def accumulate_error(self):
if self.config['error_evaluation'] == 'classification':
predicted_labels = gpu.argmax(self.root.out)
target_labels = gpu.argmax(self.root.target)
gpu.equal(predicted_labels, target_labels, target_labels)
#print target_labels.sum(), target_labels.shape_tensor
error = 1.0-(target_labels.sum()/self.out.shape[2])
elif self.config['error_evaluation'] == 'logloss':
size = self.root.out.shape_tensor[2]
error = (self.root.target*gpu.log(self.root.out+1e-15)).sum()/np.float32(-size)
elif self.config['error_evaluation'] == 'regression':
error = gpu.sum((self.activation-self.root.out)**2)/np.float32(self.root.out.shape_tensor[2]*self.root.out.shape_tensor[3])
self.current_error.append(error)
self.current_SE.append(np.array(self.current_error).std()/len(self.current_error))
def predict(self, data):
self.forward(data, None,False)
if type(self.root.funcs) == Softmax:
if self.config['error_evaluation'] == 'classification':
return gpu.argmax(self.root.out)
elif self.config['error_evaluation'] == 'logloss':
return self.root.out
else: return self.root.out
def predict(self, data):
self.forward(data, None, False)
if type(self.root.funcs) == Softmax:
if self.config['error_evaluation'] == 'classification':
return gpu.argmax(self.root.out)
elif self.config['error_evaluation'] == 'logloss':
return self.root.out
else:
return self.root.out
def accumulate_error(self):
if self.config['error_evaluation'] == 'classification':
predicted_labels = gpu.argmax(self.root.out)
target_labels = gpu.argmax(self.root.target)
gpu.equal(predicted_labels, target_labels, target_labels)
#print target_labels.sum(), target_labels.shape_tensor
error = 1.0 - (target_labels.sum() / self.out.shape[2])
elif self.config['error_evaluation'] == 'logloss':
size = self.root.out.shape_tensor[2]
error = (self.root.target *
gpu.log(self.root.out + 1e-15)).sum() / np.float32(-size)
elif self.config['error_evaluation'] == 'regression':
error = gpu.sum((self.activation - self.root.out)**2) / np.float32(
self.root.out.shape_tensor[2] * self.root.out.shape_tensor[3])
self.current_error.append(error)
self.current_SE.append(
np.array(self.current_error).std() / len(self.current_error))