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chapter_programming_model/Neural_Network_Programming.md

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+# Neural Network Programming
+
+To implement an AI model, a machine learning framework often takes a
+neural-network-centric programming interface. Regardless of their
+structures, neural networks are comprised of three elements: (1) Nodes
+serve as computational units that carry out the processing of a neural
+network, (2) Node Weights are variables updated by gradients during the
+training process, and (3) Node Connections specify how data (for
+example, activation and gradients) are transmitted within a neural
+network.
+
+## Neural Network Layers
+
+In order to simplify the construction of a neural network, many machine
+learning frameworks utilize a layer-oriented approach. This method
+organizes nodes, their weights, and their connections into cohesive
+neural network layers.
+
+To illustrate this, we can examine the use of fully connected layers, a
+type of neural network layer. A distinguishing characteristic of fully
+connected layers is that every node in one layer is linked to every node
+in the succeeding layer. This method facilitates an extensive linear
+transformation of the feature space. By doing so, data can be transposed
+from a high-dimensional space to a lower-dimensional one, and
+conversely.
+
+As shown in Figure :numref:`ch03/fc_layer_1`, the fully connected process transforms
+*n* data points from the input into an *m* sized feature space. This is
+followed by a further transformation into a *p* sized feature space.
+It's important to highlight that the quantity of parameters in a fully
+connected layer grows substantially --- from n$\times$m during the
+initial transformation to m$\times$p in the subsequent one.
+
+<figure id="fig:ch03/fc_layer_1">
+<img src="../img/ch03/fc_layer_1.png" style="width:60.0%" />
+<figcaption>Fully connected layer illustration</figcaption>
+</figure>
+
+Several types of neural network layers are widely used in various
+applications, including fully connected, convolution, pooling,
+recurrent, attention, batch normalization, and dropout layers. When
+dealing with problems related to time series association in sequential
+data, recurrent neural layers are commonly employed. However, recurrent
+neural layers encounter difficulties with vanishing or exploding
+gradients as the sequence length increases during the training process.
+The Long Short-Term Memory (LSTM) model was developed as a solution to
+this problem, enabling the capturing of long-term dependencies in
+sequential data. Code `ch02/code2.3.1` shows some examples of NN Layers in Pytorch:
+
+**ch02/code2.3.1**
+```python
+fc_layer = nn.Linear(16, 5) # A fully connected layer with 16 input features and 5 output features
+    relu_layer = nn.ReLU() # A ReLU activation layer
+    conv_layer = nn.Conv2d(3, 16, 3, padding=1) # A convolutional layer with 3 input channels, 16 output channels, and a 3x3 kernel
+    dropout_layer = nn.Dropout(0.2) # A dropout layer with 20% dropout rate
+    batch_norm_layer = nn.BatchNorm2d(16) # A batch normalization layer with 16 channels
+    layers = nn.Sequential(conv_layer, batch_norm_layer, relu_layer, fc_layer, dropout_layer) # A sequential container to combine layers
+```
+
+In tasks related to natural language processing, the
+Sequence-to-Sequence (Seq2Seq) architecture applies recurrent neural
+layers in an encoder-decoder framework. Often, the decoder component of
+Seq2Seq integrates the attention mechanism, allowing the model to
+concentrate on pertinent segments of the input sequence. This
+amalgamation contributed to the inception of the *Transformer* model, a
+pivotal element in the architecture of the Bidirectional Encoder
+Representations from Transformers (BERT) and Generative Pre-trained
+Transformers (GPT) models. Both BERT and GPT have propelled significant
+progress in diverse language-related tasks.
+
+## Neural Network Implementation
+
+With an increase in the number of network layers, the manual management
+of training variables becomes progressively complex. Thankfully, most
+machine learning frameworks provide user-friendly APIs that encapsulate
+neural network layers into a base class, which is then inherited by all
+other layers. Notable examples include `mindspore.nn.Cell` in MindSpore
+and `torch.nn.Module` in PyTorch. Code
+`ch02/code2.3.2` gives a MLP Implementation using Pytorch.
+
+**ch02/code2.3.2**
+```python
+class MLP(nn.Module):
+    def __init__(self, input_size, hidden_size, num_classes, dropout_rate=0.5):
+        super(MLP, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.bn1 = nn.BatchNorm1d(hidden_size)
+        self.relu = nn.ReLU()
+        self.dropout = nn.Dropout(dropout_rate)
+        self.fc2 = nn.Linear(hidden_size, num_classes)
+    
+    def forward(self, x):
+        out = self.fc1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+        out = self.dropout(out)
+        out = self.fc2(out)
+        return out
+```
+
+Figure :numref:`ch03/model_build` demonstrates the intricate process of
+constructing a neural network. The base class plays a pivotal role in
+initializing training parameters, managing their status, and outlining
+the computation process. Conversely, the neural network model implements
+functions to administer the network layers and their associated
+parameters. Both MindSpore's Cell and PyTorch's Module efficiently serve
+these functions. Notably, Cell and Module function not just as model
+abstraction methods but also as base classes for all networks.
+
+Existing model abstraction strategies can be divided into two
+categories. The first involves the abstraction of two methods: Layer
+(which oversees parameter construction and forward computation for an
+individual neural network layer) and Model (which manages the
+connection, combination of neural network layers, and administration of
+layer parameters). The second category combines Layer and Model into a
+single method, representing both an individual neural network layer and
+a model composed of multiple layers. Cell and Module implementations
+fall into this second category.
+
+<figure id="fig:ch03/model_build">
+<embed src="../img/ch03/model_build.pdf" style="width:90.0%" />
+<figcaption>Comprehensive neural network construction
+process</figcaption>
+</figure>
+
+Figure :numref:`ch03/cell_abstract` portrays a universal method for
+designing the abstraction of a neural network layer. The constructor
+uses the `OrderedDict` class from the Python `collections` module to
+store initialized neural network layers and their corresponding
+parameters. This results in an ordered output, which is more compatible
+with stacked deep learning models compared to an unordered `Dict`. The
+management of neural network layers and parameters is conducted within
+the `__setattr__` method. Upon detecting that an attribute pertains to a
+neural network layer or represents a layer parameter, `__setattr__`
+records the attribute appropriately.
+
+In the neural network model, the computation process is vital. This
+process is defined by reloading the `__call__` method during the
+implementation of neural network layers. To acquire the training
+parameters, the base class traverses all network layers. All retrieved
+training parameters are then conveyed to the optimizer through the
+assigned interface that returns such parameters. This text, however,
+only touches on a few significant methods.
+
+Concerning custom methods, it is often required to implement techniques
+for inserting/deleting parameters, adding/removing neural network
+layers, and retrieving neural network model information.
+
+<figure id="fig:ch03/cell_abstract">
+<img src="../img/ch03/cell_abstract.png" style="width:90.0%" />
+<figcaption>Abstraction technique of neural network base
+classes</figcaption>
+</figure>
+
+In order to preserve simplicity, we provide a condensed overview of the
+base class implementation for neural network interface layers. In
+practical applications, users are typically unable to directly reload
+the `__call__` method responsible for computation. Instead, an operation
+method is usually defined outside of `__call__`, which users can invoke
+to utilize `__call__`.