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分层嵌套操作¶

We show how to create multiple operations on a computational graph and how to visualize them using Tensorboard.

多层操作¶

Here we extend the usage of the computational graph to create multiple layers and show how they appear in Tensorboard.

载入损失函数¶

In order to train a model, we must be able to evaluate how well it is doing. This is given by loss functions. We plot various loss functions and talk about the benefits and limitations of some.

载入反向传播¶

Here we show how to use loss functions to iterate through data and back propagate errors for regression and classification.

随机和批量训练¶

TensorFlow makes it easy to use both batch and stochastic training. We show how to implement both and talk about the benefits and limitations of each.

结合训练¶

We now combine everything together that we have learned and create a simple classifier.

模型评估¶

Any model is only as good as it’s evaluation. Here we show two examples of (1) evaluating a regression algorithm and (2) a classification algorithm.

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)