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[英]tensowflow keras - model.predict giving all same outputs
[英]Keras model.predict() giving only one prediction
我訓練了一個模型,它顯示了所有4個預測並將其保存為json格式。 當我嘗試加載它並進行預測時,它僅顯示一個預測。 可能會發生什么?
我的代碼:
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)
如果我正確理解了您的問題,那么問題就出在這里: predict_classes
將為您返回最終的預測標簽,而不是概率。 它將返回概率最高的四個標簽之一。 如果需要每個類的概率,則可能應該使用predict_proba
或predict
相同的概率,例如:
prediction = model.predict(X_test,verbose=1)
錯誤已解決,無法正確調整測試值。在以下命令中刪除錯誤
tokenizer.fit_on_texts(testing.values)
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