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hyperas_approach.py
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hyperas_approach.py
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__author__ = 'Dimitris'
from hyperopt import Trials, STATUS_OK, tpe
from hyperas import optim
from hyperas.distributions import choice, uniform
from pprint import pprint
def keras_model():
from keras.models import Sequential
from keras.layers.embeddings import Embedding
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.core import Dense, Reshape, Activation, Flatten, Dropout
from keras.regularizers import l1, activity_l1, l2, activity_l2
from aiding_funcs.embeddings_handling import get_the_folds, join_folds
import pickle
embeddings = pickle.load( open( "/data/dpappas/personality/emb.p", "rb" ) )
train = pickle.load( open( "/data/dpappas/personality/train.p", "rb" ) )
no_of_folds = 10
folds = get_the_folds(train,no_of_folds)
train_data = join_folds(folds,folds.keys()[:-1])
validation_data = folds[folds.keys()[-1]]
max_input_length = validation_data['features'].shape[1]
CNN_filters = {{choice([5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95])}}
CNN_rows = {{choice([1,2,3,4,5,6])}}
Dense_size = {{choice([50, 100, 150, 200, 250, 300, 350, 400, 450, 500])}}
opt = {{choice([ 'adadelta','sgd','rmsprop', 'adagrad', 'adadelta', 'adam'])}}
is_trainable = {{choice([ True, False ])}}
D = embeddings.shape[-1]
cols = D
out_dim = 5
model = Sequential()
model.add(Embedding(input_dim = embeddings.shape[0], output_dim=D, weights=[embeddings], trainable=is_trainable, input_length = max_input_length))
model.add(Reshape((1, max_input_length, D)))
model.add(Convolution2D( CNN_filters, CNN_rows, cols, dim_ordering='th', activation='sigmoid' ))
sh = model.layers[-1].output_shape
model.add(MaxPooling2D(pool_size=(sh[-2], sh[-1]),dim_ordering = 'th'))
model.add(Flatten())
model.add(Dense(Dense_size, activation='sigmoid',W_regularizer=l2({{uniform(0, 1)}}),activity_regularizer=activity_l2({{uniform(0, 1)}})))
model.add(Dense(out_dim, activation='linear',W_regularizer=l2({{uniform(0, 1)}}),activity_regularizer=activity_l2({{uniform(0, 1)}})))
model.compile(loss='mse', optimizer=opt)
model.fit(train_data['features'], train_data['labels'], nb_epoch=50, show_accuracy=False, verbose=2)
#score = model.evaluate( validation_data['features'], validation_data['labels'])
score = model.evaluate( train_data['features'], train_data['labels'])
return {'loss': score, 'status': STATUS_OK}
if __name__ == '__main__':
best_run = optim.minimize(keras_model, algo=tpe.suggest, max_evals=1000, trials=Trials())
pprint(best_run)
'''
Best on Validation set
{'CNN_filters': 17, aka 90
'CNN_rows': 5, aka 6
'Dense_size': 9, 500
'activity_l2': 0.569606301223449,
'is_trainable': 0, TRUE
'l2': 0.36456791622174234,
'opt': 2} aka 'rmsprop'
'''
'''
Best on Training set
{'CNN_filters': 0, 5
'CNN_rows': 5, 6
'Dense_size': 5, 300
'activity_l2': 0.9593094177755246,
'activity_l2_1': 0.0011426757779919388,
'is_trainable': 1, False
'l2': 0.37173327555716984,
'l2_1': 0.000165584846072854,
'opt': 0 adadelta
}
'''