Loss Function
A loss function is also called objective function and is used to measure the difference between a predicted value and an actual value.
In deep learning, model training is a process of reducing the loss function value through continuous iteration. Therefore, it is very important to select a loss function in a model training process, and a good loss function can effectively improve model performance.
The mindspore.nn module provides many general loss functions, but these functions cannot meet all requirements. In many cases, you need to customize the required loss functions. The following describes how to customize loss functions.
Built-in Loss Functions
The following introduces loss functions built in the mindspore.nn module.
For example, use nn.L1Loss to compute the mean absolute error between the predicted value and the target value.
N is the value of batch_size in the dataset.
A value of the reduction parameter in nn.L1Loss may be mean, sum, or none. If reduction is set to mean or sum, a scalar tensor (dimension reduced) after mean or sum is output. If reduction is set to none, the shape of the output tensor is the broadcast shape.
import numpy as np
from mindspore import nn
from mindspore import Tensor
# Output a mean loss value.
loss = nn.L1Loss()
# Output a sum loss value.
loss_sum = nn.L1Loss(reduction='sum')
# Output the original loss value.
loss_none = nn.L1Loss(reduction='none')
input_data = Tensor(np.array([[1, 2, 3], [2, 3, 4]]).astype(np.float32))
target_data = Tensor(np.array([[0, 2, 5], [3, 1, 1]]).astype(np.float32))
print("loss:", loss(input_data, target_data))
print("loss_sum:", loss_sum(input_data, target_data))
print("loss_none:", loss_none(input_data, target_data))
loss: 1.5
loss_sum: 9.0
loss_none: [[1. 0. 2.]
[1. 2. 3.]]
Customized Loss Functions
You can customize a loss function by defining the loss function based on either nn.Cell or nn.LossBase. nn.LossBase is inherited from nn.Cell and provides the get_loss method. The reduction parameter is used to obtain a sum or mean loss value and output a scalar.
The following describes how to define the mean absolute error (MAE) function by inheriting Cell and LossBase. The formula of the MAE algorithm is as follows:
In the preceding formula, \(f(x)\) indicates the predicted value, \(y\) indicates the actual value of the sample, and \(loss\) indicates the mean distance between the predicted value and the actual value.
Building nn.Cell-based Loss Function
nn.Cell is the base class of MindSpore. It can be used to build networks and define loss functions. The process of defining a loss function using nn.Cell is similar to that of defining a common network. The difference is that the execution logic is to compute the error between the feedforward network output and the actual value.
The following describes how to customize the loss function MAELoss based on nn.Cell.
from mindspore import ops
import mindspore as ms
class MAELoss(nn.Cell):
"""Customize the loss function MAELoss."""
def construct(self, base, target):
return ops.abs(base - target).mean()
loss = MAELoss()
input_data = Tensor(np.array([0.1, 0.2, 0.3]).astype(np.float32)) # Generate a predicted value.
target_data = Tensor(np.array([0.1, 0.2, 0.2]).astype(np.float32)) # Generate the actual value.
output = loss(input_data, target_data)
print(output)
0.033333335
nn.LossBase-based Loss Function Build
The process of building the loss function MAELoss based on nn.LossBase is similar to that of building the loss function based on nn.Cell. The __init__ and construct methods need to be rewritten.
nn.LossBase can use the get_loss method to apply reduction to loss computation.
class MAELoss(nn.LossBase):
"""Customize the loss function MAELoss."""
def construct(self, base, target):
x = ops.abs(base - target)
return self.get_loss(x) # Return the mean loss value.
loss = MAELoss()
input_data = Tensor(np.array([0.1, 0.2, 0.3]).astype(np.float32)) # Generate a predicted value.
target_data = Tensor(np.array([0.1, 0.2, 0.2]).astype(np.float32)) # Generate the actual value.
output = loss(input_data, target_data)
print(output)
0.033333335
Loss Function and Model Training
After the loss function MAELoss is customized, you can use the train API in the Model API of MindSpore to train a model. When building a model, you need to transfer the feedforward network, loss function, and optimizer. The Model associates them internally to generate a network model that can be used for training.
In Model, the feedforward network and loss function are associated through nn.WithLossCell. nn.WithLossCell supports two inputs: data and label.
from mindspore.train import Model, LossMonitor
from mindspore.dataset import GeneratorDataset
def get_data(num, w=2.0, b=3.0):
"""Generate data and corresponding labels."""
for _ in range(num):
x = np.random.uniform(-10.0, 10.0)
noise = np.random.normal(0, 1)
y = x * w + b + noise
yield np.array([x]).astype(np.float32), np.array([y]).astype(np.float32)
def create_dataset(num_data, batch_size=16):
"""Load the dataset."""
dataset = GeneratorDataset(list(get_data(num_data)), column_names=['data', 'label'])
dataset = dataset.batch(batch_size)
return dataset
train_dataset = create_dataset(num_data=160)
network = nn.Dense(1, 1)
loss_fn = MAELoss()
optimizer = nn.Momentum(network.trainable_params(), learning_rate=0.005, momentum=0.9)
# Use the model API to associate the network, loss function, and optimizer.
model = Model(network, loss_fn, optimizer)
model.train(10, train_dataset, callbacks=[LossMonitor(10)])
epoch: 1 step: 10, loss is 6.525373935699463
epoch: 2 step: 10, loss is 4.005467414855957
epoch: 3 step: 10, loss is 2.1115174293518066
epoch: 4 step: 10, loss is 2.7334954738616943
epoch: 5 step: 10, loss is 1.7042752504348755
epoch: 6 step: 10, loss is 1.6317998170852661
epoch: 7 step: 10, loss is 1.035435438156128
epoch: 8 step: 10, loss is 0.6060740351676941
epoch: 9 step: 10, loss is 1.0374044179916382
epoch: 10 step: 10, loss is 0.736151397228241
Multi-label Loss Function and Model Training
A simple mean absolute error loss function MAELoss is defined above. However, datasets of many deep learning applications are relatively complex. For example, data of an object detection network Faster R-CNN includes a plurality of labels, instead of simply one piece of data corresponding to one label. In this case, the definition and usage of the loss function are slightly different.
The following describes how to define a multi-label loss function in a multi-label dataset scenario and use a model for model training.
Multi-label Dataset
In the following example, two groups of linear data \(y1\) and \(y2\) are fitted by using the get_multilabel_data function. The fitting target function is:
The final dataset should be randomly distributed around the function. The dataset is generated according to the following formula, where noise is a random value that complies with the standard normal distribution. The get_multilabel_data function returns data \(x\), \(y1\), and \(y2\).
Use create_multilabel_dataset to generate a multi-label dataset and set column_names in GeneratorDataset to [‘data’, ‘label1’, ‘label2’]. The returned dataset is in the format that one piece of data corresponds to two labels label1 and label2.
def get_multilabel_data(num, w=2.0, b=3.0):
for _ in range(num):
x = np.random.uniform(-10.0, 10.0)
noise1 = np.random.normal(0, 1)
noise2 = np.random.normal(-1, 1)
y1 = x * w + b + noise1
y2 = x * w + b + noise2
yield np.array([x]).astype(np.float32), np.array([y1]).astype(np.float32), np.array([y2]).astype(np.float32)
def create_multilabel_dataset(num_data, batch_size=16):
dataset = GeneratorDataset(list(get_multilabel_data(num_data)), column_names=['data', 'label1', 'label2'])
dataset = dataset.batch(batch_size) # Each batch has 16 pieces of data.
return dataset
Multi-label Loss Function
Define the multi-label loss function MAELossForMultiLabel for the multi-label dataset created in the previous step.
In the preceding formula, \(f(x)\) is the predicted value of the sample label, \(y1\) and \(y2\) are the actual values of the sample label, and \(loss1\) is the mean distance between the predicted value and the actual value \(y1\), \(loss2\) is the mean distance between the predicted value and the actual value \(y2\), and \(loss\) is the mean value of the loss value \(loss1\) and the loss value \(loss2\).
The construct method in MAELossForMultiLabel has three inputs: predicted value base, actual values target1 and target2. In construct, compute the errors between the predicted value and the actual value target1 and between the predicted value and the actual value target2, the mean value of the two errors is used as the final loss function value.
The sample code is as follows:
class MAELossForMultiLabel(nn.LossBase):
def construct(self, base, target1, target2):
x1 = ops.abs(base - target1)
x2 = ops.abs(base - target2)
return (self.get_loss(x1) + self.get_loss(x2)) / 2
Multi-label Model Training
When Model is used to connect the feedforward network, multi-label loss function, and optimizer, the network of Model is specified as the customized loss network loss_net, the loss function loss_fn is not specified, and the optimizer is still Momentum.
If loss_fn is not specified, the Model considers that the logic of the loss function has been implemented in the network by default, and does not use nn.WithLossCell to associate the feedforward network with the loss function.
train_dataset = create_multilabel_dataset(num_data=160)
# Define a multi-label loss function.
loss_fn = MAELossForMultiLabel()
# Define the optimizer.
opt = nn.Momentum(network.trainable_params(), learning_rate=0.005, momentum=0.9)
def forward_fn(data, label1, label2):
output = network(data)
return loss_fn(output, label1, label2)
grad_fn = ms.value_and_grad(forward_fn, None, opt.parameters)
def train_step(data, label1, label2):
loss, grads = grad_fn(data, label1, label2)
opt(grads)
return loss
def train(model, dataset):
size = dataset.get_dataset_size()
model.set_train()
for batch, (data, label1, label2) in enumerate(dataset.create_tuple_iterator()):
loss = train_step(data, label1, label2)
if batch % 2 == 0:
loss, current = loss.asnumpy(), batch
print(f"loss: {loss:>7f} [{current:>3d}/{size:>3d}]")
epochs = 5
for t in range(epochs):
print(f"Epoch {t+1}\n-------------------------------")
train(network, train_dataset)
print("Done!")
Epoch 1
-------------------------------
loss: 0.739832 [ 0/ 10]
loss: 0.949316 [ 2/ 10]
loss: 1.052085 [ 4/ 10]
loss: 0.982260 [ 6/ 10]
loss: 0.784400 [ 8/ 10]
Epoch 2
-------------------------------
loss: 0.963160 [ 0/ 10]
loss: 0.899232 [ 2/ 10]
loss: 0.934914 [ 4/ 10]
loss: 0.757601 [ 6/ 10]
loss: 0.965961 [ 8/ 10]
Epoch 3
-------------------------------
loss: 0.815042 [ 0/ 10]
loss: 0.999898 [ 2/ 10]
loss: 1.008266 [ 4/ 10]
loss: 1.024307 [ 6/ 10]
loss: 0.798073 [ 8/ 10]
Epoch 4
-------------------------------
loss: 0.844747 [ 0/ 10]
loss: 0.958094 [ 2/ 10]
loss: 0.898447 [ 4/ 10]
loss: 0.879910 [ 6/ 10]
loss: 0.969592 [ 8/ 10]
Epoch 5
-------------------------------
loss: 0.917983 [ 0/ 10]
loss: 0.862990 [ 2/ 10]
loss: 0.947069 [ 4/ 10]
loss: 0.854086 [ 6/ 10]
loss: 0.910622 [ 8/ 10]
Done!
The preceding describes how to define a loss function and use a Model for model training in the multi-label dataset scenario. In many other scenarios, this method may also be used for model training.