SoftMax

import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

(train_image,train_label),(test_image,test_label) = tf.keras.datasets.fashion_mnist.load_data()
train_image = train_image/255
test_image = test_image/255

# model = tf.keras.Sequential()
# model.add(tf.keras.layers.Flatten(input_shape=(28,28)))
# model.add(tf.keras.layers.Dense(128,activation=‘relu‘))
# model.add(tf.keras.layers.Dense(10,activation=‘softmax‘))
# model.compile(optimizer=‘adam‘,loss=‘sparse_categorical_crossentropy‘,metrics=[‘acc‘])
# model.fit(train_image,train_label,epochs=5)
# model.evaluate(test_image,test_label)

# 独热编码
train_label_onehot = tf.keras.utils.to_categorical(train_label)
test_label_onehot = tf.keras.utils.to_categorical(test_label)
model = tf.keras.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28,28)))
model.add(tf.keras.layers.Dense(128,activation=‘relu‘))
model.add(tf.keras.layers.Dense(10,activation=‘softmax‘))
model.compile(optimizer=‘adam‘,loss=‘categorical_crossentropy‘,metrics=[‘acc‘])
model.fit(train_image,train_label_onehot,epochs=5)
predict = model.predict(test_image)
print(np.argmax(predict[0]))
print(test_label[0])