def load_data(self):
mn = MNIST('.')
mn.test()
data = mn.train_images
data = np.array(data)
data.astype(np.float32)
data = data/255.0
return data
def load_targets(self):
mn = MNIST('.')
mn.test()
targets = []
for t in mn.train_labels:
#print t
out = np.zeros(self.output)
out[t] = 1
targets.append(out)
targets = np.array(targets)
return targets
data = data/255.0
return data
def load_targets(self):
mn = MNIST('.')
mn.test()
targets = []
for t in mn.train_labels:
#print t
out = np.zeros(self.output)
out[t] = 1
targets.append(out)
targets = np.array(targets)
return targets
if __name__ == '__main__':
mn = MNIST('.')
mn.test()
MLP = MLP_Classifier(28*28+1,40,10)
datas = MLP.load_data()
targets = MLP.load_targets()
MLP.fit(datas,targets)
result = MLP.predict(mn.test_images)
accurate = 0
for i in range(len(result)):
if result[i] == mn.test_labels[i]:
accurate+=1
print accurate
