Sunday, July 1, 2018

Simple Binary Image Classification with Keras

This article is a simple introduction to simple binary classification for images with Keras deep learning library.

There are many ways to do image classification with Keras. Here are the detail of this particular implementation:
Dogs vs Cats classification problem

Prepare Working Directories

First step is to prepare working directory.

Binary classification directory structure

This is the directory structure used in this article.

It's better to use a structured working directory, don't just mix all files in the same directory. You may modify the directory structure to suit your needs.

flow_from_director() expects each class to have its own directory. The directory names must match class names.

Download Dataset

  • Download dataset from Dataset: Dogs vs Cats Redux: Kernels Edition 
  • Put cat images in <root>/data/train/cat
  • Put dog images in <root>/data/train/dog
  • Put test images in <root>/data/test 
Now  we can jump straight into the code. First step is to import libraries.

 import tensorflow as tf  
 import keras as keras  
 import os  
 from keras.layers import Flatten, Dense, AveragePooling2D, GlobalAveragePooling2D  
 from keras.models import Model  
 from keras.optimizers import RMSprop, SGD  
 from keras.callbacks import ModelCheckpoint  
 from keras.callbacks import EarlyStopping  
 from keras.preprocessing.image import ImageDataGenerator  
 from keras.callbacks import CSVLogger  
 from keras.layers.normalization import BatchNormalization  
 import numpy as np  
 from keras.models import load_model  
 import numpy as np  
 from pathlib import Path  
 import os  
 import shutil  

The next step is to define parameters for our deep learning model.
 # preparing parameters     
 image_dir_cat='../data/train/cat' # assuming cat & dog images has been separated in different directories  
 session = "simple1000" # to differentiate between runs  
 ClassNames = ['cat', 'dog']  
 data_dir="../simple1000" # to differentiate between runs  
 learning_rate = 0.0001  
 img_width = 331 # 331 for pre-trained nasnet  
 img_height = 331  
 nbr_epochs = 10   
 batch_size = 4 # batch size depends on available memory on GPU. GTX 1080 Ti use (4)  
 train_dir = data_dir + "/train"  
 valid_dir = data_dir + "/valid"    
 print("train directory : ", train_dir)  
 print("valid directory : ", valid_dir)    
 print("number of classes: "+ str(number_of_class))  
 logfile = session + '-train' + '.log'  
 print("logfile  :", logfile)  
  • image_dir_cat & image_dir_dog must match the directory where we put our training dataset.
  • session string is useful if we want to make several different run. There will be many weights files, prediction files. If we don't stick to a naming structure, the whole thing can become a jumbled mess
The next step is to prepare files for training step. We have 12500 images of cats and 12500 images  of dogs in the dataset, but in this experiment, we only use 1000 images of cats and 1000 of dogs , to speed up the experiment. We can easily add more files later.

The following code prepares files for the training. For training we use 800 cat images and 800 dog images, while for validation we use 200 cat images and 200 dog images.
 # make training directory  
 # make validation directory  
 # copy images to respective directories  
 print("copy start")      
 def MakeDir(newdir):  
   if not os.path.exists(newdir):  
     # make validation & training directories, if not exist yet      
 # copy files to working directories  
 print("copy cats")  
 for root, dirs, files in os.walk(image_dir_cat):  
   for file in files:  
     fullfilename = os.path.join(root, file)  
 #    print(str(counter) + ": " + fullfilename)  
     if(counter>=800 and counter<1000):  
 print("copy dogs")        
 for root, dirs, files in os.walk(image_dir_dog):  
   for file in files:  
     fullfilename = os.path.join(root, file)  
 #    print(str(counter) + ": " + fullfilename)  
     if(counter>=800 and counter<1000):  
 print("copy finished")    

Building Model

 # make model with transfer learning  
   model_notop = keras.applications.nasnet.NASNetLarge(input_shape=(img_width, img_height, 3),  
                                  weights='imagenet', input_tensor=None,  
     # add a global spatial average pooling layer  
   x = model_notop.output  
   x = GlobalAveragePooling2D()(x)      
   x = Dense(1024, activation='relu')(x) # let's add a fully-connected layer      
   x = BatchNormalization()(x)  
   predictions = Dense(1, activation='sigmoid')(x)  
   deep_model = Model(model_notop.input, predictions)  
  • For the first layers, we use model & weight from NASNet, without its fully connected layer. 
  • We replace the NASNet final layer with our own, with 1024 hidden neurons (Dense) and 1 in output layer.
  • Since this is a binary classification, the final layer activation is sigmoid, and only consist of 1 cell.
  • Batch Normalization is added to reduce overfitting
  • The number of hidden layer (1024) is arbitrary, it can be increased or decreased later to find better result.

Train The Model

 # training  
   sgd_optimizer = SGD(lr=learning_rate, momentum=0.9, decay=0.0, nesterov=True)  
   deep_model.compile(loss='binary_crossentropy', optimizer=sgd_optimizer, metrics=['accuracy'])  
   # set up callbacks  
   csv_logger = CSVLogger(logfile, append=True)  
   early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=2, verbose=1, mode='auto')  
   # best_model_file = session + '-weights' + '.h5'  
   best_model = ModelCheckpoint(best_model_file, monitor='val_acc', verbose=1, save_best_only=True)  
   # this is the augmentation configuration we will use for training  
   train_datagen = ImageDataGenerator(  
     rescale=1. / 255,  
   val_datagen = ImageDataGenerator(rescale=1. / 255)  
   print('prepare train generator')  
   train_generator = train_datagen.flow_from_directory(  
     target_size=(img_width, img_height),  
   print('prepare validation generator')  
   validation_generator = val_datagen.flow_from_directory(  
     target_size=(img_width, img_height),  
   print('fit generator')  
     #    steps_per_epoch=nbr_train_samples/batch_size, # in Keras 2.2.0, automatically acquired from train generator  
     #    validation_steps=nbr_validation_samples/batch_size, # automatically acquired from validation generator  
     callbacks=[best_model, csv_logger, early_stopping])  

training progress
 prepare train generator  
 Found 1600 images belonging to 2 classes.  
 prepare validation generator  
 Found 400 images belonging to 2 classes.  
 fit generator  
 Epoch 1/10  
 400/400 [==============================] - 279s 697ms/step - loss: 0.3509 - acc: 0.8500 - val_loss: 0.1920 - val_acc: 0.9525  
 Epoch 00001: val_acc improved from -inf to 0.95250, saving model to simple1000-weights.01-0.19.h5  
 Epoch 2/10  
 400/400 [==============================] - 230s 574ms/step - loss: 0.3015 - acc: 0.8769 - val_loss: 0.1307 - val_acc: 0.9725  
 Epoch 00002: val_acc improved from 0.95250 to 0.97250, saving model to simple1000-weights.02-0.13.h5  
 Epoch 3/10  
 400/400 [==============================] - 231s 578ms/step - loss: 0.2886 - acc: 0.8869 - val_loss: 0.1337 - val_acc: 0.9675  
 Epoch 00003: val_acc did not improve from 0.97250  
 Epoch 4/10  
 400/400 [==============================] - 233s 581ms/step - loss: 0.3108 - acc: 0.8744 - val_loss: 0.1299 - val_acc: 0.9750  
 Epoch 00004: val_acc improved from 0.97250 to 0.97500, saving model to simple1000-weights.04-0.13.h5  
 Epoch 5/10  
 400/400 [==============================] - 232s 580ms/step - loss: 0.2880 - acc: 0.8863 - val_loss: 0.1093 - val_acc: 0.9775  
 Epoch 00005: val_acc improved from 0.97500 to 0.97750, saving model to simple1000-weights.05-0.11.h5  
 Epoch 6/10  
 400/400 [==============================] - 231s 576ms/step - loss: 0.2284 - acc: 0.9113 - val_loss: 0.0928 - val_acc: 0.9775  
 Epoch 00006: val_acc did not improve from 0.97750  
 Epoch 7/10  
 400/400 [==============================] - 230s 575ms/step - loss: 0.2560 - acc: 0.8969 - val_loss: 0.0935 - val_acc: 0.9825  
 Epoch 00007: val_acc improved from 0.97750 to 0.98250, saving model to simple1000-weights.07-0.09.h5  
 Epoch 8/10  
 400/400 [==============================] - 231s 577ms/step - loss: 0.2461 - acc: 0.9019 - val_loss: 0.0821 - val_acc: 0.9775  
 Epoch 00008: val_acc did not improve from 0.98250  
 Epoch 9/10  
 400/400 [==============================] - 231s 578ms/step - loss: 0.2606 - acc: 0.8981 - val_loss: 0.0722 - val_acc: 0.9825  
 Epoch 00009: val_acc did not improve from 0.98250  
 Epoch 10/10  
 400/400 [==============================] - 231s 578ms/step - loss: 0.2267 - acc: 0.9113 - val_loss: 0.1130 - val_acc: 0.9775  
 Epoch 00010: val_acc did not improve from 0.98250  

Prediction & Submit

Prediction step
 #choose weights file manually  
 weights_path = 'simple1000-weights.07-0.09.h5'  
 test_data_dir = '../data/test/'  
 test_datagen = ImageDataGenerator(rescale=1./255)  
 test_generator = test_datagen.flow_from_directory(  
     target_size=(img_width, img_height),  
     shuffle = False, # no shuffling, since filenames must match predictions. Shuffling may change file sequence  
     classes = None, #   
     class_mode = None)  
 test_image_list = test_generator.filenames  
 print('Loading model and weights')  
 predict_model = load_model(weights_path)  
 print('Begin to predict for testing data ...')  
 predictions = predict_model.predict_generator(test_generator, nbr_test_samples)  
 np.savetxt(session+'-predictions.txt', predictions) # store prediction matrix, for later analysis if necessary  

Make submission file

Make submission file, format must match given sample_submission.csv

 # submission  
 print('Begin to write submission file:'+submission_file)  
 f_submit = open(submission_file, 'w')  
 for i, image_name in enumerate(test_image_list):  
   filename, fileext = os.path.splitext(basename)    
   prediction_class  =predictions[i][0] # get predictions from array      
   f_submit.write('%s,%s\n' % (filename, prediction_class))  
 print('Finished write submission file ..')  

Submit the result to , click on "Late Submission"

We got score of 0.10979, still long way from the top (0.03) but not too bad for only 1000 samples.


  • Tutorial on using Keras flow_from_directory and generators
  • Code formatter:


  1. In what capacity would marketers be able to utilize enormous information investigation to their support?
    machine learning course in pune

  2. The development of artificial intelligence (AI) has propelled more programming architects, information scientists, and different experts to investigate the plausibility of a vocation in machine learning. Notwithstanding, a few newcomers will in general spotlight a lot on hypothesis and insufficient on commonsense application. machine learning projects for final year In case you will succeed, you have to begin building machine learning projects in the near future.

    Projects assist you with improving your applied ML skills rapidly while allowing you to investigate an intriguing point. Furthermore, you can include projects into your portfolio, making it simpler to get a vocation, discover cool profession openings, and Final Year Project Centers in Chennai even arrange a more significant compensation.

    Data analytics is the study of dissecting crude data so as to make decisions about that data. Data analytics advances and procedures are generally utilized in business ventures to empower associations to settle on progressively Python Training in Chennai educated business choices. In the present worldwide commercial center, it isn't sufficient to assemble data and do the math; you should realize how to apply that data to genuine situations such that will affect conduct. In the program you will initially gain proficiency with the specialized skills, including R and Python dialects most usually utilized in data analytics programming and usage; Python Training in Chennai at that point center around the commonsense application, in view of genuine business issues in a scope of industry segments, for example, wellbeing, promoting and account.


Simple Image Classification with Keras

Keras logo There are several kind of image classification: Binary classification Multiclass classification Multi label classificat...