#?The?architecture?is?inspired?by?LeNet-5?（LeCun?1998）
import?os

import?tensorflow?as?tf

#?Parameter
IMAGE_HEIGHT?=?240
IMAGE_WIDTH?=?320
BATCH_SIZE?=?5
NUM_EPOCHS?=?2
NUM_CLASS?=?5
NUM_CHANNELS?=?3
CONV1_FILTER_SIZE??=?32
CONV1_FILTER_COUNT??=?4
CONV2_FILTER_SIZE??=?16
CONV2_FILTER_COUNT??=?6
HIDDEN_layer_SIZE?=?400

def?read_images（data_dir）:
????pattern?=?os.path.join（data_dir?‘*.png‘）
????filenames?=?tf.train.match_filenames_once（pattern?name=‘list_files‘）
????
????queue?=?tf.train.string_input_producer（
????????filenames?
????????num_epochs=NUM_EPOCHS?
????????shuffle=True?
????????name=‘queue‘）
????
????reader?=?tf.WholeFileReader（）
????filename?content?=?reader.read（queue?name=‘read_image‘）
????filename?=?tf.Print（
????????filename?
????????data=[filename]
????????message=‘loading:?‘）
????filename_split?=?tf.string_split（[filename]?delimiter=‘/‘）
????label_id?=?tf.string_to_number（tf.substr（filename_split.values[1]?
????????0?1）?out_type=tf.int32）
????label?=?tf.one_hot（
????????label_id-1?
????????5?
????????on_value=1.0?
????????off_value=0.0?
????????dtype=tf.float32）

????img_tensor?=?tf.image.decode_png（
????????content?
????????dtype=tf.uint8?
????????channels=3
????????name=‘img_decode‘）

????#?Preprocess?the?image?Performs?random?transformations
????#?Random?flip
????img_tensor_flip?=?tf.image.random_flip_left_right（img_tensor）

????#?Random?brightness
????img_tensor_bri?=?tf.image.random_brightness（img_tensor_flip?
????????max_delta=0.2）

????#?Per-image?scaling
????img_tensor_std?=?tf.image.per_image_standardization（img_tensor_bri）

????min_after_dequeue?=?1000
????capacity?=?min_after_dequeue?+?3?*?BATCH_SIZE
????example_batch?label_batch?=?tf.train.shuffle_batch（
????????[img_tensor_std?label]?
????????batch_size=BATCH_SIZE
????????shapes=[（IMAGE_HEIGHT?IMAGE_WIDTH?NUM_CHANNELS）?（NUM_CLASS）]
????????capacity=capacity?
????????min_after_dequeue=min_after_dequeue
????????name=‘train_shuffle‘）

????return?example_batch?label_batch

#?‘images‘?is?a?4-D?tensor?with?the?shape:
#?[n_batch?img_height?img_width?n_channel]
def?inference（images）:
????#?Convolutional?layer?1
????with?tf.name_scope（‘conv1‘）:
????????W?=?tf.Variable（
????????????tf.truncated_normal（
????????????????shape=（
????????????????????CONV1_FILTER_SIZE?
????????????????????CONV1_FILTER_SIZE
????????????????????NUM_CHANNELS
????????????????????CONV1_FILTER_COUNT）
????????????????dtype=tf.float32
????????????????stddev=5e-2）?
????????????name=‘weights‘）
????????b?=?tf.Variable（
????????????tf.zeros（
????????????????shape=（CONV1_FILTER_COUNT）?
????????????????dtype=tf.float32）?
????????????name=‘biases‘）
????????conv?=?tf.nn.conv2d（
????????????input=images?
????????????filter=W
????????????strides=（1?1?1?1）?
????????????padding=‘SAME‘
????????????name=‘convolutional‘）
????????conv_bias?=?tf.nn.bias_add（conv?b）
????????conv_act?=?tf.nn.relu（
????????????features=conv_bias?
????????????name=‘a(chǎn)ctivation‘）
????????poo

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2017-02-25?07:21??hsr-master\
?????文件?????????569??2017-02-25?07:21??hsr-master\README.md
?????文件??????138259??2017-02-25?07:21??hsr-master\hsr-eval.png
?????文件????????6113??2017-02-25?07:21??hsr-master\hsr.py
?????文件????????2129??2017-02-25?07:21??hsr-master\train.py