Food101Model.py

# -*- coding: utf-8 -*-
"""
@author: Robert Kamunde
"""


import tensorflow as tf
import tensorflow.keras.backend as K
from tensorflow.keras import regularizers
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D, GlobalAveragePooling2D, AveragePooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.regularizers import l2
from tensorflow import keras
import numpy as np

import tensorflow as tf
print(tf.__version__)
print(tf.test.gpu_device_name())

K.clear_session()

n_classes = 101
img_width, img_height = 299, 299
train_data_dir = 'food-101/train'
validation_data_dir = 'food-101/test'
nb_train_samples = 75750
nb_validation_samples = 25250
batch_size = 20

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_height, img_width),
    batch_size=batch_size,
    class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='categorical')


mbv2 = MobileNetV2(weights='imagenet', include_top=False, input_shape = (299,299,3))
x = mbv2.output
x = GlobalAveragePooling2D()(x)
x = Dense(128,activation='relu')(x)
x = Dropout(0.2)(x)

predictions = Dense(101,kernel_regularizer=regularizers.l2(0.005), activation='softmax')(x)

model = Model(inputs=mbv2.input, outputs=predictions)
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])
checkpointer = ModelCheckpoint(filepath='best_model_3class_sept.hdf5', verbose=1, save_best_only=True)
csv_logger = CSVLogger('history.log')

history = model.fit_generator(train_generator,
                    steps_per_epoch = nb_train_samples // batch_size,
                    validation_data=validation_generator,
                    validation_steps=nb_validation_samples // batch_size,
                    epochs=10,
                    verbose=1,
                    callbacks=[csv_logger, checkpointer])

model.save('model_trained.h5')