TensorFlow模型出现0损失值

Question

import tensorflow as tf
import numpy as np
def weight(shape):
return tf.Variable(tf.truncated_normal(shape, stddev=0.1))
def bias(shape):
return tf.Variable(tf.constant(0.1, shape=shape))
def output(input,w,b):
return tf.matmul(input,w)+b
x_columns = 33
y_columns = 1
layer1_num = 7
layer2_num = 7
epoch_num = 10
train_num = 1000
batch_size = 100
display_size = 1
x = tf.placeholder(tf.float32,[None,x_columns])
y = tf.placeholder(tf.float32,[None,y_columns])

layer1 = 
tf.nn.relu(output(x,weight([x_columns,layer1_num]),bias([layer1_num])))
layer2=tf.nn.relu
(output(layer1,weight([layer1_num,layer2_num]),bias([layer2_num])))
prediction = output(layer2,weight([layer2_num,y_columns]),bias([y_columns]))

loss=tf.reduce_mean
(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
train_step = tf.train.AdamOptimizer().minimize(loss)

sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
for epoch in range(epoch_num):
   avg_loss = 0.
   for i in range(train_num):
      index = np.random.choice(len(x_train),batch_size)
      x_train_batch = x_train[index]
      y_train_batch = y_train[index]
      _,c = sess.run([train_step,loss],feed_dict=
{x:x_train_batch,y:y_train_batch})
      avg_loss += c/train_num
   if epoch % display_size == 0:
      print("Epoch:{0},Loss:{1}".format(epoch+1,avg_loss))
print("Training Finished")

我的模型输出结果为：Epoch:2,Loss:0.0，Epoch:3,Loss:0.0，Epoch:4,Loss:0.0，Epoch:5,Loss:0.0，Epoch:6,Loss:0.0，Epoch:7,Loss:0.0，Epoch:8,Loss:0.0，Epoch:9,Loss:0.0，Epoch:10,Loss:0.0。训练已经完成。

针对这个问题，您可以参考以下解决方法：

- yoshi

Answer 1

softmax_cross_entropy_with_logits函数需要使用独热编码形式的标签，即具有形状为[batch_size, num_classes]的标签。在这里，您仅有一个类别y_columns = 1，因此无论权重如何，输出始终正确，即预测结果和“地面真实值”（从网络的角度看）相同。因此，loss=0。

我猜您可能有不同的类别，并且y_train包含标签的ID。然后，predictions应该具有形状[batch_size, num_classes]，并且您应该使用tf.nn.sparse_softmax_cross_entropy_with_logits而不是softmax_cross_entropy_with_logits函数。

- gdelab

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非常感谢！您的回答教会了我有关输入类的错误。我已经能够预测了！ - yoshi