Question

我是tensorflow和python的新手。我通过添加一个具有50个单位的隐藏层来修改样本张量流代码，但是准确度结果变得错误并且无论模型进行多少次训练都没有改变。我发现代码没有任何问题。数据集是MNIST：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data", one_hot = True)

batch_size = 100
n_batch = mnist.train.num_examples // batch_size

x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])


W = tf.Variable(tf.zeros([784, 50]))
b = tf.Variable(tf.zeros([50]))

Wx_plus_b_L1 = tf.matmul(x,W) + b
L1 = tf.nn.relu(Wx_plus_b_L1)

W_2 = tf.Variable(tf.zeros([50, 10]))
b_2 = tf.Variable(tf.zeros([10]))

prediction = tf.nn.softmax(tf.matmul(L1, W_2) + b_2)


loss = tf.reduce_mean(tf.square(y - prediction))
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)


correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(prediction,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

init = tf.global_variables_initializer()


with tf.Session() as sess:
   sess.run(init)
   for epoch in range(21):
    for batch in range(n_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        sess.run(train_step, feed_dict={x:batch_xs, y:batch_ys})
    acc = sess.run(accuracy, feed_dict = {x:mnist.test.images, y:mnist.test.labels})
    print("Iter:" + str(epoch) + ", Testing Accuray:" + str(acc))

输出始终具有相同的精度： Iter:0, Testing Accuray:0.1135 2018-05-31 18:05:21.039188: W tensorflow/core/framework/allocator.cc:101] Allocation of 31360000 exceeds 10% of system memory. Iter:1, Testing Accuray:0.1135 2018-05-31 18:05:22.551525: W tensorflow/core/framework/allocator.cc:101] Allocation of 31360000 exceeds 10% of system memory. Iter:2, Testing Accuray:0.1135 2018-05-31 18:05:24.070686: W tensorflow/core/framework/allocator.cc:101] Allocation of 31360000 exceeds 10% of system memory. 这段代码有什么问题？谢谢~~

Answer 1

我认为这与图表有关。准确性永远不会更新，因为您调用的唯一操作会更新将此代码更改为

with tf.Session() as sess:
   sess.run(init)
   for epoch in range(21):
    for batch in range(n_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        sess.run([train_step,accuracy], feed_dict={x:batch_xs, y:batch_ys})
    acc = sess.run(accuracy, feed_dict = {x:mnist.test.images, y:mnist.test.labels})
    print("Iter:" + str(epoch) + ", Testing Accuray:" + str(acc))

Answer 2

原因是我初始化所有权重并偏向零。如果是这样，那么神经元的所有输出都是相同的。同一层内所有神经元的反向传播行为是相同的 - 相同的梯度，重量更新是相同的。这显然是一个不可接受的结果。

Answer 3

我在Titanic数据集上遇到了同样的问题。帮助了学习率的改变：

optimize = tf.train.AdamOptimizer(learning_rate=0.000001).minimize(mean_loss)

当我从0.001更改时，精度最终开始变化。在此之前，我尝试使用层数，批处理大小，隐藏层大小，但没有任何帮助。

为什么在张量流代码中不改变训练模型的准确性？

3 个答案: