def main(cube_dir, hyperband_iterations, max_epochs):
device_name = tf.test.gpu_device_name()
if device_name != '/device:GPU:0':
raise SystemError('GPU device not found')
logging.info('Found GPU at: {}'.format(device_name))
x, y, val_x, val_y = deep_emulator.data(cube_dir, rescale=True)
tuner = Hyperband(
build_model,
objective='val_mean_absolute_error',
hyperband_iterations=hyperband_iterations,
max_epochs=max_epochs,
directory='results/hyperband',
project_name='agnfinder_5layer_dropout'
)
early_stopping = keras.callbacks.EarlyStopping(restore_best_weights=True)
tuner.search(
x,
y,
callbacks=[early_stopping],
validation_data=(val_x, val_y),
batch_size=1024
)
tuner.results_summary()
models = tuner.get_best_models(num_models=5)
for n, model in enumerate(models):
logging.info(f'Model {n}')
logging.info(model.summary())
def TuneNetwork():
#Function to tune hyperparameters for the neural network
tuner = Hyperband(CreateNNmodel,
max_epochs=50,
objective='val_accuracy',
executions_per_trial=7,
directory=os.path.normpath('C:/'))
tuner.search(x=TrainingInput,
y=TrainingOutput,
validation_data=(TestingInput, TestingOutput),
epochs=50,
batch_size=Features)
best_model = tuner.get_best_models(num_models=1)[0]
loss, accuracy = best_model.evaluate(TestingInput, TestingOutput)
print(accuracy)
def hypermodel_exec(x_train, x_test, y_train, y_test):
SEED = 17
MAX_TRIALS = 40
EXECUTION_PER_TRIAL = 3
HYPERBAND_MAX_EPOCHS = 20
NUM_CLASSES = 1 # One sigmoid neuron for binary classification
INPUT_SHAPE = (32, 32, 3
) # Depends on embedding type and bp lenght of dataset
N_EPOCH_SEARCH = 40
np.random.seed(SEED)
hypermodel = HotCNNHyperModel(input_shape=INPUT_SHAPE,
num_classes=NUM_CLASSES)
# tuner = RandomSearch(
# hypermodel,
# objective='val_loss',
# seed=SEED,
# max_trials=MAX_TRIALS,
# executions_per_trial=EXECUTION_PER_TRIAL,
# directory='random_search',
# project_name='hot_cnn_promoter_01'
# )
tuner = Hyperband(hypermodel,
max_epochs=HYPERBAND_MAX_EPOCHS,
objective='val_accuracy',
seed=SEED,
executions_per_trial=EXECUTION_PER_TRIAL,
directory='hyperband',
project_name='hot_cnn_promoter_01')
tuner.search_space_summary()
tuner.search(x_train, y_train, epochs=N_EPOCH_SEARCH, validation_split=0.1)
# Show a summary of the search
tuner.results_summary()
# Retrieve the best model.
best_model = tuner.get_best_models(num_models=1)[0]
# Evaluate the best model.
loss, accuracy = best_model.evaluate(x_test, y_test)
def __train_neural_networks(self, estimator):
if estimator.get_metric() == "mse":
tuner = Hyperband(estimator,
max_epochs=20,
objective="val_mse",
executions_per_trial=1,
directory="regression_nn" +
str(random.randint(0, 1000)))
else:
tuner = Hyperband(estimator,
max_epochs=20,
objective="val_accuracy",
executions_per_trial=1,
directory="classification_nn" +
str(random.randint(0, 1000)))
tuner.search(self.X_train,
self.y_train,
epochs=1,
validation_split=0.1,
verbose=0)
return tuner.get_best_models(num_models=1)[0]
def tuneHyperband(X,
y,
max_trials=3):
"""
Perform Hyperband Tuning to search for the best model and Hyperparameters
Arguments:
X: Input dataset
y: Label or output dataset
max_trials: Trials required to perform tuning
"""
hypermodel = HyperResNet(input_shape=(128, 128, 3), num_classes=10)
tuner = Hyperband(
hypermodel,
max_epochs=max_trials,
objective='val_accuracy',
seed=42,
)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=49)
# searches the tuner space defined by hyperparameters (hp) and returns the
# best model
tuner.search(X_train, y_train,
epochs=5,
validation_data=(X_test, y_test))
hyp = tuner.get_best_hyperparameters(num_trials=1)[0]
#hyp = tuner.oracle.get_best_trials(num_trials=1)[0].hyperparameters.values
#best_hps = np.stack(hyp).astype(None)
history = tuner_hist(X, y, tuner, hyp)
"""
Return:
models[0] : best model obtained after tuning
best_hps : best Hyperprameters obtained after tuning, stored as array
history : history of the data executed from the given model
"""
return tuner.get_best_models(1)[0], hyp, history
def main():
metadata = load_metadata()
# train_metadata2, test_metadata2 = load_metadata2()
''' Fraction of positive samples wanted in both test and validation set, the same as it is in the test set of
the Kaggle Chest X-Ray Images (Pneumonia) dataset'''
train_metadata, val_metadata, test_metadata = split_dataset(metadata, augmentation + 1)
# train_metadata = train_metadata.head(8)
# val_metadata = val_metadata.head(256)
if augmentation > 0:
pos_train_metadata = train_metadata[train_metadata['Pneumonia'] == 1]
neg_train_metadata = train_metadata[train_metadata['Pneumonia'] == 0]
pos_train_metadata_augmented = augment(metadata=pos_train_metadata, rate=augmentation, batch_size=16, seed=seed)
train_metadata = pd.concat([pos_train_metadata, pos_train_metadata_augmented, neg_train_metadata],
ignore_index=True)
# train_metadata = pd.concat(([train_metadata, train_metadata2]), ignore_index=True)
train_metadata = shuffle_DataFrame(train_metadata, seed)
"""val_metadata2, test_metadata2 = split_dataset2(test_metadata2)
val_metadata = pd.concat(([val_metadata, val_metadata2]), ignore_index=True)
test_metadata = pd.concat(([test_metadata, test_metadata2]), ignore_index=True)"""
print('Train:')
print(Counter(train_metadata['Pneumonia']))
print('Validation:')
print(Counter(val_metadata['Pneumonia']))
print('Test:')
print(Counter(test_metadata['Pneumonia']))
train_ds = make_pipeline(file_paths=train_metadata['File Path'].to_numpy(),
y=train_metadata['Pneumonia'].to_numpy(),
shuffle=True,
batch_size=batch_size,
seed=seed)
val_ds = make_pipeline(file_paths=val_metadata['File Path'].to_numpy(),
y=val_metadata['Pneumonia'].to_numpy(),
shuffle=False,
batch_size=val_batch_size)
samples_ds = make_pipeline(file_paths=train_metadata['File Path'].to_numpy(),
y=train_metadata['Pneumonia'].to_numpy(),
shuffle=True,
batch_size=16,
seed=seed)
show_samples(samples_ds)
del samples_ds # Not needed anymore
"""str_time = datetime.datetime.now().strftime("%m%d-%H%M%S")
log_dir = f'{logs_root}/{str_time}'
logs_cb = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1, profile_batch=0)
history = model.fit(x=train_ds, validation_data=val_ds, epochs=300, shuffle=False, callbacks=[logs_cb])"""
# TODO what if I use val_loss instead?
tuner = Hyperband(make_model_DenseNet121,
objective=kt.Objective("val_auc", direction="max"), # Careful to keep the direction updated
max_epochs=epochs_base,
hyperband_iterations=2,
directory='computations',
project_name='base-1dataset-densenet121-auc-auc')
early_stopping_cb = tf.keras.callbacks.EarlyStopping(monitor='val_auc', patience=patience)
tuner.search(x=train_ds,
validation_data=val_ds,
epochs=epochs_base,
shuffle=False,
callbacks=[early_stopping_cb])
model_maker = ModelMaker(tuner)
tuner_ft = Hyperband(model_maker.make_model_DenseNet121,
objective=kt.Objective("val_auc", direction="max"), # Careful to keep the direction updated
max_epochs=epochs_ft,
hyperband_iterations=2,
directory='computations',
project_name='fine-1dataset-densenet121-auc-auc')
early_stopping_cb = tf.keras.callbacks.EarlyStopping(monitor='val_auc', patience=patience)
tuner_ft.search(x=train_ds,
validation_data=val_ds,
epochs=epochs_ft,
shuffle=False,
callbacks=[early_stopping_cb])
best_ft_model = tuner_ft.get_best_models()[0]
test_ds = make_pipeline(file_paths=test_metadata['File Path'].to_numpy(),
y=test_metadata['Pneumonia'].to_numpy(),
shuffle=False,
batch_size=val_batch_size)
test_results = best_ft_model.evaluate(x=test_ds, return_dict=True, verbose=1)
print('\nHyper-parameters for the fine-tuned model:')
print(tuner_ft.get_best_hyperparameters()[0].values)
print('\nTest results on the fine-tuned model:')
print(test_results)
hps = tuner_ft.get_best_hyperparameters()[0].values
dev_metadata = pd.concat([train_metadata, val_metadata], ignore_index=True)
dev_metadata = shuffle_DataFrame(dev_metadata, seed=seed)
dev_metadata.reset_index(inplace=True, drop=True)
fold_size = int(np.ceil(len(dev_metadata) / n_folds))
train_datasets, val_datasets = [], []
for fold in range(n_folds):
fold_val_metadata = dev_metadata.iloc[fold * fold_size:fold * fold_size + fold_size, :]
val_datasets.append(make_pipeline(file_paths=fold_val_metadata['File Path'].to_numpy(),
y=fold_val_metadata['Pneumonia'].to_numpy(),
batch_size=val_batch_size,
shuffle=False))
fold_train_metadata1 = dev_metadata.iloc[:fold * fold_size, :]
fold_train_metadata2 = dev_metadata.iloc[fold * fold_size + fold_size:, :]
fold_train_metadata = pd.concat([fold_train_metadata1, fold_train_metadata2], ignore_index=False)
train_datasets.append(make_pipeline(file_paths=fold_train_metadata['File Path'].to_numpy(),
y=fold_train_metadata['Pneumonia'].to_numpy(),
batch_size=batch_size,
shuffle=True,
seed=seed))
histories, means = k_fold_resumable_fit(model_maker=model_maker.make_model_DenseNet121,
hp=hps,
comp_dir='computations',
project='xval',
train_datasets=train_datasets,
val_datasets=val_datasets,
log_dir='computations/xval/logs',
epochs=epochs_ft)
print(histories)
print(means)
pass
hyperband_iterations=2,
directory='HyperBandTrials',
project_name='PressureOpti_hl')
# Defining the Early Stopping Function
early_stopping_callback = EarlyStopping(monitor='val_mape',
patience=500,
min_delta=1e-4,
restore_best_weights=True,
mode='auto',
verbose=True)
tuner.search_space_summary()
print(model.summary())
tuner.search(X_train,
y_train,
epochs=2000,
validation_data=(X_test, y_test),
callbacks=[early_stopping_callback])
models = tuner.get_best_models(num_models=2)
models[0].save(
'/mnt/IMP/Work/Thesis/NeuralNetwork/DeepLearning-RarefiedFlows/SavedModels/Pressure/HyperBand_hl/Best_Model_1'
)
models[1].save(
'/mnt/IMP/Work/Thesis/NeuralNetwork/DeepLearning-RarefiedFlows/SavedModels/Pressure/HyperBand_hl/Best_Model_2'
)
tuner.results_summary()
max_epochs=MAX_EPOCHS,
factor=FACTOR,
directory='my_dir',
project_name='val_' + obj + '_' + time.ctime())
tuner.search(X_res,
pd.get_dummies(y_res).values,
epochs=MAX_EPOCHS,
batch_size=2048,
verbose=0,
callbacks=[early_stops(obj)],
validation_data=(X_val, pd.get_dummies(y_val).values))
end = time.time()
print('Tuning time is %.2f' % (end - start))
print(tuner.oracle.get_best_trials(num_trials=1)[0].hyperparameters.values)
bp = pd.Series(
tuner.oracle.get_best_trials(num_trials=1)[0].hyperparameters.values,
name=obj)
#bp = bp.append(pd.Series(end-start,index=['Tuning_time']))
bps = pd.concat((bps, bp), axis=1)
models = tuner.get_best_models(num_models=FACTOR)
for i in range(FACTOR):
Y_pred = models[i].predict(X_test)
rsts = rsts.append(
get_accs(y_test.values, Y_pred, end - start,
'MLP-' + obj + '-' + str(i + 1)))
rsts.to_csv('VCA_Tune2.csv')
print(bps)
print(rsts.iloc[:, 5:])
bps.to_csv('VCA_Tune2_bps.csv')
def run_model_fitting(PROJECT_NAME):
"""
"""
SENTENCE_DECODER = pd.read_pickle(
f'results\\objects\\{PROJECT_NAME}\\sentence_decoder.pkl')
TARGET_DECODER = pd.read_pickle(
f'results\\objects\\{PROJECT_NAME}\\target_decoder.pkl')
TUNING_FRACTION = 0.1
BTCH_LIST = os.listdir(f'data\\preprocessed\\{PROJECT_NAME}\\inputs')
BTCH = [i for i in range(len(BTCH_LIST))]
BTCH = np.random.choice(BTCH,
int(len(BTCH) * TUNING_FRACTION),
replace=False)
TR_BTCH = BTCH[:int(len(BTCH) * 0.8)]
TS_BTCH = BTCH[int(len(BTCH) * 0.8):]
tr_generator = DataGenerator(list_batches=TR_BTCH,
project_name=PROJECT_NAME,
shuffle=True,
multi_target=True)
ts_generator = DataGenerator(list_batches=TS_BTCH,
project_name=PROJECT_NAME,
shuffle=True,
multi_target=True)
model = LanguageModel(max_vocab=len(SENTENCE_DECODER) + 1,
multi_target=True,
max_target_1=len(SENTENCE_DECODER) + 1,
max_target_2=len(TARGET_DECODER))
ES = EarlyStopping(monitor='val_loss',
min_delta=0.0001,
patience=5,
verbose=1,
mode='auto',
restore_best_weights=True)
tuner_obj = Hyperband(hypermodel=model,
max_epochs=30,
hyperband_iterations=1,
objective='val_loss',
directory='o',
project_name=f'{PROJECT_NAME}')
tuner_obj.search(tr_generator,
epochs=30,
callbacks=[ES],
verbose=2,
validation_data=ts_generator)
BTCH = [i for i in range(len(BTCH_LIST))]
BTCH = np.random.choice(BTCH, int(len(BTCH)), replace=False)
TR_BTCH = BTCH[:int(len(BTCH) * 0.8)]
TS_BTCH = BTCH[int(len(BTCH) * 0.8):]
tr_generator = DataGenerator(list_batches=TR_BTCH,
project_name=PROJECT_NAME,
shuffle=True,
multi_target=True)
ts_generator = DataGenerator(list_batches=TS_BTCH,
project_name=PROJECT_NAME,
shuffle=True,
multi_target=True)
model = tuner_obj.get_best_models(1)[0]
ES = EarlyStopping(monitor='val_loss',
min_delta=0.0001,
patience=5,
verbose=1,
mode='auto',
restore_best_weights=True)
model.fit(tr_generator,
epochs=50,
verbose=2,
callbacks=[ES],
validation_data=ts_generator)
model.save(f'results\\models\\{PROJECT_NAME}')
X,Y = next(imagenet_gen)
trainX, testX, trainY, testY = train_test_split(X, Y, test_size=0.25)
tuner = Hyperband(
tuner_model,
objective='val_categorical_accuracy',
max_epochs=40,
project_name='tuning_categorical'
)
tuner.search_space_summary()
tuner.search(trainX, trainY,epochs=25,validation_data=(testX, testY),verbose=0)
tuner.results_summary()
best_model = tuner.get_best_models(num_models=1)[0]
# Evaluate the best model.
loss, accuracy = best_model.evaluate(testX, testY)
tuner.get_best_hyperparameters()[0].values
"""Pretty good results : 0.898 vs 0.7551 categorical accuracy
## Testing different applications (ResNet, Inception)
"""
from keras.applications import VGG16
from keras import Model
from keras.layers import AveragePooling2D, Input, Flatten, Dense, Dropout
from keras.optimizers import Adam
metrics=[iou, iou_thresholded, 'mse'],
distribution_strategy=tf.distribute.MirroredStrategy(),
seed=42,
hyperband_iterations=3)
#print total search space
tuner_hb.search_space_summary()
#search through the total search space
tuner_hb.search(train_generator,
epochs=500,
verbose=1,
validation_data=(img_test, mask_test))
#save best model and hyperparameters
best_model = tuner_hb.get_best_models(1)[0]
best_hyperparameters = tuner_hb.get_best_hyperparameters(1)[0]
print(tuner_hb.get_best_hyperparameters(1))
#
model_json = best_model.to_json()
with open("hp_bce_all_basicUNet_model.json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
best_model.save_weights("hp_bce_all_tune_basicUNet_tuner_model.h5")
with open('best_LGG_basicUNet_Param.txt', 'w') as f:
print(best_hyperparameters, file=f)
print("Saved model to disk")
INPUT_SHAPE = images[0].shape
NUM_CLASSES = 1
HYPERBAND_MAX_EPOCHS = 22
SEED = 2
hypermodel = HyperUNet(input_shape=INPUT_SHAPE, classes=NUM_CLASSES)
tuner = Hyperband(hypermodel,
max_epochs=HYPERBAND_MAX_EPOCHS,
objective='val_accuracy',
seed=SEED,
executions_per_trial=22)
tuner.search(train_generator, epochs=20, validation_data=val_generator)
best_model = tuner.get_best_models(1)[0]
best_hyperparameters = tuner.get_best_hyperparameters(1)[0]
#
model_json = best_model.to_json()
with open("LGG_basicUNet_model.json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
best_model.save_weights("LGG_basicUNet_tuner_model.h5")
with open('best_LGG_basicUNet_Param.txt', 'w') as f:
print(best_hyperparameters, file=f)
print("Saved model to disk")
