Python | Image Classification using keras

Prerequisite: Image Classifier using CNN

Image classification is a method to classify the images into their respective category classes using some method like :

  • Training a small network from scratch
  • Fine tuning the top layers of the model using VGG16

Let’s discuss how to train model from scratch and classify the data containing cars and planes.



Train Data : Train data contains the 200 images of each cars and planes i.e. total their are 400 images in the training dataset
Test Data : Test data contains 50 images of each cars and planes i.e. total their are 100 images in the test dataset

To download the complete dataset, click here.

Model Description: Before starting with the model firstly prepare the dataset and it’s arrangement. Look at the following image given below:

For feeding the dataset folders the should be made and provided into this format only. So now, Let’s begins with the model:
For training the model we don’t need a large high end machine and GPU’s, we can work with CPU’s also. Firstly, in given code include following libraries:

filter_none

edit
close

play_arrow

link
brightness_4
code

# Importing all necessary libraries
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K

chevron_right


Every image in the dataset is of the size 224*224.

filter_none

edit
close

play_arrow

link
brightness_4
code

train_data_dir = 'v_data/train'
validation_data_dir = 'v_data/test'
nb_train_samples =400 
nb_validation_samples = 100
epochs = 10
batch_size = 16

chevron_right


Here, the train_data_dir is the train dataset directory. validation_data_dir is the directory for validation data. nb_train_samples is the total number train samples. nb_validation_samples is the total number of validation samples.

Checking format of Image:

filter_none

edit
close

play_arrow

link
brightness_4
code

if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)
else:
    input_shape = (img_width, img_height, 3)

chevron_right


This part is to check the data format i.e the RGB channel is coming first or last so, whatever it may be, model will check first and then input shape will be feeded accordingly.

filter_none

edit
close

play_arrow

link
brightness_4
code

model = Sequential()
model.add(Conv2D(32, (2, 2), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
  
model.add(Conv2D(32, (2, 2)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
  
model.add(Conv2D(64, (2, 2)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
  
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))

chevron_right


About the following terms used above:

Conv2D is the layer to convolve the image into multiple images
Activation is the activation function.
MaxPooling2D is used to max pool the value from the given size matrix and same is used for the next 2 layers. then, Flatten is used to flatten the dimensions of the image obtained after convolving it.
Dense is used to make this a fully connected model and is the hidden layer.
Dropout is used to avoid overfitting on the dataset.
Dense is the output layer contains only one neuron which decide to which category image belongs.


Compile Function:

filter_none

edit
close

play_arrow

link
brightness_4
code

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

chevron_right


Compile function is used here that involve use of loss, optimizers and metrics.here loss function used is binary_crossentropy, optimizer used is rmsprop.

Using DataGenerator:

filter_none

edit
close

play_arrow

link
brightness_4
code

train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)
  
test_datagen = ImageDataGenerator(rescale=1. / 255)
  
train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')
  
validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')
  
model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size)

chevron_right


Now, the part of dataGenerator comes into figure.In which we have used:

ImageDataGenerator that rescales the image, applies shear in some range, zooms the image and does horizontal flipping with the image. This ImageDataGenerator includes all possible orientation of the image.
train_datagen.flow_from_directory is the function that is used to prepare data from the train_dataset directory Target_size specifies the target size of the image.
test_datagen.flow_from_directory is used to prepare test data for the model and all is similar as above.
fit_generator is used to fit the data into the model made above, other factors used are steps_per_epochs tells us about the number of times the model will execute for the training data.
epochs tells us the number of times model will be trained in forward and backward pass.
validation_data is used to feed the validation/test data into the model.
validation_steps denotes the number of validation/test samples.

filter_none

edit
close

play_arrow

link
brightness_4
code

model.save_weights('model_saved.h5')

chevron_right


At last we can also save the model.
 
Below is the complete implementation:

filter_none

edit
close

play_arrow

link
brightness_4
code

# importing libraries
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
  
  
img_width, img_height = 224, 224
  
train_data_dir = 'v_data/train'
validation_data_dir = 'v_data/test'
nb_train_samples = 400 
nb_validation_samples = 100
epochs = 10
batch_size = 16
  
if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)
else:
    input_shape = (img_width, img_height, 3)
  
model = Sequential()
model.add(Conv2D(32, (2, 2), input_shape = input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size =(2, 2)))
  
model.add(Conv2D(32, (2, 2)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size =(2, 2)))
  
model.add(Conv2D(64, (2, 2)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size =(2, 2)))
  
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
  
model.compile(loss ='binary_crossentropy',
                     optimizer ='rmsprop',
                   metrics =['accuracy'])
  
train_datagen = ImageDataGenerator(
                rescale = 1. / 255,
                 shear_range = 0.2,
                  zoom_range = 0.2,
            horizontal_flip = True)
  
test_datagen = ImageDataGenerator(rescale = 1. / 255)
  
train_generator = train_datagen.flow_from_directory(train_data_dir,
                              target_size =(img_width, img_height),
                     batch_size = batch_size, class_mode ='binary')
  
validation_generator = test_datagen.flow_from_directory(
                                    validation_data_dir,
                   target_size =(img_width, img_height),
          batch_size = batch_size, class_mode ='binary')
  
model.fit_generator(train_generator,
    steps_per_epoch = nb_train_samples // batch_size,
    epochs = epochs, validation_data = validation_generator,
    validation_steps = nb_validation_samples // batch_size)
  
model.save_weights('model_saved.h5')

chevron_right


Output:



My Personal Notes arrow_drop_up

Check out this Author's contributed articles.

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.




Article Tags :
Practice Tags :


3


Please write to us at contribute@geeksforgeeks.org to report any issue with the above content.