정확도가 길쌈 신경 네트워크 감소

많은 수의 훈련 샘플 (100.000)에 대해 증가하는 대신 내 길쌈 신경 네트워크의 정확도가 감소합니다. 적은 수의 훈련 견본 (6.000)의 경우 정확도는 한 지점까지 증가한 다음 사망하기 시작합니다.정확도가 길쌈 신경 네트워크 감소

예 : 내가 잘못

nr_training_examples 100000 
tb 2500 
epoch 0 loss 0.19646 acc 18.52 
nr_test_examples 5000 
Accuract test set 0.00 
nr_training_examples 100000 
tb 2500 
epoch 1 loss 0.20000 acc 0.00 
nr_test_examples 5000 
Accuract test set 0.00 
nr_training_examples 100000 
tb 2500

을 뭐하는 거지?

저는 얼굴 사진을 훈련 샘플 (70 x 70 픽셀)로 사용하고 있습니다.

2 x cov-3 
max_pooling 
2 x conv-3 
max_pooling 
2 X conv-3 
1 X conv-1 
max_pooling 
2 X conv-3 
1 X conv-1 
max_pooling 
fully_connected 1024 
fully_connected 1024 - output 128

을 그리고 여기 모델의 :

네트워크는 VGG 모델에서 영감을

def siamese_convnet(x): 
    global keep_rate 
    #reshape input 

    w_conv1_1 = tf.get_variable(name='w_conv1_1', initializer=tf.random_normal([3, 3, 1, 64])) 
    w_conv1_2 = tf.get_variable(name='w_conv1_2', initializer=tf.random_normal([3, 3, 64, 64])) 

    w_conv2_1 = tf.get_variable(name='w_conv2_1', initializer=tf.random_normal([3, 3, 64, 128])) 
    w_conv2_2 = tf.get_variable(name='w_conv2_2', initializer=tf.random_normal([3, 3, 128, 128])) 

    w_conv3_1 = tf.get_variable(name='w_conv3_1', initializer=tf.random_normal([3, 3, 128, 256])) 
    w_conv3_2 = tf.get_variable(name='w_conv3_2', initializer=tf.random_normal([3, 3, 256, 256])) 
    w_conv3_3 = tf.get_variable(name='w_conv3_3', initializer=tf.random_normal([1, 1, 256, 256])) 

    w_conv4_1 = tf.get_variable(name='w_conv4_1', initializer=tf.random_normal([3, 3, 256, 512])) 
    w_conv4_2 = tf.get_variable(name='w_conv4_2', initializer=tf.random_normal([3, 3, 512, 512])) 
    w_conv4_3 = tf.get_variable(name='w_conv4_3', initializer=tf.random_normal([1, 1, 512, 512])) 

    w_conv5_1 = tf.get_variable(name='w_conv5_1', initializer=tf.random_normal([3, 3, 512, 512])) 
    w_conv5_2 = tf.get_variable(name='w_conv5_2', initializer=tf.random_normal([3, 3, 512, 512])) 
    w_conv5_3 = tf.get_variable(name='w_conv5_3', initializer=tf.random_normal([1, 1, 512, 512])) 

    w_fc_1 = tf.get_variable(name='fc_1', initializer=tf.random_normal([2*2*512, 1024])) 
    w_fc_2 = tf.get_variable(name='fc_2', initializer=tf.random_normal([1024, 1024])) 

    fc_layer = tf.get_variable(name='fc_layer', initializer=tf.random_normal([1024, 1024])) 
    w_out = tf.get_variable(name='w_out', initializer=tf.random_normal([1024, 128])) 

    bias_conv1_1 = tf.get_variable(name='bias_conv1_1', initializer=tf.random_normal([64])) 
    bias_conv1_2 = tf.get_variable(name='bias_conv1_2', initializer=tf.random_normal([64])) 

    bias_conv2_1 = tf.get_variable(name='bias_conv2_1', initializer=tf.random_normal([128])) 
    bias_conv2_2 = tf.get_variable(name='bias_conv2_2', initializer=tf.random_normal([128])) 

    bias_conv3_1 = tf.get_variable(name='bias_conv3_1', initializer=tf.random_normal([256])) 
    bias_conv3_2 = tf.get_variable(name='bias_conv3_2', initializer=tf.random_normal([256])) 
    bias_conv3_3 = tf.get_variable(name='bias_conv3_3', initializer=tf.random_normal([256])) 

    bias_conv4_1 = tf.get_variable(name='bias_conv4_1', initializer=tf.random_normal([512])) 
    bias_conv4_2 = tf.get_variable(name='bias_conv4_2', initializer=tf.random_normal([512])) 
    bias_conv4_3 = tf.get_variable(name='bias_conv4_3', initializer=tf.random_normal([512])) 

    bias_conv5_1 = tf.get_variable(name='bias_conv5_1', initializer=tf.random_normal([512])) 
    bias_conv5_2 = tf.get_variable(name='bias_conv5_2', initializer=tf.random_normal([512])) 
    bias_conv5_3 = tf.get_variable(name='bias_conv5_3', initializer=tf.random_normal([512])) 

    bias_fc_1 = tf.get_variable(name='bias_fc_1', initializer=tf.random_normal([1024])) 
    bias_fc_2 = tf.get_variable(name='bias_fc_2', initializer=tf.random_normal([1024])) 

    bias_fc = tf.get_variable(name='bias_fc', initializer=tf.random_normal([1024])) 
    out = tf.get_variable(name='out', initializer=tf.random_normal([128])) 

    x = tf.reshape(x , [-1, 70, 70, 1]); 

    conv1_1 = tf.nn.relu(conv2d(x, w_conv1_1) + bias_conv1_1); 
    conv1_2= tf.nn.relu(conv2d(conv1_1, w_conv1_2) + bias_conv1_2); 

    max_pool1 = max_pool(conv1_2); 

    conv2_1 = tf.nn.relu(conv2d(max_pool1, w_conv2_1) + bias_conv2_1); 
    conv2_2 = tf.nn.relu(conv2d(conv2_1, w_conv2_2) + bias_conv2_2); 

    max_pool2 = max_pool(conv2_2) 

    conv3_1 = tf.nn.relu(conv2d(max_pool2, w_conv3_1) + bias_conv3_1); 
    conv3_2 = tf.nn.relu(conv2d(conv3_1, w_conv3_2) + bias_conv3_2); 
    conv3_3 = tf.nn.relu(conv2d(conv3_2, w_conv3_3) + bias_conv3_3); 

    max_pool3 = max_pool(conv3_3) 

    conv4_1 = tf.nn.relu(conv2d(max_pool3, w_conv4_1) + bias_conv4_1); 
    conv4_2 = tf.nn.relu(conv2d(conv4_1, w_conv4_2) + bias_conv4_2); 
    conv4_3 = tf.nn.relu(conv2d(conv4_2, w_conv4_3) + bias_conv4_3); 

    max_pool4 = max_pool(conv4_3) 

    conv5_1 = tf.nn.relu(conv2d(max_pool4, w_conv5_1) + bias_conv5_1); 
    conv5_2 = tf.nn.relu(conv2d(conv5_1, w_conv5_2) + bias_conv5_2); 
    conv5_3 = tf.nn.relu(conv2d(conv5_2, w_conv5_3) + bias_conv5_3); 

    max_pool5 = max_pool(conv5_3) 

    fc_helper = tf.reshape(max_pool4, [-1, 2*2*512]); 
    fc_1 = tf.nn.relu(tf.matmul(fc_helper, w_fc_1) + bias_fc_1); 
    #fc_2 = tf.nn.relu(tf.matmul(fc_1, w_fc_2) + bias_fc_1); 

    fc = tf.nn.relu(tf.matmul(fc_1, fc_layer) + bias_fc); 

    output = tf.matmul(fc, w_out) + out 

    output = tf.nn.l2_normalize(output, 0) 

    return output

출처

2017-09-10 Hello Lili