Keras model.predict（）仅给出一个预测

Question

I trained a model, it shows all 4 predictions and saved it in json format. When I try to load it and do the prediction, it shows only one prediction. What could be going on?

My code:

test = pd.read_csv('./Data/test.tsv', sep="\t")
from nltk.tokenize import word_tokenize
from nltk import FreqDist
from nltk.stem import SnowballStemmer,WordNetLemmatizer
stemmer=SnowballStemmer('english')
lemma=WordNetLemmatizer()
from string import punctuation
import re
testing = test.Phrase.apply(lambda x: x.lower())
tokenizer = Tokenizer(num_words= 10000)
X_test = tokenizer.texts_to_sequences(testing.values)

X_test = sequence.pad_sequences(X_test, maxlen=48)
json_file = open('model1.json', 'r')
loaded_model_json = json_file.read()    
json_file.close()
loaded_model = model_from_json(loaded_model_json)

# Load weights into new model
loaded_model.load_weights('model1.h5') 
loaded_model.compile(loss='categorical_crossentropy',
                     optimizer=Adam(lr=0.001),
                     metrics=['accuracy'])

prediction = model.predict_classes(X_test,verbose=1)
model.summary()#while training
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_1 (Embedding)      (None, None, 100)         1373200   
_________________________________________________________________
lstm_1 (LSTM)                (None, None, 64)          42240     
_________________________________________________________________
lstm_2 (LSTM)                (None, 32)                12416     
_________________________________________________________________
dense_1 (Dense)              (None, 5)                 165       
=================================================================
Total params: 1,428,021
Trainable params: 1,428,021
Non-trainable params: 0
print(X_test.shape)
(66292, 48)

Answer 1

If I understand your issue correctly the problem is here: predict_classes will return you the final predicted label, not probabilities. It will return one of four labels which has the highest probability. If you want the probabilities for each class, you should probably use predict_proba or predict which are same, eg:

prediction = model.predict(X_test,verbose=1)

Answer 2

错误已解决，无法正确调整测试值。在以下命令中删除错误

tokenizer.fit_on_texts(testing.values)