1.导入所需模块：
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
from multiprocessing import pool
from operator import mod
from pickletools import optimize
2.准备数据：
num_classes = 10
input_shape = (28,28,1)
(x_train, y_train),(x_test, y_test) = keras.datasets.mnist.load_data()
x_train = x_train.astype("float32")/255
x_test = x_test.astype("float32")/255
#make sure images have shape(28,28,1)
x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
3.建立训练模型
model = keras.Sequential(
[
keras.Input(shape=input_shape),
layers.Conv2D(32,kernel_size=(3,3),activation="relu"),
layers.MaxPooling2D(pool_size=(2,2)),
layers.Conv2D(64,kernel_size=(3,3),activation="relu"),
layers.MaxPooling2D(pool_size=(2,2)),
layers.Flatten(),
layers.Dropout(0.5),
layers.Dense(num_classes,activation="softmax"),
]
)
model.summary()
4.开始训练
batch_size = 128
epochs = 15
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(x_train,y_train,batch_size=batch_size, epochs=epochs,validation_split= 0.1)
5.评价模型
score = model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:",score[1])
6.训练结果
