Sep-25-2019, 09:03 AM
Hi!
I am designing a Neural Network for a classification of time series. I have 4 classes of functions and around 50000 samples for each class. The functions have a length of about 500 points normalized so that max(abs(f(t)))=1. The 4 classes are roughly speaking 'continuous', 'discontinuous', 'in-between' and 'trash'.
What Neural Network design would you use for this particular problem?
The design I'm using currently is a CNN.
Thank you for your help!
I am designing a Neural Network for a classification of time series. I have 4 classes of functions and around 50000 samples for each class. The functions have a length of about 500 points normalized so that max(abs(f(t)))=1. The 4 classes are roughly speaking 'continuous', 'discontinuous', 'in-between' and 'trash'.
What Neural Network design would you use for this particular problem?
The design I'm using currently is a CNN.
model = Sequential()
model.add(Conv1D(16, 8,input_shape=(500,1)))
model.add(BatchNormalization())
model.add(Activation("relu"))
model.add(Conv1D(8, 5))
model.add(BatchNormalization())
model.add(Activation("relu"))
model.add(Conv1D(8, 3))
model.add(BatchNormalization())
model.add(Activation("relu"))
model.add(GlobalAveragePooling1D())
model.add(Dense(4, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])I'm open to all suggestions. The Network is not performing very well (85% accuracy).Thank you for your help!