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INCEPTIONV3-recipe1m.py
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INCEPTIONV3-recipe1m.py
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import numpy as np
import os
import json
import pandas as pd
import tensorflow as tf
from keras.preprocessing import image
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras.applications.inception_v3 import InceptionV3
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
import keras
currentPath = os.getcwd()
os.chdir("../..")
newPath = os.getcwd()
image_size = 299
trainFile = '/home/eleni/code/Recipe1M-602/train.json'
valFile = '/home/eleni/code/Recipe1M-602/val.json'
batch_size = 64
train_datagen = ImageDataGenerator(featurewise_center=True,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True)
def openJson(file):
with open(file) as File:
dict = json.load(File)
return dict
def train_generator():
with open(trainFile) as trainfile:
dict_train = json.load(trainfile)
train = pd.DataFrame.from_dict(dict_train, orient='index')
train.reset_index(level=0, inplace=True)
train.columns = ['Id', 'Ingredients', 'Binary']
nb_samples = len(train)
while True:
for start in range(0, nb_samples, batch_size):
train_image =[]
y_batch = []
end = min(start + batch_size, nb_samples)
for i in range(start, end):
name = train['Id'][i]
img = image.load_img("/home/shared/data/Recipe1M-602/train/" + name[0] + "/" + name[1] + "/" + name[2] + "/" + name[3] + "/" + name, target_size=(image_size, image_size, 3))
img = image.img_to_array(img)
img = (img / 255)
train_image.append(img)
aaaaaaa = np.array(train_image)
y_batch.append(train['Binary'][i])
# return np.array(train_image), np.array(y_batch)
yield (np.array(train_image), np.array(y_batch))
def val_generator():
with open(valFile) as valfile:
dict_val = json.load(valfile)
val = pd.DataFrame.from_dict(dict_val, orient='index')
val.reset_index(level=0, inplace=True)
val.columns = ['Id', 'Ingredients', 'Binary']
nb_samples = len(val)
while True:
for start in range(0, nb_samples, batch_size):
val_image = []
y_batch = []
end = min(start + batch_size, nb_samples)
for i in range(start, end):
name = val['Id'][i]
img = image.load_img("/home/shared/data/Recipe1M-602/val/" + name[0] + "/" + name[1] + "/" + name[2] + "/" + name[3] + "/" + name, target_size=(image_size, image_size, 3))
img = image.img_to_array(img)
img = (img / 255)
val_image.append(img)
y_batch.append(val['Binary'][i])
yield (np.array(val_image), np.array(y_batch))
# return np.array(val_image), np.array(y_batch)
if __name__ == "__main__":
nb_train_samples = len(openJson(trainFile))
nb_valid_samples = len(openJson(valFile))
print("TRAIN LEN", nb_train_samples)
print("VALID LEN", nb_valid_samples)
train_gen = train_generator()
val_gen = val_generator()
with tf.device('/gpu:1'):
base_model = InceptionV3(weights='imagenet', include_top=False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
predictions = Dense(602, activation='sigmoid')(x)
model = Model(inputs=base_model.input, outputs=predictions)
# model.summary()
for layer in base_model.layers:
layer.trainable = True
model.compile(optimizer=Adam(lr=1e-05), loss='categorical_crossentropy', metrics=['acc']) #1e-05, 0.01
checkpoint1 = ModelCheckpoint ('/home/eleni/code/models/Recipe1M602.h5', save_weights_only=False, monitor='val_loss', save_best_only=True, verbose=1, mode='min')
es_callback = keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)
# my_lr_scheduler = LearningRateScheduler(adapt_learning_rate) # def adapt_learning_rate()
model.fit_generator(train_gen, epochs=100, steps_per_epoch= nb_train_samples // batch_size+ 1, validation_data=val_gen, validation_steps = nb_valid_samples // batch_size+ 1,callbacks=[es_callback,checkpoint1], verbose=1, use_multiprocessing=True)