我正在用张量流写一个简单的逻辑回归。我发现当使用tf.nn.softmax时,算法收敛得更快,最终精度更高。 如果切换到我自己的softmax实现,网络收敛速度较慢,结束准确性不是很好。
以下是代码:
SEED = 1025
W = tf.Variable(tf.truncated_normal([image_size * image_size, num_labels], seed=SEED))
b = tf.Variable(tf.zeros([num_labels]))
logits = tf.matmul(train_dataset, W) + b
# My softmax:
y_ = tf.exp(logits) / tf.reduce_sum(tf.exp(logits), axis=0)
# Tensorflow softmax:
y_ = tf.nn.softmax(logits)
y_clipped = tf.clip_by_value(y_, 1e-10, 0.9999999)
loss = -tf.reduce_mean(tf.reduce_sum(train_labels * tf.log(y_clipped), axis=1))
使用我的softmax:
Loss at step 0: 22.213934
Training accuracy: 12.7%
Validation accuracy: 13.2%
Loss at step 100: 12.777291
Training accuracy: 45.3%
Validation accuracy: 45.5%
Loss at step 200: 11.361242
Training accuracy: 48.2%
Validation accuracy: 47.4%
Loss at step 300: 10.658278
Training accuracy: 51.4%
Validation accuracy: 49.7%
Loss at step 400: 9.297832
Training accuracy: 59.2%
Validation accuracy: 56.8%
Loss at step 500: 8.902699
Training accuracy: 62.0%
Validation accuracy: 59.2%
Loss at step 600: 8.681184
Training accuracy: 64.2%
Validation accuracy: 61.0%
Loss at step 700: 8.529438
Training accuracy: 65.8%
Validation accuracy: 62.3%
Loss at step 800: 8.416442
Training accuracy: 66.8%
Validation accuracy: 63.3%
Test accuracy: 70.4%
使用tensorflow的softmax:
Loss at step 0: 13.555875
Training accuracy: 12.7%
Validation accuracy: 14.5%
Loss at step 100: 2.194562
Training accuracy: 72.5%
Validation accuracy: 72.0%
Loss at step 200: 1.808641
Training accuracy: 75.5%
Validation accuracy: 74.5%
Loss at step 300: 1.593390
Training accuracy: 76.8%
Validation accuracy: 75.0%
Loss at step 400: 1.442661
Training accuracy: 77.7%
Validation accuracy: 75.2%
Loss at step 500: 1.327751
Training accuracy: 78.2%
Validation accuracy: 75.4%
Loss at step 600: 1.236314
Training accuracy: 78.5%
Validation accuracy: 75.6%
Loss at step 700: 1.161479
Training accuracy: 78.9%
Validation accuracy: 75.6%
Loss at step 800: 1.098717
Training accuracy: 79.4%
Validation accuracy: 75.8%
Test accuracy: 83.3%
从documentation开始,理论上张量流量的softmax应该和我实现的完全相同,不是吗?
此功能执行等效的
softmax = tf.exp(logits)/ tf.reduce_sum(tf.exp(logits),axis)
编辑:我在从正态分布初始化时添加了种子,现在我可以重现准确性结果。 在"我的softmax"中设置轴值时线,只有轴= 0不会导致错误。设置轴= 1或轴= -1都会导致此错误:
tensorflow.python.framework.errors_impl.InvalidArgumentError: Dimensions must be equal, but are 10 and 10000 for 'truediv' (op: 'RealDiv') with input shapes: [10000,10], [10000].
答案 0 :(得分:2)
W = tf.Variable(tf.truncated_normal([image_size * image_size, num_labels]))
在你的程序中引入了随机性,因为权重是随机设置的,因此每次运行程序时都会得到不同的结果tf.truncated_normal
函数确实采用种子参数...您可以使用该参数并查看结果是什么答案 1 :(得分:1)
您正在将axis=0
传递给"您的" SOFTMAX。虽然我不知道您的数据看起来如何,但0通常是批处理轴,沿此轴执行softmax是不正确的。请参阅tf.nn.softmax
的文档:axis
的默认值为-1。通常,axis
应该是包含不同类的维度。
答案 2 :(得分:0)
总而言之,以下实现有效。您可以通过MNIST beginner example运行此命令,并获得相同的准确性。
# My softmax:
y1 = tf.exp(logits)
y_ = y1 / tf.reduce_sum(y1, keepdims=True, axis=1)