比较与tf.compat.v1.train.ProximalAdagradOptimizer的功能差异

查看源文件

tf.compat.v1.train.ProximalAdagradOptimizer

tf.compat.v1.train.ProximalAdagradOptimizer(
    learning_rate,
    initial_accumulator_value=0.1,
    l1_regularization_strength=0.0,
    l2_regularization_strength=0.0,
    use_locking=False,
    name='ProximalAdagrad'
) -> Tensor

更多内容详见tf.compat.v1.train.ProximalAdagradOptimizer

mindspore.nn.ProximalAdagrad

class mindspore.nn.ProximalAdagrad(
    params,
    accum=0.1,
    learning_rate=0.001,
    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.ProximalAdagrad

差异对比

TensorFlow:实现ProximalAdagrad算法的优化器功能。

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,TensorFlow无默认值

参数2

initial_accumulator_value

accum

功能一致,参数名不同

参数3

l1_regularization_strength

l1

功能一致,参数名不同

参数4

l2_regularization_strength

l2

功能一致,参数名不同

参数5

use_locking

use_locking

-

参数6

name

-

不涉及

参数7

-

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8

-

loss_scale

梯度缩放系数,默认值:1.0,TensorFlow中无此参数

参数9

-

weight_decay

权重衰减(L2 penalty),默认值:0.0,TensorFlow中无此参数

参数10

-

grads

优化器中 params 的梯度,TensorFlow中无此参数

代码示例

两API实现功能一致。

# TensorFlow
import tensorflow as tf
import numpy as np

tf.compat.v1.disable_v2_behavior()
tf.compat.v1.disable_eager_execution()

param_np = np.ones(7).astype(np.float32)
indices = np.array([1, 2, 3, 4, 5, 6]).astype(np.int32)
label = np.zeros((2, 3)).astype(np.float32)
label_shape = (2, 3)
axis = 0
epoch = 3
param_tf = tf.Variable(param_np)
indices_tf = tf.Variable(indices)
label = tf.Variable(label)
net = tf.raw_ops.GatherV2(params=param_tf, indices=indices_tf, axis=axis, batch_dims=0, name=None)
net = tf.reshape(net, label_shape)
criterion = tf.compat.v1.losses.softmax_cross_entropy(onehot_labels=label, logits=net,
                                                      reduction=tf.compat.v1.losses.Reduction.MEAN)
opt = tf.compat.v1.train.ProximalAdagradOptimizer(learning_rate=0.001).minimize(criterion)
init = tf.compat.v1.global_variables_initializer()
with tf.compat.v1.Session() as ss:
    ss.run(init)
    for i in range(epoch):
        loss = criterion.eval()
        ss.run(opt)
    output = net.eval()
    net = ss.run(net)
out_tf = output.astype(np.float32)
print(out_tf)
# [[0.9987219 0.9987219 0.9987219]
#  [0.9987219 0.9987219 0.9987219]]

# MindSpore
import numpy as np
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.ops import operations as op
from mindspore import Parameter
from mindspore.nn import SoftmaxCrossEntropyWithLogits
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn import Cell

class NetWithGatherV2(Cell):
    def __init__(self, param_np, label, axis=0):
        super(NetWithGatherV2, self).__init__()
        self.param = Parameter(Tensor(param_np), name="w1")
        self.gatherv2 = op.GatherV2()
        self.reshape = op.Reshape()
        self.axis = axis
        self.label = label

    def construct(self, indices):
        x = self.gatherv2(self.param, indices, self.axis)
        return self.reshape(x, self.label)


param_np = np.ones(7).astype(np.float32)
indices = np.array([1, 2, 3, 4, 5, 6]).astype(np.int32)
label = np.zeros((2, 3)).astype(np.float32)
label_shape = (2, 3)
epoch = 3
inputs = Tensor(indices)
label = Tensor(label)
net = NetWithGatherV2(param_np, label_shape, axis=0)
criterion = SoftmaxCrossEntropyWithLogits(reduction='mean')
optimizer = nn.ProximalAdagrad(params=net.trainable_params())
net_with_criterion = WithLossCell(net, criterion)
train_network = TrainOneStepCell(net_with_criterion, optimizer)
train_network.set_train()
for i in range(epoch):
    train_network(inputs, label)
out_ms = net(inputs).asnumpy()
print(out_ms)
# [[0.9987219 0.9987219 0.9987219]
#  [0.9987219 0.9987219 0.9987219]]