CNN part1

cnn_conv.py

import tensorflow as tf
import matplotlib.pyplot as plt
import os
%matplotlib inline

images = "dataset/test_dataset_png/"
image_dir = os.path.join(os.getcwd(), images)
imagenames = [os.path.join(image_dir, f) for f in os.listdir(image_dir)]

label = "dataset/test_dataset_csv/label.csv"
labelname = [os.path.join(os.getcwd(), label)]

imagename_queue = tf.train.string_input_producer(imagenames)
labelname_queue = tf.train.string_input_producer(labelname)

img_reader = tf.WholeFileReader()
label_reader = tf.TextLineReader()

_, image = img_reader.read(imagename_queue)
_, label = label_reader.read(labelname_queue)

decoded_img = tf.image.decode_png(image)
reshaped_img = tf.reshape(decoded_img, shape=[61, 49, 1])
reshaped_img = tf.cast(reshaped_img, tf.float32)

decoded_label = tf.decode_csv(label, record_defaults=[[0]])

x, y_ = tf.train.batch([reshaped_img, decoded_label], 10)

conv1 = tf.layers.conv2d(x, filters=10, kernel_size=[3, 3], padding="SAME")
conv2 = tf.layers.conv2d(conv1, filters=10, kernel_size=[3, 3], padding="SAME")
# pool2 = tf.layers.max_pooling2d(conv2, pool_size=[2, 2], strides=[2, 2])

conv3 = tf.layers.conv2d(conv2, filters=10, kernel_size=[3, 3], padding="SAME")
# pool3 = tf.layers.max_pooling2d(conv3, pool_size=[2, 2], strides=[2, 2])

conv4 = tf.layers.conv2d(conv3, filters=10, kernel_size=[3, 3], padding="SAME")
# pool4 = tf.layers.max_pooling2d(conv4, pool_size=[2, 2], strides=[2, 2])

flat = tf.reshape(conv4, shape=[-1, 61*49*10])

fc1 = tf.layers.dense(flat, 5000)
fc2 = tf.layers.dense(fc1, 1000)
out = tf.layers.dense(fc2, 3)

with tf.Session() as sess:
    coord = tf.train.Coordinator()
    thread = tf.train.start_queue_runners(sess, coord)
    for i in range(100):
        age = sess.run(decoded_label)
        print(age)
    coord.request_stop()
    coord.join(thread)