2016-12-13 45 views
5

私は畳み込みニューラルネットワークを使って分類をしようとしていますが、クラスは2つしかなく、入力画像が見えないかネットワークに問題がありますが、なぜ結果(精度)が常に私は同じ価値を返しますか?TensorFlowは常に同じ結果を返します

私はこの参照して私のモデルを構築:以下 https://github.com/MorvanZhou/tutorials/blob/master/tensorflowTUT/tf18_CNN3/full_code.py

from __future__ import print_function 
import tensorflow as tf 
import matplotlib.image as mpimg 
import matplotlib.pyplot as plt 
def getTrainLabels(): 
    labels=[] 
    file = open('data/Class1/Class1/Train/Label/Labels.txt', 'r') 
    for line in file: 
     if len(line)<=25: 
      labels.append([0,1]) 
     else: 
      labels.append([1,0]) 
    return labels 

def getTrainImages(): 
    images = [] 
    for i in range(576,1151):#1151 
     if i<1000: 
      filename = 'data/Class1/Class1/Train/0'+str(i)+'.PNG' 
      raw_image_data = mpimg.imread(filename) 
      images.append(raw_image_data) 
     else: 
      filename = 'data/Class1/Class1/Train/'+str(i)+'.PNG' 
      raw_image_data = mpimg.imread(filename) 
      images.append(raw_image_data) 
    # step 2 
    return images 



def getTestImages(): 
    images = [] 
    for i in range(1,576): 
     if i<10: 
      filename = 'data/Class1/Class1/Test/000'+str(i)+'.PNG' 
      raw_image_data = mpimg.imread(filename) 
      images.append(raw_image_data) 
     elif i<100: 
      filename = 'data/Class1/Class1/Test/00'+str(i)+'.PNG' 
      raw_image_data = mpimg.imread(filename) 
      images.append(raw_image_data) 
     elif i<1000: 
      filename = 'data/Class1/Class1/Test/0'+str(i)+'.PNG' 
      raw_image_data = mpimg.imread(filename) 
      images.append(raw_image_data) 
     else: 
      filename = 'data/Class1/Class1/Test/'+str(i)+'.PNG' 
      raw_image_data = mpimg.imread(filename) 
      images.append(raw_image_data) 
    # step 2 
    return images 

def getTestLabels(): 
    labels=[] 
    file = open('data/Class1/Class1/Test/Label/Labels.txt', 'r') 
    for line in file: 
     if len(line)<=25: 
      labels.append([0,1]) 
     else: 
      labels.append([1,0]) 
    return labels 

def compute_accuracy(v_xs, v_ys): 
    global prediction 
    y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1}) 
    correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1)) 
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 
    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob: 1}) 
    return result 

def weight_variable(shape): 
    initial = tf.truncated_normal(shape, stddev=0.1) 
    return tf.Variable(initial) 

def bias_variable(shape): 
    initial = tf.constant(0.1, shape=shape) 
    return tf.Variable(initial) 

def conv2d(x, W): 
    # stride [1, x_movement, y_movement, 1] 
    # Must have strides[0] = strides[3] = 1 
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') #SAME or VALID 

def max_pool_2x2(x): 
    # stride [1, x_movement, y_movement, 1] 
    return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME') 

# define placeholder for inputs to network 
xs = tf.placeholder(tf.float32, [None, 512, 512]) # 512x512 
ys = tf.placeholder(tf.float32, [None,2]) 
keep_prob = tf.placeholder(tf.float32) 
x_image = tf.reshape(xs, [-1, 512, 512, 1]) 
# print(x_image.shape) # [n_samples, 512,512,1] 

## conv1 layer ## 
W_conv1 = weight_variable([5,5, 1,8]) # patch 5x5, in size 1, out size 32 
b_conv1 = bias_variable([8]) 
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # output size 512x512x32 
h_pool1 = max_pool_2x2(h_conv1)           # output size 256x256x32 

## conv2 layer ## 
W_conv2 = weight_variable([5,5, 8, 8]) # patch 5x5, in size 32, out size 64 
b_conv2 = bias_variable([8]) 
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 256x256x64 
h_pool2 = max_pool_2x2(h_conv2)           # output size 128x128x64 

## func1 layer ## 
W_fc1 = weight_variable([128*128*8, 8]) 
b_fc1 = bias_variable([8]) 
# [n_samples, 7, 7, 64] ->> [n_samples, 7*7*64] 
h_pool2_flat = tf.reshape(h_pool2, [-1, 128*128*8]) 
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) 
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) 

## func2 layer ## 
W_fc2 = weight_variable([8, 2]) # only 2 class, defect or defect-free 
b_fc2 = bias_variable([2]) 
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) 


# the error between prediction and real data 
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), 
               reduction_indices=[1]))  # loss 
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) 

sess = tf.Session() 
# important step 
sess.run(tf.initialize_all_variables()) 

batch_xs = getTrainImages() 
batch_ys = getTrainLabels() 
test_images = getTestImages() 
test_labels = getTestLabels() 
m_oH = 0 
m_oT = 5 
for i in range(1,116): 
    #batch_xs, batch_ys = mnist.train.next_batch(100) 
    sess.run(train_step, feed_dict={xs: batch_xs[m_oH:m_oT], ys: batch_ys[m_oH:m_oT],keep_prob:1}) 
    m_oH=m_oH+5 
    m_oT=m_oT+5 
    if i % 50 == 0: 
     print(compute_accuracy(
      test_images, test_labels)) 

print(compute_accuracy(test_images, test_labels)) 

は結果である:それは常に0.876522

enter image description here

を返す誰も私を助けることはできますか?ありがとう。

答えて

0

すべての入力データとラベルを正規化することをおすすめします。また、トレーニングデータとテストデータが同じスケールで正規化されていることを確認してください。

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