### 1.Creat data ### #number 1 to 10 data train_dir = 'D:\\pythonworkspace\\TensorflowTraining\\exercises\\Shen\\Practice\\ACFdata\\IR_database\\dataset5\\IR_train7\\' validate_dir = 'D:\\pythonworkspace\\TensorflowTraining\\exercises\\Shen\\Practice\\ACFdata\\IR_database\\dataset5\\IR_validate7\\' test_dir = 'D:\\pythonworkspace\\TensorflowTraining\\exercises\\Shen\\Practice\\ACFdata\\IR_database\\dataset5\\IR_validate8\\' logs_train_dir = 'D:\\pythonworkspace\\TensorflowTraining\\exercises\\Shen\\Practice\\ACFdata\\IR_logs\\IR_TrainLogs\\dataset5_DianziZangwu\\' model_train_dir = 'D:\\pythonworkspace\\TensorflowTraining\\exercises\\Shen\\Practice\\ACFdata\\IR_Models\\dataset5_DianziZangwu\\' if DELETE == True: shutil.rmtree(logs_train_dir, ignore_errors=False, onerror=None) shutil.rmtree(model_train_dir, ignore_errors=False, onerror=None) else: pass train, train_label = input_data.get_files(train_dir) train_batch, train_label_batch = input_data.get_batch(train, train_label, IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNEL, BATCH_SIZE, shuffle=True, number_thread=1000, capacity=CAPACITY) validate, validate_label = input_data.get_files(validate_dir) VALIDATE_BATCH_SIZE = len(validate) print(VALIDATE_BATCH_SIZE)
CONV3_KENEL_NUM = 70 CONV3_KENEL_SIZE = 5 CONV4_KENEL_NUM = 2 CONV4_KENEL_SIZE = 3 ### 1.Creat data ### #number 1 to 10 data train_dir = 'D:\\pythonworkspace\\TensorflowTraining\\exercises\\Shen\\Practice\\ACFdata\\IR_database\\Test\\IR_test7\\' logs_validation_dir = 'D:\\pythonworkspace\\TensorflowTraining\\exercises\\Shen\\Practice\\ACFdata\\IR_logs\\IR_TestLogs\\IR_TAN_4CONV_GAP_2BN_dropoff_3class\\' if DELETE == True: shutil.rmtree(logs_validation_dir, ignore_errors=False, onerror=None) else: pass test, test_label = input_data.get_files(train_dir) test_batch, test_label_batch = input_data.get_batch(test, test_label, IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNEL, BATCH_SIZE, CAPACITY) trainphase = tf.placeholder(tf.bool, name='trainphase') ### 2.Define placeholder for inputs to network ### ### 3. Setup Network ### # conv1 layer ## BN0 = layer.batch_norm_layer(test_batch, trainphase, 'BN0')