Simple Multiclass Image Classification with Keras

Simple Multiclass Image Classification with Keras

This tutorial shows how to do multiclass image classification with Keras, using keras.preprocessing.image.flow_from_directory() to feed the image files for training and prediction.

Plant Seedlings Classification dataset

 

Prepare Directory Structure

  • download dataset from  https://www.kaggle.com/c/plant-seedlings-classification/data
  • put original training files in <root>/data/train
  • put original test files in <root>/data/test/0 . Caution: test files must be put into a directory under /data/test. For simplicity, we create /data/test/0, but any directory name is okay, as long as it is under /data/test
  • create <root>/plant-src to store source codes

Here is the directory structure after previous steps:

 

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  
 from pathlib import Path  
 import shutil   

Make Training & Validation Directories

Create directories for training and validation set. A little bit complicated, since flow_from_directory() required that each class has it’s own directory.

 #making training & validation directories  
 import pathlib  
 session='simpleNASNet'  
 classnames=['Black-grass','Charlock','Cleavers','Common Chickweed','Common wheat','Fat Hen','Loose Silky-bent','Maize','Scentless Mayweed','Shepherds Purse','Small-flowered Cranesbill','Sugar beet']  
 train_dir="../"+session+"/train"  
 valid_dir="../"+session+"/valid"  
 for dirname in classnames:  
 #  print(dirname)  
   fulldirname=train_dir+'/'+dirname    
   print(fulldirname)  
   pathlib.Path(fulldirname).mkdir(parents=True, exist_ok=True)  
   fulldirname=valid_dir+'/'+dirname    
   print(fulldirname)  
   pathlib.Path(fulldirname).mkdir(parents=True, exist_ok=True)  

Split training data between training set and validation set. Usual 80%-20% split is used.

 #copy image files, split 80% training- 20% validation  
 counter=0  
 for root, dirs, files in os.walk(original_data_dir):  
    for file in files:  
       fullfilename = os.path.join(root, file)        
       basename=os.path.basename(fullfilename)  
       #detect image classification from directory name  
       split1=os.path.split(fullfilename)        
       split2=os.path.split(split1[0])  
       classname=str(split2[1])#classname for this particular file  
       if((counter%5)==0): #copy validation  
         dst_filename=valid_dir+"/"+classname+"/"+basename  
         shutil.copyfile(fullfilename,dst_filename)      
       else:       #copy training    
         dst_filename=train_dir+"/"+classname+"/"+basename  
         shutil.copyfile(fullfilename,dst_filename)      
       counter=counter+1  

 

Model

Prepare model, we use NASNet with 331×331 input, using pre-trained weight from Imagenet. Top layers are omitted, and replaced with a Dense layer of 1024 cells and 12 cells output layer for each class. Output activation is softmax, which is usual for multiclass classification.

 #prepare model  
 img_width=331  
 img_height=331  
 network_notop = keras.applications.nasnet.NASNetLarge(input_shape=(img_width, img_height, 3),  
                                  include_top=False,  
                                  weights='imagenet', input_tensor=None,  
                                  pooling=None)      
 x = network_notop.output  
 x = GlobalAveragePooling2D()(x)      
 x = Dense(1024, activation='relu')(x)      
 x = BatchNormalization()(x)  
 predictions = Dense(12, activation='softmax')(x)  
 the_model = Model(network_notop.input, predictions)  

 

Training

Standard training.
Specific parameter for multiclass classification:

  • loss=’categorical_crossentropy’ in model.compile()
  • class_mode=’categorical’ in flow_from_directory()

 

 #training  
 learning_rate = 0.0001   
 logfile = session + '-train' + '.log'   
 batch_size=4  
 nbr_epochs=10  
 print("training  directory: "+train_dir)  
 print("valication directory: "+valid_dir)  
 optimizer = SGD(lr=learning_rate, momentum=0.9, decay=0.0, nesterov=True)  
 the_model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])  
 csv_logger = CSVLogger(logfile, append=True)  
 early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1, mode='auto')  
 best_model_filename=session+'-weights.{epoch:02d}-{val_loss:.2f}.h5'   
 best_model = ModelCheckpoint(best_model_filename, 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,  
     shear_range=0.2,  
     zoom_range=0.2,  
     rotation_range=90,  
     width_shift_range=0.2,  
     height_shift_range=0.2,  
     horizontal_flip=True,  
     vertical_flip=True)  
 val_datagen = ImageDataGenerator(rescale=1. / 255)  
 print('prepare train generator')   
 train_generator = train_datagen.flow_from_directory(  
     train_dir,  
     target_size=(img_width, img_height),  
     batch_size=batch_size,  
     shuffle=True,  
     classes=classnames,  
     class_mode='categorical')  
 print('prepare validation generator')   
 validation_generator = val_datagen.flow_from_directory(  
     valid_dir,  
     target_size=(img_width, img_height),  
     batch_size=batch_size,  
     shuffle=True,  
     classes=classnames,  
     class_mode='categorical')  
 print('fit generator')   
 the_model.fit_generator(  
     generator=train_generator,  
     epochs=nbr_epochs,  
     verbose=1,  
     validation_data=validation_generator,  
     callbacks=[best_model, csv_logger, early_stopping])  

Training progress

 training  directory: ../simpleNASNet/train  
 valication directory: ../simpleNASNet/valid  
 prepare train generator  
 Found 3800 images belonging to 12 classes.  
 prepare validation generator  
 Found 950 images belonging to 12 classes.  
 fit generator  
 Epoch 1/10  
 950/950 [==============================] - 635s 669ms/step - loss: 1.2295 - acc: 0.6039 - val_loss: 0.6469 - val_acc: 0.7979  
 Epoch 00001: val_acc improved from -inf to 0.79789, saving model to simpleNASNet-weights.01-0.65.h5  
 Epoch 2/10  
 950/950 [==============================] - 557s 586ms/step - loss: 0.6281 - acc: 0.7929 - val_loss: 0.3840 - val_acc: 0.8674  
 Epoch 00002: val_acc improved from 0.79789 to 0.86737, saving model to simpleNASNet-weights.02-0.38.h5  
 Epoch 3/10  
 950/950 [==============================] - 557s 586ms/step - loss: 0.5220 - acc: 0.8345 - val_loss: 0.3026 - val_acc: 0.9000  
 Epoch 00003: val_acc improved from 0.86737 to 0.90000, saving model to simpleNASNet-weights.03-0.30.h5  
 Epoch 4/10  
 950/950 [==============================] - 558s 587ms/step - loss: 0.4369 - acc: 0.8566 - val_loss: 0.2830 - val_acc: 0.9105  
 Epoch 00004: val_acc improved from 0.90000 to 0.91053, saving model to simpleNASNet-weights.04-0.28.h5  
 Epoch 5/10  
 950/950 [==============================] - 558s 588ms/step - loss: 0.3722 - acc: 0.8842 - val_loss: 0.2310 - val_acc: 0.9253  
 Epoch 00005: val_acc improved from 0.91053 to 0.92526, saving model to simpleNASNet-weights.05-0.23.h5  
 Epoch 6/10  
 950/950 [==============================] - 559s 588ms/step - loss: 0.3213 - acc: 0.8966 - val_loss: 0.2210 - val_acc: 0.9232  
 Epoch 00006: val_acc did not improve from 0.92526  
 Epoch 7/10  
 950/950 [==============================] - 556s 585ms/step - loss: 0.3202 - acc: 0.8939 - val_loss: 0.2190 - val_acc: 0.9263  
 Epoch 00007: val_acc improved from 0.92526 to 0.92632, saving model to simpleNASNet-weights.07-0.22.h5  
 Epoch 8/10  
 950/950 [==============================] - 559s 589ms/step - loss: 0.2997 - acc: 0.9063 - val_loss: 0.1861 - val_acc: 0.9389  
 Epoch 00008: val_acc improved from 0.92632 to 0.93895, saving model to simpleNASNet-weights.08-0.19.h5  
 Epoch 9/10  
 950/950 [==============================] - 554s 584ms/step - loss: 0.2469 - acc: 0.9203 - val_loss: 0.1942 - val_acc: 0.9379  
 Epoch 00009: val_acc did not improve from 0.93895  
 Epoch 10/10  
 950/950 [==============================] - 557s 587ms/step - loss: 0.2619 - acc: 0.9147 - val_loss: 0.1695 - val_acc: 0.9421  
 Epoch 00010: val_acc improved from 0.93895 to 0.94211, saving model to simpleNASNet-weights.10-0.17.h5  

 

Prediction & Submission

Caution: test files must be put into a directory under /data/test. For simplicity, we create /data/test/0, but any directory name is okay, as long as it is under /data/test . This behavior is quite strange, but maybe to make flow_from_directory() work the same way for training phase and prediction/inference phase.

#prediction   
 batch_size=4  
 nbr_test_samples=794    
 img_width=331  
 img_height=331  
  #choose weights file manually   
 weights_path = 'simpleNASNet-weights.10-0.17.h5' # choose file manually, filename may be different  
 test_data_dir = '../data/test/'   
 test_datagen = ImageDataGenerator(rescale=1./255)   
 test_generator = test_datagen.flow_from_directory(   
    test_data_dir,   
    target_size=(img_width, img_height),   
    batch_size=batch_size,   
    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   

Constructing submission file

 #submission  
 submission_file=session+'-submit.csv'   
 print('Begin to write submission file:'+submission_file)   
 f_submit = open(submission_file, 'w')   
 f_submit.write('file,speciesn')   
 for i, image_name in enumerate(test_image_list):   
   # find maximum prediction of 12  
   max_index=0  
   max_value=0  
   for x in range(0, 12):  
     if(predictions[i][x]>max_value):  
       max_value=predictions[i][x]  
       max_index=x  
   basename=os.path.basename(image_name)   
   prediction_class = classnames[max_index] # get predictions from array     
   f_submit.write('%s,%sn' % (basename, prediction_class))   
 f_submit.close()   
 print('Finished write submission file ..')  

To check final score, let’s go to Late Submission page in Kaggle Plant Seedlings Classification. The score is 0.96095,  which ranks about 400 in leaderboard.

Late submission score

 Reference

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