Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Better cifar10.py #33

Open
wants to merge 3 commits into
base: master
Choose a base branch
from
Open

Better cifar10.py #33

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
25b6d59
cifar10.py provides more debug output, performance charts, configurab…
ahundt Feb 7, 2017
3030210
use parameterized directory
ahundt Feb 7, 2017
8f1ff60
cifar10.py use K.backend() for tf check
ahundt Feb 8, 2017
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
219 changes: 161 additions & 58 deletions cifar10.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,21 +5,35 @@
GPU run command with Theano backend (with TensorFlow, the GPU is automatically used):
THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python cifar10.py
"""

from __future__ import print_function
import datetime
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import np_utils
from keras.callbacks import ReduceLROnPlateau, CSVLogger, EarlyStopping
from keras.callbacks import ReduceLROnPlateau, CSVLogger, EarlyStopping, ModelCheckpoint
from keras import backend as K
import numpy as np
import matplotlib.pyplot as plt
import time
import csv
import os


if K.backend() == 'tensorflow':
from keras.callbacks import TensorBoard
import tensorflow as tf

import resnet

# http://stackoverflow.com/a/5215012/99379
def timeStamped(fname, fmt='%Y-%m-%d-%H-%M-%S_{fname}'):
return datetime.datetime.now().strftime(fmt).format(fname=fname)

lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=np.sqrt(0.1), cooldown=0, patience=5, min_lr=0.5e-6)
early_stopper = EarlyStopping(monitor='val_acc', min_delta=0.001, patience=10)
csv_logger = CSVLogger('resnet18_cifar10.csv')

batch_size = 32
batch_sizes = [32]#[16,32,64]
nb_classes = 10
nb_epoch = 200
data_augmentation = True
Expand All @@ -29,58 +43,147 @@
# The CIFAR10 images are RGB.
img_channels = 3

# The data, shuffled and split between train and test sets:
(X_train, y_train), (X_test, y_test) = cifar10.load_data()

# Convert class vectors to binary class matrices.
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

X_train = X_train.astype('float32')
X_test = X_test.astype('float32')

# subtract mean and normalize
mean_image = np.mean(X_train, axis=0)
X_train -= mean_image
X_test -= mean_image
X_train /= 128.
X_test /= 128.

model = resnet.ResnetBuilder.build_resnet_18((img_channels, img_rows, img_cols), nb_classes)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])

if not data_augmentation:
print('Not using data augmentation.')
model.fit(X_train, Y_train,
batch_size=batch_size,
nb_epoch=nb_epoch,
validation_data=(X_test, Y_test),
shuffle=True,
callbacks=[lr_reducer, early_stopper, csv_logger])
else:
print('Using real-time data augmentation.')
# This will do preprocessing and realtime data augmentation:
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=0, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
vertical_flip=False) # randomly flip images

# Compute quantities required for featurewise normalization
# (std, mean, and principal components if ZCA whitening is applied).
datagen.fit(X_train)

# Fit the model on the batches generated by datagen.flow().
model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size),
samples_per_epoch=X_train.shape[0],
validation_data=(X_test, Y_test),
nb_epoch=nb_epoch, verbose=1, max_q_size=100,
callbacks=[lr_reducer, early_stopper, csv_logger])
out_dir=''
dirname=''

# Compile and train different models while meauring performance.
results = []
avg_time_results = []
for batch_size in batch_sizes:

if K.backend() == 'tensorflow':
sess = tf.Session()
from keras import backend as K
K.set_session(sess)

dirname = timeStamped(str(batch_size) + 'batch_cifar10_resnet')
out_dir=dirname+'/'

print('Running a new session in : ' + out_dir)

# The data, shuffled and split between train and test sets:
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')

# Convert class vectors to binary class matrices.
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
# TODO switch to ResnetBuilder.build calls, permute parameters in 3x3 grid, generate plots for final assignment, save models?
# use https://github.com/fchollet/keras/blob/master/examples/lstm_benchmark.py
model = resnet.ResnetBuilder.build_resnet_18((img_channels, img_rows, img_cols), nb_classes)

# subtract mean and normalize
mean_image = np.mean(X_train, axis=0)
X_train -= mean_image
X_test -= mean_image
X_train /= 128.
X_test /= 128.

model = resnet.ResnetBuilder.build_resnet_18((img_channels, img_rows, img_cols), nb_classes)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])

if not data_augmentation:
print('Not using data augmentation.')
model.fit(X_train, Y_train,
batch_size=batch_size,
nb_epoch=nb_epoch,
validation_data=(X_test, Y_test),
shuffle=True,
callbacks=[lr_reducer, early_stopper, csv_logger])
else:
print('Using real-time data augmentation.')
# This will do preprocessing and realtime data augmentation:
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=0, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
vertical_flip=False) # randomly flip images

# Compute quantities required for featurewise normalization
# (std, mean, and principal components if ZCA whitening is applied).
datagen.fit(X_train)
if not os.path.exists(out_dir):
os.makedirs(out_dir)

csv = CSVLogger(out_dir+dirname+'.csv', separator=',', append=True)
model_checkpoint = ModelCheckpoint(out_dir+'weights.hdf5', monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=False, mode='auto')
callbacks=[lr_reducer, early_stopper, csv]

if K.backend() == 'tensorflow':
tensorboard = TensorBoard(log_dir=out_dir, histogram_freq=10, write_graph=True)
callbacks.append(tensorboard)

start_time = time.time()
# Fit the model on the batches generated by datagen.flow().
history = model.fit_generator(datagen.flow(X_train, Y_train,
batch_size=batch_size),
samples_per_epoch=X_train.shape[0],
nb_epoch=nb_epoch,
validation_data=(X_test, Y_test),
verbose=1, max_q_size=100,
callbacks=callbacks)

end_fit_time = time.time()
average_time_per_epoch = (end_fit_time - start_time) / nb_epoch

model.predict(X_test, batch_size=batch_size, verbose=1)

end_predict_time = time.time()
average_time_to_predict = (end_predict_time - end_fit_time) / nb_epoch

results.append((history, average_time_per_epoch, average_time_to_predict))
print ('--------------------------------------------------------------------')
print ('[run_name,batch_size,average_time_per_epoch,average_time_to_predict]')
print ([dirname,batch_size,average_time_per_epoch,average_time_to_predict])
print ('--------------------------------------------------------------------')

# Close the Session when we're done.
sess.close()



# Compare models' accuracy, loss and elapsed time per epoch.
plt.ioff()
plt.style.use('ggplot')
ax1 = plt.subplot2grid((2, 2), (0, 0))
ax1.set_title('Accuracy')
ax1.set_ylabel('Validation Accuracy')
ax1.set_xlabel('Epochs')
ax2 = plt.subplot2grid((2, 2), (1, 0))
ax2.set_title('Loss')
ax2.set_ylabel('Validation Loss')
ax2.set_xlabel('Epochs')
ax3 = plt.subplot2grid((2, 2), (0, 1), rowspan=2)
ax3.set_title('Time')
ax3.set_ylabel('Seconds')
for mode, result in zip(batch_sizes, results):
ax1.plot(result[0].epoch, result[0].history['val_acc'], label=mode)
ax2.plot(result[0].epoch, result[0].history['val_loss'], label=mode)
ax1.legend()
ax2.legend()
ax3.bar(np.arange(len(results)), [x[1] for x in results],
tick_label=batch_sizes, align='center')
plt.tight_layout()
plt.savefig(out_dir + dirname + '/' + dirname + '_fig.png')


# with open(out_dir+dirname+"_avg_time_results.csv", "wb") as f:
# w = csv.writer(f)
# w.writerows(avg_time_results)
#
# with open(out_dir+dirname+"_results.csv", "wb") as f:
# w = csv.writer(f)
# w.writerows(results)