注解
This chapter shows how to implement various SVM methods with TensorFlow. We first create a linear SVM and also show how it can be used for regression. We then introduce kernels (RBF Gaussian kernel) and show how to use it to split up non-linear data. We finish with a multi-dimensional implementation of non-linear SVMs to work with multiple classes.
引言¶
We introduce the concept of SVMs and how we will go about implementing them in the TensorFlow framework.
线性支持向量机¶
We create a linear SVM to separate I. setosa based on sepal length and pedal width in the Iris data set.
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回归线性回归¶
The heart of SVMs is separating classes with a line. We change tweek the algorithm slightly to perform SVM regression.
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TensorFlow中的核¶
In order to extend SVMs into non-linear data, we explain and show how to implement different kernels in TensorFlow.
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多类支持向量机¶
SVMs are inherently binary predictors. We show how to extend them in a one-vs-all strategy in TensorFlow.
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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). |