我正在使用这个IGN数据集来查找有关游戏的积极和消极反馈，基本上有两列，sentiment包含0或1（差或好）和title可能是＆＃ 39;失去了世界＆＃39;
主要是我使用tflearn来做核心的东西。一切顺利，只有问题在于预测新的例子。
#lets load the ign dataset
dataframe = pd.read_csv('ign.csv')
# Convert score_phrase to binary sentiments and add a new column called sentiment
bad_phrases = ['Bad', 'Awful', 'Painful', 'Unbearable', 'Disaster']
dataframe['sentiment'] = dataframe.score_phrase.isin(bad_phrases).map({True: 0, False: 1})
# lets remove everything besides title and score_phrase
dataframe = dataframe.drop(["score_phrase","Unnamed: 0","url","platform", "score", "genre", "editors_choice", "release_year", "release_month","release_day"], axis=1)
#lets fill in any empty space with random spaces
dataframe.fillna(value='', inplace=True)
#preprocessing
word_processor = VocabularyProcessor(100)
#converting all the title as input featurres X
trainX = np.array(list(word_processor.fit_transform(dataframe["title"])))
#converting the score_pharse to trainY since its counted as the label
trainY = dataframe.loc[:, ["sentiment"]].as_matrix()
# Network building
def build_model():
# This resets all parameters and variables,
tf.reset_default_graph()
net = tflearn.input_data([None, 100]) # Input
net = tflearn.fully_connected(net, 200, activation='ReLU') # Hidden
net = tflearn.fully_connected(net, 200, activation='ReLU')
net = tflearn.fully_connected(net, 1, activation='softmax') # Output
net = tflearn.regression(net, optimizer='sgd', learning_rate=0.01, loss='categorical_crossentropy')
model = tflearn.DNN(net)
return model
model = build_model()
# Training
model.fit(trainX, trainY, validation_set=0.1, show_metric=True, batch_size=128, n_epoch=10)
在预测的同时，这一切都变成了地狱。
example = 'Little Big Planet'
text = np.array(word_processor.fit(example))
pred_class = np.argmax(model.predict([text]))
我得到的错误是
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-59-14b2aecc0f49> in <module>()
1 example = 'Little Big Planet'
2 text = np.array(word_processor.fit(example))
----> 3 pred_class = np.argmax(model.predict([text]))
4 pred_class
/anaconda/envs/MLHardCore/lib/python3.5/site-packages/tflearn/models/dnn.py in predict(self, X)
229 """
230 feed_dict = feed_dict_builder(X, None, self.inputs, None)
--> 231 return self.predictor.predict(feed_dict)
232
233 def predict_label(self, X):
/anaconda/envs/MLHardCore/lib/python3.5/site-packages/tflearn/helpers/evaluator.py in predict(self, feed_dict)
67 prediction = []
68 for output in self.tensors:
---> 69 o_pred = self.session.run(output, feed_dict=feed_dict).tolist()
70 for i, val in enumerate(o_pred): # Reshape pred per sample
71 if len(self.tensors) > 1:
/anaconda/envs/MLHardCore/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
765 try:
766 result = self._run(None, fetches, feed_dict, options_ptr,
--> 767 run_metadata_ptr)
768 if run_metadata:
769 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/anaconda/envs/MLHardCore/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
936 ' to a larger type (e.g. int64).')
937
--> 938 np_val = np.asarray(subfeed_val, dtype=subfeed_dtype)
939
940 if not subfeed_t.get_shape().is_compatible_with(np_val.shape):
/anaconda/envs/MLHardCore/lib/python3.5/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)
529
530 """
--> 531 return array(a, dtype, copy=False, order=order)
532
533
ValueError: setting an array element with a sequence.
答案 0 :(得分：0)
我不知道这是否会有所帮助，但我能够做出预测。
这些是我的导入（上面的代码中缺少）：
import pandas as pd
from tflearn.data_utils import VocabularyProcessor
import numpy as np
import tensorflow as tf
import tflearn
正在运行model.predict([text])会重新创建错误。
潜在的解决方案：
example = 'Little Big Planet'
text = list(word_processor.transform([example]))[0].reshape(1, 100)
model.predict(text)
输出：
[[1.0]]
修改
要查看单词的映射：
>>> vocab_dict = word_processor.vocabulary_._mapping
>>> vocab_dict['Little']
1
>>> vocab_dict['Big']
2
>>> vocab_dict['Planet']
3
>>> vocab_dict['World']
75
要查看这些数字是否有意义，请查看：
>>> example = 'Little Big Planet'
>>> text = list(word_processor.transform([example]))[0].reshape(1, 100)
>>> text
array([[1, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int64)
>>> example = 'Little Big World'
>>> text = list(word_processor.transform([example]))[0].reshape(1, 100)
>>> text
array([[ 1, 2, 75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int64)