Visualizing the graph using TensorBoard is one of the most useful capabilities of TensorFlow, especially when building complicated neural networks. The computational graph that we built can be viewed with the help of Graph Object.
All constants, variables, and placeholders will be defined in the computation graph section of the code. If we use the print statement in the definition section, we will only get information about the type of tensor, and not its value.
@@ -906,7 +906,7 @@ with tf.device \ ("/job:localhost/replica:0/task:0/device:XLA_GPU:0"):
TensorFlow.org 讲述了 tfcompile：
tfcompile takes a subgraph, identified by the TensorFlow concepts of feeds and fetches, and generates a function that implements that subgraph. The feeds are the input arguments for the function, and the fetches are the output arguments for the function. All inputs must be fully specified by the feeds; the resulting pruned subgraph cannot contain placeholder or variable nodes. It is common to specify all placeholders and variables as feeds, which ensures the resulting subgraph no longer contains these nodes. The generated function is packaged as a cc_library, with a header file exporting the function signature, and an object file containing the implementation. The user writes code to invoke the generated function as appropriate.
到目前为止，我们已经描述了 ConvNets 的基本概念。 CNN 在沿时间维度的一维中对音频和文本数据应用卷积和池化操作，在沿（高度 x 宽度）维的图像中对二维图像应用卷积和池化操作，对于沿（高度 x 宽度 x 时间）维的视频中的三个维度应用卷积和池化操作。 对于图像，在输入体积上滑动滤镜会生成一个贴图，该贴图为每个空间位置提供滤镜的响应。
到目前为止，我们已经描述了 ConvNets 的基本概念。 CNN 在沿时间维度的一维中对音频和文本数据应用卷积和池化操作，在沿（高度 x 宽度）维的图像中对二维图像应用卷积和池化操作，对于沿（高度 x 宽度 x 时间）维的视频中的三个维度应用卷积和池化操作。 对于图像，在输入体积上滑动过滤器会生成一个贴图，该贴图为每个空间位置提供过滤器的响应。
In other words, a ConvNet has multiple filters stacked together that learn to recognize specific visual features independently of the location in the image. Those visual features are simple in the initial layers of the network and then more and more sophisticated deeper in the network.
Note that regardless of the specific choice adopted for our basic units, we need to have a 1:1 map from basic units into integer IDs so that a text can be seen as a matrix. For instance, if we have a document with 10 lines of text and each line is a 100-dimensional embedding, then we will represent our text with a matrix 10 x 100\. In this very particular *image*, a *pixel* is turned on if that sentence x contains the embedding represented by position y. You might also notice that a text is not really a matrix but more a vector because two words located in adjacent rows of text have very little in common. Indeed, there is a major difference with images where two pixels located in adjacent columns most likely have some correlation.
请注意，无论我们的基本单位采用哪种具体选择，我们都需要从基本单位到整数 ID 的 1：1 映射，以便可以将文本视为矩阵。 例如，如果我们有一个包含 10 行文本的文档，并且每行都是 100 维嵌入，那么我们将用`10 x 100`的矩阵表示文本。 在这个非常特殊的图像中，如果该句子`x`包含位置`y`表示的嵌入，则打开像素。 您可能还会注意到，文本实际上不是矩阵，而是向量，因为位于文本相邻行中的两个单词几乎没有共同点。 确实，与图像的主要区别在于，相邻列中的两个像素最有可能具有某种相关性。
Note that the model uses multiple filters (with varying window sizes) to obtain multiple features. After that, there is a max pool operation where the idea is to capture the most important feature-the one with the highest value for each feature map. For regularization, the article proposed to adopt a dropout on the penultimate layer with a constraint on L2-norms of the weight vectors. The final layer will output the sentiment as positive or negative.
Adapted from Reinforcement Learning: an Introduction by Sutton and BartoEven our brain has a group of subcortical nuclei situated at the base of the forebrain called **basal ganglia**, which, according to neuroscience, are responsible for action selection, that is, help us decide which of several possible actions to execute at any given time.
This recipe is based on the original DQN paper, Playing Atari with Deep Reinforcement Learning by DeepMind. In the paper, they used a concept called **experience replay**, which involved randomly sampling the previous game moves (state, action reward, next state).
