為什么張量流代碼中的訓練模型的准確性沒有改變？

Question

我是tensorflow和python的新手。 我通過添加一個包含50個單位的隱藏層來修改了一個示例tensorflow代碼，但是精度結果變成錯誤的，並且無論模型進行多少次訓練，它都沒有改變。 我找不到任何代碼問題。 數據集是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))

輸出始終是相同的精度： 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. 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. 這段代碼有什么問題？ 謝謝~~

Answer 1

我認為這與圖表有關。 准確性永遠不會更新，因為您正在調用的唯一操作已被更新，請將此代碼更改為

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

原因是我初始化了所有權重並將零偏。 如果這樣，神經元的所有輸出將是相同的。 同一層內所有神經元的反向傳播行為都相同-相同的梯度，權重更新相同，這顯然是不可接受的結果。

Answer 3

我在Titanic數據集上遇到了同樣的問題。 幫助了學習率的改變：

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

當我從0.001更改時，精度最終開始變化。 在此之前，我嘗試使用層數 ， 批處理大小 ， 隱藏層大小，但沒有任何幫助。