How to train images in CNN with Tensorflow - tensorflow

Question

I am a beginner of TensorFlow, and I am trying to build to CNN model.
Here is the sample code I refer to:
https://github.com/MorvanZhou/tutorials/blob/master/tensorflowTUT/tf18_CNN3/full_code.py
Currently I am facing a problem. I don't know how to insert training data (Images) to my model.
The sample code is using :
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
While I need to use my own images as training data.
From the sample code:
batch_xs, batch_ys = mnist.train.next_batch(100)
I not really understand this, and how can I do in my code to achieve this function ? Thanks.
Below is my code:
from __future__ import print_function
import tensorflow as tf
def getTrainImages():
filenames=[]
for i in range(576,1151):
if i<1000:
filenames.append('data/Class1/Class1/Train/0'+str(i)+'.PNG')
else:
filenames.append('data/Class1/Class1/Train/'+str(i)+'.PNG')
# step 2
filename_queue = tf.train.string_input_producer(filenames)
# step 3: read, decode and resize images
reader = tf.WholeFileReader()
filename, content = reader.read(filename_queue)
image = tf.image.decode_jpeg(content, channels=1)
image = tf.cast(image, tf.float32)
resized_image = tf.image.resize_images(image, 512, 512)
# step 4: Batching
image_batch = tf.train.batch([resized_image], batch_size=100)
return image_batch
def getTrainLabels():
labels=[]
file = open('data/Class1/Class1/Train/Label/Labels.txt', 'r')
for line in file:
if len(line)<=25:
labels.append(0)
else:
labels.append(1)
return labels
def getTestImages():
filenames=[]
for i in range(1,576):
if i<10:
filenames.append('data/Class1/Class1/Test/000'+str(i)+'.PNG')
elif i<100:
filenames.append('data/Class1/Class1/Test/00'+str(i)+'.PNG')
elif i<1000:
filenames.append('data/Class1/Class1/Test/0'+str(i)+'.PNG')
else:
filenames.append('data/Class1/Class1/Test/'+str(i)+'.PNG')
# step 2
filename_queue = tf.train.string_input_producer(filenames)
# step 3: read, decode and resize images
reader = tf.WholeFileReader()
filename, content = reader.read(filename_queue)
image = tf.image.decode_jpeg(content, channels=1)
image = tf.cast(image, tf.float32)
resized_image = tf.image.resize_images(image, 512, 512)
# step 4: Batching
image_batch = tf.train.batch([resized_image], batch_size=100)
return image_batch
def getTestLabels():
labels=[]
file = open('data/Class1/Class1/Test/Label/Labels.txt', 'r')
for line in file:
if len(line)<=25:
labels.append(0)
else:
labels.append(1)
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, 262144]) # 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, 1,32]) # patch 5x5, in size 1, out size 32
b_conv1 = bias_variable([32])
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, 32, 64]) # patch 5x5, in size 32, out size 64
b_conv2 = bias_variable([64])
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*64, 256])
b_fc1 = bias_variable([256])
# [n_samples, 7, 7, 64] ->> [n_samples, 7*7*64]
h_pool2_flat = tf.reshape(h_pool2, [-1, 128*128*64])
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([256, 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()
for i in range(1000):
#batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: 0.5})
if i % 50 == 0:
print(compute_accuracy(
test_images, test_labels))
Here is error screen after I run my code.

Answer 1

image_batch = tf.train.batch([resized_image], batch_size=100)
This is the main problem. When you are inserting image into input queue, you did not specify the label together with it.
If you look at Tensorflow tutorial example, https://github.com/tensorflow/tensorflow/blob/r0.11/tensorflow/models/image/cifar10/cifar10_input.py#L126
images, label_batch = tf.train.batch(
[image, label],
batch_size=batch_size,
num_threads=num_preprocess_threads,
capacity=min_queue_examples + 3 * batch_size)
So remove getTrainLabels() function and push the label together with resize_image into queue.