import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
%matplotlib inline
fashion_mnist = keras.datasets.fashion_mnist
(train_images,train_labels),(test_images,test_labels) = fashion_mnist.load_data()
train_images = train_images/255.0
test_images = test_images/255.0
train_images.shape
![在这里插入图片描述](https://img-blog.csdnimg.cn/20201031234221311.png#pic_center)
input = keras.Input(shape=(28, 28))
x = keras.layers.Flatten()(input)
x = keras.layers.Dense(32,activation="relu")(x)
x = keras.layers.Dropout(0.5)(x)
x = keras.layers.Dense(64,activation="relu")(x)
output = keras.layers.Dense(10,activation="softmax")(x)
model = keras.Model(inputs=input,outputs=output)
model.summary()
![在这里插入图片描述](https://img-blog.csdnimg.cn/2020103123434962.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM3OTc4ODAw,size_16,color_FFFFFF,t_70#pic_center)
model.compile(optimizer="adam",
loss="sparse_categorical_crossentropy",
metrics=["accuracy"]
)
history = model.fit(train_images,
train_labels,
epochs=30,
validation_data=(test_images,test_labels))
![在这里插入图片描述](https://img-blog.csdnimg.cn/20201031234416802.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM3OTc4ODAw,size_16,color_FFFFFF,t_70#pic_center)