# read data into numpy array
mat = scipy.io.loadmat('PaviaU.mat')
img = mat['paviaU']
# create a hyperspectral dataset object from the numpy array
hypData = data.HypImg(img)
# pre-process data to make the model easier to train
hypData.pre_process('minmax')
# create data iterator objects for training and validation using the pre-processed data
trainSamples = 200000
valSamples = 100
dataTrain = data.Iterator(
dataSamples=hypData.spectraPrep[:trainSamples, :],
targets=hypData.spectraPrep[:trainSamples, :],
batchSize=1000)
dataVal = data.Iterator(
dataSamples=hypData.spectraPrep[trainSamples:trainSamples +
valSamples, :],
targets=hypData.spectraPrep[trainSamples:trainSamples + valSamples, :])
# shuffle training data
dataTrain.shuffle()
# setup a fully-connected autoencoder neural network with 3 encoder layers
net_mlp = autoencoder.mlp_1D_network(inputSize=hypData.numBands,
encoderSize=[50, 30, 10, 3],
activationFunc='relu',
weightInitOpt='truncated_normal',
tiedWeights=None,
# get indices for training and validation data
trainSamples = 50 # per class
valSamples = 15 # per class
train_indices = []
for i in range(1, 10):
train_indices += np.nonzero(
hypData.labels == i)[0][:trainSamples].tolist()
val_indices = []
for i in range(1, 10):
val_indices += np.nonzero(
hypData.labels == i)[0][trainSamples:trainSamples +
valSamples].tolist()
# create data iterator objects for training and validation using the pre-processed data
dataTrain = data.Iterator(
dataSamples=hypData.spectraPrep[train_indices, :],
targets=hypData.labelsOnehot[train_indices, :],
batchSize=50)
dataVal = data.Iterator(dataSamples=hypData.spectraPrep[val_indices, :],
targets=hypData.labelsOnehot[val_indices, :])
# shuffle training data
dataTrain.shuffle()
# setup a cnn classifier with 3 convolutional layers and 2 fully-connected layers
net = classifier.cnn_1D_network(inputSize=hypData.numBands,
numClasses=9,
convFilterSize=[20, 10, 10],
convNumFilters=[10, 10, 10],
convStride=[1, 1, 1],
fcSize=[20, 20],
activationFunc='relu',