def __init__(self,
modelfilename,
conf,
model_name='dcgan',
gen_input='z:0',
gen_output='gen_/tanh:0',
gen_loss='reduced_mean:0',
z_dim=100,
batch_size=64):
self.conf = conf
print(config)
print("lala")
self.args = train.configure()
print("aha")
self.batch_size = batch_size
self.z_dim = z_dim
# self.gi = self.graph.get_tensor_by_name(model_name+'/'+gen_input)
# self.go = self.graph.get_tensor_by_name(model_name+'/'+gen_output)
# self.gl = self.graph.get_tensor_by_name(model_name+'/'+gen_loss)
self.gcgan = gan.GAN(args)
self.gi = gcgan.z
self.go = gcgan.gen_output
self.gl = gcgan.G_loss
self.image_shape = self.go.shape[1:].as_list()
self.lamb = config.lambda_p
self.sess = tf.Session(graph=self.graph)
self.z = np.random.randn(self.batch_size, self.z_dim)
for i in range(batch_size):
example_id = batch_map['example_ids'][i]
tokens = sentences[i].tolist()
words = [idx2word[idx] for idx in tokens]
if length == 2:
o = dict(example_id=example_id,
tree=(words[0], words[1]))
elif length == 1:
o = dict(example_id=example_id, tree=words[0])
print(json.dumps(o))
continue
trainer.step(batch_map, train=False, compute_loss=False)
trees = parse_predictor.parse_batch(batch_map)
for ii, tr in enumerate(trees):
example_id = batch_map['example_ids'][ii]
s = [idx2word[idx] for idx in sentences[ii].tolist()]
tr = replace_leaves(tr, s)
o = dict(example_id=example_id, tree=tr)
print(json.dumps(o))
if __name__ == '__main__':
parser = argument_parser()
options = parse_args(parser)
configure(options)
run(options)