Need help

[aldiak]

how to modify the following code for Indian Pines? because Pavia University dataset has 9 class and Indian Pines 16.

w=2 num_PC=1 israndom=True randtime = 1

OASpectral_IP = np.zeros((9+2,randtime)) s1s2=1 OASpectral_Pavia1 = 'spec1' time_step = 3

for r in range(0,randtime):

#################Pavia#################
dataID=2
data = HyperspectralSamples(dataID=dataID, timestep=time_step, w=w, num_PC=num_PC, israndom=israndom, s1s2=s1s2)
X = data[0]
X_train = data[1]
X_test = data[2]
XP = data[3]
XP_train = data[4]
XP_test = data[5]
Y = data[6]-1
Y_train = data[7]-1
Y_test = data[8]-1

batch_size = 128

nb_classes = Y_train.max()+1
nb_epoch = 50
nb_features = X.shape[-1]

img_rows, img_cols = XP.shape[1],XP.shape[1]
# convert class vectors to binary class matrices
y_train = np_utils.to_categorical(Y_train, nb_classes)
y_test = np_utils.to_categorical(Y_test, nb_classes)

model = LSTM_RS(time_step=time_step,nb_features=nb_features)
tic1 = time.clock()
histloss=model.fit([X_train], [y_train], nb_epoch=nb_epoch, batch_size=batch_size, verbose=1, shuffle=True)
losses = histloss.history
toc1 = time.clock()

tic2 = time.clock()

PredictLabel = model.predict([X_test],verbose=1).argmax(axis=-1)
toc2 = time.clock()

OA,Kappa,ProducerA = CalAccuracy(PredictLabel,Y_test[:,0])    
OASpectral_IP[0:9,r] = ProducerA
OASpectral_IP[-2,r] = OA
OASpectral_IP[-1,r] = Kappa