當我將MNIST tensorflow代碼應用於我的數據集時，爲什麼我會得到「nan」損失

Question

我的項目目標是檢查我的數據集的神經網絡權重圖。

我跟着MNIST example code工作正常。

MNIST數據集具有784（28 * 28）pixel_data輸入和10 class_data輸出。 我的數據集有72（8 * 9）pixel_data輸入和4 class_data輸出。 我做了代碼來處理我的數據集相同的格式作爲MNIST數據集 ，但是當我訓練時，損失繼續給出「NAN」值。您可以檢查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

兩個建議：首先將您的權重初始化爲0以外的值。例如：

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

其次，建議您使用tf.nn.softmax_cross_entropy_with_logits而不是自己計算。它更高效，更穩定。要做到這一點，擺脫你的softmax功能，改變你的成本：

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)