MachineProblem

机器学习学习过程中出现的问题

keras和sklearn联合使用

tensorflow keras 混合编程

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from keras.layers import Input, Dense, Dropout, BatchNormalization,\
                         LeakyReLU, concatenate,Conv2D, MaxPooling2D,\
                         AveragePooling2D, GlobalAveragePooling2D,Conv1D,\
                         Flatten,merge,Reshape,MaxPooling1D,Lambda,LSTM,\
                         GlobalMaxPooling2D
 
# build module
 
img = tf.placeholder(tf.float32, shape=(None, 784))
labels = tf.placeholder(tf.float32, shape=(None, 10))
 
x = Dense(128, activation='relu')(img)
x = Dense(128, activation='relu')(x)
prediction = Dense(10, activation='softmax')(x)
 
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=prediction, labels=labels))

train_optim = tf.train.AdamOptimizer().minimize(loss)
 
mnist_data = input_data.read_data_sets('MNIST_data/', one_hot=True)
 
with tf.Session() as sess:
    init = tf.global_variables_initializer()
    sess.run(init)
    for i in range(1000):
        batch_x, batch_y = mnist_data.train.next_batch(50)
        sess.run(train_optim, feed_dict={img: batch_x, labels: batch_y})
         
        acc_pred = tf.keras.metrics.categorical_accuracy(labels, prediction)
        pred = sess.run(acc_pred, feed_dict={labels: mnist_data.test.labels, img: mnist_data.test.images})
         
        print('accuracy: %.3f' % (sum(pred)/len(mnist_data.test.labels)))
``` 
## [keras 输出中间层](https://blog.csdn.net/tszupup/article/details/89192737)
1 通过K.function()函数打印模型中间层的输出
2 通过函数API打印模型中间层的输出

## 训练过程可视化

```python
import matplotlib.pyplot as plt

history = model.fit(x, y, validation_split=0.25, epochs=50, batch_size=16, verbose=1)

# 绘制训练 & 验证的准确率值
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()

# 绘制训练 & 验证的损失值
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()

keras训练多输入

https://www.jb51.net/article/189193.htm

Cuda10 RTX2070 安装
同一系统下安装不同的Cuda版本
Cuda版本
CudaNN对应版本

Python小知识

[1]class继承类重写类的方法

classFoo(object):
  def__new__(cls,*args,**kwargs):
    obj=object.__new__(cls,*args,**kwargs) 
    # 这里的object.__new__(cls, *args, **kwargs)  等价于
    # super(Foo, cls).__new__(cls, *args, **kwargs) 
    # object.__new__(Foo, *args, **kwargs)
    # Bar.__new__(cls, *args, **kwargs)
    # Student.__new__(cls, *args, **kwargs)，即使Student跟Foo没有关系，也是允许的，因为Student是从object派生的新式类
  
    # 在任何新式类，不能调用自身的“__new__”来创建实例，因为这会造成死循环
    # 所以要避免return Foo.__new__(cls, *args, **kwargs)或return cls.__new__(cls, *args, **kwargs)
    print"Call __new__ for %s"%obj.__class__
    returnobj  
  
classBar(Foo):
  def__new__(cls,*args,**kwargs):
    obj=object.__new__(cls,*args,**kwargs) 
    print"Call __new__ for %s"%obj.__class__
    returnobj

[2]super()函数是用于调用父类(超类)的一个方法。

class A:
     def add(self, x):
         y = x+1
         print(y)
class B(A):
    def add(self, x):
        super().add(x)
b = B()
b.add(2)  # 3

python问题集锦

anaconda特定环境下运行jupyter notebook

$ conda activate ENV  #启动相应环境
$ conda install jupyter
$ conda install ipython
$ jupyter notebook    #启动jupyter notebook