Function Differences with torch.optim.Adagrad

torch.optim.Adagrad

class torch.optim.Adagrad(
    params,
    lr=0.01,
    lr_decay=0,
    weight_decay=0,
    initial_accumulator_value=0,
    eps=1e-10
)

For more information, see torch.optim.Adagrad.

mindspore.nn.Adagrad

class mindspore.nn.Adagrad(
    params,
    accum=0.1,
    learning_rate=0.001,
    update_slots=True,
    loss_scale=1.0,
    weight_decay=0.0
)(grads)

For more information, see mindspore.nn.Adagrad.

Differences

PyTorch: Parameters to be optimized should be put into an iterable parameter then passed as a whole. The step method is also implemented to perform one single step optimization and return loss.

MindSpore: The ways of the same learning rate for all parameters and different values for different parameter groups are supported.

Categories	Subcategories	TensorFlow	MindSpore	Differences
Parameters	Parameter 1	learning_rate	learning_rate	-
	Parameter 2	initial_accumulator_value	accum	Same function, different parameter names
	Parameter 3	epsilon	-	TensorFlow is used to maintain numerical stability of small floating point values. MindSpore does not have this parameter
	Parameter 4	name	-	Not involved
	Parameter 5	**kwargs	-	Not involved
	Parameter 6	-	params	A list of parameters or a list of dictionaries, not available in TensorFlow
	Parameter 7	-	update_slots	If the value is True, the accumulator is updated. TensorFlow does not have this parameter
	Parameter 8	-	loss_scale	gradient scaling factor, default value: 1.0. TensorFlow does not have this parameter
	Parameter 9	-	weight_decay	weight decay (L2 penalty), default value: 0.0. TensorFlow does not have this parameter
Input	Single input	-	grads	The gradient of params in the optimizer. TensorFlow does not have this parameter

Code Example

The two APIs basically achieve the same function.

# TensorFlow
import tensorflow as tf

opt = tf.keras.optimizers.Adagrad(initial_accumulator_value=0.1, 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()])
# [0.9046537]
step_count = opt.minimize(loss, [var]).numpy()
val2 = var.value()
print([val2.numpy()])
# [0.8393387]

# 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.Adagrad(params=net.trainable_params(), accum=0.1, learning_rate=0.1)
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)
# [0.9046537]
model.train(1, data)
y2 = net(input_x)
print(y2)
# [0.8393387]