import lasagne as nn

import theano

import theano.tensor as T

from lasagne.layers import InputLayer, DenseLayer, NonlinearityLayer

from lasagne.layers import ReshapeLayer, ConcatLayer, SliceLayer, LSTMLayer

from lasagne.layers import Conv2DLayer as ConvLayer

from lasagne.layers import Pool2DLayer as PoolLayer

# theano's built-in softmax and binary cross-entropy can be numerically unstable

# so it is necessary to use equivalent log-domain functions

def log_softmax(x):

xdev = x - x.max(1, keepdims=True)

return xdev - T.log(T.sum(T.exp(xdev), axis=1, keepdims=True))

def categorical_crossentropy_logdomain(log_predictions, targets):

return -T.sum(targets * log_predictions, axis=1)

def build_rnn(conv_input_var, seq_input_var, conv_shape, word_dims, n_hid, lstm_layers):

return ret

# originally from

# https://github.com/Lasagne/Recipes/blob/master/examples/styletransfer/Art%20Style%20Transfer.ipynb

def build_vgg(shape, input_var):

return net