def runTest2():
inputArray = np.array([[[[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3]],
[[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3]],
[[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3]],
[[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3]],
[[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3]],
[[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3]],
[[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3]],
[[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3]],
[[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3]]]])
labels = [1]
#print(inputArray.shape)
#Conv layer config
numKernels = 4
kernelSize = 3
#Create network
N = Network(sigmoid, ALPHA)
N.addLayer(inputArray[0].shape)
N.addConvLayer(inputArray[0].shape, numKernels, kernelSize, flatten=True) #We flatten it if it's gonna be followed by a FC layer
N.addLayer(10, biased=True)
N.addLayer(1)
## return inputArray
## return N
#Train network
N.fit(inputArray, labels, NUM_EPOCH, verbose=True)
return N
def run():
#Unpickle data and put it in usable shape
data = unpickle(PATH)
inputs = shapeAsImage(data['data'])
labels = oneHotEncode(data['labels'])
print(labels[0])
#Check what it looks like
plt.imshow(inputs[0], interpolation='nearest')
plt.show()
#Create classification network
N = Network(sigmoid, ALPHA)
N.addLayer(IMAGE_SHAPE)
N.addConvLayer(IMAGE_SHAPE, NUM_KERNELS, SIZE_KERNELS, flatten=True)
N.addLayer(NUM_LABELS)
import cProfile
fit = N.fit
#Train network
command = 'N.fit(inputs[:10], labels[:10], NUM_EPOCH, verbose=True)'
cProfile.runctx(command, globals(), locals(), filename=None)
#cProfile.run('fit(inputs[:10], labels[:10], NUM_EPOCH, verbose=True)')
return N
