深度学习精度变化

Question

每次我更改数据集时，它都会给出不同的精度。有时它提供97%，50%和92%。这是一种文本分类。这一切为什么要发生？另外95%来自两个数据集，这些数据集大小相同，结果几乎相同。

#Split DatA
X_train, X_test, label_train, label_test = train_test_split(X, Y, test_size=0.2,random_state=42)
                                                           
#Size of train and test data:
print("Training:", len(X_train), len(label_train))
print("Testing: ", len(X_test), len(label_test))

#Function defined to test the models in the test set
def test_model(model, epoch_stop):
   model.fit(X_test
             , Y_test
             , epochs=epoch_stop
             , batch_size=batch_size
             , verbose=0)
   results = model.evaluate(X_test, Y_test)
   return results
#############3

maxlen = 300
#Bidirectional LSTM model
embedding_dim = 100
dropout = 0.5
opt = 'adam'
####################
#embed_dim = 128 #dimension of the word embedding vector for each word in a sequence 
lstm_out = 196  #no of lstm layers
lstm_model = Sequential()
#Adding dropout
#lstm_model.add(LSTM(lstm_out, dropout=0.2, recurrent_dropout=0.2))##############################
lstm_model = Sequential()
lstm_model.add(layers.Embedding(input_dim=num_words, 
                          output_dim=embedding_dim, 
                          input_length=X_train.shape[1]))

#lstm_model.add(Bidirectional(LSTM(lstm_out, return_sequences=True, dropout=0.2, recurrent_dropout=0.2))) 
#lstm_model.add(Bidirectional(LSTM(lstm_out, dropout=0.2, recurrent_dropout=0.2)))
#lstm_model.add(Bidirectional(LSTM(64, return_sequences=True)))
lstm_model.add(Bidirectional(LSTM(64, return_sequences=True)))
lstm_model.add(layers.GlobalMaxPool1D())
#Adding a regularized dense layer
lstm_model.add(layers.Dense(32,kernel_regularizer=regularizers.l2(0.001),activation='relu'))
lstm_model.add(layers.Dropout(0.25))
lstm_model.add(Dense(3,activation='softmax'))
lstm_model.compile(loss = 'categorical_crossentropy', optimizer='adam',metrics = ['accuracy'])
print(lstm_model.summary())

#TRANING 

history = lstm_model.fit(X_train, label_train,
                   epochs=4,
                   verbose=True,**strong text**
                   validation_data=(X_test, label_test),
                   batch_size=64)
loss, accuracy = lstm_model.evaluate(X_train, label_train, verbose=True)
print("Training Accuracy: {:.4f}".format(accuracy))
loss_val, accuracy_val = lstm_model.evaluate(X_test, label_test, verbose=True)
print("Testing Accuracy:  {:.4f}".format(accuracy_val))

Answer 1

ML模型将根据以前训练过的数据进行预测，如果训练数据发生变化，结果就会不同，这是很自然的。另外，使用不同的超级参数，不同的数据集可能表现得更好。