当我将MNIST tensorflow代码应用于我的数据集时，为什么我会得到“nan”损失

Question

我的项目目标是检查我的数据集的神经网络权重图。

我跟着MNIST example code工作正常。

MNIST数据集具有784（28 * 28）pixel_data输入和10 class_data输出。 我的数据集有72（8 * 9）pixel_data输入和4 class_data输出。 我做了代码来处理我的数据集相同的格式作为MNIST数据集 ，但是当我训练时，损失继续给出“NAN”值。您可以检查my code and dataset in my github。

import os 
import glob 
import pandas as pd 
import numpy as np 
import tensorflow as tf 
import matplotlib.pyplot as plt 
import random as ran 


#데이터를 8x9이미지 형태로 display 
def display_digit(num): 
    print(y_data[num]) 
    label = y_data[num].argmax(axis=0) 
    image = x_data[num].reshape([8,9]) 
    plt.title('Example: %d Label: %d' % (num, label)) 
    plt.imshow(image, cmap=plt.get_cmap('gray_r')) 
    plt.show() 

#데이터를 vector형태로 dispaly 
def display_mult_flat(start, stop): 
    images = x_data[start].reshape([1,72]) 
    for i in range(start+1,stop): 
     images = np.concatenate((images, x_data[i].reshape([1,72]))) 
    plt.imshow(images, cmap=plt.get_cmap('gray_r')) 
    plt.show() 

#y_data을 oneshot방법으로 표현 
def oneshot(n): 
    if n=="1": 
     return [1,0,0,0] 
    elif n=="2": 
     return [0,1,0,0] 
    elif n=="3": 
     return [0,0,1,0] 
    elif n=="4": 
     return [0,0,0,1] 

# input, out data 반환, (MNIST에서 사용되는 형태) 
def Get_data(): 
    Glass_dir='./glass_data/' 
    csv_filenames = [i for i in glob.glob('./glass_data/*.{}'.format('csv'))] 

    y_data=[] 
    x_data=[] 
    for filename in csv_filenames: 
     y=oneshot(filename[13]) 

     csv_file = pd.read_csv(filename) 
     df = pd.DataFrame(csv_file).T 
     df.columns = df.iloc[0] 
     df = df[1:] 
     df = df.ffill() 
     for i in range(len(df.index)): 
      y_data.append(y) 
     for row in df.iterrows(): 
      index, data = row 
      x_data.append(data.tolist()) 


    combined = list(zip(x_data, y_data)) 
    ran.shuffle(combined) 
    x_data[:], y_data[:] = zip(*combined) 

    y_data=np.array(y_data) 
    x_data=np.array(x_data, dtype=np.float32) 

    return x_data, y_data 

#각 class의 5개씩을 test로 사용 
def Get_testdata(): 
    Glass_dir='./glass_data/' 
    csv_filenames = [i for i in glob.glob('./glass_data/*.{}'.format('csv'))] 

    y_test=[] 
    x_test=[] 
    for filename in csv_filenames: 
     y=oneshot(filename[13]) 

     csv_file = pd.read_csv(filename) 
     df = pd.DataFrame(csv_file).T 
     df.columns = df.iloc[0] 
     df = df[1:] 
     df = df.ffill() 
     for i in range(5): 
      y_test.append(y) 
     df2=df.head() 
     for row in df2.iterrows(): 
      index, data = row 
      x_test.append(data.tolist()) 

    y_test=np.array(y_test) 
    x_test=np.array(x_test, dtype=np.float32) 
    return x_test, y_test 


# In[3]: 


x_data, y_data = Get_data() 


# In[4]: 


x_data 


# In[5]: 


y_data 


# In[6]: 


display_digit(ran.randint(0, x_data.shape[0])) 


# In[7]: 


display_mult_flat(0,200) 


# In[8]: 


sess = tf.Session() 
x = tf.placeholder(tf.float32, shape=[None, 72]) 
y_ = tf.placeholder(tf.float32, shape=[None, 4]) 
W = tf.Variable(tf.zeros([72,4])) 
b = tf.Variable(tf.zeros([4])) 
y = tf.nn.softmax(tf.matmul(x,W) + b) 
print(y) 


# In[9]: 


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


# In[10]: 


x_train, y_train = Get_data() 
x_test, y_test= Get_testdata() 
LEARNING_RATE = 0.01 
TRAIN_STEPS = 2500 


# In[11]: 


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


# In[12]: 


training = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(cross_entropy) 
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 


# In[13]: 


for i in range(TRAIN_STEPS+1): 
    sess.run(training, feed_dict={x: x_train, y_: y_train}) 
    if i%100 == 0: 
     print('Training Step:' + str(i) + ' Accuracy = ' + str(sess.run(accuracy, feed_dict={x: x_train, y_: y_train})) + ' Loss = ' + str(sess.run(cross_entropy, {x: x_train, y_: y_train}))) 


# In[14]: 


for i in range(4): 
    plt.subplot(2, 5, i+1) 
    weight = sess.run(W)[:,i] 
    plt.title(i) 
    plt.imshow(weight.reshape([8,9]), cmap=plt.get_cmap('seismic')) 
    frame1 = plt.gca() 
    frame1.axes.get_xaxis().set_visible(False) 
    frame1.axes.get_yaxis().set_visible(False) 


# In[15]: 


plt.show() 

Answer 1

两个建议：首先将您的权重初始化为0以外的值。例如：

W = tf.Variable(tf.truncated_normal(shape=[72, 4])) 

其次，建议您使用tf.nn.softmax_cross_entropy_with_logits而不是自己计算。它更高效，更稳定。要做到这一点，摆脱你的softmax功能，改变你的成本：

y = tf.matmul(x,W) + b # your network output before softmax (aka "logits") 
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y) 
cross_entropy = tf.reduce_mean(cross_entropy)