我有这些特征和标签，它们的线性度不够，无法满足线性解决方案。我从sklearn训练了SVR(kernel='rbf')模型，但现在是时候用tensorflow来训练了，很难说应该写什么来达到相同或更好的效果。

你看到下面那条慵懒的橙色线了吗？它不会让你充满决心

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()

如何激发那条橙色线？

之后部分。

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()

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

您正在使用tf.tanh作为激活，这意味着你的输出被限制在[-1,1]范围内。因此它永远不会适合您的数据。

编辑：我删除了注意到已修复的拼写错误的部分。