Use tf.keras.model.Sequential build a classification model

A: Use tf.keras.model.Sequential build a classification model includes seven steps:

1. Import packet module
2. Loading a data set (used here keras.datasets.fashion_mnist packet)
3. Segmentation training and validation sets
4. Data normalization
5. Build a classification model
6. Trainer
7. The model is applied to the test set

II: Import Package

There will be a one-time import machine learning to use the library frequently.

1 import numpy as np
2 import pandas as pd
3 import matplotlib as mpl
4 import matplotlib.pyplot as plt
5 import sklearn
6 import tensorflow as tf
7 import tensorflow.keras as keras

 

 

Print information about the library

1 for module in np, pd, mpl, sklearn, tf, keras:
2 　　print (module.__name__, module.__version__)

 

Three: Load data set

As used herein, the keras carrying data set for identifying the image data

1 fashion_mnist = keras.datasets.fashion_mnist
2 (x_train_all, y_train_all), (x_test, y_test) = fashion_mnist.load_data()

Before processing the data, we should look at the data in the dataset sample, we define two functions to see what the data set of pictures

1  # -defined function, see picture 
2  DEF show_single_img (img_arr):
 . 3      plt.imshow (img_arr, CMap = ' binary ' )
 . 4      plt.show ()
 . 5 show_single_img (x_train_all [0])

 

Showing results:

1  # -defined functions, display multiple rows and columns picture. N_rows x_data display before the pictures * n_cols 
2  DEF show_images (n_rows, n_cols, x_data, y_data, class_names):
 . 3      Assert len (x_data) == len (y_data) # is the number of image data to be set and the number of its category same 
. 4      Assert n_rows n_cols * <len (x_data) # amount of data to be displayed must be less than the amount of data in the data set 
. 5      plt.figure (figsize = (* n_rows for 1.5, for 1.5 n_cols * ))
 . 6      for Row in Range (n_rows):
 . 7          for COL in Range (n_cols):
 . 8              index = n_cols * Row + COL
 . 9             plt.subplot(n_rows, n_cols, index+1)
10             plt.imshow(x_data[index], cmap='binary', interpolation='nearest')
11             plt.axis('off')# 不显示坐标轴
12             plt.title(class_names[y_data[index]])
13     plt.show()
14 class_names = ['T-shirt', 'Trouser', 'Pullover', 'Dress', 'Coat ' , ' of Sandal ' , ' Shirt ' ,
 15                ' of Sandal ' , ' Shirt ' , ' the Sneaker ' , ' Bag ' , ' Ankle_boot ' ] # fashion_mnist data set of image categories 
16 show_images (. 3,. 3, x_test, android.permission.FACTOR., class_names)

 

Showing results:

Four: segmentation training and validation sets