Transfer Learning in AI is a method where a model is developed for a specific task, which is used as the initial steps for another model for other tasks.
Deep Convolutional Neural Networks in deep learning take an hour or day to train the mode if the dataset we are playing is vast.
The approach is we reuse the weights of the pre-trained model, which was trained for some standard Computer Vision datasets such as Image classification (Image Net).
Extensive deep Convolutional networks for large-scale image classification are available in Keras, which we can directly import and can be used with their pre-trained weights.
Various pre-trained model is available in Keras are:
- VGG16 and VGG19
- Inception, etc.
- For more: https://keras.io/api/applications/
Let’s now understand how to use VGG16 pre-trained on 10,000 categories(Image Net) for the Distracted driver Detection dataset.
Dataset has ten categories to classify, but VGG16 was trained for 10,000 categories, so to apply VGG16 to the Distracted Driver dataset, Fully connected layers need some changes. At the same time, the Convolutional network of VGG16 is used as such with their pre-trained weights.
Importing the required library
from tensorflow.keras.layers import Input, Lambda, Dense, Flatten from tensorflow.keras.models import Model, Sequential from tensorflow.keras.applications.vgg16 import VGG16 ,preprocess_input #importing VGG16 model from tensorflow.keras.preprocessing import image from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.optimizers import Adam from tensorflow.keras.callbacks import CSVLogger import numpy as np import os import matplotlib.pyplot as plt import tensorflow as tf
The code below is to Shift the computation to GPU’s
# --------------------------------------# #shifting computation to GPU gpus = tf.config.experimental.list_physical_devices('GPU') if gpus: gpu = gpus tf.config.experimental.set_memory_growth(gpu, True) tf.config.set_visible_devices(gpu, 'GPU') #--------------------------------------#
The code of the main model is given below:
# Path to my train and val dataset base = os.path.join(os.getcwd(),'imgs') # include_top = False means that we doesnt include fully connected top layer we will add them accordingly vgg16 = VGG16(include_top = False, input_shape = (224,224,3), weights = 'imagenet') # training of all the convolution is set to false for layer in vgg16.layers: layer.trainable = False x = Flatten()(vgg16.output) predictions = Dense(units = 10, activation = 'sigmoid')(x) model = Model(inputs = vgg16.input, outputs = predictions) model.compile(optimizer = Adam(learning_rate = 0.01 ), loss = 'binary_crossentropy',metrics = ['accuracy'] ) model.summary()
To load the dataset batch-wise, we use an in-built Image Data generator:
train_gen = ImageDataGenerator(rescale = 1./255) val_gen = ImageDataGenerator(rescale = 1./255) train_data = train_gen.flow_from_directory(os.path.join(base,'train'), target_size = (244,244),batch_size = 16, class_mode = 'categorical') val_data = val_gen.flow_from_directory(os.path.join(base,'val'), target_size = (244,244),batch_size = 16, class_mode = 'categorical') model.fit(train_data,validation_data = val_data,epochs = 30,callbacks = [CSVLogger("distracted_driver.csv",append = True)])
Anant is a consulting intern at Marktechpost. He is an undergraduate, pursuing his Btech from Jaypee Institute of Information Technology, Noida. He is having a good knowledge of Machine Learning algorithms and looking forward in various complex ML algorithms.