TensorFlow多任务多标签分类

本文是应用TensorFlow进行多任务&多标签分类的一个示例(TensorFlow Multi-Task Multi-Label Example)。

以Mnist数据集为燃料,以简单的MLP为引擎,代码走起。

本文代码见:https://github.com/xylcbd/blogs_code/blob/master/tensorflow-multitask-multilabel/main.py

任务概述

任务共有2个,分别如下;

  • 第1个任务是识别数字(即10类分类问题)
  • 第2个任务是识别属性(图片中的数字是否是奇数、图片中的数字是否大于5)

可以看到第2个任务是有多个属性,即多标签任务。

  • [0, 0]代表[不是奇数, 不大于5]
  • [1, 0]代表[是奇数, 不大于5]
  • [0, 1]代表[不是奇数, 大于5]
  • [1, 1]代表[是奇数, 大于5]

属性可以再增加,不是固定为2个属性。

模型概述

简单的解释一下。

这里模型采用的是一个3层MLP模型,前2层共用,最后一层为2个任务各自所有。

  • 对于预测数字的任务,采用的是tf.losses.softmax_cross_entropy
  • 对于预测属性的任务,采用的是tf.losses.sigmoid_cross_entropy

示例代码

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#coding: utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import time
import os
import sys
import numpy as np
######################################
#tools
class Tools(object):
@staticmethod
def currect_time():
return time.strftime("%H:%M:%S", time.localtime()) + '.%03d' % (time.time() % 1 * 1000)
@staticmethod
def log_print(content):
print("[" + Tools.currect_time() + "] " + content)
######################################
#setting
class Setting(object):
@staticmethod
def checkpoint_dir():
return "model"
######################################
#network
class Network(object):
def __init__(self):
# Network Parameters
self.num_input = 28 # MNIST data input (img shape: 28*28)
self.num_class = 10 # MNIST total class (0-9 digits)
self.num_attrs = 2 #MNIST number attrs, is_odd_number & is_larger_than_5
self.num_hidden = 128
#inputs <-> goldens
self.inputs = tf.placeholder("float", [None, self.num_input * self.num_input])
self.golden_class = tf.placeholder("float", [None, self.num_class])
self.golden_attrs = tf.placeholder("float", [None, self.num_attrs])
out = self.fc('fc1', self.inputs, self.num_input*self.num_input, self.num_hidden, True)
out = self.fc('fc2', out, self.num_hidden, self.num_hidden, True)
#last tail layer
self.logits_class = self.fc('fc_class', out, self.num_hidden, self.num_class, False)
self.logits_attrs = self.fc('fc_attrs', out, self.num_hidden, self.num_attrs, False)
def get_weight_varible(self, name, shape):
return tf.get_variable(name, shape=shape,
initializer=tf.contrib.layers.xavier_initializer())
def get_bias_varible(self, name, shape):
return tf.get_variable(name, shape=shape,
initializer=tf.contrib.layers.xavier_initializer())
def fc(self, layer_name, x, inp_dim, out_dim, with_act):
with tf.variable_scope(layer_name):
y = tf.reshape(x, shape=[-1, inp_dim])
w = self.get_weight_varible('w', [inp_dim, out_dim])
b = self.get_bias_varible('b', [out_dim])
y = tf.add(tf.matmul(y, w), b)
if with_act:
y = tf.nn.relu(y)
return y
#####################################
#main route
def save_model(saver, sess, model_path):
Tools.log_print('save model to {0}.'.format(model_path))
saver.save(sess, model_path)
def load_model(saver, sess, model_path):
Tools.log_print('try to load model from {0}.'.format(model_path))
saver.restore(sess, model_path)
Tools.log_print('load model success')
return True
def custom_lables(batch_class):
batch_attrs = []
for label in batch_class:
idx = np.argmax(label)
attrs = [float(idx % 2), float(idx > 5)]
batch_attrs.append(attrs)
return batch_class, batch_attrs
def train():
Tools.log_print('loading dataset...')
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
Tools.log_print('loade dataset success.\n')
Tools.log_print('building network...')
model_dir = 'model'
model_path = os.path.join(model_dir, 'mnist_MLP')
network = Network()
#training parameters
num_input = 28
learning_rate = 0.01
display_step = 100
train_epochs = 10
train_batchsize = 64
test_batchsize = 64
#loss of class
loss_class_op = tf.reduce_mean(tf.losses.softmax_cross_entropy(
logits= network.logits_class, onehot_labels=network.golden_class))
#loss of attrs
loss_attrs_op = tf.reduce_mean(tf.losses.sigmoid_cross_entropy(
logits= network.logits_attrs, multi_class_labels=network.golden_attrs))
#total loss
loss_op = loss_class_op + loss_attrs_op
#optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
#train operator
train_op = optimizer.minimize(loss_op)
#accuracy of class
correct_class_op = tf.equal(tf.argmax(network.logits_class, 1), tf.argmax(network.golden_class, 1))
accuracy_class_op = tf.reduce_mean(tf.cast(correct_class_op, tf.float32))
#accuracy of attrs
correct_attrs_op = tf.equal(tf.cast(tf.greater_equal(tf.sigmoid(network.logits_attrs), 0.5), tf.int32), tf.cast(network.golden_attrs, tf.int32))
accuracy_attrs_op = tf.reduce_mean(tf.reduce_min(tf.cast(correct_attrs_op, tf.float32), 1))
#initialize the variables (i.e. assign their default value)
init_op = tf.global_variables_initializer()
Tools.log_print('build network success.\n')
#start training
with tf.Session() as sess:
#run the initializer
sess.run(init_op)
#model dir
saver = tf.train.Saver(tf.global_variables())
if not os.path.exists(model_dir):
os.makedirs(model_dir)
#run epochs
Tools.log_print('start training...')
for epoch in range(1, train_epochs+1):
train_steps = len(mnist.train.labels) / train_batchsize
total_loss = 0.0
total_acc_class = 0.0
total_acc_attrs = 0.0
for step in range(1, train_steps+1):
batch_x, batch_y = mnist.train.next_batch(train_batchsize)
batch_class, batch_attrs = custom_lables(batch_y)
batch_x = batch_x.reshape((train_batchsize, num_input * num_input))
_, batch_loss, batch_acc_class, batch_acc_attrs = sess.run([train_op, loss_op, accuracy_class_op, accuracy_attrs_op], feed_dict={network.inputs: batch_x, network.golden_class: batch_class, network.golden_attrs: batch_attrs})
total_loss += batch_loss
total_acc_class += batch_acc_class
total_acc_attrs += batch_acc_attrs
if step % display_step == 0:
avg_loss = total_loss / display_step
avg_acc_class = total_acc_class / display_step
avg_acc_attrs = total_acc_attrs / display_step
Tools.log_print("Epoch[%d/%d] Step[%d/%d] Train Minibatch Loss= %.4f, Class Accuracy= %.4f, Attrs Accuracy= %.4f" % (epoch, train_epochs, step, train_steps, avg_loss, avg_acc_class, avg_acc_attrs))
total_loss = 0.0
total_acc_class = 0.0
total_acc_attrs = 0.0
Tools.log_print("finished training!")
#save model
save_model(saver, sess, model_path)
#load model and test
Tools.log_print('start testing...')
if load_model(saver, sess, model_path):
test_steps = len(mnist.test.labels) / test_batchsize
acc_class_list = []
acc_attrs_list = []
for step in range(1, test_steps+1):
batch_x, batch_y = mnist.test.next_batch(test_batchsize)
batch_x = batch_x.reshape((test_batchsize, num_input * num_input))
batch_class, batch_attrs = custom_lables(batch_y)
batch_acc_class, batch_acc_attrs = sess.run([accuracy_class_op, accuracy_attrs_op], feed_dict={network.inputs: batch_x, network.golden_class: batch_class, network.golden_attrs: batch_attrs})
acc_class_list.append(batch_acc_class)
acc_attrs_list.append(batch_acc_attrs)
acc_class = np.mean(acc_class_list)
acc_attrs = np.mean(acc_attrs_list)
Tools.log_print("Testing Class Accuracy: %.4f, Attrs Accuracy: %.4f" % (acc_class, acc_attrs))
Tools.log_print('finished testing...')
if __name__ == '__main__':
train()

模型效果

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[17:46:51.972] Epoch[10/10] Step[100/859] Train Minibatch Loss= 0.1084, Class Accuracy= 0.9791, Attrs Accuracy= 0.9848
[17:46:52.442] Epoch[10/10] Step[200/859] Train Minibatch Loss= 0.1188, Class Accuracy= 0.9802, Attrs Accuracy= 0.9852
[17:46:52.905] Epoch[10/10] Step[300/859] Train Minibatch Loss= 0.1122, Class Accuracy= 0.9797, Attrs Accuracy= 0.9859
[17:46:53.357] Epoch[10/10] Step[400/859] Train Minibatch Loss= 0.1019, Class Accuracy= 0.9814, Attrs Accuracy= 0.9875
[17:46:53.833] Epoch[10/10] Step[500/859] Train Minibatch Loss= 0.0833, Class Accuracy= 0.9841, Attrs Accuracy= 0.9886
[17:46:54.297] Epoch[10/10] Step[600/859] Train Minibatch Loss= 0.1323, Class Accuracy= 0.9745, Attrs Accuracy= 0.9828
[17:46:54.759] Epoch[10/10] Step[700/859] Train Minibatch Loss= 0.1333, Class Accuracy= 0.9752, Attrs Accuracy= 0.9808
[17:46:55.224] Epoch[10/10] Step[800/859] Train Minibatch Loss= 0.1227, Class Accuracy= 0.9767, Attrs Accuracy= 0.9838
[17:46:55.493] finished training!
[17:46:55.493] save model to model/mnist_MLP.
[17:46:55.584] start testing...
[17:46:55.584] try to load model from model/mnist_MLP.
[17:46:55.636] load model success
[17:46:56.014] Testing Class Accuracy: 0.9637, Attrs Accuracy: 0.9722
[17:46:56.014] finished testing...
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