def transform(data, label):
data = nd.moveaxis(data, 2, 0).astype("float32") / 255
label = label
return data, label
def data_xform(data):
"""Move channel axis to the beginning, cast to float32, and normalize to [0, 1]."""
return nd.moveaxis(data, 2, 0).astype('float32') / 255
def to_tensor(X):
X = X.astype(np.float32) / 255.0
X = nd.moveaxis(X, 3, 1)
return X
def accuracy(predictions, targets):
predictions = nd.argmax(predictions, 1)
return nd.mean(nd.equal(predictions, targets)).asscalar() * 100
x = []
y = []
names = []
for wbc_type in os.listdir('dataset'):
if 'normal' in wbc_type:
label = 0
elif 'tuberculosis' in wbc_type:
label = 1
img = mx.image.imread('dataset/' + wbc_type).astype('float32')
img = mx.image.imresize(img, 128, 128)
names.append(wbc_type)
x.append(nd.moveaxis(img, 2, 0).asnumpy())
y.append(label)
x = nd.array(x)
y = nd.array(y)
x /= 255.0
print(x.shape)
print(y.shape)
plt.imshow(mx.nd.moveaxis(x[0], 0, 2).asnumpy())
plt.show()
predictions = model.predict(x)
print('Test Accuracy:', accuracy(predictions, y))
def moveaxis(tensor, source, target):
return np.moveaxis(tensor, source, target)
def data_xform(data):
return nd.moveaxis(data, 2, 0).astype('float32') / 255