Python of keras  ব্যবহার করে চিত্র শ্রেণিবদ্ধের জন্য Transfer Learning কীভাবে ব্যবহার করবেন।

ট্রান্সফার লার্নিং কি

• প্রশিক্ষণের সময় বাড়িয়ে তোলে।
• এর জন্য কম ডেটা দরকার।
• গভীর শিক্ষণ বিশেষজ্ঞদের দ্বারা তৈরি করা হয়েছে এমন অত্যাধুনিক মডেলগুলি ব্যবহার করুন।

ডেটাসেট লোড এবং প্রস্তুত করা হচ্ছে

pip3 install tensorflow keras numpy matplotlib

import tensorflow as tf
from keras.models import Model
from keras.applications import MobileNetV2, ResNet50, InceptionV3 # try to use them and see which is better
from keras.layers import Dense
from keras.callbacks import ModelCheckpoint, TensorBoard
from keras.utils import get_file
from keras.preprocessing.image import ImageDataGenerator
import os
import pathlib
import numpy as np

batch_size = 32
# 5 types of flowers
num_classes = 5
# training for 10 epochs
epochs = 10
# size of each image
IMAGE_SHAPE = (224, 224, 3)

def load_data():
    """This function downloads, extracts, loads, normalizes and one-hot encodes Flower Photos dataset"""
    # download the dataset and extract it
    data_dir = get_file(origin='https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',
                                         fname='flower_photos', untar=True)
    data_dir = pathlib.Path(data_dir)
    # count how many images are there
    image_count = len(list(data_dir.glob('*/*.jpg')))
    print("Number of images:", image_count)
    # get all classes for this dataset (types of flowers) excluding LICENSE file
    CLASS_NAMES = np.array([item.name for item in data_dir.glob('*') if item.name != "LICENSE.txt"])
    # roses = list(data_dir.glob('roses/*'))
    # 20% validation set 80% training set
    image_generator = ImageDataGenerator(rescale=1/255, validation_split=0.2)
    # make the training dataset generator
    train_data_gen = image_generator.flow_from_directory(directory=str(data_dir), batch_size=batch_size,
                                                        classes=list(CLASS_NAMES), target_size=(IMAGE_SHAPE[0], IMAGE_SHAPE[1]),
                                                        shuffle=True, subset="training")
    # make the validation dataset generator
    test_data_gen = image_generator.flow_from_directory(directory=str(data_dir), batch_size=batch_size, 
                                                        classes=list(CLASS_NAMES), target_size=(IMAGE_SHAPE[0], IMAGE_SHAPE[1]),
                                                        shuffle=True, subset="validation")
    return train_data_gen, test_data_gen, CLASS_NAMES

মডেল নির্মাণ

def create_model(input_shape):
    # load MobileNetV2
    model = MobileNetV2(input_shape=input_shape)
    # remove the last fully connected layer
    model.layers.pop()
    # freeze all the weights of the model except the last 4 layers
    for layer in model.layers[:-4]:
        layer.trainable = False
    # construct our own fully connected layer for classification
    output = Dense(num_classes, activation="softmax")
    # connect that dense layer to the model
    output = output(model.layers[-1].output)
    model = Model(inputs=model.inputs, outputs=output)
    # print the summary of the model architecture
    model.summary()
    # training the model using rmsprop optimizer
    model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
    return model

মডেল প্রশিক্ষণ

if __name__ == "__main__":
    # load the data generators
    train_generator, validation_generator, class_names = load_data()
    # constructs the model
    model = create_model(input_shape=IMAGE_SHAPE)
    # model name
    model_name = "MobileNetV2_finetune_last5"
    # some nice callbacks
    tensorboard = TensorBoard(log_dir=f"logs/{model_name}")
    checkpoint = ModelCheckpoint(f"results/{model_name}" + "-loss-{val_loss:.2f}-acc-{val_acc:.2f}.h5",
                                save_best_only=True,
                                verbose=1)
    # make sure results folder exist
    if not os.path.isdir("results"):
        os.mkdir("results")
    # count number of steps per epoch
    training_steps_per_epoch = np.ceil(train_generator.samples / batch_size)
    validation_steps_per_epoch = np.ceil(validation_generator.samples / batch_size)
    # train using the generators
    model.fit_generator(train_generator, steps_per_epoch=training_steps_per_epoch,
                        validation_data=validation_generator, validation_steps=validation_steps_per_epoch,
                        epochs=epochs, verbose=1, callbacks=[tensorboard, checkpoint])

Total params: 2,264,389
Trainable params: 418,565
Non-trainable params: 1,845,824

মডেল পরীক্ষা করা হচ্ছে

# load the data generators
train_generator, validation_generator, class_names = load_data()
# constructs the model
model = create_model(input_shape=IMAGE_SHAPE)
# load the optimal weights
model.load_weights("results/MobileNetV2_finetune_last5_less_lr-loss-0.45-acc-0.86.h5")
validation_steps_per_epoch = np.ceil(validation_generator.samples / batch_size)
# print the validation loss & accuracy
evaluation = model.evaluate_generator(validation_generator, steps=validation_steps_per_epoch, verbose=1)
print("Val loss:", evaluation[0])
print("Val Accuracy:", evaluation[1])

23/23 [==============================] - 6s 264ms/step
Val loss: 0.5659930361524
Val Accuracy: 0.8166894659134987

# get a random batch of images
image_batch, label_batch = next(iter(validation_generator))
# turn the original labels into human-readable text
label_batch = [class_names[np.argmax(label_batch[i])] for i in range(batch_size)]
# predict the images on the model
predicted_class_names = model.predict(image_batch)
predicted_ids = [np.argmax(predicted_class_names[i]) for i in range(batch_size)]
# turn the predicted vectors to human readable labels
predicted_class_names = np.array([class_names[id] for id in predicted_ids])
# some nice plotting
plt.figure(figsize=(10,9))
for n in range(30):
    plt.subplot(6,5,n+1)
    plt.subplots_adjust(hspace = 0.3)
    plt.imshow(image_batch[n])
    if predicted_class_names[n] == label_batch[n]:
        color = "blue"
        title = predicted_class_names[n].title()
    else:
        color = "red"
        title = f"{predicted_class_names[n].title()}, correct:{label_batch[n]}"
    plt.title(title, color=color)
    plt.axis('off')
_ = plt.suptitle("Model predictions (blue: correct, red: incorrect)")
plt.show()

• সিএস 231 এন দ্বারা স্থানান্তর লার্নিং ।
• কুকুর এবং বিড়ালের ফটো কীভাবে শ্রেণিবদ্ধ করা যায় ।
• পাইথনে কেরাস ব্যবহার করে কীভাবে স্প্যাম শ্রেণিবদ্ধ করা যায় ।