Github address: github.com/yangjinghit…

import pandas as pd
import numpy as np
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data = pd.read_csv('./tt/train.csv')
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data.columns
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Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],
      dtype='object')
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data = data[['Survived'.'Pclass'.'Sex'.'Age'.'SibSp'.'Parch'.'Fare'.'Cabin'.'Embarked']]
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data.head(3)
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Survived Pclass Sex Age SibSp Parch Fare Cabin Embarked
0 0 3 male 22.0 1 0 7.2500 NaN S
1 1 1 female 38.0 1 0 71.2833 C85 C
2 1 3 female 26.0 0 0 7.9250 NaN S 
data['Age'] = data['Age'].fillna(data['Age'].mean())
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data['Cabin'] = pd.factorize(data.Cabin)[0]
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data.fillna(0, inplace = True)
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data['Sex'] = [1 if x=='male' else 0 for x in data.Sex]
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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)
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del data['Pclass']
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data.Embarked.unique()
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array(['S', 'C', 'Q', 0], dtype=object)
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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)
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del data['Embarked']
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data.values.dtype
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dtype('float64')
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data.columns
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Index(['Survived', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Cabin', 'p1', 'p2',
       'p3', 'e1', 'e2', 'e3'],
      dtype='object')
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data_train = data[['Sex'.'Age'.'SibSp'.'Parch'.'Fare'.'Cabin'.'p1'.'p2'.'p3'.'e1'.'e2'.'e3']]
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data_target = data['Survived'].values.reshape(len(data),1)

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np.shape(data_train), np.shape(data_target)
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((891, 12), (891, 1))
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import tensorflow as tf
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/ anaconda3 / envs/py35 / lib/python3.5 importlib / _bootstrap py: 222: RuntimeWarning: Compiletime version 3.6 of the module 'tensorflow. Python. Framework. Fast_tensor_util' does not match the runtime version 3.5 Return (* args, f * * KWDS)/anaconda3 envs py35 / lib/python3.5 / site - packages/h5py/set py: 36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`. from ._conv import register_converters as _register_convertersCopy the code
x = tf.placeholder("float", shape=[None.12])
y = tf.placeholder("float", shape=[None.1])
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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)
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loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels = y, logits = output))
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train_step = tf.train.GradientDescentOptimizer(0.003).minimize(loss)
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accuracy = tf.reduce_mean(tf.cast(tf.equal(pred,y), tf.float32))
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data_test = pd.read_csv('./tt/test.csv')
data_test = data_test[['Pclass'.'Sex'.'Age'.'SibSp'.'Parch'.'Fare'.'Cabin'.'Embarked']]
data_test['Age'] = data_test['Age'].fillna(data_test['Age'].mean())
data_test['Cabin'] = pd.factorize(data_test.Cabin)[0]
data_test['Sex'] = [1 if x=='male' else 0 for x in data_test.Sex]
data_test.fillna(0, inplace = True)
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']
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test_label = pd.read_csv('./tt/gender_submission.csv')
test_label = np.reshape(test_label.Survived.values.astype(np.float32), (418.1))
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sess = tf.Session()
sess.run(tf.global_variables_initializer())
loss_train = []
train_acc = []
test_acc = []
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for i in range(5000) : index = np.random.permutation(len(data_target)) data_train = np.array(data_train)[index] data_target = np.array(data_target)[index]for n in range(len(data_target)//100 + 1):
        batch_xs = data_train[n*100:n*100 + 100]
        batch_ys = data_target[n*100:n*100 + 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)
            test_acc_temp = sess.run(accuracy, feed_dict={x:data_test, y:test_label})
            test_acc.append(test_acc_temp)
            print(loss_temp, train_acc_temp, test_acc_temp)
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7.3604326 0.57 0.5717703
8.014492 0.44 0.45454547
7.1409583 0.31 0.4617225
5.1470814 0.36 0.43301436
6.3511767 0.45 0.4784689
5.792799 0.44 0.47368422
4.681352 0.4 0.44258374
5.842095 0.39 0.46411484
3.0389767 0.48351648 0.42105263
0.48493686 0.75 0.8779904
0.4163903 0.79 0.8732057
0.4867239 0.78 0.88755983
0.44354805 0.82 0.88755983
0.52813506 0.76 0.8755981
0.4696804 0.76 0.8851675
0.49193314 0.77 0.8779904
0.45960158 0.83 0.8755981
0.32401434 0.8681319 0.83732057
0.44543365 0.79 0.9114832
0.44734138 0.78 0.8971292
0.4601239 0.8 0.8971292
0.4851056 0.79 0.90430623
0.4117188 0.83 0.8995215
0.39182335 0.78 0.8827751
0.36042503 0.86 0.8923445
0.5273975 0.77 0.8827751
0.4674853 0.8021978 0.8899522
0.3866866 0.87 0.8684211
0.46905473 0.79 0.8947368
0.39205357 0.83 0.94736844
0.52698064 0.78 0.94736844
0.45487815 0.83 0.9569378
0.51008093 0.76 0.9521531
0.36122188 0.85 0.9569378
0.38313925 0.84 0.9186603
0.46178767 0.7582418 0.8779904
0.4290183 0.79 0.90909094
0.38088027 0.86 0.94258374
0.50604784 0.78 0.92105263
0.42591545 0.83 0.9138756
0.44238362 0.81 0.8923445
0.5280578 0.73 0.93779904
0.37598786 0.84 0.95454544
0.44755325 0.83 0.9354067
0.45485118 0.7912088 0.93779904
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sess.run(weight)
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Array ([[2.7579787], [0.02792009], [0.33448917], [0.16113752], [0.01580716], [0.01695831], [0.94773066]. [1.2085476], [0.1693252], [-0.52223164], [0.05860335], [-0.6202243]], DType = FLOAT32Copy the code
sess.run(bias)
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Array ([1.7377106], dtype = float32)Copy the code
import matplotlib.pyplot as plt
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plt.plot(loss_train, 'k-')
plt.title('train loss')
plt.show()
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plt.plot(train_acc,'b-', label ='train_acc')
plt.plot(test_acc, 'r--', label = 'test_acc')
plt.title('train and test accuracy')
plt.legend()
plt.show()
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