比较与tf.keras.optimizers.SGD的功能差异
tf.keras.optimizers.SGD
tf.keras.optimizers.SGD(
learning_rate=0.01,
momentum=0.0,
nesterov=False,
name='SGD',
**kwargs
) -> Tensor
更多内容详见tf.keras.optimizers.SGD。
mindspore.nn.SGD
class mindspore.nn.SGD(
params,
learning_rate=0.1,
momentum=0.0,
dampening=0.0,
weight_decay=0.0,
nesterov=False,
loss_scale=1.0
)(gradients) -> Tensor
更多内容详见mindspore.nn.SGD。
差异对比
TensorFlow:实现的是随机梯度下降(带动量)的优化器功能。
MindSpore:MindSpore此API实现功能与TensorFlow基本一致。
分类 |
子类 |
TensorFlow |
MindSpore |
差异 |
---|---|---|---|---|
参数 |
参数1 |
learning_rate |
learning_rate |
功能一致,默认值不同 |
参数2 |
momentum |
momentum |
- |
|
参数3 |
nesterov |
nesterov |
- |
|
参数4 |
name |
- |
不涉及 |
|
参数5 |
**kwargs |
- |
不涉及 |
|
参数6 |
- |
params |
由Parameter类组成的列表或由字典组成的列表,TensorFlow中无此参数 |
|
参数7 |
- |
dampening |
浮点动量阻尼值,默认值:0.0,TensorFlow中无此参数 |
|
参数8 |
- |
weight_decay |
权重衰减(L2 penalty),默认值:0.0,TensorFlow中无此参数 |
|
参数9 |
- |
loss_scale |
梯度缩放系数,默认值:1.0,TensorFlow中无此参数 |
|
参数10 |
- |
gradients |
优化器中 |
代码示例
两API实现功能一致。
# TensorFlow
import tensorflow as tf
opt = tf.keras.optimizers.SGD(learning_rate=0.1, momentum=0.9)
var = tf.Variable(1.0)
val0 = var.value()
loss = lambda: (var ** 2)/2.0
step_count1 = opt.minimize(loss, [var]).numpy()
val1 = var.value()
print([val1.numpy()])
# [0.9]
step_count2 = opt.minimize(loss, [var]).numpy()
val2 = var.value()
print([val2.numpy()])
# [0.71999997]
# MindSpore
import mindspore.nn as nn
import mindspore as ms
import numpy as np
from mindspore.dataset import NumpySlicesDataset
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.w = ms.Parameter(ms.Tensor(np.array([1.0], np.float32)), name='w')
def construct(self, x):
f = self.w * x
return f
class MyLoss(nn.LossBase):
def __init__(self, reduction='none'):
super(MyLoss, self).__init__()
def construct(self, y, y_pred):
return (y - y_pred) ** 2 / 2.0
net = Net()
loss = MyLoss()
optim = nn.SGD(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
model = ms.Model(net, loss_fn=loss, optimizer=optim)
data_x = np.array([1.0], dtype=np.float32)
data_y = np.array([0.0], dtype=np.float32)
data = NumpySlicesDataset((data_x, data_y), ["x", "y"])
input_x = ms.Tensor(np.array([1.0], np.float32))
y0 = net(input_x)
model.train(1, data)
y1 = net(input_x)
print(y1)
# [0.9]
model.train(1, data)
y2 = net(input_x)
print(y2)
# [0.71999997]