10 훈련된 모델로 예측
10518 단어 심도 있는 학습
10.1 predict1 프로그램:
import numpy as np
import sys
predictData = None
argt = sys.argv[1:]
for v in argt:
if v.startswith("-predict="):
tmpStr = v[len("-predict="):]
print("tmpStr: %s" % tmpStr)
predictData = np.fromstring(tmpStr, dtype=np.float32, sep=",")
print("predictData: %s" % predictData)
결과:predictData:None
10.2 predict2 프로그램:
import tensorflow as tf
import numpy as np
import random
import os
import sys
ifRestartT = False
predictData = None
argt = sys.argv[1:]
for v in argt:
if v == "-restart":
ifRestartT = True
if v.startswith("-predict="):
tmpStr = v[len("-predict="):]
predictData = np.fromstring(tmpStr, dtype=np.float32, sep=",")
print("predictData: %s" % predictData)
trainResultPath = "./save/idcard2"
random.seed()
x = tf.placeholder(tf.float32)
yTrain = tf.placeholder(tf.float32)
w1 = tf.Variable(tf.random_normal([4, 8], mean=0.5, stddev=0.1), dtype=tf.float32)
b1 = tf.Variable(0, dtype=tf.float32)
xr = tf.reshape(x, [1, 4])
n1 = tf.nn.tanh(tf.matmul(xr, w1) + b1)
w2 = tf.Variable(tf.random_normal([8, 2], mean=0.5, stddev=0.1), dtype=tf.float32)
b2 = tf.Variable(0, dtype=tf.float32)
n2 = tf.matmul(n1, w2) + b2
y = tf.nn.softmax(tf.reshape(n2, [2]))
loss = tf.reduce_mean(tf.square(y - yTrain))
optimizer = tf.train.RMSPropOptimizer(0.01)
train = optimizer.minimize(loss)
sess = tf.Session()
if ifRestartT:
print("force restart...")
sess.run(tf.global_variables_initializer())
elif os.path.exists(trainResultPath + ".index"):
print("loading: %s" % trainResultPath)
tf.train.Saver().restore(sess, save_path=trainResultPath)
else:
print("train result path not exists: %s" % trainResultPath)
sess.run(tf.global_variables_initializer())
if predictData is not None:
result = sess.run([x, y], feed_dict={x: predictData})
print(result[1])
print(y.eval(session=sess, feed_dict={x: predictData}))
sys.exit(0)
lossSum = 0.0
for i in range(5):
xDataRandom = [int(random.random() * 10), int(random.random() * 10), int(random.random() * 10), int(random.random() * 10)]
if xDataRandom[2] % 2 == 0:
yTrainDataRandom = [0, 1]
else:
yTrainDataRandom = [1, 0]
result = sess.run([train, x, yTrain, y, loss], feed_dict={x: xDataRandom, yTrain: yTrainDataRandom})
lossSum = lossSum + float(result[len(result) - 1])
print("i: %d, loss: %10.10f, avgLoss: %10.10f" % (i, float(result[len(result) - 1]), lossSum / (i + 1)))
if os.path.exists("save.txt"):
os.remove("save.txt")
print("saving...")
tf.train.Saver().save(sess, save_path=trainResultPath)
resultT = input('Would you like to save? (y/n)')
if resultT == "y":
print("saving...")
tf.train.Saver().save(sess, save_path=trainResultPath)
결과:
predictData: None
loading: ./save/idcard2
i: 0, loss: 0.0000723387, avgLoss: 0.0000723387
i: 1, loss: 0.1840327382, avgLoss: 0.0920525385
i: 2, loss: 0.1533217579, avgLoss: 0.1124756116
i: 3, loss: 0.0000722930, avgLoss: 0.0843747820
i: 4, loss: 0.3304394484, avgLoss: 0.1335877152
Would you like to save? (y/n)y
saving...
10.3 predict3
코드 동급 디렉터리 아래 데이터 2.txt 파일:
1,2,3,4
2,4,6,8
5,6,1,2
7,9,0,3
test0712.py 프로그램:
import tensorflow as tf
import numpy as np
import random
import os
import sys
ifRestartT = False
predictData = None
argt = sys.argv[1:]
for v in argt:
if v == "-restart":
ifRestartT = True
if v.startswith("-file="):# ，predictData
tmpStr = v[len("-file="):]
print(tmpStr)
predictData = np.loadtxt(tmpStr, dtype=np.float32, delimiter=",")
predictRowCount = predictData.shape[0]#
print("predictRowCount: %s" % predictRowCount)
if v.startswith("-predict="):
tmpStr = v[len("-predict="):]
predictData = [np.fromstring(tmpStr, dtype=np.float32, sep=",")]
print("predictData: %s" % predictData)
trainResultPath = "./save/idcard2"
random.seed()
x = tf.placeholder(tf.float32)
yTrain = tf.placeholder(tf.float32)
w1 = tf.Variable(tf.random_normal([4, 8], mean=0.5, stddev=0.1), dtype=tf.float32)
b1 = tf.Variable(0, dtype=tf.float32)
xr = tf.reshape(x, [1, 4])
n1 = tf.nn.tanh(tf.matmul(xr, w1) + b1)
w2 = tf.Variable(tf.random_normal([8, 2], mean=0.5, stddev=0.1), dtype=tf.float32)
b2 = tf.Variable(0, dtype=tf.float32)
n2 = tf.matmul(n1, w2) + b2
y = tf.nn.softmax(tf.reshape(n2, [2]))
loss = tf.reduce_mean(tf.square(y - yTrain))
optimizer = tf.train.RMSPropOptimizer(0.01)
train = optimizer.minimize(loss)
sess = tf.Session()
if ifRestartT:
print("force restart...")
sess.run(tf.global_variables_initializer())
elif os.path.exists(trainResultPath + ".index"):
print("loading: %s" % trainResultPath)
tf.train.Saver().restore(sess, save_path=trainResultPath)
else:
print("train result path not exists: %s" % trainResultPath)
sess.run(tf.global_variables_initializer())
if predictData is not None:
for i in range(predictRowCount):
print(y.eval(session=sess, feed_dict={x: predictData[i]}))
sys.exit(0)
lossSum = 0.0
for i in range(500000):
xDataRandom = [int(random.random() * 10), int(random.random() * 10), int(random.random() * 10), int(random.random() * 10)]
if xDataRandom[2] % 2 == 0:
yTrainDataRandom = [0, 1]
else:
yTrainDataRandom = [1, 0]
result = sess.run([train, x, yTrain, y, loss], feed_dict={x: xDataRandom, yTrain: yTrainDataRandom})
lossSum = lossSum + float(result[len(result) - 1])
print("i: %d, loss: %10.10f, avgLoss: %10.10f" % (i, float(result[len(result) - 1]), lossSum / (i + 1)))
if os.path.exists("save.txt"):
os.remove("save.txt")
print("saving...")
tf.train.Saver().save(sess, save_path=trainResultPath)
resultT = input('Would you like to save? (y/n)')
if resultT == "y":
print("saving...")
tf.train.Saver().save(sess, save_path=trainResultPath)
명령줄 입력:
e:
cd E:\PycharmProjects\test20190712
python test0712.py -file=data2.txt
결과:
data2.txt
predictRowCount: 4
predictData: [[1. 2. 3. 4.]
[2. 4. 6. 8.]
[5. 6. 1. 2.]
[7. 9. 0. 3.]]
loading: ./save/idcard2
[0.4777626 0.5222374]
[0.46356556 0.5364345 ]
[0.99819934 0.0018006 ]
[0.00806343 0.99193656]
임의의 문자열에서 데이터를 읽어서 예측합니다
10.4 predict4
코드 동급 디렉터리 아래 데이터 3.txt 파일:
[[1,2,3,4], [2,4,6,8], [5,6,1,2], [7,9,0,3]]
test0712.py 프로그램:
import tensorflow as tf
import numpy as np
import random
import os
import sys
ifRestartT = False
predictData = None
argt = sys.argv[1:]
for v in argt:
if v == "-restart":
ifRestartT = True
if v.startswith("-file="):
tmpStr = v[len("-file="):]
predictData = np.loadtxt(tmpStr, dtype=np.float32, delimiter=",")
predictRowCount = predictData.shape[0]
print("predictRowCount: %s" % predictRowCount)
if v.startswith("-dataFile="):# ， filStr
tmpStr = v[len("-dataFile="):]
fileStr = open(tmpStr).read()
predictData = np.array(eval(fileStr))
predictRowCount = predictData.shape[0]
print("predictRowCount: %s" % predictRowCount)
if v.startswith("-predict="):
tmpStr = v[len("-predict="):]
predictData = [np.fromstring(tmpStr, dtype=np.float32, sep=",")]
print("predictData: %s" % predictData)
trainResultPath = "./save/idcard2"
random.seed()
x = tf.placeholder(tf.float32)
yTrain = tf.placeholder(tf.float32)
w1 = tf.Variable(tf.random_normal([4, 8], mean=0.5, stddev=0.1), dtype=tf.float32)
b1 = tf.Variable(0, dtype=tf.float32)
xr = tf.reshape(x, [1, 4])
n1 = tf.nn.tanh(tf.matmul(xr, w1) + b1)
w2 = tf.Variable(tf.random_normal([8, 2], mean=0.5, stddev=0.1), dtype=tf.float32)
b2 = tf.Variable(0, dtype=tf.float32)
n2 = tf.matmul(n1, w2) + b2
y = tf.nn.softmax(tf.reshape(n2, [2]))
loss = tf.reduce_mean(tf.square(y - yTrain))
optimizer = tf.train.RMSPropOptimizer(0.01)
train = optimizer.minimize(loss)
sess = tf.Session()
if ifRestartT:
print("force restart...")
sess.run(tf.global_variables_initializer())
elif os.path.exists(trainResultPath + ".index"):
print("loading: %s" % trainResultPath)
tf.train.Saver().restore(sess, save_path=trainResultPath)
else:
print("train result path not exists: %s" % trainResultPath)
sess.run(tf.global_variables_initializer())
if predictData is not None:
for i in range(predictRowCount):
print(y.eval(session=sess, feed_dict={x: predictData[i]}))
sys.exit(0)
lossSum = 0.0
for i in range(500000):
xDataRandom = [int(random.random() * 10), int(random.random() * 10), int(random.random() * 10), int(random.random() * 10)]
if xDataRandom[2] % 2 == 0:
yTrainDataRandom = [0, 1]
else:
yTrainDataRandom = [1, 0]
result = sess.run([train, x, yTrain, y, loss], feed_dict={x: xDataRandom, yTrain: yTrainDataRandom})
lossSum = lossSum + float(result[len(result) - 1])
print("i: %d, loss: %10.10f, avgLoss: %10.10f" % (i, float(result[len(result) - 1]), lossSum / (i + 1)))
if os.path.exists("save.txt"):
os.remove("save.txt")
print("saving...")
tf.train.Saver().save(sess, save_path=trainResultPath)
resultT = input('Would you like to save? (y/n)')
if resultT == "y":
print("saving...")
tf.train.Saver().save(sess, save_path=trainResultPath)
명령줄 입력:
e:
cd E:\PycharmProjects\test20190712
python test0712.py -dataFile=data3.txt
결과:
predictRowCount: 4
predictData: [[1 2 3 4]
[2 4 6 8]
[5 6 1 2]
[7 9 0 3]]
loading: ./save/idcard2
[0.4777626 0.5222374]
[0.46356556 0.5364345 ]
[0.99819934 0.0018006 ]
[0.00806343 0.99193656]
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현재 기사가 여러분의 문제를 해결하지 못하는 경우 AI 엔진은 머신러닝 분석(스마트 모델이 방금 만들어져 부정확한 경우가 있을 수 있음)을 통해 가장 유사한 기사를 추천합니다:
[Caffe] mnist 인식 프로세스cd $CAFFE_ROOT 트레이닝 데이터 다운로드 ./data/mnist/get_mnist.sh 데이터 세트 만들기: ./examples/mnist/create_mnist.sh 트레이닝 모델: ./examples/...
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