关于慕课第四讲中Fashion的各种优化人工智能weixin43981664的博客-

代码和上一讲只是相差了保存权重和增加了预测功能
1.权重保存的路径和读取方法

checkpoint_save_path = "./checkpoint/mnist.ckpt" if os.path.exists(checkpoint_save_path+'.index'): print('---------------------load the model------------------')     model.load_weights(checkpoint_save_path)

2.callback权重填写函数

cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,                                                 save_best_only=True,                                                 save_weights_only=True)

3.训练时调用

history = model.fit(x_train,y_train,batch_size=32,epochs=10,validation_data=(x_test,y_test),validation_freq=2,callbacks=[cp_callback])

4 预测部分
①网络复现

checkpoint_save_path = "./checkpoint/mnist.ckpt" model = tf.keras.models.Sequential([     tf.keras.layers.Flatten(),     tf.keras.layers.Dense(128,activation='relu'),     tf.keras.layers.Dense(10,activation='softmax') ]) model.load_weights(checkpoint_save_path) 

#预测函数

def preidct(img_path):     img = Image.open(img_path)     img =img.resize((28,28),Image.ANTIALIAS)     img_arr = np.array(img.convert('L')) import matplotlib.pyplot as plt #    plt.imshow(img,cmap='gray') #    plt.show() for i in range(28): for j in range(28): if img_arr[i][j] < 200:                 img_arr[i][j] = 255 else:                 img_arr[i][j] = 0 #    plt.imshow(img_arr,cmap='gray') #    plt.show()     img_arr = img_arr/255.0     x_predict = img_arr[tf.newaxis,...]     result = model.predict(x_predict)     pred = tf.argmax(result,axis=1) print('n')     class_names=['T-shirt/top','Trouser','Pullover','Dress','Coat', 'Sandal','Shirt','Sneaker','Bag','Ankle boot']     pred = pred.numpy() print(class_names[pred[0]])

对于前面几张的预测效果