python训练一个简单的感知机用于手写数据集识别

import keras
from keras import layers
import matplotlib.pyplot as plt
import joblib
import keras.datasets.mnist as mnist
import pandas as pd
import numpy as np

(train_image, train_label), (test_image, test_label) = mnist.load_data()

#建立感知机
model = keras.Sequential()
model.add(layers.Flatten())#Flatten层可以将数据展平成1维的
model.add(layers.Dense(64, activation='relu'))#全连接层
model.add(layers.Dense(10, activation='softmax'))#全连接层，0-10手写数字，所以10个输出

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['acc'])

model.fit(train_image, train_label, epochs=50, batch_size=512, validation_data=(test_image, test_label))


#np.argmax(model_mnist.predict(test_image[:10], axis=1))
y = model.predict(test_image)

print(y)