def run(self, epochs):
#CIFAR
dataset = DataSet.Cifar()
self.trX, self.trY, self.teX, self.teY = dataset.create_sets()
epochs = 50000 * epochs
self.Train()
self.Test()
with tf.train.MonitoredTrainingSession(
checkpoint_dir=
"Networkfile/tfconvtest", # 세이브, 그래프, 초기화 모두 다 포함되있음.
hooks=[tf.train.StopAtStepHook(last_step=epochs)],
save_checkpoint_secs=300,
save_summaries_steps=50) as sess:
while not (sess.should_stop()):
_, loss, step = sess.run(
[self.train_op, self.cost, self.global_step],
feed_dict={
self.p_keep_conv: 0.8,
self.p_keep_hidden: 0.5
})
if (step % 1000 == 0):
accuracy = sess.run(self.acc_op,
feed_dict={
self.p_keep_conv: 1.0,
self.p_keep_hidden: 1.0
})
print(accuracy)
import tensorflow as tf
import numpy as np
import NNutils
import DataSet
from datetime import datetime
dataset = DataSet.Cifar()
trX, trY, teX, teY = dataset.create_sets()
def NN(X):
w1 = tf.Variable(tf.random_normal([32 * 32 * 3, 2048], stddev=0.1))
b1 = tf.Variable(tf.zeros([2048]))
w2 = tf.Variable(tf.random_normal(
[2048, 1400], stddev=0.1)) #MNIST 숫자가 10개이므로(0~9) 10이다.
b2 = tf.Variable(tf.zeros([1400]))
w3 = tf.Variable(tf.random_normal([1400, 900], stddev=0.1))
b3 = tf.Variable(tf.zeros([900]))
w4 = tf.Variable(tf.random_normal([900, 600], stddev=0.1))
b4 = tf.Variable(tf.zeros([600]))
w5 = tf.Variable(tf.random_normal([600, 300], stddev=0.1))
b5 = tf.Variable(tf.zeros([300]))
w6 = tf.Variable(tf.random_normal([300, 10], stddev=0.1))
b6 = tf.Variable(tf.zeros([10]))
