I am using transfer learning to train a binary image classification model using keras' pretrained VGG16 model. The code can be found below :
training_dir = '/Users/rishabh/Desktop/CyberBoxer/data/train'
validation_dir = '/Users/rishabh/Desktop/CyberBoxer/data/validation'
image_files = glob(training_dir + '/*/*.jpg')
valid_image_files = glob(validation_dir + '/*/*.jpg')
# importing the libraries
from keras.models import Model
from keras.layers import Flatten, Dense
from keras.applications import VGG16
#from keras.preprocessing import image
IMAGE_SIZE = [64, 64] # we will keep the image size as (64,64). You
can increase the size for better results.
# loading the weights of VGG16 without the top layer. These weights are
trained on Imagenet dataset.
vgg = VGG16(input_shape = IMAGE_SIZE + [3], weights = 'imagenet',
include_top = False) # input_shape = (64,64,3) as required by VGG
# this will exclude the initial layers from training phase as there are
already been trained.
for layer in vgg.layers:
layer.trainable = False
x = Flatten()(vgg.output)
#x = Dense(128, activation = 'relu')(x) # we can add a new fully
connected layer but it will increase the execution time.
x = Dense(num_classes, activation = 'softmax')(x) # adding the output
layer with softmax function as this is a multi label classification
problem.
model = Model(inputs = vgg.input, outputs = x)
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
from keras.preprocessing.image import ImageDataGenerator
from keras.applications.vgg16 import preprocess_input
training_datagen = ImageDataGenerator(
rescale=1./255, # all pixel values will be between 0 an 1
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
preprocessing_function=preprocess_input)
validation_datagen = ImageDataGenerator(rescale = 1./255, preprocessing_function=preprocess_input)
training_generator = training_datagen.flow_from_directory(training_dir,
target_size = IMAGE_SIZE, batch_size = 200, class_mode = 'categorical')
validation_generator =
validation_datagen.flow_from_directory(validation_dir, target_size =
IMAGE_SIZE, batch_size = 200, class_mode = 'categorical')
training_images = 3717
validation_images = 885
history = model.fit_generator(training_generator,
steps_per_epoch = 3717, # this should be equal to total number of images in training set. But to speed up the execution, I am only using 10000 images. Change this for better results.
epochs = 1, # change this for better results
validation_data = validation_generator,
validation_steps = 885) # this should be equal to total number of images in validation set.
I am training it on just 3700 images but still a single epoch is taking around 10-12 hours. Is this supposed to happen ? Am I doing anything wrong ? I had to downgrade my keras to 2.1.4 for the code to run so is it something affecteing the learning ?
