本文主要是介绍tensorflow泰坦尼克号沉船数据预测模型，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
首先下载数据
https://www.kaggle.com/c/titanic/data
kaggle上面的数据
import pandas as pd
import numpy as np
import os,sys
os.getcwd()
data = pd.read_csv(’./tt/train.csv’)
data.columns
data = data[[‘Survived’, ‘Pclass’, ‘Sex’, ‘Age’, ‘SibSp’,
‘Parch’, ‘Fare’, ‘Cabin’, ‘Embarked’]]
data[‘Age’] = data[‘Age’].fillna(data[‘Age’].mean())
data[‘Cabin’] = pd.factorize(data.Cabin)[0]
data.fillna(0,inplace = True)
data[‘p1’] = np.array(data[‘Pclass’] == 1).astype(np.int32)
data[‘p2’] = np.array(data[‘Pclass’] == 2).astype(np.int32)
data[‘p3’] = np.array(data[‘Pclass’] == 3).astype(np.int32)
del data[‘Pclass’]
data.Embarked.unique()
data[‘e1’] = np.array(data[‘Embarked’] == ‘S’).astype(np.int32)
data[‘e2’] = np.array(data[‘Embarked’] == ‘C’).astype(np.int32)
data[‘e3’] = np.array(data[‘Embarked’] == ‘Q’).astype(np.int32)
del data[‘Embarked’]
data[‘Sex’] = [1 if x == ‘male’ else 0 for x in data.Sex]
data.values.dtype
data_train = data[[ ‘Sex’, ‘Age’, ‘SibSp’,
‘Parch’, ‘Fare’, ‘Cabin’, ‘p1’,‘p2’,‘p3’,‘e1’,‘e2’,‘e3’]]
data_target = data[‘Survived’].values.reshape(len(data),1)
np.shape(data_train),np.shape(data_target)
import tensorflow as tf
x = tf.placeholder(“float”,shape=[None,12])
y = tf.placeholder(“float”,shape=[None,1])
weight = tf.Variable(tf.random_normal([12,1]))
bias = tf.Variable(tf.random_normal([1]))
output = tf.matmul(x,weight) + bias
pred = tf.cast(tf.sigmoid(output)>0.5,tf.float32)
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels = y,logits = output))
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels = y,logits = output))
train_step = tf.train.GradientDescentOptimizer(0.0003).minimize(loss)
accuracy = tf.reduce_mean(tf.cast(tf.equal(pred,y),tf.float32))
data_test = pd.read_csv(’./tt/test.csv’)
data_test.column
data_test.columns
In[42]:
date_test = data_test[[‘Pclass’, ‘Sex’, ‘Age’, ‘SibSp’, ‘Parch’,
‘Fare’, ‘Cabin’, ‘Embarked’]].copy()
In[43]:
data_test
In[44]:
data_test = data_test[[‘Pclass’, ‘Sex’, ‘Age’, ‘SibSp’, ‘Parch’,
‘Fare’, ‘Cabin’, ‘Embarked’]]
In[51]:
data_test
In[46]:
data_test[‘Age’] = data_test[‘Age’].fillna(data[‘Age’].mean())
In[47]:
data_test[‘Age’] = data_test[‘Age’].fillna(data_test[‘Age’].mean())
In[48]:
data_test
In[49]:
data_test[‘Age’] = data_test[‘Age’].fillna(data_test[‘Age’].mean())
In[50]:
data_test[‘Cabin’] = pd.factorize(data_test.Cabin)[0]
In[52]:
data_test = data_test[[‘Pclass’, ‘Sex’, ‘Age’, ‘SibSp’, ‘Parch’,
‘Fare’, ‘Cabin’, ‘Embarked’]].copy()
data_test[‘Cabin’] = pd.factorize(data_test.Cabin)[0]
In[53]:
data_test[‘Age’] = data_test[‘Age’].fillna(data_test[‘Age’].mean())
In[54]:
data_test.fillna(0,inplace = True)
In[55]:
data_test[‘Sex’] = [1 if x == ‘male’ else 0 for x in data_test.Sex]
In[56]:
data_test[‘p1’] = np.array(data_test[‘Pclass’] == 1).astype(np.int32)
data_test[‘p2’] = np.array(data_test[‘Pclass’] == 2).astype(np.int32)
data_test[‘p3’] = np.array(data_test[‘Pclass’] == 3).astype(np.int32)
data_test[‘e1’] = np.array(data_test[‘Embarked’] == ‘S’).astype(np.int32)
data_test[‘e2’] = np.array(data_test[‘Embarked’] == ‘C’).astype(np.int32)
data_test[‘e3’] = np.array(data_test[‘Embarked’] == ‘Q’).astype(np.int32)
del data_test[‘Pclass’]
del data_test[‘Embarked’]
In[57]:
test_lable = pd.read_csv(’./tt/gender.csv’)
test_lable = np.reshape(test_lable.Survived.values.astype(np.float32),(418,1))
In[58]:
test_lable = pd.read_csv(’./tt/gender.csv’)
test_lable = np.reshape(test_lable.Survived.values.astype(np.float32),(418,1))
In[59]:
sess = tf.Session()
sess.run(tf.global_variables_initializer())
loss_train = []
train_acc = []
test_acc = []
In[61]:
for i in range(25000):
index = np.random.permutation(len(data_target))
data_train = data_train[index]
data_target = data_target[index]
for n in range(len(data_target)//100 + 1):
batch_xs = data_train[n100:n100 + 100]
batch_ys = data_target[n100:n100 + 100]
sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
if i % 1000==0:
loss_temp = sess.run(loss,feed_dict={x: batch_xs,y: batch_ys})
loss_train.append(loss_temp)
train_acc_temp = sess.run(accuracy,feed_dict={x: batch_xs,y: batch_ys})
train_acc.append(train_acc_temp)
print(loss_temp,train_acc_temp)
In[62]:
for i in range(25000):
index = np.random.permutation(len(data_target))
data_train = data_train[index]
data_target = data_target[index]
for n in range(len(data_target)//100 + 1):
batch_xs = data_train[n100:n100 + 100]
batch_ys = data_target[n100:n100 + 100]
sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
if i%1000 == 0:
loss_temp = sess.run(loss,feed_dict={x: batch_xs,y: batch_ys})
loss_train.append(loss_temp)
train_acc_temp = sess.run(accuracy,feed_dict={x: batch_xs,y: batch_ys})
train_acc.append(train_acc_temp)
print(loss_temp,train_acc_temp)
In[64]:
for i in range(25000):
index = np.random.permutation(len(data_target))
data_train = data_train[index]
data_target = data_target[index]
for n in range(len(data_target)//100 + 1):
batch_xs = data_train[n100:n100 + 100]
batch_ys = data_target[n100:n100 + 100]
sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
if i%1000 == 0:
loss_temp = sess.run(loss,feed_dict={x: batch_xs,y: batch_ys})
loss_train.append(loss_temp)
train_acc_temp = sess.run(accuracy,feed_dict={x: batch_xs,y: batch_ys})
train_acc.append(train_acc_temp)
print(loss_temp,train_acc_temp)
In[65]:
for i in range(25000):
index = np.random.permutation(len(data_target))
data_train = data_train[index]
data_target = data_target[index]
for n in range(len(data_target)//100 + 1):
batch_xs = data_train[n100:n100 + 100]
batch_ys = data_target[n100:n100 + 100]
sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
if i%1000 == 0:
loss_temp = sess.run(loss,feed_dict={x: batch_xs,y: batch_ys})
loss_train.append(loss_temp)
train_acc_temp = sess.run(accuracy,feed_dict={x: batch_xs,y: batch_ys})
train_acc.append(train_acc_temp)
print(loss_temp,train_acc_temp)
In[66]:
for i in range(25000):
#index = np.random.permutation(len(data_target))
#data_train = data_train[index]
#data_target = data_target[index]
for n in range(len(data_target)//100 + 1):
batch_xs = data_train[n100:n100 + 100]
batch_ys = data_target[n100:n100 + 100]
sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
if i%1000 == 0:
loss_temp = sess.run(loss,feed_dict={x: batch_xs,y: batch_ys})
loss_train.append(loss_temp)
train_acc_temp = sess.run(accuracy,feed_dict={x: batch_xs,y: batch_ys})
train_acc.append(train_acc_temp)
print(loss_temp,train_acc_temp)
In[67]:
import matplotlib.pyplot as plt
In[68]:
plt.plot(loss_train,‘k-’)
plt.title(‘train loss’)
plt.show()
In[69]:
plt.plot(train_acc,‘b–’,label = ‘train_acc’)
plt.plot(test_acc,‘r–’,label = ‘test_acc’)
plt.title(‘acc’)
plt.legend()
plt.show()
这篇关于tensorflow泰坦尼克号沉船数据预测模型的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
