Tensorflow预测全部出来真实

Question

在我的tensorflow应用程序中，所有的预测都是真实的。我试图将MNIST示例应用于我的问题，但是我担心从多个类使用该技术是错误的，并且我有二进制分类。

# In[1]: 


import tensorflow as tf 
import numpy 


# In[2]: 


X = tf.placeholder(tf.float32, [None, 3], "training-data") 
W1 = tf.Variable(tf.truncated_normal([3, 2]), "W") 
b1 = tf.Variable(tf.zeros([2]), "B") # number of neurons 

W2 = tf.Variable(tf.truncated_normal([2, 1]), "W1") 
b2 = tf.Variable(tf.zeros([1]), "B1") # number of neurons 


# In[9]: 


Y = tf.nn.sigmoid(tf.matmul(X, W1) + b1) 
Y1 = tf.nn.softmax(tf.matmul(Y, W2) + b2) 
Y_ = tf.placeholder(tf.float32, [None, 1], "labels") # labels 

#cross_entropy = -tf.reduce_sum(Y_ * tf.log(Y1)) # error function 
cross_entropy = tf.reduce_sum(tf.abs(Y1 - Y_)) 
is_correct = tf.equal(Y1, Y_) 
# All the predictions are coming out True ?!? 
accuracy = tf.reduce_sum(tf.cast(is_correct, tf.int32))/tf.size(is_correct) 

print("X", X) 
print("Y", Y) 
print("Y1", Y1) 
print("Y_", Y_) 
print("cross-entropy", cross_entropy) 
print("is-correct", is_correct) 
print("accuracy", accuracy) 


# In[10]: 


optimizer = tf.train.GradientDescentOptimizer(0.005) 
train_step = optimizer.minimize(cross_entropy) 


# In[11]: 


def load(filename): 
    filename_queue = tf.train.string_input_producer([filename]) 
    key, value = tf.TextLineReader(skip_header_lines=1).read(filename_queue) 
    col1, col2, col3, col4, col5 = tf.decode_csv(records = value, record_defaults=[[1.0], [1.0], [1.0], [1.0], [1.0]]) 
    batch_size=100 
    # A tensor for each column of the CSV 
    load_time, is_east, is_west, is_europe, labels = tf.train.shuffle_batch([col1, col2, col3, col4, col5], batch_size=batch_size, capacity=batch_size*50, min_after_dequeue=batch_size) 
    #features = tf.stack([load_time, is_east, is_west, is_europe], 1) 
    features = tf.stack([is_east, is_west, is_europe], 1) 
    return features, tf.reshape(labels, [-1, 1]) 


# In[12]: 


features, labels = load("/Users/andrew.ehrlich/Desktop/labelled_clicks.csv") 


# In[13]: 


# Run! 

test_features = numpy.loadtxt(open("/Users/andrew.ehrlich/Desktop/labelled_clicks_test.csv", "rb"), delimiter=",", skiprows=1, usecols = [1,2,3]) 
test_labels = numpy.loadtxt(open("/Users/andrew.ehrlich/Desktop/labelled_clicks_test.csv", "rb"), delimiter=",", skiprows=1, usecols = [4], ndmin = 2) 

summ = tf.reduce_sum(test_labels) 
size = tf.size(test_labels) 

with tf.Session() as sess: 
    file_writer = tf.summary.FileWriter('/Users/andrew.ehrlich/tf.log', sess.graph) 
    init = tf.global_variables_initializer() 

    coord = tf.train.Coordinator() 
    threads = tf.train.start_queue_runners(sess=sess, coord=coord) 

    sess.run(init) 
    for i in range(1000): 

     ran_features = sess.run(features) 
     ran_labels = sess.run(labels) 

     train_data = {X: ran_features, Y_: ran_labels} 
     sess.run(train_step, feed_dict=train_data) # I guess this updates the tensors behind train_step (W and b) 

     if (i % 100 == 0): 
      train_acc, train_ent = sess.run([accuracy, cross_entropy], feed_dict=train_data) 

      test_data = {X: test_features, Y_: test_labels} 
      test_acc, test_ent = sess.run([accuracy, cross_entropy], feed_dict=test_data) 

      size = sess.run(tf.size(ran_labels)) 
      print("batch size: %d [TRAIN - acc:%1.4f ent: %10.4f] [TEST - acc:%1.4f ent: %10.4f]" % (size, train_acc, train_ent, test_acc, test_ent)) 


# In[ ]: 

输出：

batch size: 100 [TRAIN - acc:0.4100 ent: 59.0000] [TEST - acc:0.4787 ent: 9423.0000] 
batch size: 100 [TRAIN - acc:0.5300 ent: 47.0000] [TEST - acc:0.4787 ent: 9423.0000] 
batch size: 100 [TRAIN - acc:0.5900 ent: 41.0000] [TEST - acc:0.4787 ent: 9423.0000] 
batch size: 100 [TRAIN - acc:0.4700 ent: 53.0000] [TEST - acc:0.4787 ent: 9423.0000] 
batch size: 100 [TRAIN - acc:0.5200 ent: 48.0000] [TEST - acc:0.4787 ent: 9423.0000] 
batch size: 100 [TRAIN - acc:0.6000 ent: 40.0000] [TEST - acc:0.4787 ent: 9423.0000] 
batch size: 100 [TRAIN - acc:0.5500 ent: 45.0000] [TEST - acc:0.4787 ent: 9423.0000] 
batch size: 100 [TRAIN - acc:0.6100 ent: 39.0000] [TEST - acc:0.4787 ent: 9423.0000] 
batch size: 100 [TRAIN - acc:0.4100 ent: 59.0000] [TEST - acc:0.4787 ent: 9423.0000] 
batch size: 100 [TRAIN - acc:0.5300 ent: 47.0000] [TEST - acc:0.4787 ent: 9423.0000] 

精度不改变，因为Y_的值始终都是真的，这导致了许多，只是表示在测试组正标签的数量。请让我知道任何反馈！我很感激！

Answer 1

当您在最后一层使用softmax，然后计算cross_entropy时，将它们组合到一个数字稳定的tf.softmax_cross_entropy_with_logits。一旦你看到损失正在减少，但你的准确性不好，那么你可以通过增加更多的层来增加网络的复杂性。

做以下修改：

Y1 = (tf.matmul(Y, W2) + b2) 
cross_entropy = tf.reduce_mean(tf.softmax_cross_entropy_with_logits(logits=Y1, labels=Y_)) 

Answer 2

你有一个单一的输出，我假设你希望它是0和1的虚假和真实。如果是这样的话，其中一个问题就是softmax函数。 Softmax适用于多个类别，因为它的一个属性是它使所有类别的总和等于1，并且将结果解释为概率分布变得更容易。在你的情况下，它只是因为这个属性而将你的单个输出设置为1。

有两种基本的解决方法。或者在你的输出层上使用softmax，或者使用不同的损失函数（均方可能工作），或者让输出层返回两个值，保持softmax并将结果两个数字解释为一个热点编码，其中实际值是一个度量网络的信心。