Python 用神经网络比较两个数

我想建立一个接受两个输入的NN模型，如果输入1<输入2，我想要输出[[1,0]]，否则我想要输出[[0,1]]

我的尝试是

import numpy as np
import random
import pickle
import bitstring
import tensorflow as tf
# this is for converting numbers to binary form
def binary(num):
    f1 = bitstring.BitArray(float=num, length=32)
    return f1.bin
def num2bin(num):
    return [int(x) for x in binary(num)[0:]]
N=60;
pos=10*np.random.rand(N)
pos1=10*np.random.rand(N)
pos1_test=10*np.random.rand(N)
pos_test=10*np.random.rand(N)

neg=-10*np.random.rand(N)
neg_test=-10*np.random.rand(N)
m=np.concatenate((pos,neg),axis=0)
b=np.concatenate((pos*0,pos1),axis=0)
m_test=np.concatenate((pos_test,neg_test),axis=0)
b_test=np.concatenate((pos*0,pos1_test),axis=0)
# this creates the training data.
def create_label_feature(m,m_test,b,b_test,test_size=0.1):
    train_x=[]
    train_y=[]
    test_x=[]
    test_y=[]
    for x in m:
        for y in b:
            train_x +=[num2bin(x)+num2bin(y)]
            if y !=0 and abs(x)<abs(y):
                train_y+=[[1,0]]
            elif y==0 and x>0:
                train_y+=[[1,0]]
            elif y==0 and x<=0:
                train_y+=[[0,1]]
            else:
                train_y+=[[0,1]]    

    for x in m_test:
        for y in b_test:
            test_x +=[num2bin(x)+num2bin(y)]
            if y !=0 and abs(x)<abs(y):
                test_y+=[[1,0]]
            elif y==0 and x>0:
                test_y+=[[1,0]]
            elif y==0 and x<=0:
                test_y+=[[0,1]]
            else:
                test_y+=[[0,1]]






    return train_x, train_y, test_x, test_y

train_x ,train_y ,test_x, test_y=create_label_feature(m,m_test,b,b_test,test_size=0.1)

将numpy导入为np
随机输入
进口泡菜
导入位字符串
导入tensorflow作为tf
#这是用于将数字转换为二进制形式
def二进制（num）：
f1=位字符串.位数组（浮点=num，长度=32）
返回f1.bin
def num2bin（num）：
返回二进制（num）[0:]
N=60；
pos=10*np.随机随机随机数（N）
位置1=10*np.rand.rand（N）
pos1_检验=10*np.rand.rand（N）
pos_检验=10*np.rand.rand（N）
负=-10*np.rand.rand（N）
负检验=-10*np.rand.rand（N）
m=np.连接（（正，负），轴=0）
b=np.连接（（位置*0，位置1），轴=0）
m_检验=np.连接（（正检验，负检验），轴=0）
b_测试=np。连接（（位置*0，位置1_测试），轴=0）
#这将创建训练数据。
def创建_标签_功能（m，m_测试，b，b_测试，测试_大小=0.1）：
列车x=[]
列车y=[]
测试_x=[]
测试_y=[]
对于x（单位：m）：
对于b中的y：
列车x+=[num2bin（x）+num2bin（y）]
如果你是我的话=0和abs（x）0：
列车y+=[[1,0]]
elif y==0和x有什么问题？问题是在第33个历元之后损失一直保持在零，我不明白为什么。准确度仅为0.881944。
n_nodes_hl1 = 1500
n_nodes_hl2= 1500
n_nodes_hl3= 1500


n_classes = 2
batch_size = 144
hm_epochs = 100

x = tf.placeholder('float',shape=[None,64])
y = tf.placeholder('float')

hidden_1_layer = {'f_fum':n_nodes_hl1,
                  'weight':tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])),
                  'bias':tf.Variable(tf.random_normal([n_nodes_hl1]))}

hidden_2_layer = {'f_fum':n_nodes_hl2,
                  'weight':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
                  'bias':tf.Variable(tf.random_normal([n_nodes_hl2]))}

hidden_3_layer = {'f_fum':n_nodes_hl3,
                  'weight':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
                  'bias':tf.Variable(tf.random_normal([n_nodes_hl3]))}



output_layer = {'f_fum':None,
                'weight':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
                'bias':tf.Variable(tf.random_normal([n_classes])),}


# Nothing changes
def neural_network_model(data):

    l1 = tf.add(tf.matmul(data,hidden_1_layer['weight']), hidden_1_layer['bias'])
    l1 = tf.nn.relu(l1)
    l2 = tf.add(tf.matmul(l1,hidden_2_layer['weight']), hidden_2_layer['bias'])
    l2 = tf.nn.relu(l2)
    l3 = tf.add(tf.matmul(l2,hidden_2_layer['weight']), hidden_2_layer['bias'])
    l3 = tf.nn.relu(l3)


    output = tf.matmul(l3,output_layer['weight']) + output_layer['bias']

    return output


saver = tf.train.Saver()
tf_log = 'tf.log'

def train_neural_network(x):
    prediction = neural_network_model(x)
    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y) )
    optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)

    with tf.Session() as sess:

        sess.run(tf.initialize_all_variables())

        for epoch in range(hm_epochs):
            epoch_loss = 0
            i=0
            while i < len(train_x):
                start = i
                end = i+batch_size
                batch_x = np.array(train_x[start:end])
                batch_y = np.array(train_y[start:end])

                _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,
                                                              y: batch_y})
                epoch_loss += c
                i+=batch_size

            print('Epoch', epoch+1, 'completed out of',hm_epochs,'loss:',epoch_loss)
        saver.save(sess, 'C:\\Users\\HP\\Documents\\Python Deep Learning Learning\\model_2.ckpt')    
        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))

        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))

        print('Accuracy:',accuracy.eval({x:test_x, y:test_y}))
#       a=num2bin(98)        
#       a=np.reshape(a,(1,64))

        for k in [1,2,3,0,15]:
                    a=num2bin(k)
                    a=np.concatenate((a,num2bin(10)),axis=0)
                    a=np.reshape(a,(1,64))

                    a=sess.run(prediction, {x:a})

                    prediction_tensor = tf.nn.softmax(a)     
                    print(sess.run(prediction_tensor))


train_neural_network(x)

    WARNING:tensorflow:From <ipython-input-43-bef948531bdc>:58: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
Epoch 1 completed out of 100 loss: 1656465.87708
Epoch 2 completed out of 100 loss: 965547.536972
Epoch 3 completed out of 100 loss: 523595.430435
Epoch 4 completed out of 100 loss: 977268.598053
Epoch 5 completed out of 100 loss: 668892.447758
Epoch 6 completed out of 100 loss: 533576.244034
Epoch 7 completed out of 100 loss: 447366.692417
Epoch 8 completed out of 100 loss: 201797.375396
Epoch 9 completed out of 100 loss: 123624.894434
Epoch 10 completed out of 100 loss: 74161.0360594
Epoch 11 completed out of 100 loss: 62320.2841816
Epoch 12 completed out of 100 loss: 82650.6948113
Epoch 13 completed out of 100 loss: 97856.969979
Epoch 14 completed out of 100 loss: 86694.9150848
Epoch 15 completed out of 100 loss: 104940.329952
Epoch 16 completed out of 100 loss: 55279.0731068
Epoch 17 completed out of 100 loss: 42239.2455358
Epoch 18 completed out of 100 loss: 26236.6220574
Epoch 19 completed out of 100 loss: 15605.1042328
Epoch 20 completed out of 100 loss: 7502.51530361
Epoch 21 completed out of 100 loss: 4281.0073503
Epoch 22 completed out of 100 loss: 2421.28072715
Epoch 23 completed out of 100 loss: 1674.25452423
Epoch 24 completed out of 100 loss: 1491.07880789
Epoch 25 completed out of 100 loss: 1245.5299934
Epoch 26 completed out of 100 loss: 932.400794029
Epoch 27 completed out of 100 loss: 182.700586021
Epoch 28 completed out of 100 loss: 246.078451872
Epoch 29 completed out of 100 loss: 295.789887428
Epoch 30 completed out of 100 loss: 144.528374732
Epoch 31 completed out of 100 loss: 95.6009635925
Epoch 32 completed out of 100 loss: 88.4947395325
Epoch 33 completed out of 100 loss: 201.378837585
Epoch 34 completed out of 100 loss: 0.0
Epoch 35 completed out of 100 loss: 0.0
Epoch 36 completed out of 100 loss: 0.0
Epoch 37 completed out of 100 loss: 0.0
Epoch 38 completed out of 100 loss: 0.0
Epoch 39 completed out of 100 loss: 0.0
Epoch 40 completed out of 100 loss: 0.0
Epoch 41 completed out of 100 loss: 0.0
Epoch 42 completed out of 100 loss: 0.0
Epoch 43 completed out of 100 loss: 0.0
Epoch 44 completed out of 100 loss: 0.0
Epoch 45 completed out of 100 loss: 0.0
Epoch 46 completed out of 100 loss: 0.0
Epoch 47 completed out of 100 loss: 0.0
Epoch 48 completed out of 100 loss: 0.0
Epoch 49 completed out of 100 loss: 0.0
Epoch 50 completed out of 100 loss: 0.0
Epoch 51 completed out of 100 loss: 0.0
Epoch 52 completed out of 100 loss: 0.0
Epoch 53 completed out of 100 loss: 0.0
Epoch 54 completed out of 100 loss: 0.0
Epoch 55 completed out of 100 loss: 0.0
Epoch 56 completed out of 100 loss: 0.0
Epoch 57 completed out of 100 loss: 0.0
Epoch 58 completed out of 100 loss: 0.0
Epoch 59 completed out of 100 loss: 0.0
Epoch 60 completed out of 100 loss: 0.0
Epoch 61 completed out of 100 loss: 0.0
Epoch 62 completed out of 100 loss: 0.0
Epoch 63 completed out of 100 loss: 0.0
Epoch 64 completed out of 100 loss: 0.0
Epoch 65 completed out of 100 loss: 0.0
Epoch 66 completed out of 100 loss: 0.0
Epoch 67 completed out of 100 loss: 0.0
Epoch 68 completed out of 100 loss: 0.0
Epoch 69 completed out of 100 loss: 0.0
Epoch 70 completed out of 100 loss: 0.0
Epoch 71 completed out of 100 loss: 0.0
Epoch 72 completed out of 100 loss: 0.0
Epoch 73 completed out of 100 loss: 0.0
Epoch 74 completed out of 100 loss: 0.0
Epoch 75 completed out of 100 loss: 0.0
Epoch 76 completed out of 100 loss: 0.0
Epoch 77 completed out of 100 loss: 0.0
Epoch 78 completed out of 100 loss: 0.0
Epoch 79 completed out of 100 loss: 0.0
Epoch 80 completed out of 100 loss: 0.0
Epoch 81 completed out of 100 loss: 0.0
Epoch 82 completed out of 100 loss: 0.0
Epoch 83 completed out of 100 loss: 0.0
Epoch 84 completed out of 100 loss: 0.0
Epoch 85 completed out of 100 loss: 0.0
Epoch 86 completed out of 100 loss: 0.0
Epoch 87 completed out of 100 loss: 0.0
Epoch 88 completed out of 100 loss: 0.0
Epoch 89 completed out of 100 loss: 0.0
Epoch 90 completed out of 100 loss: 0.0
Epoch 91 completed out of 100 loss: 0.0
Epoch 92 completed out of 100 loss: 0.0
Epoch 93 completed out of 100 loss: 0.0
Epoch 94 completed out of 100 loss: 0.0
Epoch 95 completed out of 100 loss: 0.0
Epoch 96 completed out of 100 loss: 0.0
Epoch 97 completed out of 100 loss: 0.0
Epoch 98 completed out of 100 loss: 0.0
Epoch 99 completed out of 100 loss: 0.0
Epoch 100 completed out of 100 loss: 0.0
Accuracy: 0.881944
[[ 1.  0.]]
[[ 1.  0.]]
[[ 1.  0.]]
[[ 1.  0.]]
[[ 1.  0.]]