Value Error: Sequence is shorter then the required number of steps about skip-thoughts HOT 5 OPEN

I'm having the same issue. Any luck fixing this?

Edit: I managed to solve this. A blank line in my file was the cause of this.

from skip-thoughts.

I'm having same issue can you help me with how to fix that?
ng f_log_probs... Done
Building f_cost... Done
Done
Building f_grad... Building optimizers... Optimization
Epoch 0

ValueError Traceback (most recent call last)
in ()
1 import train
----> 2 train.trainer (X)

/home/pratyusha/Documents/skip-thoughts/training/train.py in trainer(X, dim_word, dim, encoder, decoder, max_epochs, dispFreq, decay_c, grad_clip, n_words, maxlen_w, optimizer, batch_size, saveto, dictionary, saveFreq, reload_)
155 x, x_mask, y, y_mask, z, z_mask = homogeneous_data.prepare_data(x, y, z, worddict, maxlen=maxlen_w, n_words=n_words)
156
--> 157 if x == None:
158 print 'Minibatch with zero sample under length ', maxlen_w
159 uidx -= 1

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

could any one help me?
Thank you

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@Pratyusha1796
Change x == None in training/train.py to
x.any() == None or x.all() == None.

from skip-thoughts.

I'm having the same issue. Any luck fixing this?

Edit: I managed to solve this. A blank line in my file was the cause of this.

Hi @TitusTom : Can you please help me with which file did you fix to fix this issue?

from skip-thoughts.

I am getting following error , can anyone please help ,

vectors = encoder.encode(['love is good'])

TypeError Traceback (most recent call last)
in ()
----> 1 vectors = encoder.encode(['love is good'])

~/Downloads/skip-thoughts-master/skipthoughts.py in encode(self, X, use_norm, verbose, batch_size, use_eos)
123 Encode sentences in the list X. Each entry will return a vector
124 """
--> 125 return encode(self._model, X, use_norm, verbose, batch_size, use_eos)
126
127

~/Downloads/skip-thoughts-master/skipthoughts.py in encode(model, X, use_norm, verbose, batch_size, use_eos)
151 print(k)
152 numbatches = len(ds[k]) / batch_size + 1
--> 153 for minibatch in range(numbatches):
154 caps = ds[k][minibatch::numbatches]
155

TypeError: 'float' object cannot be interpreted as an integer

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