# 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)
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Flatten())
model.add(Dense(128, 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=search_name)
experiment.run(
normalize_image_data(images[train_idx]),
labels[train_idx],
normalize_image_data(images[val_idx]),
labels[val_idx],
)
experiment.save()
id_param[experiment.id] = {
'batch_size': b_size,
}
search_path = get_git_root() + "experiments/searches/"
with open(search_path + search_name + ".json", "w") as fp:
json.dump(id_param, fp, indent=2)