tensorflow模型继续训练 fineturn实例

TensorFlow模型继续训练finetune实例

在机器学习中，模型的训练是一个持续的过程。有时候，我们需要在已经训练好的模型上继续训练，以提高模型的准确性。这个过程被称为finetune。本攻略将介绍如何在TensorFlow中进行模型finetune，并提供两个示例。

示例1：在已经训练好的模型上继续训练

以下是示例步骤：

1. 导入必要的库。

python
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

1. 准备数据。

python
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

1. 定义模型。

python
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
init = tf.global_variables_initializer()
saver = tf.train.Saver()

1. 训练模型。

python
with tf.Session() as sess:
sess.run(init)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
if i % 100 == 0:
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
saver.save(sess, "model.ckpt")

1. 加载模型并继续训练。

python
with tf.Session() as sess:
saver.restore(sess, "model.ckpt")
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
if i % 100 == 0:
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
saver.save(sess, "model.ckpt")

在这个示例中，我们演示了如何在已经训练好的模型上继续训练。

示例2：使用预训练模型进行图像分类

以下是示例步骤：

1. 导入必要的库。

python
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import urllib.request
import os
from tensorflow.contrib.slim.nets import vgg
from tensorflow.contrib import slim

1. 准备数据。

```python
def download(url):
filename = url.split("/")[-1]
if not os.path.exists(filename):
urllib.request.urlretrieve(url, filename)

def load_image(path):
img = plt.imread(path)
img = img.astype(np.float32)
return img

download("https://upload.wikimedia.org/wikipedia/commons/thumb/3/32/House_sparrow04.jpg/800px-House_sparrow04.jpg")
img = load_image("800px-House_sparrow04.jpg")
```

1. 定义模型。

```python
with tf.Graph().as_default():
input_tensor = tf.placeholder(tf.float32, [None, None, 3])
processed_image = tf.expand_dims(input_tensor, 0)
processed_image = tf.image.resize_bilinear(processed_image, [224, 224], align_corners=False)
processed_image = tf.subtract(processed_image, 0.5)
processed_image = tf.multiply(processed_image, 2.0)

   with slim.arg_scope(vgg.vgg_arg_scope()):
       logits, _ = vgg.vgg_16(processed_image, num_classes=1000, is_training=False)

   probabilities = tf.nn.softmax(logits)

   init_fn = slim.assign_from_checkpoint_fn(
       "vgg_16.ckpt",
       slim.get_model_variables("vgg_16"))

   saver = tf.train.Saver()

   with tf.Session() as sess:
       init_fn(sess)
       saver.save(sess, "model.ckpt")

```

1. 加载模型并进行finetune。

```python
with tf.Graph().as_default():
input_tensor = tf.placeholder(tf.float32, [None, None, 3])
processed_image = tf.expand_dims(input_tensor, 0)
processed_image = tf.image.resize_bilinear(processed_image, [224, 224], align_corners=False)
processed_image = tf.subtract(processed_image, 0.5)
processed_image = tf.multiply(processed_image, 2.0)

   with slim.arg_scope(vgg.vgg_arg_scope()):
       logits, _ = vgg.vgg_16(processed_image, num_classes=1000, is_training=False)

   probabilities = tf.nn.softmax(logits)

   init_fn = slim.assign_from_checkpoint_fn(
       "model.ckpt",
       slim.get_model_variables("vgg_16"))

   saver = tf.train.Saver()

   with tf.Session() as sess:
       init_fn(sess)
       # finetune
       # ...
       saver.save(sess, "model.ckpt")

```

在这个示例中，我们演示了如何使用预训练模型进行图像分类，并进行finetune。