Tensorflow2.0实现断点续训
作者：互联网
参考: https://www.bilibili.com/video/BV16A41157LW?p=17
视频及课件来源 北京大学 曹建
使用的识别图片
获取训练数据集
def get_mnist_data():
# 参考: https://www.codenong.com/53310656/
# 获取数据 return (x_train, y_train), (x_test, y_test)
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
# 数据归一化 0-255之间的灰度值变成 变成0 或者 1
# 把输入特征的数值变小 更利于神经网络的吸收
x_train, x_test = (x_train > 128).astype(int), (x_test > 128).astype(int)
return (x_train, y_train), (x_test, y_test)
加载训练模型
def local_load_weights(checkpoint_save_path):
local_weights_model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
local_weights_model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
metrics=['sparse_categorical_accuracy'])
if os.path.exists(checkpoint_save_path + '.index'):
tf.print('-------------load the model-----------------')
local_weights_model.load_weights(checkpoint_save_path)
return local_weights_model
断点续练
def weights_train(local_weights_model, checkpoint_save_path):
# 回调函数 用于保存模型
cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path, # 文件存储路径
save_weights_only=True, # 是否只保留模型参数
save_best_only=True) # 是否只保留最优结果
(x_train, y_train), (x_test, y_test) = get_mnist_data()
history = local_weights_model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test),
validation_freq=1,
callbacks=[cp_callback]) # fit中假如回调函数
还有一种方法
加载模型
def local_load_model(model_path):
if os.path.exists(model_path + '/saved_model.pb'):
tf.print('-------------load the model-----------------')
local_model = tf.keras.models.load_model(model_path)
else:
local_model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(), # 拉直层 将数据拉直成1维
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
local_model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
metrics=['sparse_categorical_accuracy'])
return local_model
训练
(x_train, y_train), (x_test, y_test) = get_mnist_data()
history = local_model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test),
validation_freq=1)
整个文件如下
# -*- coding: utf-8 -*-
import tensorflow as tf
import os
import numpy as np
from PIL import Image
from matplotlib import pyplot as plt
def get_mnist_data():
# 参考: https://www.codenong.com/53310656/
# 获取数据 return (x_train, y_train), (x_test, y_test)
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
# 数据归一化 0-255之间的灰度值变成 变成0 或者 1
# 把输入特征的数值变小 更利于神经网络的吸收
x_train, x_test = (x_train > 128).astype(int), (x_test > 128).astype(int)
return (x_train, y_train), (x_test, y_test)
def get_local_image():
img = Image.open('./data/image/2.jpg') # 载入自己的图片
img = img.resize((28, 28)) # 设置图片大小
gray_img = img.convert('L')
mun_img = np.array(gray_img)
# convert.show()
mun_img = (mun_img > 64).astype(int)
x_test__reshape = mun_img.reshape(1, 28, 28)
return x_test__reshape
def local_load_weights(checkpoint_save_path):
local_weights_model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
local_weights_model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
metrics=['sparse_categorical_accuracy'])
if os.path.exists(checkpoint_save_path + '.index'):
tf.print('-------------load the model-----------------')
local_weights_model.load_weights(checkpoint_save_path)
tf.print("加载文件参数")
return local_weights_model
def local_load_model(model_path):
if os.path.exists(model_path + '/saved_model.pb'):
tf.print('-------------load the model-----------------')
local_model = tf.keras.models.load_model(model_path)
else:
local_model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(), # 拉直层 将数据拉直成1维
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
local_model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
metrics=['sparse_categorical_accuracy'])
return local_model
# 断点续练
def weights_train(local_weights_model, checkpoint_save_path):
# 回调函数 用于保存模型
cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path, # 文件存储路径
save_weights_only=True, # 是否只保留模型参数
save_best_only=True) # 是否只保留最优结果
(x_train, y_train), (x_test, y_test) = get_mnist_data()
history = local_weights_model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test),
validation_freq=1,
callbacks=[cp_callback]) # fit中假如回调函数
local_weights_model.summary()
show_train_line(history)
def show_train_line(history):
# 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()
def forecast_demo(model):
# 预测
local_image = get_local_image()
loaded_evaluate = model.predict(local_image)
print(loaded_evaluate)
prediction = np.argmax(loaded_evaluate, axis=1) # 找出最大值
print('预测结果：', prediction)
def demo_1():
checkpoint_path = "./data/model/checkpoint/mnist.ckpt"
local_weights_model = local_load_weights(checkpoint_path)
# 继续训练
weights_train(local_weights_model, checkpoint_path)
forecast_demo(local_weights_model)
def demo_2():
model_path = "./data/model/breakpoint"
local_model = local_load_model(model_path)
(x_train, y_train), (x_test, y_test) = get_mnist_data()
history = local_model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test),
validation_freq=1)
show_train_line(history)
local_model.summary()
# 保存模型
local_model.save(model_path, save_format="tf")
forecast_demo(local_model)
if __name__ == '__main__':
demo_1()
# demo_2()
标签：续训,local,Tensorflow2.0,tf,train,test,path,model,断点 来源： https://blog.csdn.net/xy3233/article/details/122784726