0
現在、ニューラルネットワークは、範囲X [0、]とY [0、]の関数を予測する必要があります。Tensorflow：ニューラルネットワークが収束しない
Tensorflowを使用してネットワークを実装することに決めました。さらに、2つのレイヤーを使用することに決めました。最初のレイヤーは50、2番目のレイヤーは30です。活性化関数として、隠れ層にはsigmoidを使用し、出力層にはsoftplusを使用します（出力が1より大きくなるようにするため）。
この設定は、0と2の間のスケールされた洞の機能（XとYの場合）のような問題でうまく機能します。しかし、これを実データに適用すると、収束していないように見えます。
ここ私が書いたコード：
import numpy as np
import matplotlib.pyplot as plt
import itertools
import tensorflow as tf
import json
train = []
datax = []
datay = []
data = []
def grouper(n, iterable):
it = iter(iterable)
while True:
chunk = tuple(itertools.islice(it, n))
if not chunk:
return
yield chunk
# Load data from string into list
points = json.loads('[["445.0", "38.0"], ["416.0", "25.0"], ["439.0", "40.0"], ["450.0", "41.0"], ["448.0", "39.0"], ["467.0", "32.0"], ["419.0", "29.0"], ["430.0", "39.0"], ["459.0", "42.0"], ["482.0", "36.0"], ["477.0", "38.0"], ["449.0", "41.0"], ["465.0", "39.0"], ["464.0", "44.0"], ["431.0", "41.0"], ["399.0", "40.0"], ["369.0", "40.0"], ["377.0", "48.0"], ["403.0", "38.0"], ["374.0", "40.0"], ["393.0", "43.0"], ["368.0", "41.0"], ["340.0", "41.0"], ["357.0", "37.0"], ["356.0", "36.0"], ["397.0", "44.0"], ["388.0", "45.0"], ["355.0", "39.0"], ["392.0", "44.0"], ["346.0", "40.0"], ["462.0", "45.0"], ["426.0", "34.0"], ["481.0", "35.0"], ["454.0", "45.0"], ["470.0", "33.0"], ["452.0", "38.0"], ["444.0", "35.0"], ["475.0", "38.0"], ["458.0", "42.0"], ["461.0", "42.0"], ["483.0", "39.0"], ["457.0", "43.0"], ["447.0", "36.0"], ["417.0", "26.0"], ["460.0", "43.0"], ["455.0", "44.0"], ["427.0", "36.0"], ["415.0", "22.0"], ["456.0", "43.0"], ["418.0", "29.0"], ["606.0", "41.0"], ["580.0", 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["962.0", "27.0"], ["982.0", "30.0"], ["950.0", "31.0"], ["938.0", "29.0"], ["978.0", "26.0"], ["958.0", "24.0"], ["959.0", "26.0"], ["745.0", "23.0"], ["712.0", "34.0"], ["714.0", "32.0"], ["700.0", "39.0"], ["705.0", "36.0"], ["730.0", "36.0"], ["706.0", "32.0"], ["701.0", "40.0"], ["715.0", "34.0"], ["733.0", "37.0"], ["763.0", "31.0"], ["758.0", "29.0"], ["755.0", "29.0"], ["722.0", "36.0"], ["707.0", "37.0"], ["887.0", "24.0"], ["864.0", "24.0"], ["877.0", "23.0"], ["886.0", "25.0"], ["894.0", "22.0"], ["873.0", "21.0"], ["914.0", "28.0"], ["892.0", "27.0"], ["728.0", "39.0"], ["704.0", "37.0"], ["861.0", "25.0"], ["909.0", "26.0"], ["900.0", "24.0"], ["713.0", "33.0"], ["866.0", "25.0"], ["862.0", "25.0"], ["879.0", "26.0"], ["885.0", "24.0"], ["888.0", "21.0"], ["859.0", "22.0"], ["860.0", "24.0"], ["899.0", "25.0"], ["906.0", "26.0"], ["753.0", "31.0"], ["740.0", "36.0"], ["746.0", "24.0"], ["723.0", "37.0"], ["729.0", "38.0"], ["708.0", "37.0"], ["717.0", "35.0"], ["756.0", "27.0"], ["901.0", "25.0"], ["762.0", "28.0"], ["719.0", "35.0"], ["703.0", "37.0"], ["764.0", "32.0"], ["736.0", "36.0"], ["710.0", "33.0"], ["757.0", "27.0"], ["709.0", "37.0"], ["731.0", "37.0"], ["739.0", "37.0"], ["882.0", "27.0"], ["870.0", "21.0"], ["854.0", "27.0"]]')
# Load numbers in list into arrays of size 5
for lines in grouper(5,points):
xa = None
ya = None
set = False
for line in lines:
datax.append(line[0])
datay.append(line[1])
data.append([line[0],line[1]])
if set == False:
xa = np.array(line[0])
ya = np.array(line[1])
set = True
else:
xa = np.vstack((xa,np.array(line[0])))
ya = np.vstack((ya,np.array(line[1])))
train.append([xa,ya])
# Network Input/Output
inputsize = 1
outputsize = 1
a_0 = tf.placeholder(tf.float32, [None, inputsize])
y = tf.placeholder(tf.float32, [None, outputsize])
# Hidden layers
middle = 50
middle2 = 30
w_1 = tf.Variable(tf.truncated_normal([inputsize, middle]))
b_1 = tf.Variable(tf.truncated_normal([1, middle]))
w_12 = tf.Variable(tf.truncated_normal([middle, middle2]))
b_12 = tf.Variable(tf.truncated_normal([1, middle2]))
w_2 = tf.Variable(tf.truncated_normal([middle2, outputsize]))
b_2 = tf.Variable(tf.truncated_normal([1, outputsize]))
def sigma1(x):
return tf.nn.sigmoid(x)
def sigma2(x):
return tf.nn.softplus(x)
z_1 = tf.add(tf.matmul(a_0, w_1), b_1)
a_1 = sigma1(z_1)
z_12 = tf.add(tf.matmul(a_1, w_12), b_12)
a_12 = sigma1(z_12)
z_2 = tf.add(tf.matmul(a_12, w_2), b_2)
a_2 = sigma2(z_2)
cost = tf.nn.l2_loss((a_2 - y))
step = tf.train.GradientDescentOptimizer(0.1).minimize(cost)
sess = tf.InteractiveSession()
sess.run(tf.initialize_all_variables())
lasterror = -1
times = 0
# Train for 100 epoch (tried also longer, but no additional gain)
for i in range(100):
for tup in train:
sess.run(step, feed_dict = {a_0: tup[0],
y : tup[1]})
# Plot the predicted outcome
def _transpose(matrix):
return matrix[np.newaxis, :].T
x = np.arange(0,1400, 0.01)
y = []
res = sess.run(a_2, feed_dict = {a_0: _transpose(x)})
plt.plot(x,res)
#Plot data
plt.plot(datax,datay,"ro")
plt.show()
を私はまた、0と1の間の出力をスケーリングしようとしたが、これも収束するネットワークの助けにはなりませんでした。
結果は（予測されてラインで、データポイント）次のようになります。
が、私はここに助けることができるどのような任意の提案、のために開いています。
ありがとうございます。