Tensorflow预测总是相同的结果 [英] Tensorflow predicts always the same result

问题描述

我正在尝试获取 TensorFlow示例使用我自己的数据运行，但是以某种方式，分类器总是为每个测试示例选择相同的类.输入数据始终先洗牌.我有约4000张图像作为训练集，有500张图像作为测试集.

I'm trying to get the TensorFlow example running with my own data, but somehow the classifier always picks the same class for every test example. The input data is always shuffled prior. I have about 4000 images as a training set and 500 images as a testing set.

我得到的结果如下:

Result: [[ 1.  0.]] Actually: [ 1.  0.] 
Result: [[ 1.  0.]] Actually: [ 0.  1.] 
Result: [[ 1.  0.]] Actually: [ 1.  0.] 
Result: [[ 1.  0.]] Actually: [ 1.  0.] 
Result: [[ 1.  0.]] Actually: [ 0.  1.] 
Result: [[ 1.  0.]] Actually: [ 0.  1.]
...

所有500张图像[1. 0.]的右侧仍然保留.分类是二进制的，所以我只有两个标签.

The right side remains for all 500 images [1. 0.]. The classification is binary so I just have two labels.

这是我的源代码:

import tensorflow as tf
import input_data as id

test_images, test_labels = id.read_images_from_csv(
    "/home/johnny/Desktop/tensorflow-examples/46-model.csv")

train_images = test_images[:4000]
train_labels = test_labels[:4000]
test_images = test_images[4000:]
test_labels = test_labels[4000:]

print len(train_images)
print len(test_images)

pixels = 200 * 200
labels = 2

sess = tf.InteractiveSession()

# Create the model
x = tf.placeholder(tf.float32, [None, pixels])
W = tf.Variable(tf.zeros([pixels, labels]))
b = tf.Variable(tf.zeros([labels]))
y_prime = tf.matmul(x, W) + b
y = tf.nn.softmax(y_prime)

# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, labels])
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(y_prime, y_)
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

# Train
tf.initialize_all_variables().run()
for i in range(10):
    res = train_step.run({x: train_images, y_: train_labels})
# Test trained model
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval({x: test_images, y_: test_labels}))

for i in range(0, len(test_images)):
    res = sess.run(y, {x: [test_images[i]]})
    print("Result: " + str(res) + " Actually: " + str(test_labels[i]))

我错过了一点吗?

推荐答案

您的代码中存在三个潜在的问题:

There are three potential issues in your code:

1. 权重W被初始化为零. 来自stats.stackexchange.com 的问题很好地讨论了为什么这会导致不良的培训结果(例如陷入当地的最低要求).相反，您应该随机初始化它们，例如，如下所示:

1. The weights, W, are initialized to zero. This question from stats.stackexchange.com has a good discussion of why this can lead to poor training outcomes (such as getting stuck in a local minimum). Instead, you should initialize them randomly, for example as follows:

W = tf.Variable(tf.truncated_normal([pixels, labels],
                                    stddev=1./math.sqrt(pixels)))

在将cross_entropy最小化之前，应将其汇总为单个标量值，例如使用

The cross_entropy should be aggregated to a single, scalar value before minimizing it, using for example tf.reduce_mean():

cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(y_prime, y_))

如果您对小型批次(甚至单个示例)进行训练，而不是一次对整个数据集进行训练，则可能会更快地收敛:

You may get faster convergence if you train on mini-batches (or even single examples) rather than training on the entire dataset at once:

for i in range(10):
        for j in range(4000):
            res = train_step.run({x: train_images[j:j+1],
                                  y_: train_labels[j:j+1]})

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