# 引言¶

We introduce methods for turning text into numerical vectors. We introduce the TensorFlow ‘embedding’ feature as well.

# 词袋 (Bag of Words)¶

Here we use TensorFlow to do a one-hot-encoding of words called bag-of-words. We use this method and logistic regression to predict if a text message is spam or ham.

# 词频-逆文本频率 (TF-IDF)¶

We implement Text Frequency - Inverse Document Frequency (TFIDF) with a combination of Sci-kit Learn and TensorFlow.

# 运用Skip-Gram¶

Our first implementation of Word2Vec called, “skip-gram” on a movie review database.

# CBOW (Continuous Bag fo Words)¶

Next, we implement a form of Word2Vec called, “CBOW” (Continuous Bag of Words) on a movie review database. We also introduce method to saving and loading word embeddings.

This section introduces the convolution layer and the max-pool layer. We show how to chain these together in a 1D and 2D example with fully connected layers as well.

# Word2Vec应用实例¶

In this example, we use the prior saved CBOW word embeddings to improve on our TF-IDF logistic regression of movie review sentiment.

Here we show how to functionalize different layers and variables for a cleaner multi-layer neural network.

# Doc2Vec情感分析 (Sentiment Analysis)¶

Here, we introduce a Doc2Vec method (concatenation of doc and word embeddings) to improve out logistic model of movie review sentiment.

# 神经网络学习井字棋¶

Given a set of tic-tac-toe boards and corresponding optimal moves, we train a neural network classification model to play. At the end of the script, we can attempt to play against the trained model.

# 本章学习模块¶

## tensorflow.zeros¶

Creates a tensor with all elements set to zero.

This operation returns a tensor of type dtype with shape shape and all elements set to zero.

>>> tf.zeros([3, 4], tf.int32)
<tf.Tensor: shape=(3, 4), dtype=int32, numpy=
array([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]], dtype=int32)>

param shape: A list of integers, a tuple of integers, or a 1-D Tensor of type int32. The DType of an element in the resulting Tensor. Optional string. A name for the operation. A Tensor with all elements set to zero.

## tensorflow.ones¶

Creates a tensor with all elements set to one (1).

>>> tf.ones([3, 4], tf.int32)