1 Introduction

Building and training neural networks is a common task in the field of deep learning. The tf.keras.models.Sequential function is a simple and powerful tool provided by TensorFlow for building and training sequential models.

2. Method history

The tf.keras.models.Sequential function is a high-level API in TensorFlow based on the Keras API. Keras is an open source deep learning library that is easy to use and quickly debug models. Keras was originally developed by Google engineer François Chollet in 2015, and was integrated into TensorFlow in 2017 as part of TensorFlow.

3. Advantages of the Sequential function

Ease of use: The Sequential function allows users to build neural networks in a sequential manner without manually configuring the connections of the neural network graph.
Fast iteration: Using the Sequential function, users can quickly build and iterate different types of neural network models.
Support for common neural network layers: Sequential functions support common neural network layers, such as fully connected layers, convolutional layers, and pooling layers.

4. Differences from other methods

Compared with traditional neural network construction methods, the Sequential function has obvious advantages. Traditional methods need to manually configure the connections of the neural network graph, and the Sequential function simplifies this process by directly using the layers, making the construction of the model more intuitive and faster.

In addition, the Sequential function also provides an intuitive and easy-to-understand API interface, allowing users to quickly build models and train them. This simplified design makes Sequential functions the tool of choice for beginners and rapid prototyping.

5. Example of use

Here is an example of building a simple fully connected neural network using the tf.keras.models.Sequential function:

import tensorflow as tf
from tensorflow.keras import models, layers

# 创建Sequential模型
model = models.Sequential()

# 添加全连接层
model.add(layers.Dense(64, activation='relu', input_shape=(10,)))
model.add(layers.Dense(64, activation='relu'))

# 添加输出层
model.add(layers.Dense(1, activation='sigmoid'))

# 编译模型
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# 打印模型结构
model.summary()

In the above example, we first created a Sequential model, then added two fully connected layers and an output layer. Finally, we compilecompile the model using functions, specifying the optimizer, loss function, and evaluation metric. Through summarythe function, we can print out the structural information of the model.

6. Structural diagram

Using the Mermaid code, we can generate the following structure diagram to visualize the model structure in the example code above:

7. Theoretical derivation

For the fully connected layer added in the above example, we give the specific theoretical derivation process and calculation steps.

For each fully connected layer, first we need to initialize the weights and biases. Suppose the input feature dimension is n and the output feature dimension is m.

Initialize the weight matrix W: W is a matrix with dimensions (n, m), which can be initialized randomly, such as a normal distribution.
Initialize the bias vector b: b is a vector of dimension (m,) that can be initialized with a zero vector.
Calculate the weighted input z: z is a vector of dimension (m,), which can be obtained by calculating the product of the input feature x and the weight matrix W and adding the bias vector b.
```
z = x * W + b
```
Apply activation function a: For each element zi, the output of the neuron is obtained by activation function a(zi).
```
a = a(z)
```
The output of this layer is used as the input of the next layer, and the above steps are repeated until the last layer is reached.

The above is the theoretical derivation process and calculation steps of using the tf.keras.models.Sequential function to build a fully connected layer.

8. Summary

This article describes the history, benefits, and differences of the tf.keras.models.Sequential function from other methods. We show how to use the Sequential function and its results by giving an example and a structural diagram of using the Sequential function to build a fully connected neural network. In addition, we give the specific theoretical derivation process and calculation steps to help readers better understand the inner working principle of the Sequential function.

By using the tf.keras.models.Sequential function, we can quickly build different types of neural network models, and perform training and evaluation. It is a powerful and easy-to-use tool for beginners and rapid prototyping.

I hope this article can help readers better understand the tf.keras.models.Sequential function and play a huge role in practice. thanks for reading!

References

Official Keras documentation: https://keras.io/api/models/sequential/
TensorFlow official documentation: https://www.tensorflow.org/api_docs/python/tf/keras/models/Sequential

Introduction to tf.keras.models.Sequential function