Function Differences with tf.keras.optimizers.SGD

tf.keras.optimizers.SGD

tf.keras.optimizers.SGD(
    learning_rate=0.01,
    momentum=0.0,
    nesterov=False,
    name='SGD',
    **kwargs
) -> Tensor

For more information, see 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

For more information, see mindspore.nn.SGD.

Differences

TensorFlow: Implement optimizer function of stochastic gradient descent (driven volume).

MindSpore: MindSpore API implements the same functions as TensorFlow.

Categories	Subcategories	TensorFlow	MindSpore	Differences
Parameters	Parameter 1	learning_rate	learning_rate	Same function, different default values
	Parameter 2	momentum	momentum	-
	Parameter 3	nesterov	nesterov	-
	Parameter 4	name	-	Not involved
	Parameter 5	**kwargs	-	Not involved
	parameter 6	-	params	A list composed of Parameter classes or a list composed of dictionaries, which are not available in TensorFlow.
	parameter 7	-	dampening	Floating-point momentum damping value. Default value: 0.0. TensorFlow does not have this parameter.
	parameter 8	-	weight_decay	Weight decay (L2 penalty). Default value: 0.0. TensorFlow does not have this parameter
	parameter 9	-	loss_scale	Gradient scaling factor. Default value: 1.0. TensorFlow does not have this parameter
	parameter 10	-	gradients	Gradient of params in the optimizer. TensorFlow does not have this parameter

Code Example

Both APIs implement the same function.

# 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
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.SGD(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
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)
# [0.9]
model.train(1, data)
y2 = net(input_x)
print(y2)
# [0.71999997]