import tensorflow as tf
import train_nn
import train_nn_TrainingAugmentation
import Read_Data
IMAGE_SIZE = 256
BATCH_SIZE = 6
with open('classes.txt') as f:
con = f.read()
class_ = con.splitlines()
label = {}
for i in range(len(class_)):
a, b = class_[i].split('#')
label[int(a)] = b
print(label)
img_test, img_name = Read_Data.Read_Test_TFRecords(
'Weed_InputData_Final_Test*', IMAGE_SIZE)
img_test_batch, img_name_batch = tf.train.batch([img_test, img_name],
batch_size=BATCH_SIZE)
keep_prob = tf.placeholder(tf.float32)
#logits = train_nn_TrainingAugmentation.Model(img_test_batch, keep_prob)
logits = train_nn.Model(img_test_batch, keep_prob)
pred = tf.argmax(tf.nn.softmax(logits), 1)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(
[tf.global_variables_initializer(),
tf.local_variables_initializer()])
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# results04
saver = tf.train.import_meta_graph(
