我想编写一个神经网络来寻找没有预定义模型的 ax^2 分布。准确地说,它在 [-1,1] 中给出了一些点和它们的正方形来训练,然后它必须重现和预测类似的点,例如 [-10,10]。我或多或少地做到了——没有数据集。但后来我尝试修改它以使用数据集并学习如何使用它。现在,我成功让程序运行了,但是输出比以前更糟,主要是常数0。
以前的版本就像 [-1,1] 中的 x^2 具有线性延长,这更好..以前 的输出,蓝线现在是平的。目标是与红色相吻合..
在这里,评论是波兰语,对此感到抱歉。
# square2.py - drugie podejscie do trenowania sieci za pomocą Tensorflow
# cel: nauczyć sieć rozpoznawać rozkład x**2
# analiza skryptu z:
# https://stackoverflow.com/questions/43140591/neural-network-to-predict-nth-square
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
from tensorflow.python.framework.ops import reset_default_graph
# def. danych do trenowania sieci
# x_train = (np.random.rand(10**3)*4-2).reshape(-1,1)
# y_train = x_train**2
square2_dane = np.load("square2_dane.npz")
x_train = square2_dane['x_tren'].reshape(-1,1)
y_train = square2_dane['y_tren'].reshape(-1,1)
# zoptymalizować dzielenie danych
# x_train = square2_dane['x_tren'].reshape(-1,1)
# ds_x = tf.data.Dataset.from_tensor_slices(x_train)
# batch_x = ds_x.batch(rozm_paczki)
# iterator = ds_x.make_one_shot_iterator()
# określenie parametrów sieci
wymiary = [50,50,50,1]
epoki = 500
rozm_paczki = 200
reset_default_graph()
X = tf.placeholder(tf.float32, shape=[None,1])
Y = tf.placeholder(tf.float32, shape=[None,1])
weights = []
biases = []
n_inputs = 1
# inicjalizacja zmiennych
for i,n_outputs in enumerate(wymiary):
with tf.variable_scope("layer_{}".format(i)):
w = tf.get_variable(name="W", shape=[n_inputs,n_outputs],initializer = tf.random_normal_initializer(mean=0.0,stddev=0.02,seed=42))
b=tf.get_variable(name="b",shape=[n_outputs],initializer=tf.zeros_initializer)
weights.append(w)
biases.append(b)
n_inputs=n_outputs
def forward_pass(X,weights,biases):
h=X
for i in range(len(weights)):
h=tf.add(tf.matmul(h,weights[i]),biases[i])
h=tf.nn.relu(h)
return h
output_layer = forward_pass(X,weights,biases)
f_strat = tf.reduce_mean(tf.squared_difference(output_layer,Y),1)
f_strat = tf.reduce_sum(f_strat)
# alternatywna funkcja straty
#f_strat2 = tf.reduce_sum(tf.abs(Y-y_train)/y_train)
optimizer = tf.train.AdamOptimizer(learning_rate=0.003).minimize(f_strat)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# trenowanie
dataset = tf.data.Dataset.from_tensor_slices((x_train,y_train))
dataset = dataset.batch(rozm_paczki)
dataset = dataset.repeat(epoki)
iterator = dataset.make_one_shot_iterator()
ds_x, ds_y = iterator.get_next()
sess.run(optimizer, {X: sess.run(ds_x), Y: sess.run(ds_y)})
saver = tf.train.Saver()
save = saver.save(sess, "./model.ckpt")
print("Model zapisano jako: %s" % save)
# puszczenie sieci na danych
x_test = np.linspace(-1,1,600)
network_outputs = sess.run(output_layer,feed_dict = {X :x_test.reshape(-1,1)})
plt.plot(x_test,x_test**2,color='r',label='y=x^2')
plt.plot(x_test,network_outputs,color='b',label='sieć NN')
plt.legend(loc='right')
plt.show()
我认为问题在于训练数据的输入
sess.run(optimizer, {X: sess.run(ds_x), Y: sess.run(ds_y)})
或 ds_x、ds_y 的定义。这是我的第一个这样的程序。所以这是行的输出(在“sees”块中)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# trenowanie
for i in range(epoki):
idx = np.arange(len(x_train))
np.random.shuffle(idx)
for j in range(len(x_train)//rozm_paczki):
cur_idx = idx[rozm_paczki*j:(rozm_paczki+1)*j]
sess.run(optimizer,feed_dict = {X:x_train[cur_idx],Y:y_train[cur_idx]})
saver = tf.train.Saver()
save = saver.save(sess, "./model.ckpt")
print("Model zapisano jako: %s" % save)
谢谢!
PS:我受到神经网络的极大启发来预测第n个平方