def __init__(self,
n=32,
shape=(3, 3),
pad='same',
stride=(1, 1),
init_fn='orthogonal',
update_fn='nag'):
self.n = n
if isinstance(shape, int):
shape = (shape, shape)
self.shape = shape
if pad != 'same':
raise NotImplementedError('Only same pad supported right now!')
self.pad = pad
if isinstance(stride, int):
stride = (stride, stride)
if stride != (1, 1):
raise NotImplementedError(
'Only (1, 1) stride supported right now!')
self.stride = stride
self.init_fn = instantiate(inits, init_fn)
self.update_fn = instantiate(updates, update_fn)
def __init__(self,
activation,
init_fn=inits.Constant(c=0.25),
update_fn='nag'):
self.activation = instantiate(activations, activation)
self.init_fn = instantiate(inits, init_fn)
self.update_fn = instantiate(updates, update_fn)
def __init__(self,
n=32,
shape=(3, 3),
pad='same',
stride=(1, 1),
init_fn='orthogonal',
update_fn='nag'):
self.n = n
if isinstance(shape, int):
shape = (shape, shape)
self.shape = shape
if isinstance(pad, int):
pad = (pad, pad)
elif pad == 'same':
pad = (same_pad(shape[0]), same_pad(shape[1]))
self.pad = pad
if isinstance(stride, int):
stride = (stride, stride)
self.stride = stride
self.init_fn = instantiate(inits, init_fn)
self.update_fn = instantiate(updates, update_fn)
def __init__(self, dim=256, activation='rectify', proj_init_fn='orthogonal', rec_init_fn='identity',
bias_init_fn='constant', update_fn='nag'):
self.dim = dim
self.activation = instantiate(activations, activation)
self.proj_init_fn = instantiate(inits, proj_init_fn)
self.rec_init_fn = instantiate(inits, rec_init_fn)
self.bias_init_fn = instantiate(inits, bias_init_fn)
self.update_fn = instantiate(updates, update_fn)
def __init__(self, model, e, a=0.5, verbose=2, iterator='linear'):
self.verbose = verbose
self.model = init(model)
try:
self.iterator = instantiate(iterators, iterator)
except:
self.iterator = instantiate(async_iterators, iterator)
y_tr = self.model[-1].op({
'dropout': True,
'bn_active': True,
'infer': False
})
y_te = self.model[-1].op({
'dropout': False,
'bn_active': False,
'infer': False
})
y_inf = self.model[-1].op({
'dropout': False,
'bn_active': True,
'infer': True
})
self.X = self.model[0].X
self.Y = T.TensorType(theano.config.floatX,
(False, ) * (len(model[-1].out_shape)))()
cost = T.nnet.categorical_crossentropy(y_tr, self.Y).mean()
X_adv = self.X + e * T.sgn(T.grad(cost, self.X))
self.model[0].X = X_adv
y_tr_adv = self.model[-1].op({
'dropout': True,
'bn_active': True,
'infer': False
})
cost_adv = a * cost + (1. - a) * T.nnet.categorical_crossentropy(
y_tr_adv, self.Y).mean()
te_cost = T.nnet.categorical_crossentropy(y_te, self.Y).mean()
X_te_adv = self.X + e * T.sgn(T.grad(te_cost, self.X))
self.updates = collect_updates(self.model, cost_adv)
self.infer_updates = collect_infer_updates(self.model)
self.reset_updates = collect_reset_updates(self.model)
self._train = theano.function([self.X, self.Y],
cost_adv,
updates=self.updates)
self._predict = theano.function([self.X], y_te)
self._fast_sign = theano.function([self.X, self.Y], X_te_adv)
self._infer = theano.function([self.X],
y_inf,
updates=self.infer_updates)
self._reset = theano.function([], updates=self.reset_updates)
def __init__(self,
dim=256,
activation='rectify',
proj_init_fn='orthogonal',
rec_init_fn='identity',
bias_init_fn='constant',
update_fn='nag'):
self.dim = dim
self.activation = instantiate(activations, activation)
self.proj_init_fn = instantiate(inits, proj_init_fn)
self.rec_init_fn = instantiate(inits, rec_init_fn)
self.bias_init_fn = instantiate(inits, bias_init_fn)
self.update_fn = instantiate(updates, update_fn)
def __init__(self, n=32, shape=(3, 3), pad='same', stride=(1, 1), init_fn='orthogonal', update_fn='nag'):
self.n = n
if isinstance(shape, int):
shape = (shape, shape)
self.shape = shape
if isinstance(pad, int):
pad = (pad, pad)
elif pad == 'same':
pad = (same_pad(shape[0]), same_pad(shape[1]))
self.pad = pad
if isinstance(stride, int):
stride = (stride, stride)
self.stride = stride
self.init_fn = instantiate(inits, init_fn)
self.update_fn = instantiate(updates, update_fn)
def __init__(self, n=32, shape=(3, 3), pad='same', stride=(1, 1), init_fn='orthogonal', update_fn='nag'):
self.n = n
if isinstance(shape, int):
shape = (shape, shape)
self.shape = shape
if pad != 'same':
raise NotImplementedError('Only same pad supported right now!')
self.pad = pad
if isinstance(stride, int):
stride = (stride, stride)
if stride != (1, 1):
raise NotImplementedError('Only (1, 1) stride supported right now!')
self.stride = stride
self.init_fn = instantiate(inits, init_fn)
self.update_fn = instantiate(updates, update_fn)
def __init__(self, model, cost=None, verbose=2, iterator='linear'):
if cost is not None:
self.cost = instantiate(costs, cost)
else:
if isinstance(model[-1], ops.Activation):
if isinstance(model[-1].activation, activations.Sigmoid):
self.cost = instantiate(costs, 'bce')
elif isinstance(model[-1].activation, activations.Softmax):
self.cost = instantiate(costs, 'cce')
else:
self.cost = instantiate(costs, 'mse')
else:
self.cost = instantiate(costs, 'mse')
self.verbose = verbose
self.model = init(model)
try:
self.iterator = instantiate(iterators, iterator)
except:
self.iterator = instantiate(async_iterators, iterator)
y_tr = self.model[-1].op({
'dropout': True,
'bn_active': True,
'infer': False
})
y_te = self.model[-1].op({
'dropout': False,
'bn_active': False,
'infer': False
})
y_inf = self.model[-1].op({
'dropout': False,
'bn_active': True,
'infer': True
})
self.X = self.model[0].X
self.Y = T.TensorType(theano.config.floatX,
(False, ) * (len(model[-1].out_shape)))()
cost = self.cost(self.Y, y_tr)
self.updates = collect_updates(self.model, cost)
self.infer_updates = collect_infer_updates(self.model)
self.reset_updates = collect_reset_updates(self.model)
self._train = theano.function([self.X, self.Y],
cost,
updates=self.updates)
self._predict = theano.function([self.X], y_te)
self._infer = theano.function([self.X],
y_inf,
updates=self.infer_updates)
self._reset = theano.function([], updates=self.reset_updates)
def __init__(self, model, e, a=0.5, verbose=2, iterator='linear'):
self.verbose = verbose
self.model = init(model)
try:
self.iterator = instantiate(iterators, iterator)
except:
self.iterator = instantiate(async_iterators, iterator)
y_tr = self.model[-1].op({'dropout':True, 'bn_active':True, 'infer':False})
y_te = self.model[-1].op({'dropout':False, 'bn_active':False, 'infer':False})
y_inf = self.model[-1].op({'dropout':False, 'bn_active':True, 'infer':True})
self.X = self.model[0].X
self.Y = T.TensorType(theano.config.floatX, (False,)*(len(model[-1].out_shape)))()
cost = T.nnet.categorical_crossentropy(y_tr, self.Y).mean()
X_adv = self.X + e*T.sgn(T.grad(cost, self.X))
self.model[0].X = X_adv
y_tr_adv = self.model[-1].op({'dropout':True, 'bn_active':True, 'infer':False})
cost_adv = a*cost + (1.-a)*T.nnet.categorical_crossentropy(y_tr_adv, self.Y).mean()
te_cost = T.nnet.categorical_crossentropy(y_te, self.Y).mean()
X_te_adv = self.X + e*T.sgn(T.grad(te_cost, self.X))
self.updates = collect_updates(self.model, cost_adv)
self.infer_updates = collect_infer_updates(self.model)
self.reset_updates = collect_reset_updates(self.model)
self._train = theano.function([self.X, self.Y], cost_adv, updates=self.updates)
self._predict = theano.function([self.X], y_te)
self._fast_sign = theano.function([self.X, self.Y], X_te_adv)
self._infer = theano.function([self.X], y_inf, updates=self.infer_updates)
self._reset = theano.function([], updates=self.reset_updates)
def __init__(self, model, cost=None, verbose=2, iterator='linear'):
if cost is not None:
self.cost = instantiate(costs, cost)
else:
if isinstance(model[-1], ops.Activation):
if isinstance(model[-1].activation, activations.Sigmoid):
self.cost = instantiate(costs, 'bce')
elif isinstance(model[-1].activation, activations.Softmax):
self.cost = instantiate(costs, 'cce')
else:
self.cost = instantiate(costs, 'mse')
else:
self.cost = instantiate(costs, 'mse')
self.verbose = verbose
self.model = init(model)
try:
self.iterator = instantiate(iterators, iterator)
except:
self.iterator = instantiate(async_iterators, iterator)
y_tr = self.model[-1].op({'dropout':True, 'bn_active':True, 'infer':False})
y_te = self.model[-1].op({'dropout':False, 'bn_active':False, 'infer':False})
y_inf = self.model[-1].op({'dropout':False, 'bn_active':True, 'infer':True})
self.X = self.model[0].X
self.Y = T.TensorType(theano.config.floatX, (False,)*(len(model[-1].out_shape)))()
cost = self.cost(self.Y, y_tr)
self.updates = collect_updates(self.model, cost)
self.infer_updates = collect_infer_updates(self.model)
self.reset_updates = collect_reset_updates(self.model)
self._train = theano.function([self.X, self.Y], cost, updates=self.updates)
self._predict = theano.function([self.X], y_te)
self._infer = theano.function([self.X], y_inf, updates=self.infer_updates)
self._reset = theano.function([], updates=self.reset_updates)
def __init__(self, dim=256, init_fn='orthogonal', update_fn='nag'):
self.dim = dim
self.init_fn = instantiate(inits, init_fn)
self.update_fn = instantiate(updates, update_fn)
def __init__(self, init_fn='constant', update_fn='nag'):
self.init_fn = instantiate(inits, init_fn)
self.update_fn = instantiate(updates, update_fn)
def __init__(self, dim, n_embed, init_fn='uniform', update_fn='nag'):
self.dim = dim
self.n_embed = n_embed
self.init_fn = instantiate(inits, init_fn)
self.update_fn = instantiate(updates, update_fn)
def __init__(self, update_fn='nag', e=1e-8):
self.update_fn = instantiate(updates, update_fn)
self.e = e
def __init__(self, activation, init_fn=inits.Constant(c=0.25), update_fn='nag'):
self.activation = instantiate(activations, activation)
self.init_fn = instantiate(inits, init_fn)
self.update_fn = instantiate(updates, update_fn)
def __init__(self, init_fn='constant', update_fn='nag'):
self.init_fn = instantiate(inits, init_fn)
self.update_fn = instantiate(updates, update_fn)
def __init__(self, dim=256, init_fn='orthogonal', update_fn='nag'):
self.dim = dim
self.init_fn = instantiate(inits, init_fn)
self.update_fn = instantiate(updates, update_fn)
def __init__(self, update_fn='nag', e=1e-8):
self.update_fn = instantiate(updates, update_fn)
self.e = e
def __init__(self, dim, n_embed, init_fn='uniform', update_fn='nag'):
self.dim = dim
self.n_embed = n_embed
self.init_fn = instantiate(inits, init_fn)
self.update_fn = instantiate(updates, update_fn)
