''' 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)
''' 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)
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='kick_vs_punch_', epochs=1000, batch_size=128) evaluate_model(model, DATASET_INDEX, dataset_prefix='kick_vs_punch_', 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='walk_vs_run_', epochs=1000, batch_size=128) evaluate_model(model, DATASET_INDEX, dataset_prefix='walk_vs_run_', batch_size=128)
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='action_3d', epochs=600, batch_size=128) evaluate_model(model, DATASET_INDEX, dataset_prefix='action_3d', batch_size=128)
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='ht_sensor', epochs=600, batch_size=128) evaluate_model(model, DATASET_INDEX, dataset_prefix='ht_sensor', batch_size=128)
import json ''' Train portion ''' scores = [] for i in range(10): K.clear_session() print("Begin iteration %d" % (i + 1)) print("*" * 80) print() model = generate_model() # change to generate_model_2() #train_model(model, DATASET_INDEX, dataset_prefix='character_attention', dataset_fold_id=(i + 1), epochs=600, batch_size=128) score = evaluate_model(model, DATASET_INDEX, dataset_prefix='character_attention', dataset_fold_id=(i + 1), batch_size=128) scores.append(score) with open('data/character/scores.json', 'w') as f: json.dump({'scores': scores}, f) ''' evaluate average score ''' with open('data/character/scores.json', 'r') as f: results = json.load(f) scores = results['scores'] avg_score = sum(scores) / len(scores) print("Scores : ", scores) print("Average score over 10 epochs : ", avg_score)
''' 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: 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='net_flow_', epochs=1000, batch_size=128) evaluate_model(model, DATASET_INDEX, dataset_prefix='net_flow_', batch_size=128)
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='cmu_subject_16_', epochs=1000, batch_size=128) evaluate_model(model, DATASET_INDEX, dataset_prefix='cmu_subject_16_', 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)
import json ''' Train portion ''' scores = [] for i in range(10): K.clear_session() print("Begin iteration %d" % (i + 1)) print("*" * 80) print() model = generate_model() # change to generate_model_2() #train_model(model, DATASET_INDEX, dataset_prefix='ck', dataset_fold_id=(i + 1), epochs=600, batch_size=128) score = evaluate_model(model, DATASET_INDEX, dataset_prefix='ck', dataset_fold_id=(i + 1), batch_size=128) scores.append(score) with open('data/CK/scores.json', 'w') as f: json.dump({'scores': scores}, f) ''' evaluate average score ''' with open('data/CK/scores.json', 'r') as f: results = json.load(f) scores = results['scores'] avg_score = sum(scores) / len(scores) print("Scores : ", scores) print("Average score over 10 epochs : ", avg_score)
''' 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)
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='character_trajectories_', epochs=1000, batch_size=128) evaluate_model(model, DATASET_INDEX, dataset_prefix='character_trajectories_', 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='ozone', epochs=600, batch_size=128) evaluate_model(model, DATASET_INDEX, dataset_prefix='ozone', batch_size=128)
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='shapes_random_', epochs=1000, batch_size=128) evaluate_model(model, DATASET_INDEX, dataset_prefix='shapes_random_', 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='u_wave_', epochs=1000, batch_size=128) evaluate_model(model, DATASET_INDEX, dataset_prefix='u_wave_', batch_size=128)
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='occupancy_detect', epochs=1000, batch_size=128) evaluate_model(model, DATASET_INDEX, dataset_prefix='occupancy_detect', 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=1000, batch_size=128) evaluate_model(model, DATASET_INDEX, dataset_prefix='japanese_vowels_', batch_size=128)
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='pendigits_', epochs=1000, batch_size=128) evaluate_model(model, DATASET_INDEX, dataset_prefix='pendigits_', batch_size=128)