Jack Choi

run cargo build get the error: extern crate unicode_segmentation; | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ can't find crate lack of something?

data set

3

what kind of dataset you use in this paper can you give me a link of the dataset? THX

edward import error

4

when I import edward: File "", line 1, in File "F:\edward\edward\__init__.py", line 5, in from edward import criticisms File "F:\edward\edward\criticisms\__init__.py", line 7, in from edward.criticisms.evaluate import * File "F:\edward\edward\criticisms\evaluate.py", line...

flag error

1

I got this error: TypeError Traceback (most recent call last) in () ----> 1 tf.flags.DEFINE_string("data_dir", default="/tmp/data", help="") 2 tf.flags.DEFINE_string("out_dir", default="/tmp/out", help="") 3 tf.flags.DEFINE_integer("M", default=128, help="Batch size during training.") 4 tf.flags.DEFINE_integer("d",...

code question

4

can U explain the following code? & what's the meaning of -9e15 and adj? zero_vec = -9e15*torch.ones_like(e) attention = torch.where(adj > 0, e, zero_vec)

ocr issue

1

Problem: Interpretation of the code: "let mut host_temp_input_filename = String::from("/tmp/");"?

AttributeError Traceback (most recent call last) in () 4 input_=apply_transforms(image) 5 target_class=24 ----> 6 backprop.visualize(input,target_class,guided=True) 1 frames /usr/local/lib/python3.6/dist-packages/flashtorch/saliency/backprop.py in calculate_gradients(self, input_, target_class, take_max, guided, use_gpu) 84 self.model.zero_grad() 85 ---> 86...

What I gonna do after 24-48epochs done?

Hi.I got a problem here: When I run train.py,error exists: Traceback (most recent call last): File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1356, in _do_call return fn(*args) File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1339, in _run_fn self._extend_graph()...

按照初始化的设置 训练了100个epoch ===> Average train loss: nan ===> Average train log-likelihood: nan ===> Average train bit_per_dim: nan ===> Average validation loss: nan ===> Average validation log-likelihood: nan ===> Average validation...