小编dav*_*vid的帖子

我正在尝试使用 Keras 和 mnist 数据集冻结预测模型中某个层的权重，但它不起作用。代码是这样的：

from keras.layers import Dense, Flatten
from keras.utils import to_categorical
from keras.models import Sequential, load_model
from keras.datasets import mnist
from keras.losses import categorical_crossentropy

import numpy as np

def load_data():
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train /= 255
    x_test /= 255
    y_train = to_categorical(y_train, num_classes=10)
    y_test = to_categorical(y_test, num_classes=10)
    return x_train, y_train, x_test, y_test


def run():
    x_train, y_train, x_test, y_test = load_data()
    model = Sequential([Flatten(input_shape=(28, 28)),
                        Dense(300, name='dense1', activation='relu'),
                        Dense(100, …

我正在尝试在春季使用 mongoDB。当我尝试使用findUserByUserName如下函数查询用户时：

@Repository("usersDao")
public class UserDaoImpl implements UserDao {

    @Autowired
    MongoTemplate mongoTemplate;

    @Override
    public void saveUser(User user) {
        mongoTemplate.save(user);
    }

    @Override
    public User findUserByUserName(String userName)
    {
        return mongoTemplate.findOne(
                    new Query(Criteria.where("_id").is(userName)), User.class);
    }
}

它会抛出这样的异常：

org.springframework.data.mapping.MappingException: Parameter org.springframework.data.mapping.PreferredConstructor$Parameter@d1c79025 does not have a name!
at org.springframework.data.mapping.model.PersistentEntityParameterValueProvider.getParameterValue(PersistentEntityParameterValueProvider.java:61) ~[spring-data-commons-2.0.6.RELEASE.jar:2.0.6.RELEASE]
at org.springframework.data.mapping.model.SpELExpressionParameterValueProvider.getParameterValue(SpELExpressionParameterValueProvider.java:49) ~[spring-data-commons-2.0.6.RELEASE.jar:2.0.6.RELEASE]
at org.springframework.data.convert.ClassGeneratingEntityInstantiator$EntityInstantiatorAdapter.extractInvocationArguments(ClassGeneratingEntityInstantiator.java:248) ~[spring-data-commons-2.0.6.RELEASE.jar:2.0.6.RELEASE]
at org.springframework.data.convert.ClassGeneratingEntityInstantiator$EntityInstantiatorAdapter.createInstance(ClassGeneratingEntityInstantiator.java:221) ~[spring-data-commons-2.0.6.RELEASE.jar:2.0.6.RELEASE]
at org.springframework.data.convert.ClassGeneratingEntityInstantiator.createInstance(ClassGeneratingEntityInstantiator.java:86) ~[spring-data-commons-2.0.6.RELEASE.jar:2.0.6.RELEASE]
at org.springframework.data.mongodb.core.convert.MappingMongoConverter.read(MappingMongoConverter.java:273) ~[spring-data-mongodb-2.0.6.RELEASE.jar:2.0.6.RELEASE]
at org.springframework.data.mongodb.core.convert.MappingMongoConverter.read(MappingMongoConverter.java:253) ~[spring-data-mongodb-2.0.6.RELEASE.jar:2.0.6.RELEASE]
at org.springframework.data.mongodb.core.convert.MappingMongoConverter.readMap(MappingMongoConverter.java:1047) ~[spring-data-mongodb-2.0.6.RELEASE.jar:2.0.6.RELEASE]
at org.springframework.data.mongodb.core.convert.MappingMongoConverter.read(MappingMongoConverter.java:233) ~[spring-data-mongodb-2.0.6.RELEASE.jar:2.0.6.RELEASE]
at org.springframework.data.mongodb.core.convert.MappingMongoConverter.readValue(MappingMongoConverter.java:1388) ~[spring-data-mongodb-2.0.6.RELEASE.jar:2.0.6.RELEASE]
at org.springframework.data.mongodb.core.convert.MappingMongoConverter$MongoDbPropertyValueProvider.getPropertyValue(MappingMongoConverter.java:1335) ~[spring-data-mongodb-2.0.6.RELEASE.jar:2.0.6.RELEASE]
at org.springframework.data.mongodb.core.convert.MappingMongoConverter.readProperties(MappingMongoConverter.java:335) ~[spring-data-mongodb-2.0.6.RELEASE.jar:2.0.6.RELEASE] …

我是Keras的初学者，只写一个玩具示例。它报告一个TypeError。代码和错误如下：

码：

inputs = keras.Input(shape=(3, ))

cell = keras.layers.SimpleRNNCell(units=5, activation='softmax')
label = keras.layers.RNN(cell)(inputs)

model = keras.models.Model(inputs=inputs, outputs=label)
model.compile(optimizer='rmsprop',
              loss='mae',
              metrics=['acc'])

data = np.array([[1, 2, 3], [3, 4, 5]])
labels = np.array([1, 2])
model.fit(x=data, y=labels)

错误：

Traceback (most recent call last):
    File "/Users/david/Documents/code/python/Tensorflow/test.py", line 27, in <module>
        run()
    File "/Users/david/Documents/code/python/Tensorflow/test.py", line 21, in run
        label = keras.layers.RNN(cell)(inputs)
    File "/Users/david/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/layers/recurrent.py", line 619, in __call__
...
    File "/Users/david/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py", line 473, in __call__
        scale /= max(1., (fan_in + fan_out) / 2.)
TypeError: …

假设向量\theta是神经网络中的所有参数，我想知道如何\theta在 pytorch 中计算Hessian矩阵。

假设网络如下：

class Net(Module):
    def __init__(self, h, w):
        super(Net, self).__init__()
        self.c1 = torch.nn.Conv2d(1, 32, 3, 1, 1)
        self.f2 = torch.nn.Linear(32 * h * w, 5)

    def forward(self, x):
        x = self.c1(x)
        x = x.view(x.size(0), -1)
        x = self.f2(x)
        return x

我知道可以通过调用torch.autograd.grad()两次来计算二阶导数，但是pytorch中的参数是由 组织的net.parameters()，我不知道如何计算所有参数的hessian。

我尝试torch.autograd.functional.hessian()在 pytorch 1.5 中使用如下：

import torch
import numpy as np
from torch.nn import Module
import torch.nn.functional as F


class Net(Module):
    def __init__(self, h, w):
        super(Net, self).__init__() …

目标

• 我想在 Android 应用程序中监视我的应用程序的 CPU 使用情况（全局 CPU 使用情况对我来说也可以）。

背景

• Android 11之后，正常应用无法访问/proc/stat,
• HardwarePropertiesManager"android.permission.DEVICE_POWER"由于该权限仅授予系统应用程序，因此无法在正常应用程序中使用。
• 我在google能得到的解决方案都和上面两种方法是一样的。

问题

• 那么，在 Android 11 之后，有什么方法可以获取 Android 应用程序中的 CPU 使用情况呢？

我正在尝试使用该包matplotlib.animation在 PyCharm 中绘制动画。但是，PyCharm 仅在 PNG 图形中显示动画的第一帧。

动画是关于一个移动的矩形，python版本是2.7.14，代码在这里：

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib import animation

x = [0, 1, 2]
y = [0, 1, 2]
yaw = [0.0, 0.5, 1.3]
fig = plt.figure()
plt.axis('equal')
plt.grid()
ax = fig.add_subplot(111)
ax.set_xlim(-10, 10)
ax.set_ylim(-10, 10)

patch = patches.Rectangle((0, 0), 0, 0, fc='y')

def init():
    ax.add_patch(patch)
    return patch,

def animate(i):
    patch.set_width(1.2)
    patch.set_height(1.0)
    patch.set_xy([x[i], y[i]])
    patch._angle = -np.rad2deg(yaw[i])
    return patch,

anim = animation.FuncAnimation(fig, animate,
                               init_func=init, …