01-Tensorflow2.0 training and compilation of Fashion-Mnist data set

The main steps of Tensorflow2.0 training and compilation of Fashion-Mnist dataset

1. tf.keras.datasets import fashion_mnist data set
2. tf.keras.Sequential() build model
3. model.compile() model compilation
4. model.fit model training, get history
5. Draw a history curve graph
6. model.evaluate() Test the model on the test set

#-*- coding:utf-8 _*-
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf
from tensorflow import keras
# 输出库的名字和版本
print(sys.version_info)
for module in tf, mpl, np, pd, sklearn, tf, keras:
    print(module.__name__, module.__version__)

# 指定GPU
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = '1'
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.compat.v1.Session(config=config)

# 导入数据集 fashion_mnist
fashion_mnist = keras.datasets.fashion_mnist
(x_train_all,y_train_all),(x_test_all,y_test_all) = fashion_mnist.load_data()
x_valid , x_train = x_train_all[:5000],x_train_all[5000:]
y_valid , y_train = y_train_all[:5000],y_train_all[5000:]
x_test , y_test = x_test_all,y_test_all

print(x_train.shape,y_train.shape)
print(x_valid.shape,y_valid.shape)
print(x_test.shape,y_test.shape)

# 显示一张图片
def show_single_image(img_arr):
    plt.imshow(img_arr,cmap='binary')
    plt.show()
show_single_image(x_train[0])

# 利用tf.keras.Sequential()创建
# model = tf.keras.Sequential()
# model.add(keras.layers.Flatten(input_shape=[28,28]))
# model.add(keras.layers.Dense(300,activation='relu'))
# model.add(keras.layers.Dense(100,activation='relu'))
# model.add(keras.layers.Dense(10,activation='softmax'))
model = tf.keras.Sequential([
    keras.layers.Flatten(input_shape=[28,28]),
    keras.layers.Dense(300,activation='relu'),
    keras.layers.Dense(100,activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])

# 模型编译
model.compile(loss='sparse_categorical_crossentropy',
               optimizer='sgd',
               metrics=['accuracy'])

# 输出模型的一些参数
model.layers
model.summary() #模型参数打印

# 模型训练 history.history是一个重要的参数
history = model.fit(x_train,y_train,
                    epochs=10,
                    validation_data=(x_valid,y_valid))

# 绘制history图像
def plot_learning_curves(history):
    pd.DataFrame(history.history).plot(figsize=(8,5))
    plt.grid(True)
    plt.gca().set_ylim(0,1)
    plt.show()
plot_learning_curves(history)
# 测试集上进行测试
model.evaluate(x_test_scaled,y_test)

• The final classification accuracy image is as follows:
• We can see that the classification accuracy is very low and the effect is very bad. How to improve it will be mentioned in the following article