'''
filters = input._keras_shape[-1] # channel_axis = -1 for TF
se = GlobalAveragePooling1D()(input)
se = Reshape((1, filters))(se)
se = Dense(filters // 16,
activation='relu',
kernel_initializer='he_normal',
use_bias=False)(se)
se = Dense(filters,
activation='sigmoid',
kernel_initializer='he_normal',
use_bias=False)(se)
se = multiply([input, se])
return se
if __name__ == "__main__":
model = generate_model_2()
train_model(model,
DATASET_INDEX,
dataset_prefix='ozone',
epochs=600,
batch_size=128)
evaluate_model(model,
DATASET_INDEX,
dataset_prefix='ozone',
batch_size=128)
'''
filters = input._keras_shape[-1] # channel_axis = -1 for TF
se = GlobalAveragePooling1D()(input)
se = Reshape((1, filters))(se)
se = Dense(filters // 16,
activation='relu',
kernel_initializer='he_normal',
use_bias=False)(se)
se = Dense(filters,
activation='sigmoid',
kernel_initializer='he_normal',
use_bias=False)(se)
se = multiply([input, se])
return se
if __name__ == "__main__":
model = generate_model_2()
train_model(model,
DATASET_INDEX,
dataset_prefix='eeg2_attention',
epochs=500,
batch_size=128)
evaluate_model(model,
DATASET_INDEX,
dataset_prefix='eeg2_attention',
batch_size=128)
Returns: a keras tensor
'''
filters = input._keras_shape[-1] # channel_axis = -1 for TF
se = GlobalAveragePooling1D()(input)
se = Reshape((1, filters))(se)
se = Dense(filters // 16,
activation='relu',
kernel_initializer='he_normal',
use_bias=False)(se)
se = Dense(filters,
activation='sigmoid',
kernel_initializer='he_normal',
use_bias=False)(se)
se = multiply([input, se])
return se
if __name__ == "__main__":
from time import time
model = generate_model_2()
train_model(model,
DATASET_INDEX,
dataset_prefix='lp3_',
epochs=100,
batch_size=128)
evaluate_model(model, DATASET_INDEX, dataset_prefix='lp3_', batch_size=128)
(cfmatrix.sum(axis=1) / sum(cfmatrix.sum(axis=1))))
weightedf = metrics.f1_score(y_test, y_pred, average='weighted')
print()
print("Binary F-Score : ", microf)
print("Final F-Score : ", averagef)
print("Weighted F-Score : ", weightedf)
print("Mahis F-Score : ", averagef_mahi)
print("Mahis W F-Score : ", weightedf_mahi)
return averagef
if __name__ == "__main__":
model = generate_model_2()
model.compile('adam',
'categorical_crossentropy',
metrics=['accuracy', f1_score])
train_model(model,
DATASET_INDEX,
dataset_prefix='opportunity_new',
epochs=1000,
batch_size=128,
monitor='val_f1_score',
optimization_mode='max',
compile_model=False)
#evaluate_model(model, DATASET_INDEX, dataset_prefix='opportunity', batch_size=128)
#predict_model(model, DATASET_INDEX, dataset_prefix='opportunity_weights_attention_9208_512_lstm_128', batch_size=512)
'''
filters = input._keras_shape[-1] # channel_axis = -1 for TF
se = GlobalAveragePooling1D()(input)
se = Reshape((1, filters))(se)
se = Dense(filters // 16,
activation='relu',
kernel_initializer='he_normal',
use_bias=False)(se)
se = Dense(filters,
activation='sigmoid',
kernel_initializer='he_normal',
use_bias=False)(se)
se = multiply([input, se])
return se
if __name__ == "__main__":
model = generate_model_2()
train_model(model,
DATASET_INDEX,
dataset_prefix='arabic_voice_',
epochs=1000,
batch_size=128)
evaluate_model(model,
DATASET_INDEX,
dataset_prefix='arabic_voice_',
batch_size=128)
# add load model code here to fine-tune
return model
def squeeze_excite_block(input):
''' Create a squeeze-excite block
Args:
input: input tensor
filters: number of output filters
k: width factor
Returns: a keras tensor
'''
filters = input._keras_shape[-1] # channel_axis = -1 for TF
se = GlobalAveragePooling1D()(input)
se = Reshape((1, filters))(se)
se = Dense(filters // 16, activation='relu', kernel_initializer='he_normal', use_bias=False)(se)
se = Dense(filters, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(se)
se = multiply([input, se])
return se
if __name__ == "__main__":
model = generate_model_2()
train_model(model, DATASET_INDEX, dataset_prefix='activity_attention', epochs=1000, batch_size=128)
evaluate_model(model, DATASET_INDEX, dataset_prefix='activity_attention', batch_size=128)
'''
filters = input._keras_shape[-1] # channel_axis = -1 for TF
se = GlobalAveragePooling1D()(input)
se = Reshape((1, filters))(se)
se = Dense(filters // 16,
activation='relu',
kernel_initializer='he_normal',
use_bias=False)(se)
se = Dense(filters,
activation='sigmoid',
kernel_initializer='he_normal',
use_bias=False)(se)
se = multiply([input, se])
return se
if __name__ == "__main__":
model = generate_model()
train_model(model,
DATASET_INDEX,
dataset_prefix='daily_sport_no_attention',
epochs=500,
batch_size=128)
evaluate_model(model,
DATASET_INDEX,
dataset_prefix='daily_sport_no_attention',
batch_size=128)
'''
filters = input._keras_shape[-1] # channel_axis = -1 for TF
se = GlobalAveragePooling1D()(input)
se = Reshape((1, filters))(se)
se = Dense(filters // 16,
activation='relu',
kernel_initializer='he_normal',
use_bias=False)(se)
se = Dense(filters,
activation='sigmoid',
kernel_initializer='he_normal',
use_bias=False)(se)
se = multiply([input, se])
return se
if __name__ == "__main__":
model = generate_model_2()
train_model(model,
DATASET_INDEX,
dataset_prefix='movement_aal',
epochs=1000,
batch_size=128)
evaluate_model(model,
DATASET_INDEX,
dataset_prefix='movement_aal',
batch_size=128)
# add load model code here to fine-tune
return model
def squeeze_excite_block(input):
''' Create a squeeze-excite block
Args:
input: input tensor
filters: number of output filters
k: width factor
Returns: a keras tensor
'''
filters = input._keras_shape[-1] # channel_axis = -1 for TF
se = GlobalAveragePooling1D()(input)
se = Reshape((1, filters))(se)
se = Dense(filters // 16, activation='relu', kernel_initializer='he_normal', use_bias=False)(se)
se = Dense(filters, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(se)
se = multiply([input, se])
return se
if __name__ == "__main__":
model = generate_model_2()
train_model(model, DATASET_INDEX, dataset_prefix='japanese_vowels', epochs=600, batch_size=128)
evaluate_model(model, DATASET_INDEX, dataset_prefix='japanese_vowels', batch_size=128)
'''
filters = input._keras_shape[-1] # channel_axis = -1 for TF
se = GlobalAveragePooling1D()(input)
se = Reshape((1, filters))(se)
se = Dense(filters // 16,
activation='relu',
kernel_initializer='he_normal',
use_bias=False)(se)
se = Dense(filters,
activation='sigmoid',
kernel_initializer='he_normal',
use_bias=False)(se)
se = multiply([input, se])
return se
if __name__ == "__main__":
model = generate_model()
train_model(model,
DATASET_INDEX,
dataset_prefix='gesture_phase',
epochs=1000,
batch_size=128)
evaluate_model(model,
DATASET_INDEX,
dataset_prefix='gesture_phase',
batch_size=128)
