[PYTHON] TensorFlow> Sinuskurve lernen> Reproduktion von Lernergebnissen aus Gewicht, Bias v0.4 (Erfolg?)
Betriebsumgebung
GeForce GTX 1070 (8GB)
ASRock Z170M Pro4S [Intel Z170chipset]
Ubuntu 14.04 LTS desktop amd64
TensorFlow v0.11
cuDNN v5.1 for Linux
CUDA v8.0
Python 2.7.6
IPython 5.1.0 -- An enhanced Interactive Python.
Verwandte http://qiita.com/7of9/items/b364d897b95476a30754
Ich versuche, das Netzwerk selbst zu reproduzieren und die Ausgabe basierend auf [Gewicht und Verzerrung beim Lernen der Sinuskurve] zu berechnen (http://qiita.com/7of9/items/f7b2e0eeea3b7fdc632c).
http://qiita.com/7of9/items/b7d38e174d4052b74cae Fortsetzung.
Ein anderes Python-Programm, das das von der TensorFlow-Verarbeitung ausgegebene Gewicht und die Vorspannung liest, kann die Sinuskurve nicht reproduzieren.
input.csv-Generation
http://qiita.com/7of9/items/b364d897b95476a30754#データ生成部
code (sigmoid_onlyHidden.py)
Ich habe die Ausgabeposition von Gewicht und Bias (model_variables = slim.get_model_variables () 3 Zeilen unten) auf der TensorFlow-Seite nach unten geändert.
Die Idee ist, dass Sie, wenn Sie in einem Versuch ausgeben, möglicherweise nicht in der Lage sind, das Gewicht und die Vorspannung auszugeben, die Sie ursprünglich wollten.
sigmoid_onlyHidden.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
filename_queue = tf.train.string_input_producer(["input.csv"])
# prase CSV
reader = tf.TextLineReader()
key, value = reader.read(filename_queue)
input1, output = tf.decode_csv(value, record_defaults=[[0.], [0.]])
inputs = tf.pack([input1])
output = tf.pack([output])
batch_size=4 # [4]
inputs_batch, output_batch = tf.train.shuffle_batch([inputs, output], batch_size, capacity=40, min_after_dequeue=batch_size)
input_ph = tf.placeholder("float", [None,1])
output_ph = tf.placeholder("float",[None,1])
## network
hiddens = slim.stack(input_ph, slim.fully_connected, [7,7,7],
activation_fn=tf.nn.sigmoid, scope="hidden")
#prediction = slim.fully_connected(hiddens, 1, activation_fn=tf.nn.sigmoid, scope="output")
prediction = slim.fully_connected(hiddens, 1, activation_fn=None, scope="output")
loss = tf.contrib.losses.mean_squared_error(prediction, output_ph)
train_op = slim.learning.create_train_op(loss, tf.train.AdamOptimizer(0.001))
init_op = tf.initialize_all_variables()
with tf.Session() as sess:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
sess.run(init_op)
for i in range(30000): #[10000]
inpbt, outbt = sess.run([inputs_batch, output_batch])
_, t_loss = sess.run([train_op, loss], feed_dict={input_ph:inpbt, output_ph: outbt})
if (i+1) % 100 == 0:
print("%d,%f" % (i+1, t_loss))
# # output to npy
# model_variables = slim.get_model_variables()
# res = sess.run(model_variables)
# np.save('model_variables.npy', res)
finally:
coord.request_stop()
# output to npy
model_variables = slim.get_model_variables()
res = sess.run(model_variables)
np.save('model_variables.npy', res)
#output trained curve
print 'output' # used to separate from above lines (grep -A 200 output [outfile])
for loop in range(10):
inpbt, outbt = sess.run([inputs_batch, output_batch])
pred = sess.run([prediction], feed_dict={input_ph:inpbt, output_ph: outbt})
for din,dout in zip(inpbt, pred[0]):
print '%.5f,%.5f' % (din,dout)
coord.join(threads)
Remake model_variables.npy
Lauf
$ python sigmoid_onlyHidden.py
Klicken Sie hier, um eine TensorFlow-Vorhersagedatei zu erstellen (res.161210_1958.cut)
Lauf
$python sigmoid_onlyHidden.py > res.161210_1958.org
$grep -A 200 output res.161210_1958.org > res.161210_1958.cut
$vi res.161210_1958.cut # (Löschen Sie die erste Zeile)
Reproduktion der Sinuskurve
reproduce_sine.py
'''
v0.3 Dec. 11, 2016
- add output_debugPrint()
- fix bug > calc_sigmoid() was using positive for exp()
v0.2 Dec. 10, 2016
- calc_conv() takes [applyActFnc] argument
v0.1 Dec. 10, 2016
- add calc_sigmoid()
- add fully_connected network
- add input data for sine curve
=== [read_model_var.py] branched to [reproduce_sine.py] ===
v0.4 Dec. 10, 2016
- add 2x2 network example
v0.3 Dec. 07, 2016
- calc_conv() > add bias
v0.2 Dec. 07, 2016
- fix calc_conv() treating src as a list
v0.1 Dec. 07, 2016
- add calc_conv()
'''
import numpy as np
import math
import sys
model_var = np.load('model_variables.npy')
# to ON/OFF debug print at one place
def output_debugPrint(str):
# print(str)
pass # no operation
output_debugPrint( ("all shape:",(model_var.shape)) )
def calc_sigmoid(x):
return 1.0 / (1.0 + math.exp(-x))
def calc_conv(src, weight, bias, applyActFnc):
wgt = weight.shape
# print wgt # debug
#conv = list(range(bias.size))
conv = [0.0] * bias.size
# weight
for idx1 in range(wgt[0]):
for idx2 in range(wgt[1]):
conv[idx2] = conv[idx2] + src[idx1] * weight[idx1,idx2]
# bias
for idx2 in range(wgt[1]):
conv[idx2] = conv[idx2] + bias[idx2]
# activation function
if applyActFnc:
for idx2 in range(wgt[1]):
conv[idx2] = calc_sigmoid(conv[idx2])
return conv # return list
inpdata = np.linspace(0, 1, 30).astype(float).tolist()
#debug
for idx in range(8):
output_debugPrint(model_var[idx].shape)
#sys.exit()
for din in inpdata:
# input layer (1 node)
#
# hidden layer 1 (7 node)
inlist = [ din ]
outdata = calc_conv(inlist, model_var[0], model_var[1], applyActFnc=True)
# hidden layer 2 (7 node)
outdata = calc_conv(outdata, model_var[2], model_var[3], applyActFnc=True)
# hidden layer 3 (7 node)
outdata = calc_conv(outdata, model_var[4], model_var[5], applyActFnc=True)
# output layer (1 node)
outdata = calc_conv(outdata, model_var[6], model_var[7], applyActFnc=False)
dout = outdata[0] # output is 1 node
print '%.3f, %.3f' % (din,dout)
Lauf
$ python reproduce_sine.py > res.reprod_sine
Anzeige in Jupyter
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
data1 = np.loadtxt('res.161210_1958.cut', delimiter=',')
inp1 = data1[:,0]
out1 = data1[:,1]
data2 = np.loadtxt('res.reprod_sine', delimiter=',')
inp2 = data2[:,0]
out2 = data2[:,1]
fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)
ax1.scatter(inp1, out1, label='TensorFlow prediction', color='blue', marker='o')
ax1.scatter(inp2, out2, label='from model_var.npy', color='red',marker='x')
ax1.set_xlabel('x')
ax1.set_ylabel('sine(x) prediction')
ax1.grid(True)
ax1.legend()
ax1.set_xlim([0,1.0])
fig.show()
Während ich dem "Segelflugzeug" der Peggies zuhörte, konnte ich die Sinuskurve bequem reproduzieren.
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