用Keras搭建第一个神经网络

深度学习

1 Steps

1. Load Data
2. Define Model
3. Compile Model
4. Fit Model
5. Evaluate Model
6. Tie It All Together

2 Load Data

1. Define a certain random number seed. By doing so, you can get the same result to compare it with other model.
   Method:

   import numpy as np
   np.random.seed(7)

   We use pima-indians-diabetes.csv as our dataset. To load the .csv data, we use np.loadtxt and split it into input and output section.

   3 Define Model

   Models in Keras are defined as a sequence of layers.While defining the first layer, we can define the input number with the input_dim argument.
   In this example, we will use a fully-connected network structure with three layers.
   Fully connected layers are defined using the Dense class. We can specify:
2. the number of neurons in the layer as the first argument
3. the initialization method as the second argument as init
4. specify the activation function using the activation argument.

   4 Build the Model

   define a model class. In this problem, we use a sequential model. So we use a model = keras.sequential() to instantiate the class. And then use the .add method to add layers in the model.
   We want to add a layer into our model, so we use keras.layers.Dense() to be the parameter of add. The activation function must be in the string form like activation='relu'.

5 Compile the Model

When compiling, we must specify some additional properties required when training the network.We must specify the loss function to use to evaluate a set of weights, the optimizer used to search through different weights for the network and any optional metrics we would like to collect and report during training.

6 Fit the model

use model.fit() method to define fit parameters like epochs and batch_size.

7 Evaluate the model

use model.evaluate and a set of X and Y to calculate the model performance.

8 Prediction

use model.fit and a set of X to predict the predicted Y value.

9 Related material

1. https://machinelearningmastery.com/
2. Keras中文文档
3. Keras Example
   s-team/keras/tree/master/examples)

本文由 Eric_fish 创作，采用 知识共享署名4.0 国际许可协议进行许可
本站文章除注明转载/出处外，均为本站原创或翻译，转载前请务必署名