一、自制数据集

准备：txt和图片

需要有图片的名称信息，并且有txt文件进行汇总，下面是txt信息

制作函数

//路径
train_path = './mnist_image_label/mnist_train_jpg_60000/'
train_txt = './mnist_image_label/mnist_train_jpg_60000.txt'
x_train_savepath = './mnist_image_label/mnist_x_train.npy'
y_train_savepath = './mnist_image_label/mnist_y_train.npy'

//函数
def generateds(path, txt):
    f = open(txt, 'r')  # 以只读形式打开txt文件
    contents = f.readlines()  # 读取文件中所有行
    f.close()  # 关闭txt文件
    x, y_ = [], []  # 建立空列表
    for content in contents:  # 逐行取出
        value = content.split()  # 以空格分开，图片路径为value[0] , 标签为value[1] , 存入列表
        img_path = path + value[0]  # 拼出图片路径和文件名
        img = Image.open(img_path)  # 读入图片
        img = np.array(img.convert('L'))  # 图片变为8位宽灰度值的np.array格式
        img = img / 255.  # 数据归一化 （实现预处理）
        x.append(img)  # 归一化后的数据，贴到列表x
        y_.append(value[1])  # 标签贴到列表y_
        print('loading : ' + content)  # 打印状态提示

    x = np.array(x)  # 变为np.array格式
    y_ = np.array(y_)  # 变为np.array格式
    y_ = y_.astype(np.int64)  # 变为64位整型
    return x, y_  # 返回输入特征x，返回标签y_
//调用函数，并保存numpy格式的训练数据
    x_train, y_train = generateds(train_path, train_txt)
    x_train_save = np.reshape(x_train, (len(x_train), -1))
    np.save(x_train_savepath, x_train_save)
    np.save(y_train_savepath, y_train)

二、断点继训，存取模型

1.读取保存的模型

load_weights(路径文件名）

2.保存模型

tf.keras.callbacks.ModelCheckpoint(
filepath=路径文件名, 
save_weights_only=True/False, 
save_best_only=True/False)
history = model.fit（ callbacks=[cp_callback]

3.正确使用

# 保存训练的模型路径
checkpoint_save_path = "./checkpoint/fashion.ckpt"
if os.path.exists(checkpoint_save_path + '.index'):
    print('-------------load the model-----------------')
    # 读取文件模型名字
    model.load_weights(checkpoint_save_path)

cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,
                                                 save_weights_only=True,
                                                 save_best_only=True)
# 回调函数，将训练好的模型返回history
history = model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1,
                    callbacks=[cp_callback])

三、参数提取，把参数存入txt

提取可训练参数

model.trainable_variables 返回模型中可训练的参数

参数提取

print(model.trainable_variables)
file = open('./weights.txt', 'w')
for v in model.trainable_variables:
    file.write(str(v.name) + '\n')
    file.write(str(v.shape) + '\n')
    file.write(str(v.numpy()) + '\n')
file.close()

四、acc/loss可视化，查看效果

1.前提开启：获取history

history=model.fit(训练集数据, 训练集标签, batch_size= , epochs=, validation_split=用作测试数据的比例,validation_data=测试集, validation_freq=测试频率)

2.history参数表

训练集loss： loss

测试集loss： val_loss

训练集准确率： sparse_categorical_accuracy

测试集准确率： val_sparse_categorical_accuracy

3.代码

# 显示训练集和验证集的acc和loss曲线
acc = history.history['sparse_categorical_accuracy']
val_acc = history.history['val_sparse_categorical_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']

plt.subplot(1, 2, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.title('Training and Validation Loss')
plt.legend()
plt.show()

五、应用程序，给图识物

前向传播执行应用

predict(输入特征, batch_size=整数) 返回前向传播计算结果

代码实现

from PIL import Image
import numpy as np
import tensorflow as tf
# 训练好的模型
model_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')])
# 读取模型
model.load_weights(model_save_path)
# 测试数据
preNum = int(input("input the number of test pictures:"))
# 将输入的数据修改成我们想要的数据
for i in range(preNum):
    image_path = input("the path of test picture:")
    img = Image.open(image_path)
    img = img.resize((28, 28), Image.ANTIALIAS)
    img_arr = np.array(img.convert('L'))

    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

    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')
    tf.print(pred)