# log-scale the images if desireable config['scaling'] = "minmax" if "np.log" in config['scaling']: images = np.log1p(images) # set tf random seed tf.random.set_seed(config['random_seed']) # ================== Import Data ================== with tf.device(get_tf_device(20)): model = Sequential() model.add(Dense(2, activation='relu', input_shape=(256, ))) model.compile( loss='mse', optimizer='adam', ) print(model.summary()) # Run experiment experiment = Experiment( model=model, config=config, model_type="regression", experiment_name="generate_results_energies_double_linreg", ) experiment.run_kfold( images[double_indices], energies[double_indices], ) experiment.save(save_model=True, save_indices=False) print("Finished experiment:", experiment.id)
config['scaling'] = "minmax" if "np.log" in config['scaling']: images = np.log1p(images) # set tf random seed tf.random.set_seed(config['random_seed']) # ================== Import Data ================== with tf.device(get_tf_device(20)): padding = 'same' model = Sequential() model.add(Dense(4, activation='relu', input_shape=(256, ))) model.compile( loss='mse', optimizer='adam', ) print(model.summary()) # Run experiment experiment = Experiment( model=model, config=config, model_type="regression", experiment_name="generate_results_pos_double_linreg", ) experiment.run_kfold( images[double_indices], normalize_position_data(positions[double_indices]), ) experiment.save(save_model=True, save_indices=False) print("Finished experiment:", experiment.id)
tf.random.set_seed(config['random_seed']) # ================== Import Data ================== with tf.device(get_tf_device(20)): padding = 'same' model = Sequential() model.add( Conv2D(8, kernel_size=3, activation='relu', input_shape=(16, 16, 1), padding='same') ) model.add(Flatten()) model.add(Dense(2, activation='linear')) model.compile( loss='mse', optimizer='adam', ) print(model.summary()) # Run experiment experiment = Experiment( model=model, config=config, model_type="regression", experiment_name="generate_results_cnn_small", ) experiment.run_kfold( images[single_indices], normalize_position_data(positions[single_indices])[:, :2], ) experiment.save(save_model=True, save_indices=False) print("Finished experiment:", experiment.id)
# log-scale the images if desireable config['scaling'] = "minmax" if "np.log" in config['scaling']: images = np.log1p(images) # set tf random seed tf.random.set_seed(config['random_seed']) # ================== Import Data ================== with tf.device(get_tf_device(20)): model = Sequential() model.add(Dense(1, activation='relu', input_shape=(256, ))) model.compile( loss='mse', optimizer='adam', ) print(model.summary()) # Run experiment experiment = Experiment( model=model, config=config, model_type="regression", experiment_name="generate_results_energies_single_linreg", ) experiment.run_kfold( images[single_indices], energies[single_indices, 0], ) experiment.save(save_model=True, save_indices=False) print("Finished experiment:", experiment.id)
Conv2D(8, kernel_size=3, activation='relu', input_shape=images.shape[1:], padding='same') ) model.add(Flatten()) model.add(Dense(1, activation='sigmoid')) model.compile( optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'] ) # Run experiment experiment = Experiment( model=model, config=config, model_type="classification", experiment_name="full_training_cnn_small" ) experiment.run_kfold( images, labels, ) experiment.save(save_model=True, save_indices=False) print("Finished experiment:", experiment.id) lpath = experiment.config['path_args']['models'] + "models.log" log = open(lpath,"a") log.write(experiment.id + ":\n") log.write(os.path.basename(__file__)+"\n") log.write(repr(config) + "\n") log.close()
# set tf random seed tf.random.set_seed(config['random_seed']) with tf.device(get_tf_device(20)): # Small Dense network model = Sequential() model.add(InputLayer(input_shape=(256, ))) model.add(Dense(8, activation='relu')) model.add(Dense(1, activation='sigmoid')) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # Run experiment experiment = Experiment(model=model, config=config, model_type="classification", experiment_name="full_training_dense_small") experiment.run_kfold( images.reshape(images.shape[0], 256), labels, ) experiment.save(save_model=True, save_indices=False) print("Finished experiment:", experiment.id) lpath = experiment.config['path_args']['models'] + "models.log" log = open(lpath, "a") log.write(experiment.id + ":\n") log.write(os.path.basename(__file__) + "\n") log.write(repr(config) + "\n") log.close()