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food_xception_dp0707_2p_345p.py
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food_xception_dp0707_2p_345p.py
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import numpy as np
from os import listdir
from os.path import isfile, join
import h5py
from sklearn.model_selection import train_test_split
from keras.preprocessing import image
from keras.layers import Input
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D, GlobalAveragePooling2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint, TensorBoard, CSVLogger, EarlyStopping
import keras.backend as K
from keras.optimizers import SGD, RMSprop, Adam
from keras.models import model_from_json
from keras.utils.np_utils import to_categorical
from keras.applications.inception_v3 import InceptionV3
from keras.applications.resnet50 import ResNet50
from keras.applications.vgg19 import VGG19
from keras.applications.xception import Xception
from keras.applications.inception_v3 import preprocess_input, decode_predictions
from random import sample
import tensorflow as tf
import glob
from PIL import Image
import matplotlib.pyplot as plt
class_to_ix = {}
ix_to_class = {}
with open('meta/classes.txt', 'r') as txt:
classes = [l.strip() for l in txt.readlines()]
class_to_ix = dict(zip(classes, range(len(classes))))
ix_to_class = dict(zip(range(len(classes)), classes))
class_to_ix = {v: k for k, v in ix_to_class.items()}
print("Loading Data")
h = h5py.File('all_data_300515.hdf5', 'r')
h.keys()
print("Load Training Data")
X_train = np.array(h.get('X_train')) # Size (m, n_h = 299 , n_w = 299, n_c = 3)
y_train = np.array(h.get('y_train')) # Size (m, 101)
index_train = sample(range(X_train.shape[0]),X_train.shape[0])
X_train = X_train[index_train,:,:,:]
y_train = y_train[index_train,:]
# print("Load Dev Data")
# X_dev = np.array(h.get('X_dev')) # Size (m, n_h = 299 , n_w = 299, n_c = 3)
# y_dev = np.array(h.get('y_dev')) # Size (m, 101)
# index_dev = sample(range(X_dev.shape[0]),X_dev.shape[0])
# X_dev = X_dev[index_dev,:,:,:]
# y_dev = y_dev[index_dev,:]
print("Load Test Data")
X_test = np.array(h.get('X_test')) # Size (m, n_h = 299 , n_w = 299, n_c = 3)
y_test = np.array(h.get('y_test')) # Size (m, 101)
h.close()
index_test = sample(range(X_test.shape[0]),X_test.shape[0])
X_test = X_test[index_test,:,:,:]
y_test = y_test[index_test,:]
######## Set up Image Augmentation
print("Setting up ImageDataGenerator")
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
# rotation_range=45, # randomly rotate images in the range (degrees, 0 to 180)
# width_shift_range=0.125, # randomly shift images horizontally (fraction of total width)
# height_shift_range=0.125, # randomly shift images vertically (fraction of total height)
# horizontal_flip=True, # randomly flip images
vertical_flip=False, # randomly flip images
rescale=1./255,
fill_mode='nearest')
# datagen.fit(X_train)
generator = datagen.flow(X_train, y_train, batch_size=9)
val_generator = datagen.flow(X_test, y_test, batch_size=9)
# generator = datagen.flow(X_train, y_train_cat, batch_size=32)
# val_generator = datagen.flow(X_val, y_val_cat, batch_size=32)
## Fine tuning. 70% with image augmentation.
## 83% with pre processing (14 mins).
## 84.5% with rmsprop/img.aug/dropout
## 86.09% with batchnorm/dropout/img.aug/adam(10)/rmsprop(140)
## InceptionV3
print("Load Model")
K.clear_session()
# base_model = InceptionV3(weights='imagenet', include_top=False, input_tensor=Input(shape=(299, 299, 3)))
# base_model = ResNet50(weights='imagenet', include_top=False, input_tensor=Input(shape=(299, 299, 3)))
# base_model = VGG19(weights='imagenet', include_top=False, input_tensor=Input(shape=(299, 299, 3)))
base_model = Xception(weights='imagenet', include_top=False, input_tensor=Input(shape=(299, 299, 3)))
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dropout(0.7)(x)
x = Dense(4096)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dropout(0.7)(x)
predictions = Dense(101, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
import time
filename = time.strftime("%Y%m%d_%H%M") + "_xception_dp0608"
# serialize model to JSON
model_json = model.to_json()
with open(filename + "_model.json", "w") as json_file:
json_file.write(model_json)
model.load_weights("20180607_2237_xception_dp0608_second.12-1.81.hdf5")
# print("First pass")
# for layer in base_model.layers:
# layer.trainable = False
# model.compile(optimizer=Adam(lr=0.001, beta_1=0.9, beta_2=0.999), loss='categorical_crossentropy', metrics=['accuracy'])
# checkpointer = ModelCheckpoint(filepath=filename + '_first.{epoch:02d}-{val_loss:.2f}.hdf5', verbose=1, save_best_only=False)
# csv_logger = CSVLogger(filename + '_first.log')
# model.fit_generator(generator,
# validation_data=val_generator,
# epochs=40,
# verbose=1,
# callbacks=[csv_logger, checkpointer])
# # serialize weights to HDF5
# model.save_weights(filename + "_fp_weights.hdf5")
# print("Saved model to disk")
# nh = 8
# print("Second pass")
# for layer in model.layers[:132-nh]:
# layer.trainable = False
# for layer in model.layers[132-nh:]:
# layer.trainable = True
# model.compile(optimizer=Adam(lr=0.001, beta_1=0.9, beta_2=0.999), loss='categorical_crossentropy', metrics=['accuracy'])
# checkpointer = ModelCheckpoint(filepath=filename + '_second.{epoch:02d}-{val_loss:.2f}.hdf5', verbose=1, save_best_only=False)
# csv_logger = CSVLogger(filename + '_second.log')
# model.fit_generator(generator,
# validation_data=val_generator,
# epochs=30,
# verbose=1,
# callbacks=[csv_logger, checkpointer])
# # serialize weights to HDF5
# model.save_weights(filename + "_sp_weights.hdf5")
# print("Saved model to disk")
nh = 9
print("Third pass")
for layer in model.layers[:132-nh]:
layer.trainable = False
for layer in model.layers[132-nh:]:
layer.trainable = True
model.compile(optimizer=Adam(lr=0.0001, beta_1=0.9, beta_2=0.999), loss='categorical_crossentropy', metrics=['accuracy'])
checkpointer = ModelCheckpoint(filepath=filename + '_third.{epoch:02d}-{val_loss:.2f}.hdf5', verbose=1, save_best_only=False)
csv_logger = CSVLogger(filename + 'third.log')
model.fit_generator(generator,
validation_data=val_generator,
epochs=20,
verbose=1,
callbacks=[csv_logger, checkpointer])
# serialize weights to HDF5
model.save_weights(filename + "_3p_weights.hdf5")
print("Saved model to disk")
nh = 10
print("Fourth pass")
for layer in model.layers[:132-nh]:
layer.trainable = False
for layer in model.layers[132-nh:]:
layer.trainable = True
model.compile(optimizer=Adam(lr=0.0001, beta_1=0.9, beta_2=0.999), loss='categorical_crossentropy', metrics=['accuracy'])
checkpointer = ModelCheckpoint(filepath=filename + '_fourth.{epoch:02d}-{val_loss:.2f}.hdf5', verbose=1, save_best_only=False)
csv_logger = CSVLogger(filename + '_fourth.log')
model.fit_generator(generator,
validation_data=val_generator,
epochs=30,
verbose=1,
callbacks=[csv_logger, checkpointer])
# serialize weights to HDF5
model.save_weights(filename + "_4p_weights.hdf5")
print("Saved model to disk")
nh = 12
print("Fifth pass 16 layers")
for layer in model.layers[:132-nh]:
layer.trainable = False
for layer in model.layers[132-nh:]:
layer.trainable = True
model.compile(optimizer=Adam(lr=0.0001, beta_1=0.9, beta_2=0.999), loss='categorical_crossentropy', metrics=['accuracy'])
checkpointer = ModelCheckpoint(filepath=filename + '_fifth.{epoch:02d}-{val_loss:.2f}.hdf5', verbose=1, save_best_only=False)
csv_logger = CSVLogger(filename + 'fifth.log')
model.fit_generator(generator,
validation_data=val_generator,
epochs=30,
verbose=1,
callbacks=[csv_logger, checkpointer])
# serialize weights to HDF5
model.save_weights(filename + "_5p_weights.hdf5")
print("Saved model to disk")