Writing 100 lines of code in 10 seconds
In the process of constructing the for breast cancer prediction, we mainly divide it into three parts: 1.
Using Python to create a neural network from scratch, and using gradient descent to train the model.
The Wisconsin Breast Cancer Data Set is used in this neural network to predict whether the tumors are benign or malignant according to nine different characteristics.
Explore the working principle of back propagation and gradient descent algorithm.
In this field, many Daniels share their professional knowledge through videos and blogs, such as Jeremy Howard of fast.
They agreed that one of the keys to in-depth learning is to write a model of in-depth learning by hand as soon as possible.
At present, there are many powerful libraries available in the field of in-depth learning, such as Tensorflow, PyTorch, Fast.
If we just use these powerful libraries directly, we may miss a lot of key things, so we need to think more about the most important part of these processes.
If we can create a neural network by coding it ourselves, we have to face some problems and obstacles in the process of creating it, and excavate the amazing knowledge hidden behind in-depth learning.
At present, there are various architectures and developments in the field of deep learning: convolutional neural network, cyclic neural network and generating antagonistic network.
Behind these different kinds of networks, there are two identical algorithms: back propagation algorithm and gradient descent algorithm.
Exploring Mysterious Functions Many things in the universe can be expressed by functions.
Essentially, a function is a mathematical structure that accepts an input and produces an output, representing causality and input-output click />When we look at the world around us, we will receive a lot of information.
We can learn a lot from these data by transforming it into data.
There are many different kinds of learning using these data.
Generally speaking, there are three most common types of in-depth learning: 1.
Supervised learning: Learning functions from a set of labeled classified training data, input and output are paired data sets.
Unsupervised learning: learning functions from data without any labels or classifications.
Reinforcement learning: Agents will act in a specific environment, and 100 lines of code the function by maximizing the rewards they receive.
Supervised Learning In this paper, we mainly focus on supervised learning.
Now, we have a data set that contains input and corresponding output.
Next, we want to understand how these inputs and outputs are linked through a mysterious function.
When the data set achieves a certain degree of complexity, it is quite difficult to find this function.
Therefore, we need to use neural networks and in-depth learning to explore this mysterious function.
These weights are actually numbers.
When we use the correct structure and parameters, we can approximate the neural network to a single one through the structure and optimization algorithm of the neural network.
General function approximatorConnect input and 100 bonus veren bahis siteleri data.
Creating a Neural Network Generally speaking, a simple neural network consists of two layers input does not count layers : 1.
Input: The input of the neural network contains our source data.
Moreover, the number of neurons matches the number of features of the source data.
There are four inputs in the figure below.
When we use the Wisconsin Breast Cancer Data Set to create a neural network, we use nine inputs.
Layer 1: Hidden layer, which contains some neurons in the Hidden Layer.
These neurons will be connected to all the units in the surrounding layer.
Layer 2: There is a unit for the output of the neural network.
In the actual process of building a neural network, we can use more layers, such as 10 or 20 layers of network.
For simplicity, here we use two layers.
Never underestimate these two layers, they can achieve many functions.
How to Learn Neural Networks The question arises: in this neural network, which part of learning will be carried out?
In the neural network, each neuron has a relevant weight and a deviation.
These weights are only random numbers initialized by the neural network at the beginning of learning.
The neural network calculates according to the input data and these weights, and propagates through the neural network until the final result is produced.
The result of these calculations is a function that maps input to output.
What we need is that these neural networks can calculate an optimal weight value.
Because the network can approximate different types of functions by calculating, combining different weights with different layers.
To facilitate reading, we need to explain the names of these variables: 1.
X represents the input layer, the data set provided to the network.
Y denotes the target output corresponding to input x, and the output obtained by a series of calculations of input through the network.
Yh y hat denotes the predictive function, i.
Therefore, Https://free-jackpot-deposit.website/100/free-slots-100-pandas.html is the ideal output, and Yh is the actual output of the neural network after receiving the input data.
W represents the weight of each layer of the network.
Then a weighted sum is made: 1.
Each unit in this layer is connected to each unit in the previous layer.
Weight values exist in each connection.
To some extent, the weight represents the strength of the connection, that is, the strength of the unit connection between different layers.
This deviation can bring more flexibility to the neural network.
B stands for unit deviation.
Now, our neural network has only two layers, but remember, a neural network can have many layers, such as 20 or even 200.
Therefore, we interesting. bonus deposit 100 poker indonesia can numbers to describe which level these variables belong to.
When we write code for neural networks, we will use vector programming, that is, using matrices to put all computations at a certain level in a single mathematical operation.
The above is about a neural network with only one layer.
Now, we consider a neural network with many layers.
Each layer performs a linear operation similar to that above.
When all the linear operations are connected together, the neural network can calculate complex functions.
Generally speaking, complex functions are often non-linear.
Moreover, if the structure of the neural network is calculated only by linear functions, it is difficult to calculate the non-linear behavior.
In order to facilitate our further exploration of activation functions, we need 100 lines of code introduce them first.
The gradient of a function at a certain point is also called the derivative of the function, which represents the rate of change of the output value of the function at that point.
When the gradient derivative is very small, that is, when the output of the function changes very flat, we call it the gradient derivative.
That is to say, knowing the change of this parameter will increase or decrease the output of the network.
Gradient disappearance is a problem we face, because if the gradient of a point changes very little or tends to zero, it is difficult to determine the output direction of the neural network at that point.
Of course, we will also article source the opposite situation.
Different activation functions have their own advantages, but they will face two major problems: gradient disappearance and gradient explosion.
Nonlinearity, the output is two extreme variables 0 and 1.
It can be applied to the problem of binary classification.
The curve changes gently, so the gradient derivative is easy to control.
The main disadvantage of the activation function is that in extreme cases, the output curve wizbet no deposit bonus codes 2019 the function becomes very flat, that is to say, the derivative rate of change of the function will become very small.
In this case, the calculation efficiency and speed of the Sigmoid activation function will be very low, or even completely inefficient.
When the Sigmoid activation function appears in the last layer of the neural network, it will be particularly useful, because the Sigmoid activation function helps to change the output to 0 or 1 i.
If the Sigmoid activation function is placed in other layers of the neural network, the gradient will disappear.
The curve of Sigmoid activation function is similar to that of Sigmoid activation function, which is a reduced version of Sigmoid activation function curve.
Tanh activation function curve is steep, so the derivative rate of change of the activation function is relatively large.
The disadvantage of Tanh activation function is similar to that of Sigmoid activation function.
If the input is greater than 0, then the output value is equal to the input value; otherwise, the output is 0.
Advantages: Lightweight the neural network, because some neurons may output 0 to prevent all neurons from being activated at the same time.
There is a problem with Relu activation function, that is, when the input is 0, the output is all 0, which will lead to a gradient of 0, which will make us ignore some useful calculations of some neurons.
The calculation of Relu activation function is simple and the cost is low.
At present, Relu activation function is the most frequently used activation function in the inner layer of neural network.
Leaky Relu activation function normalizes the input into a probability distribution.
Usually used in the output layer of multi-classification scenarios.
Here, we use the Sigmoid activation function in the output layer and the Relu activation function in the hidden layer.
Well, now that we understand the activation function, we need to name it!
A: Represents the output of the activation function.
The output of the second layer is the final output of the network.
That is to say, the neural network must continue to learn, find the correct values of W and b, in order to calculate the correct function.
Therefore, the purpose of training neural more info has become clear, that is, to find the correct wizbet no deposit bonus codes 2019 of W1, b1, W2, b2.
However, before training the neural network, we must first initialize these values, i.
After initialization, we can code the neural 100 lines of code />We use Python to construct a class to initialize these main parameters.
How will we code war?
Read on to our second part: Building a neural network with Python.
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And I was (again) surprised how fast and easy it was to build the model; it took not even half an hour and only around 100 lines of code (counting only the main code; for this post, I added comments and line breaks to make it easier to read)! That's why I wanted to share it here and spread the keras love. The code
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