# 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()