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)
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