我有這些功能和標籤，線性不足以滿足線性解決方案。我從sklearn培訓了SVR（kernel ='rbf'）模型，但現在是時候用tensorflow來完成了，並且很難說出應該寫什麼來達到相同或更好的效果。Tensorflow。非線性迴歸

你看到懶橙色線下來呢？它不與決心填補你

代碼本身：

import pandas as pd 
import numpy as np 
import tensorflow as tf 
import tqdm 
import matplotlib.pyplot as plt 
from omnicomm_data.test_data import get_model, clean_df 
import os 
from sklearn import preprocessing 

graph = tf.get_default_graph() 

# tf variables 
x_ = tf.placeholder(name="input", shape=[None, 1], dtype=np.float32) 
y_ = tf.placeholder(name="output", shape=[None, 1], dtype=np.float32) 
w = tf.Variable(tf.random_normal([]), name='weight') 
b = tf.Variable(tf.random_normal([]), name='bias') 
lin_model = tf.add(tf.multiply(x_, w), b) 

#loss 
loss = tf.reduce_mean(tf.pow(lin_model - y_, 2), name='loss') 
train_step = tf.train.GradientDescentOptimizer(0.000000025).minimize(loss) 

#nonlinear part 
nonlin_model = tf.tanh(tf.add(tf.multiply(x_, w), b)) 
nonlin_loss = tf.reduce_mean(tf.pow(nonlin_model - y_, 2), name='cost') 
train_step_nonlin = tf.train.GradientDescentOptimizer(0.000000025).minimize(nonlin_loss)  


# pandas data 
df_train = pd.read_csv('me_rate.csv', header=None) 

liters = df_train.iloc[:, 0].values.reshape(-1, 1) 
parrots = df_train.iloc[:, 1].values.reshape(-1, 1) 

#model for prediction 
mms = preprocessing.MinMaxScaler() 
rbf = get_model(path_to_model) 


n_epochs = 200 
train_errors = [] 
non_train_errors = [] 
test_errors = [] 

with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer()) 
    for i in tqdm.tqdm(range(n_epochs)): 

     _, train_err, summ = sess.run([train_step, loss, summaries], 
           feed_dict={x_: parrots, y_: liters}) 
     summary_writer.add_summary(summ, i) 
     train_errors.append(train_err) 

     _, non_train_err, = sess.run([train_step_nonlin, nonlin_loss], 
             feed_dict={x_: parrots, y_: liters}) 
     non_train_errors.append(non_train_err) 


    plt.plot(list(range(n_epochs)), train_errors, label='train_lin') 
    plt.plot(list(range(n_epochs)), non_train_errors, label='train_nonlin') 
    plt.legend() 
    print(train_errors[:10]) 
    print(non_train_errors[:10]) 
    plt.show() 

    plt.scatter(parrots, liters, label='actual data') 
    plt.plot(parrots, sess.run(lin_model, feed_dict={x_: parrots}), label='linear (tf)') 
    plt.plot(parrots, sess.run(nonlin_model, feed_dict={x_: parrots}), label='nonlinear (tf)') 
    plt.plot(parrots, rbf.predict(mms.fit_transform(parrots)), label='rbf (sklearn)') 
    plt.legend() 
    plt.show()

如何激勵是橙色線？

部分之後。

代碼如下所示：

import pandas as pd 
import numpy as np 
import tensorflow as tf 
import tqdm 
import matplotlib.pyplot as plt 
from omnicomm_data.test_data import get_model 
import os 
from sklearn import preprocessing 

graph = tf.get_default_graph() 

# tf variables 
x_ = tf.placeholder(name="input", shape=[None, 1], dtype=np.float32) 
y_ = tf.placeholder(name="output", shape=[None, 1], dtype=np.float32) 
w = tf.Variable(tf.random_normal([]), name='weight') 
b = tf.Variable(tf.random_normal([]), name='bias') 

# nonlinear 
nonlin_model = tf.add(tf.multiply(tf.tanh(x_), w), b) 
nonlin_loss = tf.reduce_mean(tf.pow(nonlin_model - y_, 2), name='cost') 
train_step_nonlin = tf.train.GradientDescentOptimizer(0.01).minimize(nonlin_loss) 


# pandas data 
df_train = pd.read_csv('me_rate.csv', header=None) 


liters = df_train.iloc[:, 0].values.reshape(-1, 1) 
parrots = df_train.iloc[:, 1].values.reshape(-1, 1) 


#model for prediction 
mms = preprocessing.MinMaxScaler() 
rbf = get_model(path_to_model) 


nz = preprocessing.MaxAbsScaler() # normalization coz tanh 
norm_parrots = nz.fit_transform(parrots) 
print(norm_parrots) 

n_epochs = 20000 
train_errors = [] 
non_train_errors = [] 
test_errors = [] 
weights = [] 
biases = [] 

with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer()) 
    for i in tqdm.tqdm(range(n_epochs)): 

     _, non_train_err, weight, bias = sess.run([train_step_nonlin, nonlin_loss, w, b], 
             feed_dict={x_: norm_parrots, y_: liters}) 
     non_train_errors.append(non_train_err) 
     weights.append(weight) 
     biases.append(bias) 


    plt.scatter(norm_parrots, liters, label='actual data') 

    plt.plot(norm_parrots, sess.run(nonlin_model, feed_dict={x_: norm_parrots}), c='orange', label='nonlinear (tf)') 
    plt.plot(norm_parrots, rbf.predict(mms.fit_transform(parrots)), label='rbf (sklearn)') 
    plt.legend() 
    plt.show()

Asyoucanclearlysee我們得到了橙色系的一些改進（如RBF不太好，但它只是需要更多的工作）。