def build_model(self, train_set, test_set, validation_set=None):
super(FCAE, self).build_model(train_set, test_set, validation_set)
y_train = get_output(self.model, self.sym_x)
loss = aggregate(squared_error(y_train, self.sym_x), mode='mean')
# loss += + 1e-4 * lasagne.regularization.regularize_network_params(self.model, lasagne.regularization.l2)
y_test = get_output(self.model, self.sym_x, deterministic=True)
loss_test = aggregate(squared_error(y_test, self.sym_x), mode='mean')
all_params = get_all_params(self.model, trainable=True)
sym_beta1 = T.scalar('beta1')
sym_beta2 = T.scalar('beta2')
grads = T.grad(loss, all_params)
ngrads = lasagne.updates.total_norm_constraint(grads, 5)
cgrads = [T.clip(g, -5, 5) for g in ngrads]
updates = rmsprop(cgrads, all_params, self.sym_lr, sym_beta1,
sym_beta2)
inputs = [
self.sym_index, self.sym_batchsize, self.sym_lr, sym_beta1,
sym_beta2
]
f_train = theano.function(
inputs,
[loss],
updates=updates,
givens={
self.sym_x: self.sh_train_x[self.batch_slice],
},
)
f_test = theano.function(
[self.sym_index, self.sym_batchsize],
[loss_test],
givens={
self.sym_x: self.sh_test_x[self.batch_slice],
},
on_unused_input='ignore',
)
f_ae = None
# f_ae = theano.function(
# [self.sym_batchsize], [y_test],
# givens={
# self.sym_x: self.sh_valid_x,
# },
# on_unused_input='ignore',
# )
self.train_args['inputs']['batchsize'] = 128
self.train_args['inputs']['learningrate'] = 1e-3
self.train_args['inputs']['beta1'] = 0.9
self.train_args['inputs']['beta2'] = 1e-6
self.train_args['outputs']['loss'] = '%0.6f'
self.test_args['inputs']['batchsize'] = 128
self.test_args['outputs']['loss_test'] = '%0.6f'
# self.validate_args['inputs']['batchsize'] = 128
# self.validate_args['outputs']['loss_eval'] = '%0.6f'
# self.validate_args['outputs']['loss_acc'] = '%0.6f'
return f_train, f_test, f_ae, self.train_args, self.test_args, self.validate_args
def build_model(self, train_set, test_set, validation_set=None):
super(CNN, self).build_model(train_set, test_set, validation_set)
epsilon = 1e-8
y_train = T.clip(get_output(self.model, self.sym_x), epsilon, 1)
loss_cc = aggregate(categorical_crossentropy(y_train, self.sym_t),
mode='mean')
loss_train_acc = categorical_accuracy(y_train, self.sym_t).mean()
y = T.clip(get_output(self.model, self.sym_x, deterministic=True),
epsilon, 1)
loss_eval = aggregate(categorical_crossentropy(y, self.sym_t),
mode='mean')
loss_acc = categorical_accuracy(y, self.sym_t).mean()
all_params = get_all_params(self.model, trainable=True)
sym_beta1 = T.scalar('beta1')
sym_beta2 = T.scalar('beta2')
grads = T.grad(loss_cc, all_params)
grads = [T.clip(g, -5, 5) for g in grads]
updates = rmsprop(grads, all_params, self.sym_lr, sym_beta1, sym_beta2)
inputs = [
self.sym_index, self.sym_batchsize, self.sym_lr, sym_beta1,
sym_beta2
]
f_train = theano.function(
inputs,
[loss_cc, loss_train_acc],
updates=updates,
givens={
self.sym_x: self.sh_train_x[self.batch_slice],
self.sym_t: self.sh_train_t[self.batch_slice],
},
)
f_test = theano.function(
[self.sym_index, self.sym_batchsize],
[loss_eval, loss_acc],
givens={
self.sym_x: self.sh_test_x[self.batch_slice],
self.sym_t: self.sh_test_t[self.batch_slice],
},
)
f_validate = None
if validation_set is not None:
f_validate = theano.function(
[self.sym_index, self.sym_batchsize],
[loss_eval, loss_acc],
givens={
self.sym_x: self.sh_valid_x[self.batch_slice],
self.sym_t: self.sh_valid_t[self.batch_slice],
},
)
self.train_args['inputs']['batchsize'] = 128
self.train_args['inputs']['learningrate'] = 1e-3
self.train_args['inputs']['beta1'] = 0.9
self.train_args['inputs']['beta2'] = 0.999
self.train_args['outputs']['loss_cc'] = '%0.6f'
self.train_args['outputs']['loss_train_acc'] = '%0.6f'
self.test_args['inputs']['batchsize'] = 128
self.test_args['outputs']['loss_eval'] = '%0.6f'
self.test_args['outputs']['loss_acc'] = '%0.6f'
self.validate_args['inputs']['batchsize'] = 128
# self.validate_args['outputs']['loss_eval'] = '%0.6f'
# self.validate_args['outputs']['loss_acc'] = '%0.6f'
return f_train, f_test, f_validate, self.train_args, self.test_args, self.validate_args
def build_model(self,
train_set,
test_set,
validation_set=None,
weights=None):
super(wconvRNN, self).build_model(train_set, test_set, validation_set)
def brier_score(given, predicted, weight_vector):
return T.power(given - predicted, 2.0).dot(weight_vector).mean()
epsilon = 1e-8
y_train = T.clip(get_output(self.model, self.sym_x), epsilon, 1)
loss_brier_train = brier_score(y_train, self.sym_t, weights)
loss_cc = aggregate(categorical_crossentropy(y_train, self.sym_t),
mode='mean')
loss_train_acc = categorical_accuracy(y_train, self.sym_t).mean()
y_test = T.clip(get_output(self.model, self.sym_x, deterministic=True),
epsilon, 1)
loss_brier_test = brier_score(y_test, self.sym_t, weights)
loss_eval = aggregate(categorical_crossentropy(y_test, self.sym_t),
mode='mean')
loss_acc = categorical_accuracy(y_test, self.sym_t).mean()
all_params = get_all_params(self.model, trainable=True)
sym_beta1 = T.scalar('beta1')
sym_beta2 = T.scalar('beta2')
grads = T.grad(loss_brier_train, all_params)
grads = [T.clip(g, -5, 5) for g in grads]
updates = rmsprop(grads, all_params, self.sym_lr, sym_beta1, sym_beta2)
inputs = [
self.sym_index, self.sym_batchsize, self.sym_lr, sym_beta1,
sym_beta2
]
f_train = theano.function(
inputs,
[loss_brier_train],
updates=updates,
givens={
self.sym_x: self.sh_train_x[self.batch_slice],
self.sym_t: self.sh_train_t[self.batch_slice],
},
)
f_test = theano.function(
[],
[loss_brier_test],
givens={
self.sym_x: self.sh_test_x,
self.sym_t: self.sh_test_t,
},
on_unused_input='ignore',
)
f_validate = None
if validation_set is not None:
f_validate = theano.function(
[self.sym_batchsize],
[loss_brier_test],
givens={
self.sym_x: self.sh_valid_x,
self.sym_t: self.sh_valid_t,
},
on_unused_input='ignore',
)
predict = theano.function([self.sym_x], [y_test])
self.train_args['inputs']['batchsize'] = 128
self.train_args['inputs']['learningrate'] = 1e-3
self.train_args['inputs']['beta1'] = 0.9
self.train_args['inputs']['beta2'] = 0.999
self.train_args['outputs']['loss_brier_train'] = '%0.6f'
# self.train_args['outputs']['loss_train_acc'] = '%0.6f'
# self.test_args['inputs']['batchsize'] = 128
self.test_args['outputs']['loss_brier_test'] = '%0.6f'
# self.test_args['outputs']['loss_acc'] = '%0.6f'
# self.validate_args['inputs']['batchsize'] = 128
# self.validate_args['outputs']['loss_eval'] = '%0.6f'
# self.validate_args['outputs']['loss_acc'] = '%0.6f'
return f_train, f_test, f_validate, self.train_args, self.test_args, self.validate_args, predict