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.
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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.
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运用Skip-Gram¶
Our first implementation of Word2Vec called, “skip-gram” on a movie review database.
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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.
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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.
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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.
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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.
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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. |
---|---|
param dtype: | The DType of an element in the resulting Tensor. |
param name: | Optional string. A name for the operation. |
returns: | A Tensor with all elements set to zero. |
tensorflow.ones¶
Creates a tensor with all elements set to one (1).
See also tf.ones_like.
This operation returns a tensor of type dtype with shape shape and all elements set to one.
>>> tf.ones([3, 4], tf.int32)
<tf.Tensor: shape=(3, 4), dtype=int32, numpy=
array([[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]], dtype=int32)>
param shape: | A list of integers, a tuple of integers, or a 1-D Tensor of type int32. |
---|---|
param dtype: | Optional DType of an element in the resulting Tensor. Default is tf.float32. |
param name: | Optional string. A name for the operation. |
returns: | A Tensor with all elements set to one (1). |