Softmax返回恒等矩阵

Question

我对softmax的输入，y = tf.nn.softmax(tf.matmul(x, W) + b)，是一个值矩阵

tf.matmul(x, W) + b =
[[  9.77206726e+02]
 [  5.72391296e+02]
 [  3.53560760e+02]
 [  4.75727379e-01]
 [  6.58911804e+02]]

但是，当输入到softmax中时，我得到：

tf.nn.softmax(tf.matmul(x, W) + b) =
[[ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]]

导致我的训练输出是一个1数组，这意味着没有权重W或偏差b对每一批培训数据进行更新。这也使得我对随机测试数据集的准确性达到了1。

下面是我的代码：

x = tf.placeholder(tf.float32, [None, 2])

W = tf.Variable(tf.random_normal([2, 1]))

b = tf.Variable(tf.random_normal([1]))

y = tf.nn.softmax(tf.matmul(x, W) + b)

## placeholder for cross-entropy
y_ = tf.placeholder(tf.float32, [None, 1])

## cross-entropy function
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))

## backpropagation & gradienct descent
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

## initialize variables
init = tf.initialize_all_variables()

sess = tf.Session()
sess.run(init)

ITER_RANGE = 10
EVAL_BATCH_SIZE = ( len(training_outputs)/ITER_RANGE )
training_outputs = np.reshape(training_outputs, (300, 1))
## training
for i in range(ITER_RANGE):
  print 'iterator:'
  print i

  ## batch out training data
  BEGIN = ( i*EVAL_BATCH_SIZE )
  END = ( (i*EVAL_BATCH_SIZE) + EVAL_BATCH_SIZE )

  batch_ys = training_outputs[BEGIN:END]
  batch_xs = training_inputs[BEGIN:END]

  print 'batch_xs'
  print batch_xs

  print 'batch_ys'
  print batch_ys

  sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

  # y = tf.nn.softmax(tf.matmul(x, W) + b)
  print 'y'
  print (sess.run(y, feed_dict={x: batch_xs, y_: batch_ys}))

  #print 'x'
  #print sess.run(x)

  print 'W'
  print sess.run(W)

  print 'b'
  print sess.run(b)

  print 'tf.matmul(x, W) + b'
  print sess.run(tf.matmul(x, W) + b, feed_dict={x: batch_xs, y_: batch_ys})

  print 'tf.nn.softmaxtf.matmul(x, W) + b)'
  print sess.run((tf.nn.softmax(tf.matmul(x, W) + b)), feed_dict={x: batch_xs, y_: batch_ys})

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

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

test_outputs = np.random.rand(300, 1)

## the following prints 1
print(sess.run(accuracy, feed_dict={x: test_inputs, y_: test_outputs}))

Answer 1

根据软极的定义，它将任意实数的K维向量“压缩”为(0，1)范围内等于1的实数K维向量。

如果只有一个输出值，那么Softmax输出的分类概率分布就是1，而不是加到1的值。

Answer 2

似乎您的softmax函数应用于输出向量中的每个不同的值。尝试转换您的输出，即将tf.nn.softmax(tf.matmul(x, W) + b))更改为tf.nn.softmax(tf.transpose(tf.matmul(x, W) + b)))。

Answer 3

看起来您只有两个类{yes，no}，tf.matmul(x, W) + b表示{yes}的可能性。在这种情况下，您应该使用tf.nn.sigmoid_cross_entropy_with_logits而不是softmax。类似于：

y_pred = tf.matmul(x, W) + b
loss = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(y_pred, y_))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)