本文介绍了张量流.非线性回归的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我有这些特征和标签，它们的线性度不足以满足线性解决方案.我从 sklearn 训练了 SVR(kernel='rbf') 模型，但现在是时候用 tensorflow 来训练了，而且很难说应该写什么才能达到相同或更好的效果.

I have these feature and label, that are not linear enough to be satisfied with linear solution. I trained SVR(kernel='rbf') model from sklearn, but now its time to do it with tensorflow, and its hard to say what one should write to achieve same or better effect.

你看到下面那条懒惰的橙色线了吗?它不会让你充满决心

Do you see that lazy orange line down there? It doesn't fill you with determination

代码本身:

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()

如何激发橙色线条?

部分之后.

代码如下:

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()

正如你可以清楚地看到，我们对橙色线进行了一些改进(不如 rbf，但它只是需要更多的工作).

Asyoucanclearlysee we got some improvements for orange line (not quite good as rbf, but its just need more work).

线性

张量流.非线性回归

问题描述

如何激发橙色线条?

推荐答案