Question

在我的tensorflow应用程序中，所有预测都是真的。我试图让MNIST的例子适应我的问题，但是我担心使用这种技术是错误的，因为它用于多个类，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]

准确度不会发生变化，因为Y_的值始终为True，这导致数字只显示测试集中的正标签数。请让我知道任何反馈！我很感激！

Answer 1

当您在最后一个图层上使用softmax然后计算cross_entropy时，将它们组合在一起，形成数字稳定的tf.softmax_cross_entropy_with_logits。一旦您发现损失正在减少，但您的准确性不佳，那么您可以通过添加更多图层来增加网络的复杂性。

进行以下更改：

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

Answer 2

你有一个输出，我假设你假设它为0和1为假和真。如果是这种情况，其中一个问题是softmax功能。 Softmax适用于多个类，因为它的一个属性是它使所有类的总和等于1，并且更容易将结果解释为概率分布。在您的情况下，由于此属性，它最终会将您的单个输出设置为1.

有两种基本方法可以解决它。要么丢弃输出图层上的softmax并且可能使用不同的损失函数（均方可能有效）或使输出图层返回两个值，保持softmax并将得到的两个数字解释为单热编码，其中实际值是度量对网络的信心。

Tensorflow预测都是真实的

2 个答案: