比较与tf.keras.optimizers.Ftrl的差异

查看源文件

tf.keras.optimizers.Ftrl

tf.keras.optimizers.Ftrl(
    learning_rate=0.001,
    learning_rate_power=-0.5,
    initial_accumulator_value=0.1,
    l1_regularization_strength=0.0,
    l2_regularization_strength=0.0,
    name='Ftrl',
    l2_shrinkage_regularization_strength=0.0,
    beta=0.0,
    **kwargs) -> Tensor

更多内容详见tf.keras.optimizers.Ftrl

mindspore.nn.FTRL

class mindspore.nn.FTRL(
    params,
    initial_accum=0.1,
    learning_rate=0.001,
    lr_power=-0.5,
    l1=0.0,
    l2=0.0,
    use_locking=False,
    loss_scale=1.0,
    weight_decay=0.0)(grads) -> Tensor

更多内容详见mindspore.nn.FTRL

差异对比

TensorFlow:一种在线凸优化算法,适合具有大而稀疏特征特征空间的浅层模型。

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

-

参数2

learning_rate_power

lr_power

功能一致,参数名不同

参数3

initial_accumulator_value

initial_accum

功能一致,参数名不同

参数4

l1_regularization_strength

l1

功能一致,参数名不同

参数5

l2_regularization_strength

l2

功能一致,参数名不同

参数6

name

-

不涉及

参数7

l2_shrinkage_regularization_strength

weight_decay

功能一致,参数名不同

参数8

beta

-

一个浮点值,默认值为0.0。MindSpore无此参数

参数9

**kwargs

-

不涉及

参数10

-

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11

-

use_locking

如果为True,则更新操作使用锁保护,默认值为False。TensorFlow中无此参数

参数12

-

loss_scale

梯度缩放系数,默认值:1.0,TensorFlow中无此参数

参数13

-

grads

优化器中 params 的梯度,TensorFlow中无此参数

代码示例1

learning_rate均设置为0.1,两API功能一致,用法相同。

# TensorFlow
import tensorflow as tf

opt = tf.keras.optimizers.Ftrl(learning_rate=0.1)
var = tf.Variable(1.0)
val0 = var.value()
loss = lambda: (var ** 2) / 2.0
step_count = opt.minimize(loss, [var]).numpy()
val1 = var.value()
print([val1.numpy()])
# [0.6031424]
step_count = opt.minimize(loss, [var]).numpy()
val2 = var.value()
print([val2.numpy()])
# [0.5532904]

# MindSpore
import numpy as np
import mindspore.nn as nn
import mindspore as ms
from mindspore.dataset import NumpySlicesDataset
from mindspore.train import Model

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.FTRL(params=net.trainable_params(), learning_rate=0.1)
model = 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)
# [0.6031424]
model.train(1, data)
y2 = net(input_x)
print(y2)
# [0.5532904]

代码示例2

learning_rate均设置为0.01,两API功能一致,用法相同。

# TensorFlow
import tensorflow as tf

opt = tf.keras.optimizers.Ftrl(learning_rate=0.01)
var = tf.Variable(1.0)
val0 = var.value()
loss = lambda: (var ** 2) / 2.0
step_count = opt.minimize(loss, [var]).numpy()
val1 = var.value()
print([val1.numpy()])
# [0.688954]
step_count = opt.minimize(loss, [var]).numpy()
val2 = var.value()
print([val2.numpy()])
# [0.6834637]

# MindSpore
import numpy as np
import mindspore.nn as nn
import mindspore as ms
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.FTRL(params=net.trainable_params(), learning_rate=0.01)
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)
# [0.688954]
model.train(1, data)
y2 = net(input_x)
print(y2)
# [0.6834637]