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python之如何使用 TensorFlow 将预测值添加到 CSV 文件中的空列

2024年08月23日35jpfss

所以我有这么一段代码，它的预测准确率大约为 93%。我想知道现在该怎么做是采用经过训练的程序，让它查看没有答案的实际测试数据，并让它在不考虑准确性的情况下填写答案。这是我所拥有的预测准确率约为 93% 的代码。

import tensorflow as tf 
import numpy as np 
from numpy import genfromtxt 
import sklearn 
 
# Convert to one hot 
def convertOneHot(data): 
    y=np.array([int(i[0]) for i in data]) 
    y_onehot=[0]*len(y) 
    for i,j in enumerate(y): 
        y_onehot[i]=[0]*(y.max() + 1) 
        y_onehot[i][j]=1 
    return (y,y_onehot) 
 
data = genfromtxt('cs-training.csv',delimiter=',')  # Training data 
test_data = genfromtxt('cs-test.csv',delimiter=',')  # Test data 
 
x_train=np.array([ i[1::] for i in data]) 
y_train,y_train_onehot = convertOneHot(data) 
 
x_test=np.array([ i[1::] for i in test_data]) 
y_test,y_test_onehot = convertOneHot(test_data) 
 
A=data.shape[1]-1 # Number of features, Note first is y 
B=len(y_train_onehot[0]) 
tf_in = tf.placeholder("float", [None, A]) # Features 
tf_weight = tf.Variable(tf.zeros([A,B])) 
tf_bias = tf.Variable(tf.zeros([B])) 
tf_softmax = tf.nn.softmax(tf.matmul(tf_in,tf_weight) + tf_bias) 
 
# Training via backpropagation 
tf_softmax_correct = tf.placeholder("float", [None,B]) 
tf_cross_entropy = -tf.reduce_sum(tf_softmax_correct*tf.log(tf_softmax)) 
 
# Train using tf.train.GradientDescentOptimizer 
tf_train_step = tf.train.GradientDescentOptimizer(0.01).minimize(tf_cross_entropy) 
 
# Add accuracy checking nodes 
tf_correct_prediction = tf.equal(tf.argmax(tf_softmax,1), tf.argmax(tf_softmax_correct,1)) 
tf_accuracy = tf.reduce_mean(tf.cast(tf_correct_prediction, "float")) 
 
saver = tf.train.Saver([tf_weight,tf_bias]) 
 
# Initialize and run 
init = tf.initialize_all_variables() 
sess = tf.Session() 
sess.run(init) 
 
print("...") 
# Run the training 
for i in range(100): 
    sess.run(tf_train_step, feed_dict={tf_in: x_train, tf_softmax_correct: y_train_onehot}) 
 
#Print accuracy 
    result = sess.run(tf_accuracy, feed_dict={tf_in: x_test, tf_softmax_correct: y_test_onehot}) 
print result

现在我有了实际的测试集 cs-test-actual.csv，其中第一列完全是空的，我需要用预测的 1 或 0 填充它。我该怎么做这样做？

请您参考如下方法:

上面的程序似乎没有保存经过训练的 session 。我想你想分两步完成。

  1. 训练并保存 session
  2. 恢复保存 session ，并通过它运行测试数据。

第 1 步: 

 #!/usr/bin/env python 
 
 import tensorflow as tf 
 import numpy as np 
 from numpy import genfromtxt 
 import sklearn 
 
 # Convert to one hot 
 def convertOneHot(data): 
     y=np.array([int(i[0]) for i in data]) 
     y_onehot=[0]*len(y) 
     for i,j in enumerate(y): 
         y_onehot[i]=[0]*(y.max() + 1) 
         y_onehot[i][j]=1 
     return (y,y_onehot) 
 
 # Build Example Data is CSV format, but use Iris data 
 from sklearn import datasets 
 from sklearn.model_selection import train_test_split 
 def buildDataFromIris(): 
     iris = datasets.load_iris() 
     X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.33, random_state=42) 
     f=open('cs-training.csv','w') 
     for i,j in enumerate(X_train): 
         k=np.append(np.array(y_train[i]),j   ) 
         f.write(",".join([str(s) for s in k]) + '\n') 
     f.close() 
     f=open('cs-test.csv','w') 
     for i,j in enumerate(X_test): 
         k=np.append(np.array(y_test[i]),j   ) 
         f.write(",".join([str(s) for s in k]) + '\n') 
     f.close() 
 
 # Recreate logging and save dir 
 # Seems the tensorflow won't always overwrite 
 import shutil, os, sys 
 TMPDir='./tensorTMP' 
 try: 
  shutil.rmtree(TMPDir) 
 except: 
  print "Tmp Dir did not exist...that's okay" 
 os.mkdir(TMPDir, 0755 ) 
 
 
 
 # Populate the data 
 buildDataFromIris() 
 
 data = genfromtxt('cs-training.csv',delimiter=',')  # Training data 
 test_data = genfromtxt('cs-test.csv',delimiter=',')  # Test data 
 
 x_train=np.array([ i[1::] for i in data]) 
 y_train,y_train_onehot = convertOneHot(data) 
 
 x_test=np.array([ i[1::] for i in test_data]) 
 y_test,y_test_onehot = convertOneHot(test_data) 
 
 A=data.shape[1]-1 # Number of features, Note first is y 
 B=len(y_train_onehot[0]) 
 tf_in = tf.placeholder("float", [None, A]) # Features 
 tf_weight = tf.Variable(tf.zeros([A,B])) 
 tf_bias = tf.Variable(tf.zeros([B])) 
 tf_softmax = tf.nn.softmax(tf.matmul(tf_in,tf_weight) + tf_bias) 
 
 # Training via backpropagation 
 tf_softmax_correct = tf.placeholder("float", [None,B]) 
 tf_cross_entropy = -tf.reduce_sum(tf_softmax_correct*tf.log(tf_softmax)) 
 
 # Train using tf.train.GradientDescentOptimizer 
 tf_train_step = tf.train.GradientDescentOptimizer(0.01).minimize(tf_cross_entropy) 
 
 # Add accuracy checking nodes 
 tf_correct_prediction = tf.equal(tf.argmax(tf_softmax,1), tf.argmax(tf_softmax_correct,1)) 
 tf_accuracy = tf.reduce_mean(tf.cast(tf_correct_prediction, "float")) 
 
 saver = tf.train.Saver([tf_weight,tf_bias]) 
 
 # Initialize and run 
 init = tf.initialize_all_variables() 
 sess = tf.Session() 
 sess.run(init) 
 
 THRESHOLD = 0.98 
 saved = False 
 print("...") 
 # Run the training 
 for i in range(100): 
     sess.run(tf_train_step, feed_dict={tf_in: x_train, tf_softmax_correct: y_train_onehot}) 
     result = sess.run(tf_accuracy, feed_dict={tf_in: x_test, tf_softmax_correct: y_test_onehot}) 
     # If it's well trained on this iteration, save it. We just need one save. 
     if result > THRESHOLD  and saved == False: 
         saved = True 
         print "saving result {}".format(result) 
         saver.save(sess,TMPDir +"/savedSess")

所做的唯一修改是使用 Iris 生成样本数据，为 session 建立阈值或置信区间。如果超过阈值，则保存 session 。运行第一步后，应训练并保存模型。

第 2 步:

恢复保存的 session ，并通过它运行训练数据。

 #!/usr/bin/env python 
 
 import tensorflow as tf 
 import numpy as np 
 from numpy import genfromtxt 
 import sklearn 
 
 # Convert to one hot 
 def convertOneHot(data): 
     y=np.array([int(i[0]) for i in data]) 
     y_onehot=[0]*len(y) 
     for i,j in enumerate(y): 
         y_onehot[i]=[0]*(y.max() + 1) 
         y_onehot[i][j]=1 
     return (y,y_onehot) 
 
 
 data = genfromtxt('cs-training.csv',delimiter=',')  # Training data 
 test_data = genfromtxt('cs-test.csv',delimiter=',')  # Test data 
 
 x_train=np.array([ i[1::] for i in data]) 
 y_train,y_train_onehot = convertOneHot(data) 
 
 x_test=np.array([ i[1::] for i in test_data]) 
 y_test,y_test_onehot = convertOneHot(test_data) 
 
 A=data.shape[1]-1 # Number of features, Note first is y 
 B=len(y_train_onehot[0]) 
 tf_in = tf.placeholder("float", [None, A]) # Features 
 tf_weight = tf.Variable(tf.zeros([A,B])) 
 tf_bias = tf.Variable(tf.zeros([B])) 
 tf_softmax = tf.nn.softmax(tf.matmul(tf_in,tf_weight) + tf_bias) 
 
 # Training via backpropagation 
 tf_softmax_correct = tf.placeholder("float", [None,B]) 
 tf_cross_entropy = -tf.reduce_sum(tf_softmax_correct*tf.log(tf_softmax)) 
 
 # Train using tf.train.GradientDescentOptimizer 
 tf_train_step = tf.train.GradientDescentOptimizer(0.01).minimize(tf_cross_entropy) 
 
 # Add accuracy checking nodes 
 tf_correct_prediction = tf.equal(tf.argmax(tf_softmax,1), tf.argmax(tf_softmax_correct,1)) 
 tf_accuracy = tf.reduce_mean(tf.cast(tf_correct_prediction, "float")) 
 
 saver = tf.train.Saver([tf_weight,tf_bias]) 
 
 # Initialize and run 
 init = tf.initialize_all_variables() 
 sess = tf.Session() 
 sess.run(init) 
 
 TMPDir='./tensorTMP' 
 saver.restore(sess, TMPDir + '/savedSess') 
 ans = sess.run(tf_softmax, feed_dict={tf_in: x_test, tf_softmax_correct: y_test_onehot}) 
 
 print ans

请注意，您的输出将如下所示...

[[  6.17585704e-02   8.63590300e-01   7.46511072e-02] 
[  9.98804331e-01   1.19561062e-03   3.25832108e-13] 
[  1.52018686e-07   4.49650863e-04   9.99550164e-01]


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