def __init__(self, size_embed, size, size_out, depth, gru_activation=tanh, dropout_prob=0.0):
autoassign(locals())
self.Encode = StackedGRU(self.size_embed, self.size, self.depth,
activation=self.gru_activation, dropout_prob=self.dropout_prob)
self.FromImg = Dense(self.size_out, self.size)
self.Predict = Dense(self.size, self.size_embed)
self.params = params(self.Encode, self.FromImg, self.Predict)
def __init__(self,
size_vocab,
size_embed,
size,
size_out,
depth,
gru_activation=clipped_rectify,
visual_activation=linear,
visual_encoder=StackedGRUH0,
dropout_prob=0.0):
autoassign(locals())
self.Embed = Embedding(self.size_vocab, self.size_embed)
self.Visual = Visual(self.size_embed,
self.size,
self.size_out,
self.depth,
encoder=self.visual_encoder,
gru_activation=self.gru_activation,
visual_activation=self.visual_activation,
dropout_prob=self.dropout_prob)
self.LM = StackedGRU(self.size_embed,
self.size,
self.depth,
activation=self.gru_activation,
dropout_prob=self.dropout_prob)
self.FromImg = Dense(self.size_out, self.size)
self.ToTxt = Dense(self.size, self.size_embed) # try direct softmax
def __init__(self, size_in, size, depth=2, dropout_prob=0.0, activation=tanh):
autoassign(locals())
self.bottom = Dense(self.size_in, self.size)
layers = [ Dense(self.size, self.size) for _ in range(1, self.depth) ]
self.stack = reduce(lambda z, x: \
x.compose(WithDropout(Activation(self.activation).compose(z), self.dropout_prob)), \
layers, \
self.bottom)
self.params = self.stack.params
def __init__(self, size_vocab, size_embed, size, size_out, depth, out_depth=1, # FIXME USE THIS PARAM
gru_activation=tanh, visual_activation=linear,
dropout_prob=0.0):
autoassign(locals())
self.Embed = Embedding(self.size_vocab, self.size_embed)
self.Encode = StackedGRUH0(self.size_embed, self.size, self.depth,
activation=self.gru_activation, dropout_prob=self.dropout_prob)
self.DecodeT = StackedGRU(self.size_embed, self.size, self.depth,
activation=self.gru_activation, dropout_prob=self.dropout_prob)
self.PredictT = Dense(size_in=self.size, size_out=self.size_embed)
self.DecodeV = Dense(self.size, self.size_out)
self.params = params(self.Embed, self.DecodeT, self.PredictT, self.DecodeV)
def __init__(self,
size_repr,
size_hidden=200,
size_classify=3,
activation=tanh,
dropout=0.0):
autoassign(locals())
self.Dropout = Dropout(prob=self.dropout)
self.L1 = WithDropout(Dense(self.size_repr * 2, self.size_hidden),
prob=dropout)
self.L2 = WithDropout(Dense(self.size_hidden, self.size_hidden),
prob=dropout)
self.L3 = WithDropout(Dense(self.size_hidden, self.size_hidden),
prob=dropout)
self.classify = Dense(self.size_hidden, self.size_classify)
self.params = util.params(self.Dropout, self.L1, self.L2, self.L3,
self.classify)
def __init__(self, size_embed, size, size_out, depth, gru_activation=tanh, dropout_prob=0.0):
autoassign(locals())
encoder = lambda size_in, size:\
StackedGRUH0(size_embed, size, self.depth,
activation=self.gru_activation, dropout_prob=self.dropout_prob)
decoder = lambda size_in, size: \
StackedGRU(size_embed, size, self.depth,
activation=self.gru_activation, dropout_prob=self.dropout_prob)
self.Encdec = EncoderDecoderGRU(self.size, self.size, self.size,
encoder=encoder,
decoder=decoder)
self.Predict = Dense(size_in=self.size, size_out=self.size_embed)
self.params = params(self.Encdec, self.Predict)
def __init__(self,
size_vocab,
size_embed,
size,
depth,
size_target,
max_norm=None,
lr=0.0002):
autoassign(locals())
self.updater = util.Adam(max_norm=self.max_norm, lr=self.lr)
self.Encode = Encoder(self.size_vocab, self.size_embed, self.size,
self.depth)
self.ToImg = Dense(self.size, self.size_target)
self.inputs = [T.imatrix()]
self.target = T.fmatrix()
def __init__(self,
size_embed,
size,
size_out,
depth,
encoder=StackedGRUH0,
gru_activation=clipped_rectify,
visual_activation=linear,
dropout_prob=0.0):
autoassign(locals())
self.Encode = encoder(self.size_embed,
self.size,
self.depth,
activation=self.gru_activation,
dropout_prob=self.dropout_prob)
self.ToImg = Dense(self.size, self.size_out)
def __init__(self, size_embed, size, size_out, depth, out_depth=1, gru_activation=tanh, dropout_prob=0.0):
autoassign(locals())
self.Encode = StackedGRUH0(self.size_embed, self.size, self.depth,
activation=self.gru_activation, dropout_prob=self.dropout_prob)
self.Project = Dense(self.size, self.size_out)
self.params = params(self.Encode, self.Project)
def __init__(self, size_repr, size_classify=3, dropout=0.0):
autoassign(locals())
self.Dropout = Dropout(prob=self.dropout)
self.classify = Dense(self.size_repr * 2, self.size_classify)
self.params = util.params(self.Dropout, self.classify)