逻辑回归 Cifar10- 使用 tensorflow 进行图像分类 1.x

Question

我正在尝试使用 Cifar10 数据集为图像分类实施简单的逻辑回归。我只被允许使用 TensorFlow 1.x 进行训练。（我可以使用 Keras 和其他库来处理数据）

我的问题是我建的模型不学习... 所有 epoch 给出的测试和训练准确度均为 0.1。

我认为在发送到模型之前处理数据本身存在一些问题，我很乐意帮助理解为什么模型没有学习。

代码：

%tensorflow_version 1.x

import tensorflow as tf
import numpy as np
import keras
import cv2 as cv2
import matplotlib.pyplot as plt
from keras.utils import to_categorical
from keras.datasets import mnist, cifar10


def get_cifar10():
    """Retrieve the CIFAR dataset and process the data."""
    # Set defaults.
    nb_classes = 10
    batch_size = 64
    input_shape = (3072,)

    # Get the data.
    (x_train, y_train), (x_test, y_test) = cifar10.load_data()
    x_train = x_train.reshape(50000, 3072)
    x_test = x_test.reshape(10000, 3072)
    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    # x_train /= 255
    # x_test /= 255

    # convert class vectors to binary class matrices
    y_train = to_categorical(y_train, nb_classes)
    y_test = to_categorical(y_test, nb_classes)

    return (nb_classes, batch_size, input_shape, x_train, x_test, y_train, y_test) 

nb_classes, batch_size, input_shape, x_train, x_test, y_train, y_test = get_cifar10()


features = 3072
categories = nb_classes

x = tf.placeholder(tf.float32, [None, features])
y_ = tf.placeholder(tf.float32, [None, categories])
W = tf.Variable(tf.zeros([features,categories]))
b = tf.Variable(tf.zeros([categories]))

y = tf.nn.softmax(tf.matmul(x, W) + b)

loss = -tf.reduce_mean(y_*tf.log(y))

update = tf.train.GradientDescentOptimizer(0.0001).minimize(loss)
correct_prediction = tf.equal(tf.argmax(y, 1),tf.argmax(y_, 1)) 
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))


sess = tf.Session()
sess.run(tf.global_variables_initializer())
for epoch in range(0,1000):
    sess.run(update, feed_dict = {x:x_train, y_:y_train}) #BGD 
    train_acc = sess.run(accuracy, feed_dict={x:x_train, y_:y_train})
    test_acc = sess.run(accuracy, feed_dict={x:x_test, y_:y_test})
    if(epoch % 10 == 0):
      print ("epoch: %3d train_acc: %f test_acc: %f" % (epoch,train_acc, test_acc))

运行模型给出以下内容：


epoch:   0 train_acc: 0.099880 test_acc: 0.099900
epoch:  10 train_acc: 0.100000 test_acc: 0.100000
epoch:  20 train_acc: 0.100000 test_acc: 0.100000
epoch:  30 train_acc: 0.100000 test_acc: 0.100000
epoch:  40 train_acc: 0.100000 test_acc: 0.100000
epoch:  50 train_acc: 0.100000 test_acc: 0.100000
epoch:  60 train_acc: 0.100000 test_acc: 0.100000
epoch:  70 train_acc: 0.100000 test_acc: 0.100000
epoch:  80 train_acc: 0.100000 test_acc: 0.100000
epoch:  90 train_acc: 0.100000 test_acc: 0.100000
epoch: 100 train_acc: 0.100000 test_acc: 0.100000
epoch: 110 train_acc: 0.100000 test_acc: 0.100000
epoch: 120 train_acc: 0.100000 test_acc: 0.100000
epoch: 130 train_acc: 0.100000 test_acc: 0.100000

提前致谢！

Answer 1

所以你遇到了三个问题

取消注释这两行：
```
# x_train /= 255
# x_test /= 255
```

您应该规范化您的输入。

损失不是对数损失的均值，而只是总和（你正在使用互斥类）
```
loss = -tf.reduce_sum(y_*tf.log(y))
```

更改优化器或学习率。我已经使用了 Adam，损失现在可以了
```
update = tf.train.AdamOptimizer(0.0001).minimize(loss)
```

The run on colab

输出：

epoch:   0 train_acc: 0.099940 test_acc: 0.099900
epoch:  10 train_acc: 0.258440 test_acc: 0.258300
epoch:  20 train_acc: 0.287600 test_acc: 0.291300
epoch:  30 train_acc: 0.306160 test_acc: 0.308000
epoch:  40 train_acc: 0.320680 test_acc: 0.321400
epoch:  50 train_acc: 0.332040 test_acc: 0.331700
epoch:  60 train_acc: 0.340040 test_acc: 0.337500
epoch:  70 train_acc: 0.345100 test_acc: 0.345100
epoch:  80 train_acc: 0.350460 test_acc: 0.348900
epoch:  90 train_acc: 0.354780 test_acc: 0.353200
epoch: 100 train_acc: 0.358020 test_acc: 0.356400
epoch: 110 train_acc: 0.361180 test_acc: 0.359400
epoch: 120 train_acc: 0.364420 test_acc: 0.361600
epoch: 130 train_acc: 0.367260 test_acc: 0.362900
epoch: 140 train_acc: 0.369220 test_acc: 0.365700
epoch: 150 train_acc: 0.371540 test_acc: 0.367900
epoch: 160 train_acc: 0.373560 test_acc: 0.368700
epoch: 170 train_acc: 0.375220 test_acc: 0.371300
epoch: 180 train_acc: 0.377040 test_acc: 0.372900
epoch: 190 train_acc: 0.378840 test_acc: 0.375000
epoch: 200 train_acc: 0.380340 test_acc: 0.377500
epoch: 210 train_acc: 0.381780 test_acc: 0.379800
epoch: 220 train_acc: 0.383640 test_acc: 0.380400
epoch: 230 train_acc: 0.385340 test_acc: 0.380600
epoch: 240 train_acc: 0.386500 test_acc: 0.381300
epoch: 250 train_acc: 0.387640 test_acc: 0.381900
...

显然，在图像上使用 LogistRegressor 并不是最好的选择。为了获得更好更快的结果，最好使用 CNN

逻辑回归 Cifar10- 使用 tensorflow 进行图像分类 1.x

Logistic Regression Cifar10- image classification using tensorflow 1.x

tensorflow

machine-learning

deep-learning

logistic-regression

tensorflow1.15