基于tensorflow2对于fashion_mnist进行分类

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
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

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

fashion_mnist = keras.datasets.fashion_mnist
(x_train_all,y_train_all),(x_test,y_test) = 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:]

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

def show_single_image(img_arr):
    plt.imshow(img_arr,cmap="binary")
    plt.show()

show_single_image(x_train[0])

def show_imags(n_rows,n_cols,x_data,y_data,class_names):
   assert len(x_data)==len(y_data)
   assert n_rows*n_cols < len(x_data)
   plt.figure(figsize=(n_cols*1.4,n_rows*1.6))
   for row in range(n_rows):
       for col in range(n_cols):
           index = n_cols*row+col
           plt.subplot(n_rows,n_cols,index+1)
           plt.imshow(x_data[index],cmap = "binary",interpolation="nearest")
           plt .axis("off")#不显示座标尺寸
           plt.title(class_names[y_data[index]])
   plt.show()

class_names = ['T-shirt','Trouser','Pullover','Dress','Coat','Sandal','Shirt',
               'Sneaker','Bag','Ankle boot']
show_imags(3,5,x_train,y_train,class_names)


model = keras.models.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.compile(loss = 'sparse_categorical_crossentropy',optimizer = 'sgd',
              metrics = ['accuracy'])

model.summmary()

history = model.fit(x_train,y_train,epochs=10,validation_data=(x_valid,y_valid))

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)