On Wed, 12 Jun 2019 04:12:34 -0700 (PDT), Rishika Sen wrote: > So I am coding in Python. I have to set of samples. Set1 contains > samples of class A and the other set, Set2 contains samples of class > B. When I am predicting set1 and set2 individually, the classification > is perfect. Now when I am merging the two sets for prediction into one > set, the prediction gives the wrong result for the samples in Set2, > i.e., predicting the samples of set 2 to be in class A. However, > samples belonging to Set1 are predicted to be in class A in the merged > set. Why is this happening? > > model.add(Dense(newshape[1]+1, activation='relu', input_shape=(newshape[1],))) > model.add(Dropout(0.5)) > model.add(Dense(500, activation='relu')) > model.add(Dropout(0.5)) > model.add(Dense(250, activation='relu')) > model.add(Dropout(0.5)) > model.add(Dense(100, activation='relu')) > model.add(Dropout(0.5)) > model.add(Dense(50, activation='relu')) > model.add(Dropout(0.5)) > model.add(Dense(1, activation='sigmoid')) > model.compile(loss='binary_crossentropy', > optimizer='adam', > metrics=['binary_accuracy']) > model.fit(X_train, y_train,validation_data=(X_test, y_test), > validation_split=0.2, epochs=500, batch_size=25, verbose=0)
This is really a question about some model-fitting package that you're using, not about Python. And you don't even tell us which model-fitting package it is. Please share more information. Are you expecting that any model-fitting process that works individually on Set1 and Set2 must work on the union of the two sets? 'Cause I don't think it works that way. -- To email me, substitute nowhere->runbox, invalid->com. -- https://mail.python.org/mailman/listinfo/python-list
