带有张量流的泰坦尼克号的简单实现

Question

我是机器学习和张量流的新手。我试图以一种简单的方式在不使用任何架构，不进行大量转换的情况下实现这一目标，但是我被束缚了，不惜一切代价得到NAN。下面的代码让我知道我在这里做错了什么。

[这是我从其中一门学习课程中获得的代码段，他们已经为IRIS数据实现了它，只是试图对Titanic数据执行相同的操作。

! kaggle competitions download -c 'titanic'
import pandas as pd
train_data = pd.read_csv("train.csv")
train_data.head(), train_data.columns
n_input =7 ##7 valid columns
n_output=2 ## {0,1} survived or not survived
import tensorflow as tf
tf.reset_default_graph()
input_shape = [None,n_input]
inputplaceholder = tf.placeholder(dtype=tf.float32, shape=input_shape, name="input_placeholder")
weights = tf.Variable(tf.random_normal([n_input,n_output]), name="weights")
biases = tf.Variable(tf.zeros([n_output]), name="biases")
layer_1 = tf.matmul(inputplaceholder, weights)
layer_2 = tf.add(layer_1, biases)
outputlayer = tf.nn.sigmoid(layer_2)
learning_rate = 0.001
labelsplaceholder = tf.placeholder(dtype=tf.float32, shape=[None,n_output], name="labels_placeholder")
cost = tf.losses.mean_squared_error(labelsplaceholder, outputlayer)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost) 
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
scaled_data=train_data[[ 'Pclass',  'Sex', 'Age', 'SibSp','Parch', 'Fare',  'Embarked']]
genderMap = {"male":1,"female":2,"":""}
embarkMap = {"C":1,"Q":2,"S":3,"":""}
scaled_data['Sex'].replace(genderMap, inplace=True)
scaled_data['Embarked'].replace(embarkMap, inplace=True)
scaled_data['Sex'] = scaled_data['Sex'].astype('float32')
scaled_data['Pclass'] = scaled_data['Pclass'].astype('float32')
scaled_data['SibSp'] = scaled_data['SibSp'].astype('float32')
scaled_data['Parch'] = scaled_data['Parch'].astype('float32')
scaled_data['Embarked'] = scaled_data['Embarked'].astype('float32')
scaled_data['Age'] = scaled_data['Age'].astype('float32')
scaled_data['Fare'] = scaled_data['Fare'].astype('float32')
import random
mydata = list(zip(scaled_data.values, train_data.Survived))
batch_size = 891
iterations = 400

history_loss = list()

for _ in range(iterations):
    inputdata = list()
    output_data = list()
    for _ in range(batch_size):
        input_output_pairs = random.choice(mydata)
        inputdata.append(input_output_pairs[0])
        output_one_hot = [0,0]
        output_one_hot[input_output_pairs[1]] = 1
        output_data.append(output_one_hot)
    res_optimizer, res_cost = sess.run([optimizer, cost], feed_dict={inputplaceholder: inputdata, labelsplaceholder: output_data})
    print(res_cost)
    history_loss.append(res_cost)    

运行此程序，我期望获得一些成本数据，但将其作为NAN。我确实尝试了较低的学习率，分别为0.00001和0.00005，但结果仍然相同

Answer 1

解决了问题后，没有从原始数据中删除NAN，使用“ dropna”之后我们可以得到一些成本值