5
Próbuję dokonać klasyfikacji przy użyciu sieci neuronowej Convolution, tylko 2 klasy, nie widzę obrazów wejściowych lub sieć ma jakiekolwiek problemy, ale zastanawiam się, dlaczego wynik (dokładność) zawsze zwrócić mi tę samą wartość?TensorFlow zawsze zwraca ten sam wynik
buduję mój model, odnosząc się w ten sposób: 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))
Poniżej wynik: Zawsze powrót 0,876522
Czy ktoś może mi pomóc ?? dzięki.