Deepy | 基于 Numpy 深度学习库【巧优雅简单】

昱良

发布于 2019-07-04 19:40:47

4560

发布于 2019-07-04 19:40:47

文章被收录于专栏：机器学习算法与Python学习机器学习算法与Python学习

项目：Deepy

简介：它使用numpy进行计算。 API类似于PyTorch的API。

GitHub：

https://github.com/kaszperro/deepy

Demo：

在示例目录中有一个线性分类器，其准确率超过96%。

顺序模型的创建：

from deepy.module import Linear, Sequentialfrom deepy.autograd.activations import Softmax, ReLU
my_model = Sequential(
    Linear(28 * 28, 300),
    ReLU(),
    Linear(300, 300),
    ReLU(),
    Linear(300, 10),
    Softmax()
    )

损失：

from deepy.module import Linear
from deepy.autograd.losses import CrossEntropyLoss, MSELoss
from deepy.variable import Variable
import numpy as np

my_model = Linear(10, 10)

loss1 = CrossEntropyLoss()
loss2 = MSELoss()


good_output = Variable(np.zeros((10,10)))
model_input = Variable(np.ones((10,10)))
model_output = my_model(model_input)

error = loss1(good_output, model_output)# now you can propagate error backwards:error.backward()

优化：

from deepy.module import Linear
from deepy.autograd.losses import CrossEntropyLoss, MSELoss
from deepy.variable import Variable
from deepy.autograd.optimizers import SGD
import numpy as np


my_model = Linear(10, 10)

loss1 = CrossEntropyLoss()
loss2 = MSELoss()

optimizer1 = SGD(my_model.get_variables_list())

good_output = Variable(np.zeros((10,10)))
model_input = Variable(np.ones((10,10)))
model_output = my_model(model_input)

error = loss1(good_output, model_output)

# now you can propagate error backwards:
error.backward()

# and then optimizer can update variables:
optimizer1.zero_grad()
optimizer1.step()

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原始发表：2019-03-28，如有侵权请联系 cloudcommunity@tencent.com 删除

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