Merge pull request #1 from IdlessChaye/master

new version alexnet model scripts

Author: stephen-v

.gitignore

@@ -4,3 +4,4 @@
 test/
 tmp/
 train/
+validate/

README.md

@@ -1,30 +1,65 @@
 # tensorflow_alexnet_classify
-> This repository aims to implement a alexnet with tensorflow . it gives a pretrain weight (bvlc_alexnet.npy), you can download from 
-[here](http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/).the train file contains 25000 images (cat and dog). 
+> This repository aims to implement a alexnet with tensorflow. 
+> The train file contains 25000 images (cat and dog). 
 > We built this AlexNet in Windows ,  it's very convenient for most of you to train the net.
 
 ## Requirements
 * Python 3.5
-* TensorFlow 1.0
+* TensorFlow 1.8.0
 * Numpy
 * cat and dog images [here](https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition/data)
 
 ## Usage 
-* image_generator: it can  generate imageurl  from your image file.  
+1'  Make sure that you have already changed file directory to the right format.
 
-    **example:**
-    
-    /path/to/train/image1.png 0
+**example:**
+
+
+    /path/to/train/cat/cat_1.jpg
+
+    /path/to/train/cat/cat_2.jpg
+
+    /path/to/train/dog/dog_1.jpg
 
-    /path/to/train/image2.png 1
+    /path/to/train/dog/dog_2.jpg
 
-    /path/to/train/image3.png 2
+	/path/to/test/cat/cat_1.jpg
+
+    /path/to/test/dog/dog_1.jpg
+
+
+
 
-    /path/to/train/image4.png 0
+2'  Modify parameters of the beginning of main function in the main_alexnet.py file.
+
+**example:**
+
+
+	learning_rate = 1e-3
+	num_epochs = 17  
+	train_batch_size = 1000 
+	test_batch_size = 100
+	dropout_rate = 0.5
+	num_classes = 2  
+	display_step = 2 
+	
+	filewriter_path = "./tmp/tensorboard" 
+	checkpoint_path = "./tmp/checkpoints"  
+	
+	image_format = 'jpg' 
+	file_name_of_class = ['cat',
+	                      'dog']
+	train_dataset_paths = ['G:/Lab/Data_sets/catanddog/train/cat/', 
+	                       'G:/Lab/Data_sets/catanddog/train/dog/'] 
+	test_dataset_paths = ['G:/Lab/Data_sets/catanddog/test/cat/',
+	                      'G:/Lab/Data_sets/catanddog/test/dog/'] 
+
+
 
 ## Notes:
 * The alexnet.py and datagenerator.py files have been builded, you don't have to modify it. But if you have more concise or effective codes, please do share them with us.
-* finetune.py is aimed to tune the weights and bias in the full connected layer, you must define some varibles,functions,and class numbers according to your own classification projects.  
+* The main_alexnet.py is aimed to tune the weights and bias in the alexnet.
+* This model is easily transfered into a multi-class classification model. All you need to do is modifying parameters of the beginning of main function in the main_alexnet.py file.
 
 ## Example output:
 We choosed ten pictures from the internet to validate the AlexNet, there were three being misidentified, the accuracy is about 70%, which is similar to the accuracy we tested before. But, On the whole, the AlexNet is not as good as we expected, the reason may have something to do with the datesets. If you have more than One hundred thousand dataset, the accuracy must be better than we trained.