I'm struggling with a problem OF class probabilities (binary: 0 and 1). I don't know why but after 100 epochs the probabilities became 0 or 1 (like the class to predict). Maybe something is not working with the code or I missing something?
Train data is 20.000 rows (more or less); test data is 2000 rows. Each row is a match where 0 is lost and 1 is won.
y_train_binary = keras.utils.to_categorical(Y, 2)
y_test_binary = keras.utils.to_categorical(t_Y, 2)
model = Sequential()
model.add(Dense(40, input_dim=45, activation='relu'))
model.add(Dropout(0.05))
model.add(BatchNormalization())
model.add(Dense(30, activation='relu'))
model.add(Dropout(0.05))
model.add(BatchNormalization())
model.add(Dense(20, activation='relu'))
model.add(Dropout(0.05))
model.add(BatchNormalization())
model.add(Dense(10, activation='relu'))
model.add(Dropout(0.05))
model.add(BatchNormalization())
model.add(Dense(2, activation='softmax'))
keras.optimizers.Adam(lr=0.5, beta_1=0.9, beta_2=0.999, epsilon=0.3)
model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])
model.fit(X, y_train_binary, epochs=300, validation_split=0.2, batch_size=10, verbose=0)
prediction_classes = model.predict_proba(t_X)
numpy.savetxt("C:/Users/Megaport/Desktop/foo.csv", prediction_classes, delimiter=",")
I've tried so many parameters (learning rate, epochs, batch size, epsilon, add layer, less layer, different value of dropout) but the problem is the same: probs are not working.
The probabilities of the class are 1 or 0 and not a value between 1 and 0. A data test with prediction after 100 epoches is like this:
**RESULT** VALUE_A VALUE_B VALUE_C **PRED_0 PRED_1**
0 4 5 3 1 0
0 7 4 5 0 1
1 6 7 6 0 1
1 2 3 4 0 1
What I'm looking for:
**RESULT** VALUE_A VALUE_B VALUE_C **PRED_0 PRED_1**
0 4 5 3 0.65 0.35
0 7 4 5 0.25 0.75
1 6 7 6 0.20 0.80
1 2 3 4 0.30 0.70
Plus, someone could please give me some advice looking at accuracy and loss graph?
