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hyperparameter_search.py
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hyperparameter_search.py
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import json
import os
import traceback
from keras import backend as K
from keras.layers import Conv1D, BatchNormalization, GlobalAveragePooling1D, Permute, Dropout, Flatten
from keras.layers import Input, PReLU, Dense, LSTM, CuDNNLSTM, concatenate, Activation, GRU, SimpleRNN
from keras.models import Model
from utils.constants import MAX_SEQUENCE_LENGTH_LIST, NB_CLASSES_LIST
from utils.generic_utils import load_dataset_at
from utils.keras_utils import train_model, evaluate_model, loss_model
from utils.layer_utils import AttentionLSTM
def generate_lstmfcn(MAX_SEQUENCE_LENGTH, NB_CLASS, NUM_CELLS=8):
ip = Input(shape=(1, MAX_SEQUENCE_LENGTH))
x = LSTM(NUM_CELLS)(ip)
x = Dropout(0.8)(x)
y = Permute((2, 1))(ip)
y = Conv1D(128, 8, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = Conv1D(256, 5, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = Conv1D(128, 3, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = GlobalAveragePooling1D()(y)
x = concatenate([x, y])
out = Dense(NB_CLASS, activation='softmax')(x)
model = Model(ip, out)
model.summary()
# add load model code here to fine-tune
return model
def generate_grufcn(MAX_SEQUENCE_LENGTH, NB_CLASS, NUM_CELLS=8):
ip = Input(shape=(1, MAX_SEQUENCE_LENGTH))
x = GRU(NUM_CELLS)(ip)
x = Dropout(0.8)(x)
y = Permute((2, 1))(ip)
y = Conv1D(128, 8, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = Conv1D(256, 5, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = Conv1D(128, 3, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = GlobalAveragePooling1D()(y)
x = concatenate([x, y])
out = Dense(NB_CLASS, activation='softmax')(x)
model = Model(ip, out)
model.summary()
# add load model code here to fine-tune
return model
def generate_rnnfcn(MAX_SEQUENCE_LENGTH, NB_CLASS, NUM_CELLS=8):
ip = Input(shape=(1, MAX_SEQUENCE_LENGTH))
x = SimpleRNN(NUM_CELLS)(ip)
x = Dropout(0.8)(x)
y = Permute((2, 1))(ip)
y = Conv1D(128, 8, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = Conv1D(256, 5, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = Conv1D(128, 3, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = GlobalAveragePooling1D()(y)
x = concatenate([x, y])
out = Dense(NB_CLASS, activation='softmax')(x)
model = Model(ip, out)
model.summary()
# add load model code here to fine-tune
return model
def generate_cnnfcn(MAX_SEQUENCE_LENGTH, NB_CLASS, NUM_CELLS=8):
ip = Input(shape=(1, MAX_SEQUENCE_LENGTH))
x = Conv1D(NUM_CELLS, 3, padding='same', kernel_initializer='he_uniform')(ip)
x = Dropout(0.8)(x)
x = GlobalAveragePooling1D()(x)
y = Permute((2, 1))(ip)
y = Conv1D(128, 8, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = Conv1D(256, 5, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = Conv1D(128, 3, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = GlobalAveragePooling1D()(y)
x = concatenate([x, y])
out = Dense(NB_CLASS, activation='softmax')(x)
model = Model(ip, out)
model.summary()
# add load model code here to fine-tune
return model
def generate_densefcn(MAX_SEQUENCE_LENGTH, NB_CLASS, NUM_CELLS=8):
ip = Input(shape=(1, MAX_SEQUENCE_LENGTH))
x = Flatten()(ip)
x = Dense(NUM_CELLS, activation='sigmoid')(x)
x = Dropout(0.8)(x)
y = Permute((2, 1))(ip)
y = Conv1D(128, 8, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = Conv1D(256, 5, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = Conv1D(128, 3, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = GlobalAveragePooling1D()(y)
x = concatenate([x, y])
out = Dense(NB_CLASS, activation='softmax')(x)
model = Model(ip, out)
model.summary()
# add load model code here to fine-tune
return model
def generate_alstmfcn(MAX_SEQUENCE_LENGTH, NB_CLASS, NUM_CELLS=8):
ip = Input(shape=(1, MAX_SEQUENCE_LENGTH))
x = AttentionLSTM(NUM_CELLS)(ip)
x = Dropout(0.8)(x)
y = Permute((2, 1))(ip)
y = Conv1D(128, 8, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = Conv1D(256, 5, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = Conv1D(128, 3, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = GlobalAveragePooling1D()(y)
x = concatenate([x, y])
out = Dense(NB_CLASS, activation='softmax')(x)
model = Model(ip, out)
model.summary()
# add load model code here to fine-tune
return model
def generate_ndlstmfcn(MAX_SEQUENCE_LENGTH, NB_CLASS, NUM_CELLS=8):
ip = Input(shape=(1, MAX_SEQUENCE_LENGTH))
x = Permute((2, 1))(ip)
x = CuDNNLSTM(NUM_CELLS)(x)
x = Dropout(0.8)(x)
y = Permute((2, 1))(ip)
y = Conv1D(128, 8, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = Conv1D(256, 5, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = Conv1D(128, 3, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = GlobalAveragePooling1D()(y)
x = concatenate([x, y])
out = Dense(NB_CLASS, activation='softmax')(x)
model = Model(ip, out)
model.summary()
# add load model code here to fine-tune
return model
def generate_ndalstmfcn(MAX_SEQUENCE_LENGTH, NB_CLASS, NUM_CELLS=8):
ip = Input(shape=(1, MAX_SEQUENCE_LENGTH))
x = AttentionLSTM(NUM_CELLS)(ip)
x = Dropout(0.8)(x)
y = Permute((2, 1))(ip)
y = Conv1D(128, 8, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = Conv1D(256, 5, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = Conv1D(128, 3, padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = GlobalAveragePooling1D()(y)
x = concatenate([x, y])
out = Dense(NB_CLASS, activation='softmax')(x)
model = Model(ip, out)
model.summary()
# add load model code here to fine-tune
return model
if __name__ == "__main__":
dataset_map = [('Adiac', 0),
('ArrowHead', 1),
('ChlorineConcentration', 2),
('InsectWingbeatSound', 3),
('Lighting7', 4),
('Wine', 5),
('WordsSynonyms', 6),
('50words', 7),
('Beef', 8),
('DistalPhalanxOutlineAgeGroup', 9),
('DistalPhalanxOutlineCorrect', 10),
('DistalPhalanxTW', 11),
('ECG200', 12),
('ECGFiveDays', 13),
('BeetleFly', 14),
('BirdChicken', 15),
('ItalyPowerDemand', 16),
('SonyAIBORobotSurface', 17),
('SonyAIBORobotSurfaceII', 18),
('MiddlePhalanxOutlineAgeGroup', 19),
('MiddlePhalanxOutlineCorrect', 20),
('MiddlePhalanxTW', 21),
('ProximalPhalanxOutlineAgeGroup', 22),
('ProximalPhalanxOutlineCorrect', 23),
('ProximalPhalanxTW', 24),
('MoteStrain', 25),
('MedicalImages', 26),
('Strawberry', 27),
('ToeSegmentation1', 28),
('Coffee', 29),
('Cricket_X', 30),
('Cricket_Y', 31),
('Cricket_Z', 32),
('uWaveGestureLibrary_X', 33),
('uWaveGestureLibrary_Y', 34),
('uWaveGestureLibrary_Z', 35),
('ToeSegmentation2', 36),
('DiatomSizeReduction', 37),
('car', 38),
('CBF', 39),
('CinC_ECG_torso', 40),
('Computers', 41),
('Earthquakes', 42),
('ECG5000', 43),
('ElectricDevices', 44),
('FaceAll', 45),
('FaceFour', 46),
('FacesUCR', 47),
('Fish', 48),
('FordA', 49),
('FordB', 50),
('Gun_Point', 51),
('Ham', 52),
('HandOutlines', 53),
('Haptics', 54),
('Herring', 55),
('InlineSkate', 56),
('LargeKitchenAppliances', 57),
('Lighting2', 58),
('MALLAT', 59),
('Meat', 60),
('NonInvasiveFatalECG_Thorax1', 61),
('NonInvasiveFatalECG_Thorax2', 62),
('OliveOil', 63),
('OSULeaf', 64),
('PhalangesOutlinesCorrect', 65),
('Phoneme', 66),
('plane', 67),
('RefrigerationDevices', 68),
('ScreenType', 69),
('ShapeletSim', 70),
('ShapesAll', 71),
('SmallKitchenAppliances', 72),
('StarlightCurves', 73),
('SwedishLeaf', 74),
('Symbols', 75),
('synthetic_control', 76),
('Trace', 77),
('Patterns', 78),
('TwoLeadECG', 79),
('UWaveGestureLibraryAll', 80),
('wafer', 81),
('Worms', 82),
('WormsTwoClass', 83),
('yoga', 84),
('ACSF1', 85),
('AllGestureWiimoteX', 86),
('AllGestureWiimoteY', 87),
('AllGestureWiimoteZ', 88),
('BME', 89),
('Chinatown', 90),
('Crop', 91),
('DodgerLoopDay', 92),
('DodgerLoopGame', 93),
('DodgerLoopWeekend', 94),
('EOGHorizontalSignal', 95),
('EOGVerticalSignal', 96),
('EthanolLevel', 97),
('FreezerRegularTrain', 98),
('FreezerSmallTrain', 99),
('Fungi', 100),
('GestureMidAirD1', 101),
('GestureMidAirD2', 102),
('GestureMidAirD3', 103),
('GesturePebbleZ1', 104),
('GesturePebbleZ2', 105),
('GunPointAgeSpan', 106),
('GunPointMaleVersusFemale', 107),
('GunPointOldVersusYoung', 108),
('HouseTwenty', 109),
('InsectEPGRegularTrain', 110),
('InsectEPGSmallTrain', 111),
('MelbournePedestrian', 112),
('MixedShapesRegularTrain', 113),
('MixedShapesSmallTrain', 114),
('PickupGestureWiimoteZ', 115),
('PigAirwayPressure', 116),
('PigArtPressure', 117),
('PigCVP', 118),
('PLAID', 119),
('PowerCons', 120),
('Rock', 121),
('SemgHandGenderCh2', 122),
('SemgHandMovementCh2', 123),
('SemgHandSubjectCh2', 124),
('ShakeGestureWiimoteZ', 125),
('SmoothSubspace', 126),
('UMD', 127)
]
print("Num datasets : ", len(dataset_map))
print()
base_log_name = '%s_%d_cell_search.csv'
base_weights_dir = '%s_%d_cells_weights/'
MODELS = [
('lstmfcn', generate_lstmfcn),
# ('alstmfcn', generate_alstmfcn),
# ('ndlstmfcn', generate_ndlstmfcn),
# ('grufcn', generate_grufcn),
# ('rnnfcn', generate_rnnfcn),
# ('densefcn', generate_densefcn),
]
# Number of cells
CELLS = [8, 64, 128]
for model_id, (MODEL_NAME, model_fn) in enumerate(MODELS):
for dname, did in dataset_map:
current_loss = 1e10
MAX_SEQUENCE_LENGTH = MAX_SEQUENCE_LENGTH_LIST[did]
NB_CLASS = NB_CLASSES_LIST[did]
finalcell = None
successes = []
failures = []
for cell in CELLS:
if not os.path.exists(base_log_name % (MODEL_NAME, cell)):
file = open(base_log_name % (MODEL_NAME, cell), 'w')
file.write('%s,%s,%s,%s\n' % ('dataset_id', 'dataset_name', 'dataset_name_', 'test_accuracy'))
file.close()
# release GPU Memory
K.clear_session()
weights_dir = base_weights_dir % (MODEL_NAME, cell)
if not os.path.exists('weights/' + weights_dir):
os.makedirs('weights/' + weights_dir)
dataset_name_ = weights_dir + dname
model = model_fn(MAX_SEQUENCE_LENGTH, NB_CLASS, cell)
print('*' * 20, "Training model for dataset %s" % (dname), '*' * 20)
train_model(model, did, dataset_name_, epochs=2000, batch_size=128, normalize_timeseries=True)
model_loss = loss_model(model, did, dataset_name_, batch_size=128, normalize_timeseries=True)
if (model_loss < current_loss):
finalcell = cell
current_loss = model_loss
print('Final Cell Selected:', finalcell)
model = model_fn(MAX_SEQUENCE_LENGTH, NB_CLASS, finalcell)
weights_dir = base_weights_dir % (MODEL_NAME, finalcell)
dataset_name_ = weights_dir + dname
print(dataset_name_)
acc = evaluate_model(model, did, dataset_name_, batch_size=128, normalize_timeseries=True)
s = "%d,%s,%s,%0.6f\n" % (did, dname, dataset_name_, acc)
file = open(base_log_name % (MODEL_NAME, finalcell), 'a+')
file.write(s)
file.flush()
successes.append(s)
file.close()