batches_test = test_iter.nbatches if test_iter is not None else 0
n_classes = train_iter.non_void_nclasses
void_labels = train_iter.void_labels
#
# Build network
#
simple_net_output, net = build_model(input_var,
filter_size=filter_size,
n_filters=n_filters,
depth=depth,
block=block,
nb_in_channels=nb_in_channels,
n_classes=n_classes)
#
# Define and compile theano functions
#
print("Defining and compiling training functions")
prediction = lasagne.layers.get_output(simple_net_output[0])
loss = categorical_crossentropy(prediction, target_var)
loss = loss.mean()
if weight_decay > 0:
weightsl2 = regularize_network_params(
simple_net_output, lasagne.regularization.l2)
loss += weight_decay * weightsl2
train_acc = accuracy_metric(prediction, target_var, void_labels)
params = lasagne.layers.get_all_params(simple_net_output, trainable=True)
updates = lasagne.updates.adam(loss, params, learning_rate=learn_step)
train_fn = theano.function([input_var, target_var], [loss, train_acc], updates=updates)
print("Done")
print("Defining and compiling valid functions")
valid_prediction = lasagne.layers.get_output(simple_net_output[0], deterministic=True)
valid_loss = categorical_crossentropy(valid_prediction, target_var).mean()
valid_acc = accuracy_metric(valid_prediction, target_var, void_labels)
valid_jacc = jaccard(valid_prediction, target_var, n_classes)
valid_fn = theano.function([input_var, target_var], [valid_loss, valid_acc, valid_jacc])
print("Done")
#
# Train loop
#
err_train = []
acc_train = []
err_valid = []
acc_valid = []
jacc_valid = []
patience = 0
# Training main loop
print("Start training")
for epoch in range(num_epochs):
learn_step.set_value((learn_step.get_value() * 0.99).astype(theano.config.floatX))
# Single epoch training and validation
start_time = time.time()
# Cost train and acc train for this epoch
cost_train_epoch = 0
acc_train_epoch = 0
for i in range(n_batches_train):
# Get minibatch (comment the next line if only 1 minibatch in training)
train_batch = train_iter.next()
X_train_batch, L_train_batch, idx_train_batch = train_batch['data'], train_batch['labels'], \
train_batch['filenames'][0]
L_train_batch = np.reshape(L_train_batch, np.prod(L_train_batch.shape))
# Training step
cost_train_batch, acc_train_batch = train_fn(X_train_batch, L_train_batch)
# Update epoch results
cost_train_epoch += cost_train_batch
acc_train_epoch += acc_train_batch
# Add epoch results
err_train += [cost_train_epoch / n_batches_train]
acc_train += [acc_train_epoch / n_batches_train]
# Validation
cost_val_epoch = 0
acc_val_epoch = 0
jacc_val_epoch = np.zeros((2, n_classes))
for i in range(n_batches_val):
# Get minibatch (comment the next line if only 1 minibatch in training)
val_batch = val_iter.next()
X_val_batch, L_val_batch, idx_val_batch = val_batch['data'], val_batch['labels'], val_batch['filenames'][0]
L_val_batch = np.reshape(L_val_batch, np.prod(L_val_batch.shape))
# Validation step
cost_val_batch, acc_val_batch, jacc_val_batch = valid_fn(X_val_batch, L_val_batch)
# Update epoch results
cost_val_epoch += cost_val_batch
acc_val_epoch += acc_val_batch
jacc_val_epoch += jacc_val_batch
# Add epoch results
err_valid += [cost_val_epoch / n_batches_val]
acc_valid += [acc_val_epoch / n_batches_val]
jacc_perclass_valid = jacc_val_epoch[0, :] / jacc_val_epoch[1, :]
jacc_valid += [np.mean(jacc_perclass_valid)]
# worse_indices_valid += [worse_indices_val_epoch]
# Print results (once per epoch)
out_str = ("EPOCH %i: Avg cost train %f, acc train %f" +
", cost val %f, acc val %f, jacc val per class %s, "
"jacc val %f took %f s")
out_str = out_str % (epoch, err_train[epoch],
acc_train[epoch],
err_valid[epoch],
acc_valid[epoch],
['%d: %f' % (i, j)
for i, j in enumerate(jacc_perclass_valid)],
jacc_valid[epoch],
time.time() - start_time)
print(out_str)
# Early stopping and saving stuff
with open(os.path.join(savepath, "fcn1D_output.log"), "a") as f:
f.write(out_str + "\n")
if epoch == 0:
best_jacc_val = jacc_valid[epoch]
elif epoch > 1 and jacc_valid[epoch] > best_jacc_val:
print('saving best (and last) model')
best_jacc_val = jacc_valid[epoch]
patience = 0
np.savez(os.path.join(savepath, 'new_fcn1D_model_best.npz'),
*lasagne.layers.get_all_param_values(simple_net_output))
np.savez(os.path.join(savepath, "fcn1D_errors_best.npz"),
err_train=err_train, acc_train=acc_train,
err_valid=err_valid, acc_valid=acc_valid, jacc_valid=jacc_valid)
else:
patience += 1
print('saving last model')
np.savez(os.path.join(savepath, 'new_fcn1D_model_last.npz'),
*lasagne.layers.get_all_param_values(simple_net_output))
np.savez(os.path.join(savepath, "fcn1D_errors_last.npz"),
err_train=err_train, acc_train=acc_train,
err_valid=err_valid, acc_valid=acc_valid, jacc_valid=jacc_valid)
# Finish training if patience has expired or max nber of epochs reached
if patience == max_patience or epoch == num_epochs - 1:
if savepath != loadpath:
print('Copying model and other training files to {}'.format(loadpath))
copy_tree(savepath, loadpath)
break