tf-tutorials

some learned cases about using tensorflow

Content

• Linear_regression

  • data

    randomly generate some points

  • model

    Y=WX+b,where y is a real value

  • API

    tf.mul(X, W) + b

• Logistic_Regression

  • data

    download from https://www.kaggle.com/c/titanic/data. The Attribute like below:

    passenger_id, survived, pclass, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked
    

    Just choose the passenger_id, survived, pclass,to predict the sex.

  • model

    Y=1/(1+e^(WX+b)),where y is 0 or 1.

  • API

    tf.nn.sigmoid_cross_entropy_with_logits(tf.matmul(x_in, W) + b, y_in)

• Softmax_Classification

  • data

    iris data,it's UCI data,also you can download from https://www.kaggle.com/uciml/iris

  • model

    Y=1/(1+e^(WX+b)),where you can use multiple labels

  • API

    tf.nn.sparse_softmax_cross_entropy_with_logits tf.nn.softmax_cross_entropy_with_logit

• cnn-sentence-classification

  Convolutional Neural Networks for Sentence Classification