Tensorflow预测总是相同的结果

Question

我正在尝试使用我自己的数据运行TensorFlow示例,但不知何故,分类器总是为每个测试示例选择相同的类.输入数据总是在之前进行混洗.我有大约4000张图像作为训练集,500张图像作为测试集.

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张图片的右侧仍然是[1. 0.].分类是二进制的,所以我只有两个标签.

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？

Answer 1

您的代码中存在三个潜在问题:

1. 权重,W初始化为零.来自stats.stackexchange.com的这个问题很好地讨论了为什么这会导致不良的培训结果(例如陷入局部最低限度).相反,您应该随机初始化它们,例如,如下所示:

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

2. 的cross_entropy应该聚集到单个,标量值最小化它,使用例如之前tf.reduce_mean():

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

3. 如果您使用小批量(甚至单个示例)进行训练而不是一次训练整个数据集,则可以获得更快的收敛:

   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]})