下面就详细讲解如何实现TensorFlow中的迁移学习以及两条相关的示例。
1. 初探迁移学习
1.1 什么是迁移学习？
迁移学习（Transfer Learning）是指在训练模型时，将预训练模型的一部分参数引入到新的模型中，以加快模型的训练速度及提高模型的准确率。稳定后的模型部分或全部的函数参数作为新模型的参数来使用。
1.2 迁移学习的优势
- 可以提高模型的准确率
- 可以加快模型的训练速度
- 可以减少数据量及时间投入
1.3 迁移学习的应用
迁移学习广泛应用于计算机视觉、自然语言处理、语音识别等领域，通过对已经训练好的模型进行微调，使得模型更加适应新的任务。
2. 实现迁移学习
下面介绍两个使用TensorFlow实现迁移学习的例子。
2.1 图像识别任务
2.1.1 安装TensorFlow
使用pip工具安装TensorFlow：
pip install tensorflow
2.1.2 下载预训练模型
- 下载Inception-v3模型：http://download.tensorflow.org/models/image/imagenet/inception-v3-2016-03-01.tar.gz
- 解压到指定目录下（假设目录为./models）
2.1.3 准备数据集
准备训练集和测试集，并且根据模型的要求将图片转换为相应大小（299*299）。
2.1.4 构建网络
import tensorflow as tf
from tensorflow.python.platform import gfile
with tf.Session() as sess:
model_filename = './models/inception-v3.pb'
with gfile.FastGFile(model_filename, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sess.graph.as_default()
tf.import_graph_def(graph_def, name='')
# 获取输出层
softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
2.1.5 训练模型
# 转换数据格式，构建输入的Tensor
def read_file_list(file_list):
images = []
labels = []
for file in file_list:
image = cv2.imread(file)
image = cv2.resize(image, (299, 299))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
images.append(image)
labels.append(int(file.split('/')[-2].split('_')[1]))
return np.asarray(images, dtype=np.float32) / 255.0, np.asarray(labels, dtype=np.int32)
train_images, train_labels = read_file_list(train_list)
test_images, test_labels = read_file_list(test_list)
train_image_tensor = sess.graph.get_tensor_by_name('input:0')
train_label_tensor = sess.graph.get_tensor_by_name('output:0')
# 构造损失函数和优化器
cross_entropy = tf.losses.sparse_softmax_cross_entropy(train_label_tensor, logits_tensor)
train_step = tf.train.AdamOptimizer().minimize(cross_entropy)
# 训练模型
with tf.Session() as sess:
# 初始化全局变量
sess.run(tf.global_variables_initializer())
for i in range(steps):
_, loss = sess.run([train_step, cross_entropy], feed_dict={train_image_tensor: train_images, train_label_tensor: train_labels})
if i % 100 == 0:
print("Step: ", i, " Loss: ", loss)
accuracy = sess.run(accuracy_tensor, feed_dict={train_image_tensor: test_images, train_label_tensor: test_labels})
print("Accuracy: ", accuracy)
2.2 自然语言处理任务
2.2.1 安装TensorFlow
使用pip工具安装TensorFlow：
pip install tensorflow
2.2.2 下载预训练模型
- 下载GloVe预训练模型：http://nlp.stanford.edu/data/glove.6B.zip
- 解压到指定目录下（假设目录为./models）
2.2.3 准备数据集
准备训练集和测试集。本例中我们使用IMDB数据集。
2.2.4 构建网络
本例中我们使用LSTM来构建网络：
import tensorflow as tf
import numpy as np
from tensorflow.contrib.rnn import BasicLSTMCell
# 构建模型
class LSTM_Model(object):
def __init__(self, num_classes, num_units):
self.num_classes = num_classes
self.num_units = num_units
self.labels = tf.placeholder(tf.int32, [None])
self.inputs = tf.placeholder(tf.float32, [None, None, self.num_units])
self.seq_lens = tf.placeholder(tf.int32, [None])
self.dropout_keep_prob = tf.placeholder(tf.float32)
cell_fw = BasicLSTMCell(self.num_units)
cell_bw = BasicLSTMCell(self.num_units)
outputs, states = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, self.inputs, sequence_length=self.seq_lens, dtype=tf.float32)
output = tf.concat(outputs, 2)
output = tf.layers.dropout(output, rate=self.dropout_keep_prob)
output = tf.layers.dense(output, self.num_classes, activation=None)
self.loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.labels, logits=output))
self.train_op = tf.train.AdamOptimizer().minimize(self.loss)
self.prediction = tf.argmax(output, 1)
correct_pred = tf.equal(tf.cast(self.prediction, tf.int32), self.labels)
self.accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
def train(self, sess, x, y, seq_lens, dropout_keep_prob):
feed_dict = {self.inputs: x, self.labels: y, self.seq_lens: seq_lens, self.dropout_keep_prob:dropout_keep_prob}
_, loss, accuracy = sess.run([self.train_op, self.loss, self.accuracy], feed_dict=feed_dict)
return loss, accuracy
def predict(self, sess, x, seq_lens):
feed_dict = {self.inputs: x, self.seq_lens: seq_lens, self.dropout_keep_prob: 1.0}
pred = sess.run(self.prediction, feed_dict=feed_dict)
return pred
2.2.5 训练模型
import numpy as np
from keras.preprocessing import sequence
from keras.datasets import imdb
from keras.layers import Embedding
# 加载数据
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=10000, maxlen=100)
x_train = sequence.pad_sequences(x_train, maxlen=100, padding='post', truncating='post')
x_test = sequence.pad_sequences(x_test, maxlen=100, padding='post', truncating='post')
# 加载GloVe预训练模型
glove_file = "./models/glove.6B.100d.txt"
word2idx = imdb.get_word_index()
idx2word = {k: (v + 3) for k, v in word2idx.items()}
idx2word[0] = "<PAD/>"
idx2word[1] = "<START/>"
idx2word[2] = "<UNK/>"
idx2word[3] = "<UNUSED/>"
embedding_matrix = []
with open(glove_file, 'r') as f:
for line in f:
line = line.strip().split()
word = line[0]
if word in idx2word:
embedding = np.asarray(line[1:], dtype=np.float32)
embedding_matrix.append(embedding)
embedding_matrix = np.asarray(embedding_matrix, dtype=np.float32)
embedding_layer = Embedding(input_dim=x_train.shape[0], output_dim=100, input_length=100, weights=[embedding_matrix])
with tf.Session() as sess:
lstm_model = LSTM_Model(num_classes=2, num_units=100)
sess.run(tf.global_variables_initializer())
for i in range(steps):
idxs = np.random.permutation(np.arange(0,len(y_train)))
batch_loss = []
batch_acc = []
for j in range(0, len(y_train), batch_size):
batch_idx = idxs[j:j+batch_size]
batch_seq_lens = np.sum(np.sign(x_train[batch_idx]),axis=1)
batch_loss_j, batch_acc_j = lstm_model.train(sess, x_train[batch_idx], y_train[batch_idx], batch_seq_lens, dropout_keep_prob=0.7)
batch_loss.append(batch_loss_j)
batch_acc.append(batch_acc_j)
if i % 10 == 0:
print("Step: ", i, " Loss: ", np.mean(batch_loss), " Accuracy: ", np.mean(batch_acc))
test_seq_lens = np.sum(np.sign(x_test),axis=1)
test_accuracy = lstm_model.predict(sess, x_test, test_seq_lens)
print("Test Accuracy: ", test_accuracy)
以上两个例子分别是图像识别任务和自然语言处理任务中使用迁移学习的例子，本文档已经详细讲解完成。
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