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hyperas_hierarchy_two_output.py
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hyperas_hierarchy_two_output.py
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from __future__ import print_function
gpu = 'gpu0'
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.datasets import cifar100
from keras.utils import np_utils
from keras.models import Graph
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
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 #NOTE: need to modify this elsewhere as well
nb_evals = 100
def data():
nb_classes_fine = 100
nb_classes_coarse = 20
(X_train, y_train_fine), (X_test, y_test_fine) = cifar100.load_data(label_mode='fine')
(_, y_train_coarse), (_, y_test_coarse) = cifar100.load_data(label_mode='coarse')
# convert class vectors to binary class matrices
Y_train_fine = np_utils.to_categorical(y_train_fine, nb_classes_fine)
Y_train_coarse = np_utils.to_categorical(y_train_coarse, nb_classes_coarse)
Y_test_fine = np_utils.to_categorical(y_test_fine, nb_classes_fine)
Y_test_coarse = np_utils.to_categorical(y_test_coarse, nb_classes_coarse)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
#Y_train = np.concatenate((Y_train_coarse, Y_train_fine), axis=1)
#Y_test = np.concatenate((Y_test_coarse, Y_test_fine), axis=1)
return X_train, Y_train_fine, Y_train_coarse, X_test, Y_test_fine, Y_test_coarse
def model(X_train, Y_train_fine, Y_train_coarse, X_test, Y_test_fine, Y_test_coarse):
nb_dim = 20
img_rows, img_cols = 32, 32
img_channels = 3
#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', 'nodroporiginal', 'moredense', 'custom1', 'split', 'nodrop_split'])}}
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
}
model = Graph()
model.add_input(name='input', input_shape=(img_channels, img_rows, img_cols))
if model_style == 'original':
model.add_node(Convolution2D(32, 3, 3, border_mode='same',
input_shape=(img_channels, img_rows, img_cols)),
name='conv1', input='input')
model.add_node(Activation('relu'),
name='relu1', input='conv1')
model.add_node(Convolution2D(32, 3, 3),
name='conv2', input='relu1')
model.add_node(Activation('relu'),
name='relu2', input='conv2')
model.add_node(MaxPooling2D(pool_size=(2, 2)),
name='pool1', input='relu2')
model.add_node(Dropout(0.25),
name='drop1', input='pool1')
model.add_node(Convolution2D(64, 3, 3, border_mode='same'),
name='conv3', input='drop1')
model.add_node(Activation('relu'),
name='relu3', input='conv3')
model.add_node(Convolution2D(64, 3, 3),
name='conv4', input='relu3')
model.add_node(Activation('relu'),
name='relu4', input='conv4')
model.add_node(MaxPooling2D(pool_size=(2, 2)),
name='pool2', input='relu4')
model.add_node(Dropout(0.25),
name='drop2', input='pool2')
model.add_node(Flatten(),
name='flat1', input='drop2')
model.add_node(Dense(512),
name='dense1', input='flat1')
model.add_node(Activation('relu'),
name='relu5', input='dense1')
model.add_node(Dropout(0.5),
name='drop3', input='relu5')
#model.add_node(Dense(nb_classes_coarse + nb_classes_fine),
# name='dense2', input='drop3')
model.add_node(Dense(nb_classes_coarse),
name='dense_c', input='drop3')
model.add_node(Activation('softmax'),
name='soft_c', input='dense_c')
model.add_node(Dense(nb_classes_fine),
name='dense_f', input='drop3')
model.add_node(Activation('softmax'),
name='soft_f', input='dense_f')
if model_style == 'nodroporiginal':
model.add_node(Convolution2D(32, 3, 3, border_mode='same',
input_shape=(img_channels, img_rows, img_cols)),
name='conv1', input='input')
model.add_node(Activation('relu'),
name='relu1', input='conv1')
model.add_node(Convolution2D(32, 3, 3),
name='conv2', input='relu1')
model.add_node(Activation('relu'),
name='relu2', input='conv2')
model.add_node(MaxPooling2D(pool_size=(2, 2)),
name='pool1', input='relu2')
model.add_node(Convolution2D(64, 3, 3, border_mode='same'),
name='conv3', input='pool1')
model.add_node(Activation('relu'),
name='relu3', input='conv3')
model.add_node(Convolution2D(64, 3, 3),
name='conv4', input='relu3')
model.add_node(Activation('relu'),
name='relu4', input='conv4')
model.add_node(MaxPooling2D(pool_size=(2, 2)),
name='pool2', input='relu4')
model.add_node(Flatten(),
name='flat1', input='pool2')
model.add_node(Dense(512),
name='dense1', input='flat1')
model.add_node(Activation('relu'),
name='relu5', input='dense1')
#model.add_node(Dense(nb_classes_coarse + nb_classes_fine),
# name='dense2', input='drop3')
model.add_node(Dense(nb_classes_coarse),
name='dense_c', input='relu5')
model.add_node(Activation('softmax'),
name='soft_c', input='dense_c')
model.add_node(Dense(nb_classes_fine),
name='dense_f', input='relu5')
model.add_node(Activation('softmax'),
name='soft_f', input='dense_f')
elif model_style == 'moredense':
model.add_node(Convolution2D(32, 3, 3, border_mode='same',
input_shape=(img_channels, img_rows, img_cols)),
name='conv1', input='input')
model.add_node(Activation('relu'),
name='relu1', input='conv1')
model.add_node(Convolution2D(32, 3, 3),
name='conv2', input='relu1')
model.add_node(Activation('relu'),
name='relu2', input='conv2')
model.add_node(MaxPooling2D(pool_size=(2, 2)),
name='pool1', input='relu2')
model.add_node(Dropout(0.25),
name='drop1', input='pool1')
model.add_node(Convolution2D(64, 3, 3, border_mode='same'),
name='conv3', input='drop1')
model.add_node(Activation('relu'),
name='relu3', input='conv3')
model.add_node(Convolution2D(64, 3, 3),
name='conv4', input='relu3')
model.add_node(Activation('relu'),
name='relu4', input='conv4')
model.add_node(MaxPooling2D(pool_size=(2, 2)),
name='pool2', input='relu4')
model.add_node(Dropout(0.25),
name='drop2', input='pool2')
model.add_node(Flatten(),
name='flat1', input='drop2')
model.add_node(Dense(512),
name='dense1', input='flat1')
model.add_node(Activation('relu'),
name='relu5', input='dense1')
model.add_node(Dropout(0.25),
name='drop3', input='relu5')
model.add_node(Dense(512),
name='dense2', input='drop3')
model.add_node(Activation('relu'),
name='relu6', input='dense2')
model.add_node(Dropout(0.25),
name='drop4', input='relu6')
#model.add_node(Dense(nb_classes_coarse + nb_classes_fine),
# name='dense2', input='drop3')
model.add_node(Dense(nb_classes_coarse),
name='dense_c', input='drop4')
model.add_node(Activation('softmax'),
name='soft_c', input='dense_c')
model.add_node(Dense(nb_classes_fine),
name='dense_f', input='drop4')
model.add_node(Activation('softmax'),
name='soft_f', input='dense_f')
elif model_style == 'wider':
model.add_node(Convolution2D(48, 3, 3, border_mode='same',
input_shape=(img_channels, img_rows, img_cols)),
name='conv1', input='input')
model.add_node(Activation('relu'),
name='relu1', input='conv1')
model.add_node(Convolution2D(48, 3, 3),
name='conv2', input='relu1')
model.add_node(Activation('relu'),
name='relu2', input='conv2')
model.add_node(MaxPooling2D(pool_size=(2, 2)),
name='pool1', input='relu2')
model.add_node(Dropout(0.25),
name='drop1', input='pool1')
model.add_node(Convolution2D(96, 3, 3, border_mode='same'),
name='conv3', input='drop1')
model.add_node(Activation('relu'),
name='relu3', input='conv3')
model.add_node(Convolution2D(96, 3, 3),
name='conv4', input='relu3')
model.add_node(Activation('relu'),
name='relu4', input='conv4')
model.add_node(MaxPooling2D(pool_size=(2, 2)),
name='pool2', input='relu4')
model.add_node(Dropout(0.25),
name='drop2', input='pool2')
model.add_node(Flatten(),
name='flat1', input='drop2')
model.add_node(Dense(1024),
name='dense1', input='flat1')
model.add_node(Activation('relu'),
name='relu5', input='dense1')
model.add_node(Dropout(0.5),
name='drop3', input='relu5')
#model.add_node(Dense(nb_classes_coarse + nb_classes_fine),
# name='dense2', input='drop3')
model.add_node(Dense(nb_classes_coarse),
name='dense_c', input='drop3')
model.add_node(Activation('softmax'),
name='soft_c', input='dense_c')
model.add_node(Dense(nb_classes_fine),
name='dense_f', input='drop3')
model.add_node(Activation('softmax'),
name='soft_f', input='dense_f')
elif model_style == 'custom1':
model.add_node(Convolution2D(48, 5, 5, border_mode='same',
input_shape=(img_channels, img_rows, img_cols)),
name='conv1', input='input')
model.add_node(Activation('relu'),
name='relu1', input='conv1')
model.add_node(Convolution2D(48, 5, 5),
name='conv2', input='relu1')
model.add_node(Activation('relu'),
name='relu2', input='conv2')
model.add_node(MaxPooling2D(pool_size=(2, 2)),
name='pool1', input='relu2')
model.add_node(Dropout(0.10),
name='drop1', input='pool1')
model.add_node(Convolution2D(96, 3, 3, border_mode='same'),
name='conv3', input='drop1')
model.add_node(Activation('relu'),
name='relu3', input='conv3')
model.add_node(Convolution2D(96, 3, 3),
name='conv4', input='relu3')
model.add_node(Activation('relu'),
name='relu4', input='conv4')
model.add_node(MaxPooling2D(pool_size=(3, 3)),
name='pool2', input='relu4')
model.add_node(Dropout(0.10),
name='drop2', input='pool2')
model.add_node(Flatten(),
name='flat1', input='drop2')
model.add_node(Dense(1024),
name='dense1', input='flat1')
model.add_node(Activation('relu'),
name='relu5', input='dense1')
model.add_node(Dropout(0.10),
name='drop3', input='relu5')
#model.add_node(Dense(nb_classes_coarse + nb_classes_fine),
# name='dense2', input='drop3')
model.add_node(Dense(nb_classes_coarse),
name='dense_c', input='drop3')
model.add_node(Activation('softmax'),
name='soft_c', input='dense_c')
model.add_node(Dense(nb_classes_fine),
name='dense_f', input='drop3')
model.add_node(Activation('softmax'),
name='soft_f', input='dense_f')
elif model_style == 'split': # have some convolutions for each of coarse and fine only
model.add_node(Convolution2D(32, 3, 3, border_mode='same',
input_shape=(img_channels, img_rows, img_cols)),
name='conv1', input='input')
model.add_node(Activation('relu'),
name='relu1', input='conv1')
model.add_node(Convolution2D(32, 3, 3),
name='conv2', input='relu1')
model.add_node(Activation('relu'),
name='relu2', input='conv2')
model.add_node(MaxPooling2D(pool_size=(2, 2)),
name='pool1', input='relu2')
model.add_node(Dropout(0.25),
name='drop1', input='pool1')
model.add_node(Convolution2D(64, 3, 3, border_mode='same'),
name='conv3_c', input='drop1')
model.add_node(Activation('relu'),
name='relu3_c', input='conv3_c')
model.add_node(Convolution2D(64, 3, 3),
name='conv4_c', input='relu3_c')
model.add_node(Activation('relu'),
name='relu4_c', input='conv4_c')
model.add_node(MaxPooling2D(pool_size=(2, 2)),
name='pool2_c', input='relu4_c')
model.add_node(Dropout(0.25),
name='drop2_c', input='pool2_c')
model.add_node(Flatten(),
name='flat1_c', input='drop2_c')
model.add_node(Dense(512),
name='dense1_c', input='flat1_c')
model.add_node(Activation('relu'),
name='relu5_c', input='dense1_c')
model.add_node(Dropout(0.5),
name='drop3_c', input='relu5_c')
#model.add_node(Dense(nb_classes_coarse + nb_classes_fine),
# name='dense2', input='drop3')
model.add_node(Dense(nb_classes_coarse),
name='dense_c', input='drop3_c')
model.add_node(Activation('softmax'),
name='soft_c', input='dense_c')
model.add_node(Convolution2D(64, 3, 3, border_mode='same'),
name='conv3_f', input='drop1')
model.add_node(Activation('relu'),
name='relu3_f', input='conv3_f')
model.add_node(Convolution2D(64, 3, 3),
name='conv4_f', input='relu3_f')
model.add_node(Activation('relu'),
name='relu4_f', input='conv4_f')
model.add_node(MaxPooling2D(pool_size=(2, 2)),
name='pool2_f', input='relu4_f')
model.add_node(Dropout(0.25),
name='drop2_f', input='pool2_f')
model.add_node(Flatten(),
name='flat1_f', input='drop2_f')
model.add_node(Dense(512),
name='dense1_f', input='flat1_f')
model.add_node(Activation('relu'),
name='relu5_f', input='dense1_f')
model.add_node(Dropout(0.5),
name='drop3_f', input='relu5_f')
model.add_node(Dense(nb_classes_fine),
name='dense_f', input='drop3_f')
model.add_node(Activation('softmax'),
name='soft_f', input='dense_f')
elif model_style == 'nodrop_split': # have some convolutions for each of coarse and fine only
model.add_node(Convolution2D(32, 3, 3, border_mode='same',
input_shape=(img_channels, img_rows, img_cols)),
name='conv1', input='input')
model.add_node(Activation('relu'),
name='relu1', input='conv1')
model.add_node(Convolution2D(32, 3, 3),
name='conv2', input='relu1')
model.add_node(Activation('relu'),
name='relu2', input='conv2')
model.add_node(MaxPooling2D(pool_size=(2, 2)),
name='pool1', input='relu2')
model.add_node(Convolution2D(64, 3, 3, border_mode='same'),
name='conv3_c', input='pool1')
model.add_node(Activation('relu'),
name='relu3_c', input='conv3_c')
model.add_node(Convolution2D(64, 3, 3),
name='conv4_c', input='relu3_c')
model.add_node(Activation('relu'),
name='relu4_c', input='conv4_c')
model.add_node(MaxPooling2D(pool_size=(2, 2)),
name='pool2_c', input='relu4_c')
model.add_node(Flatten(),
name='flat1_c', input='pool2_c')
model.add_node(Dense(512),
name='dense1_c', input='flat1_c')
model.add_node(Activation('relu'),
name='relu5_c', input='dense1_c')
#model.add_node(Dense(nb_classes_coarse + nb_classes_fine),
# name='dense2', input='drop3')
model.add_node(Dense(nb_classes_coarse),
name='dense_c', input='relu5_c')
model.add_node(Activation('softmax'),
name='soft_c', input='dense_c')
model.add_node(Convolution2D(64, 3, 3, border_mode='same'),
name='conv3_f', input='pool1')
model.add_node(Activation('relu'),
name='relu3_f', input='conv3_f')
model.add_node(Convolution2D(64, 3, 3),
name='conv4_f', input='relu3_f')
model.add_node(Activation('relu'),
name='relu4_f', input='conv4_f')
model.add_node(MaxPooling2D(pool_size=(2, 2)),
name='pool2_f', input='relu4_f')
model.add_node(Flatten(),
name='flat1_f', input='pool2_f')
model.add_node(Dense(512),
name='dense1_f', input='flat1_f')
model.add_node(Activation('relu'),
name='relu5_f', input='dense1_f')
model.add_node(Dense(nb_classes_fine),
name='dense_f', input='relu5_f')
model.add_node(Activation('softmax'),
name='soft_f', input='dense_f')
model.add_output(name='output_fine', input='soft_f')
model.add_output(name='output_coarse', input='soft_c')
model.compile(loss={'output_fine':objective,'output_coarse':objective}, optimizer=optimizer, metrics=['accuracy'])
history = model.fit({'input':X_train, 'output_fine':Y_train_fine,'output_coarse':Y_train_coarse}, batch_size=batch_size,
nb_epoch=30, verbose=2,#show_accuracy=True,
validation_data={'input':X_test, 'output_fine':Y_test_fine,'output_coarse':Y_test_coarse}, shuffle=True)
#score, acc = model.evaluate({'input':X_train, 'output_fine':Y_train_fine,'output_coarse':Y_train_coarse}, verbose=0)
loss, fine_loss, coarse_loss, fine_acc, coarse_acc = model.evaluate({'input':X_train, 'output_fine':Y_train_fine,'output_coarse':Y_train_coarse}, verbose=0)
print('Test fine accuracy:', fine_acc)
print('Test coarse accuracy:', coarse_acc)
print('Combined loss', fine_loss + coarse_loss)
#return {'loss': -acc, 'status': STATUS_OK, 'model':model}
return {'loss': fine_loss + coarse_loss, 'status': STATUS_OK, 'params':params, 'fine_acc':fine_acc, 'coarse_acc':coarse_acc}
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_fine, Y_train_coarse, X_test, Y_test_fine, Y_test_coarse = data()
#print("Evaluation of best performing model:")
#print(best_model.evaluate(X_test, Y_test))
print("saving results to: net_output/trials_hier2output_epoch%s_evals%s.p"%(nb_epoch, nb_evals))
pickle.dump(trials, open('net_output/trials_hier2output_epoch%s_evals%s.p'%(nb_epoch, nb_evals),'w'))