IMAGE_SIZE = 256 BATCH_SIZE = 20 ''' 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_valid, img_label = Read_Data.Read_TFRecords('Weed_InputData_Valid_120.tfre*') img_valid, img_label = Read_Data.Read_TFRecords('Weed_InputData_Valid_Augmentation*') img_p, label_p = Read_Data.Preprocess(img_valid, img_label, IMAGE_SIZE) img_valid_batch, img_label_batch = tf.train.batch([img_p, label_p], batch_size = BATCH_SIZE) keep_prob = tf.placeholder(tf.float32) #logits = train_nn.Model(img_valid_batch, keep_prob) #logits = train_nn_Training.Model(img_valid_batch, keep_prob) logits = train_nn_TrainingAugmentation.Model(img_valid_batch, keep_prob) correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(img_label_batch, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 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) # BEST Model_Saver06_Final_Augmentation saver = tf.train.import_meta_graph('./Model_Saver06_Final_Augmentation/model_save.ckpt-13.meta') saver.restore(sess, './Model_Saver06_Final_Augmentation/model_save.ckpt-13')