问：这是我得到错误的地方。我只想问问我该怎么做才能使此代码运行。我正在使用张量流构建一个聊天机器人。大部分错误是在if-else语句中遇到的。所以请看一看。并尽快告诉我，谢谢:)
def run_step(sess, model, encoder_inputs, decoder_inputs, decoder_masks, bucket_id, forward_only):
""" Run one step in training.
@forward_only: boolean value to decide whether a backward path should be created
forward_only is set to True when you just want to evaluate on the test set,
or when you want to the bot to be in chat mode. """
encoder_size, decoder_size = config.BUCKETS[bucket_id]
_assert_lengths(encoder_size, decoder_size, encoder_inputs, decoder_inputs, decoder_masks)
# input feed: encoder inputs, decoder inputs, target_weights, as provided.
input_feed = {}
for step in range(encoder_size):
input_feed[model.encoder_inputs[step].name] = encoder_inputs[step]
for step in range(decoder_size):
input_feed[model.decoder_inputs[step].name] = decoder_inputs[step]
input_feed[model.decoder_masks[step].name] = decoder_masks[step]
last_target = model.decoder_inputs[decoder_size].name
input_feed[last_target] = np.zeros([model.batch_size], dtype=np.int32)
# output feed: depends on whether we do a backward step or not.
if not forward_only:
output_feed = [model.train_ops[bucket_id], # update op that does SGD.
model.gradient_norms[bucket_id], # gradient norm.
model.losses[bucket_id]] # loss for this batch.
else:
output_feed = [model.losses[bucket_id]] # loss for this batch.
for step in range(decoder_size): # output logits.
output_feed.append(model.outputs[bucket_id][step])
outputs = sess.run(output_feed, input_feed)
if not forward_only:
return outputs[1], outputs[2], None # Gradient norm, loss, no outputs.
else:
return None, outputs[0], outputs[1:] # No gradient norm, loss, outputs.
答：您首先可以在第一行中检查意外的缩进错误，然后才能使用
将tensorflow导入为tf然后它将工作。
