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hyperas_w2v_struct.py
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hyperas_w2v_struct.py
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from __future__ import print_function
# this version just varies large structures of the model, but not things like size of layers
gpu = 'gpu3'
import os
os.environ["THEANO_FLAGS"] = "mode=FAST_RUN,device=%s,floatX=float32" % gpu
from hyperopt import Trials, STATUS_OK, tpe
from hyperas import optim
from hyperas.distributions import choice, uniform, conditional, quniform
from keras.preprocessing.image import ImageDataGenerator
from keras.datasets import cifar100
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.optimizers import SGD
from network_utils import get_w2v_labels, accuracy_w2v
import numpy as np
import cPickle as pickle
# Open an IPython session if an exception is found
import sys
from IPython.core import ultratb
sys.excepthook = ultratb.FormattedTB(mode='Verbose', color_scheme='Linux', call_pdb=1)
nb_epoch = 30#10 #NOTE: need to modify this elsewhere as well
nb_evals = 100#50
nb_dim = 50 #NOTE: this needs to be modified in two other places as well
def data():
nb_dim=50
(X_train, y_train), (X_test, y_test) = cifar100.load_data(label_mode='fine')
# convert class vectors to w2v class matrices
Y_train = get_w2v_labels(y_train, dim=nb_dim)
Y_test = get_w2v_labels(y_test, dim=nb_dim)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
return X_train, Y_train, X_test, Y_test
def model(X_train, Y_train, X_test, Y_test):
img_rows, img_cols = 32, 32
img_channels = 3
nb_dim = 50
nb_epoch=30
#dense_layer_size = {{choice([256, 512, 1024])}}
objective = 'mse'
optimizer = {{choice(['rmsprop', 'adam', 'sgd'])}}
batch_size = {{choice([32, 64, 128])}}
#num_conv1 = int({{quniform(24, 64, 1)}})
#num_conv2 = int({{quniform(32, 96, 1)}})
#model_style = {{choice(['original', 'wider', 'deeper', 'wider_activation', 'nodrop_original', 'nodrop_wider'])}}
model_style = {{choice(['original', 'wider', 'deeper', 'wider_activation'])}}
data_augmentation = {{choice(['True','False'])}}
params = {#'dense_layer_size':dense_layer_size,
'optimizer':optimizer,
'batch_size':batch_size,
#'num_conv1':num_conv1,
#'num_conv2':num_conv2,
'model_style':model_style
}
if optimizer == 'sgd':
learning_rate = {{loguniform(np.log(0.001),np.log(0.999))}}
params['learning_rate'] = learning_rate
if data_augmentation:
more_augmentation = {{choice(['True','False'])}}
params['more_augmentation'] = more_augmentation
model = Sequential()
if model_style == 'original':
model.add(Convolution2D(32, 3, 3, border_mode='same',
input_shape=(img_channels, img_rows, img_cols)))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_dim))
#model.add(Activation('softmax'))
#TODO: might want a linear activation function here
elif model_style == 'wider':
model.add(Convolution2D(48, 3, 3, border_mode='same',
input_shape=(img_channels, img_rows, img_cols)))
model.add(Activation('relu'))
model.add(Convolution2D(48, 5, 5))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Convolution2D(96, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(96, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_dim))
#model.add(Activation('softmax'))
#TODO: might want a linear activation function here
elif model_style == 'deeper':
model.add(Convolution2D(32, 3, 3, border_mode='same',
input_shape=(img_channels, img_rows, img_cols)))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Convolution2D(96, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(96, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_dim))
#model.add(Activation('softmax'))
#TODO: might want a linear activation function here
elif model_style == 'wider_activation':
model.add(Convolution2D(48, 3, 3, border_mode='same',
input_shape=(img_channels, img_rows, img_cols)))
model.add(Activation('relu'))
model.add(Convolution2D(48, 5, 5))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Convolution2D(96, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(96, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_dim))
model.add(Activation('linear'))
#TODO: might want a linear activation function here
if model_style == 'nodrop_original':
model.add(Convolution2D(32, 3, 3, border_mode='same',
input_shape=(img_channels, img_rows, img_cols)))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dense(nb_dim))
#model.add(Activation('softmax'))
#TODO: might want a linear activation function here
elif model_style == 'nodrop_wider':
model.add(Convolution2D(48, 3, 3, border_mode='same',
input_shape=(img_channels, img_rows, img_cols)))
model.add(Activation('relu'))
model.add(Convolution2D(48, 5, 5))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(96, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(96, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))
model.add(Dense(nb_dim))
#model.add(Activation('softmax'))
#TODO: might want a linear activation function here
if optimizer == 'sgd':
# let's train the model using SGD + momentum (how original).
sgd = SGD(lr=learning_rate, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss=objective, optimizer=sgd)
elif optimizer == 'rmsprop':
model.compile(loss=objective, optimizer='rmsprop')
else:
model.compile(loss=objective, optimizer=optimizer)
if not data_augmentation:
print('Not using data augmentation.')
history = model.fit(X_train, Y_train, batch_size=batch_size,
nb_epoch=nb_epoch, show_accuracy=True,
validation_data=(X_test, Y_test), shuffle=True)
else:
print('Using real-time data augmentation.')
if more_augmentation:
# this will do preprocessing and realtime data augmentation
datagen = ImageDataGenerator(
featurewise_center=True, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=True, # 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
else:
# 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()
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, show_accuracy=True,
validation_data=(X_test, Y_test),
nb_worker=1)
#score, acc = model.evaluate(X_test, Y_test, verbose=0)
loss = model.evaluate(X_test, Y_test, verbose=0)
print('Test loss:', loss)
return {'loss': loss, 'status': STATUS_OK, 'params':params}
if __name__ == '__main__':
trials = Trials()
best_run, best_model = optim.minimize(model=model,
data=data,
algo=tpe.suggest,
max_evals=nb_evals,
trials=trials)
X_train, Y_train, X_test, Y_test = data()
#print("Evaluation of best performing model:")
#print(best_model.evaluate(X_test, Y_test))
pickle.dump(trials, open('net_output/trials_w2v_struct_dim%s_epoch%s_evals%s.p'%(nb_dim, nb_epoch, nb_evals),'w'))