Advertisement
Not a member of Pastebin yet?
Sign Up,
it unlocks many cool features!
- 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)
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement