def run_1(exp_folder, exp, run, cd2x, max_filt, l1, c_filters):
name = f'baseline4_whatwhere_Cd2x{cd2x}xmax_filt{max_filt}_filtxl1{l1}_convsize{c_filters}_x5000'
exp = f"{exp}x{run}"
def get_hsm_params_custom(input, output, i):
_, output_shape = output.shape
_, input_shape = input.shape
pprint(f"in: {input_shape} out: {output_shape}")
intput_w, input_h = int(math.sqrt(input_shape)), int(
math.sqrt(input_shape))
hsm_params = NDNutils.ffnetwork_params(
verbose=False,
input_dims=[1, intput_w, input_h],
layer_sizes=[c_filters, c_filters, c_filters,
output_shape], # paper: 9, 0.2*output_shape
ei_layers=[None, None, None, None],
normalization=[0, 0, 0, 0],
layer_types=['conv', 'conv', 'conv', 'sep'],
act_funcs=['softplus', 'softplus', 'softplus', 'softplus'],
shift_spacing=[2, 2, 2, 0],
conv_filter_widths=[13, 3, 3, 0],
reg_list={
'd2x': [cd2x, cd2x, cd2x, None],
'max_filt': [max_filt, max_filt, max_filt, None],
'l1': [None, None, None, l1]
})
hsm_params['weights_initializers'] = [
'normal', 'normal', 'normal', 'normal'
]
hsm_params['biases_initializers'] = [
'trunc_normal', 'trunc_normal', 'trunc_normal', 'trunc_normal'
]
return hsm_params
def get_training_params():
epochs = 5000
return {
'batch_size': 16,
'use_gpu': False,
'epochs_summary': epochs // 50,
'epochs_training': epochs,
'learning_rate': 0.001
}
input_tr_processed, output_tr, output_tr_mask = udata.load_data_multiple(
[1], 'training', udata.normalize_mean_std)
input_val_processed, output_val, output_val_mask = udata.load_data_multiple(
[1], 'validation', udata.normalize_mean_std)
for i in range(10):
seed = i
hsm_params = get_hsm_params_custom(input_tr_processed, output_tr, i)
pprint(hsm_params)
hsm, input_tuple = unet.get_network(
input_tr_processed,
output_tr,
'adam',
get_training_params(),
hsm_params,
'poisson',
input_val_processed,
output_val,
output_tr_mask,
output_val_mask,
f"{name}__{i}",
seed,
)
hsm.log_correlation = 'zero-NaNs'
(input, output, train_indxs, test_indxs, data_filters, larg,
opt_params, name_str) = input_tuple
hsm.train(
input_data=input,
output_data=output,
train_indxs=train_indxs,
test_indxs=test_indxs,
data_filters=data_filters,
learning_alg=larg,
opt_params=opt_params,
output_dir=f"training_data/logs/{exp_folder}/{exp}/{name_str}")
res, naeval, corr = uasp.evaluate_all(hsm, input_val_processed,
output_val, output_val_mask)
hsm.save_model(
f"./training_data/models/{exp_folder}/{exp}/{name}__{i}.ndnmod")
with open("./training_data/experiments.txt", "a+") as f:
f.write(f"{exp_folder}/{exp}/{name}\n")
def run_1(exp_folder, exp, run, hidden, reg_lambda, reg_type):
name = f'baseline3_{reg_type}{reg_lambda}xN{hidden}x5000'
exp = f"{exp}x{run}"
def get_hsm_params_custom(input, output, i):
_, output_shape = output.shape
_, input_shape = input.shape
pprint(f"in: {input_shape} out: {output_shape}")
intput_w, input_h = int(math.sqrt(input_shape)), int(
math.sqrt(input_shape))
hsm_params = NDNutils.ffnetwork_params(
verbose=False,
input_dims=[1, intput_w, input_h],
layer_sizes=[9, int(hidden * output_shape),
output_shape], # paper: 9, 0.2*output_shape
ei_layers=[None, None, None],
normalization=[0, 0, 0],
layer_types=['diff_of_gaussians', 'normal', 'normal'],
act_funcs=['lin', 'softplus', 'softplus'],
reg_list={f'{reg_type}': [None, reg_lambda, reg_lambda]})
hsm_params['weights_initializers'] = ['random', 'normal', 'normal']
hsm_params['biases_initializers'] = [
'trunc_normal', 'trunc_normal', 'trunc_normal'
]
return hsm_params
def get_training_params():
epochs = 5000
return {
'batch_size': 16,
'use_gpu': False,
'epochs_summary': epochs // 50,
'epochs_training': epochs,
'learning_rate': 0.001
}
input_tr_processed, output_tr, output_tr_mask = udata.load_data_multiple(
[1], 'training', udata.normalize_mean_std)
input_val_processed, output_val, output_val_mask = udata.load_data_multiple(
[1], 'validation', udata.normalize_mean_std)
for i in range(10):
seed = i
hsm_params = get_hsm_params_custom(input_tr_processed, output_tr, i)
pprint(hsm_params)
hsm, input_tuple = unet.get_network(
input_tr_processed,
output_tr,
'adam',
get_training_params(),
hsm_params,
'poisson',
input_val_processed,
output_val,
output_tr_mask,
output_val_mask,
f"{name}__{i}",
seed,
)
hsm.log_correlation = 'zero-NaNs'
(input, output, train_indxs, test_indxs, data_filters, larg,
opt_params, name_str) = input_tuple
hsm.train(
input_data=input,
output_data=output,
train_indxs=train_indxs,
test_indxs=test_indxs,
data_filters=data_filters,
learning_alg=larg,
opt_params=opt_params,
output_dir=f"training_data/logs/{exp_folder}/{exp}/{name_str}")
res, naeval, corr = uasp.evaluate_all(hsm, input_val_processed,
output_val, output_val_mask)
hsm.save_model(
f"./training_data/models/{exp_folder}/{exp}/{name}__{i}.ndnmod")
with open("./training_data/experiments.txt", "a+") as f:
f.write(f"{exp_folder}/{exp}/{name}\n")
f"{name}__{i}",
seed,
)
hsm.log_correlation = 'zero-NaNs'
(input, output, train_indxs, test_indxs, data_filters, larg, opt_params,
name_str) = input_tuple
hsm.train(input_data=input,
output_data=output,
train_indxs=train_indxs,
test_indxs=test_indxs,
data_filters=data_filters,
learning_alg=larg,
opt_params=opt_params,
output_dir=f"training_data/logs/{exp_folder}/{exp}/{name_str}")
res, naeval, corr = uasp.evaluate_all(hsm, input_val_processed, output_val,
output_val_mask)
hsm.save_model(
f"./training_data/models/{exp_folder}/{exp}/{name}__{i}.ndnmod")
with open("./training_data/experiments.txt", "a+") as f:
f.write(f"{exp_folder}/{exp}/{name}\n")
#
# 1
#
name = 'baselineshort_exprep12_1mult1e6_Dog9xN0x2N_LxSpxSp_Nonorm_1e3x16x5000'
exp_folder = "experiments_repeatability"
exp = "bs1_exp12"
def get_hsm_params_custom(input, output, i):
_, output_shape = output.shape
def run_1(exp_folder, exp, c_size, c_filters, cd2x, hidden_t, hidden_s,
hidden_lt, region):
name = f'multieval_exp5_{region}'
exp = f"{exp}"
def get_hsm_params_custom(input, output, i):
_, output_shape = output.shape
_, input_shape = input.shape
pprint(f"in: {input_shape} out: {output_shape}")
intput_w, input_h = int(math.sqrt(input_shape)), int(
math.sqrt(input_shape))
hsm_params = NDNutils.ffnetwork_params(
verbose=False,
input_dims=[1, intput_w, input_h],
layer_sizes=[c_filters,
int(0.2 * output_shape),
output_shape], # paper: 9, 0.2*output_shape
ei_layers=[None, None, None],
normalization=[0, 0, 0],
layer_types=['conv', hidden_lt, 'normal'],
act_funcs=['softplus', 'softplus', 'softplus'],
shift_spacing=[(c_size + 1) // 2, 0],
conv_filter_widths=[c_size, 0, 0],
reg_list={
'd2x': [cd2x, None, None],
hidden_t: [None, hidden_s, None],
'l2': [None, None, 0.1],
})
hsm_params['weights_initializers'] = ['normal', 'normal', 'normal']
hsm_params['biases_initializers'] = [
'trunc_normal', 'trunc_normal', 'trunc_normal'
]
return hsm_params
def get_training_params():
epochs = 35000
return {
'batch_size': 16,
'use_gpu': False,
'epochs_summary': epochs // 50,
'epochs_training': epochs,
'learning_rate': 0.001
}
input_tr_processed, output_tr, output_tr_mask = udata.load_data_multiple(
[region], 'training', udata.normalize_mean_std)
input_val_processed, output_val, output_val_mask = udata.load_data_multiple(
[region], 'validation', udata.normalize_mean_std)
for i in range(10):
seed = i
hsm_params = get_hsm_params_custom(input_tr_processed, output_tr, i)
pprint(hsm_params)
hsm, input_tuple = unet.get_network(
input_tr_processed,
output_tr,
'adam',
get_training_params(),
hsm_params,
'poisson',
input_val_processed,
output_val,
output_tr_mask,
output_val_mask,
f"{name}__{i}",
seed,
)
hsm.log_correlation = 'zero-NaNs'
(input, output, train_indxs, test_indxs, data_filters, larg,
opt_params, name_str) = input_tuple
hsm.train(
input_data=input,
output_data=output,
train_indxs=train_indxs,
test_indxs=test_indxs,
data_filters=data_filters,
learning_alg=larg,
opt_params=opt_params,
output_dir=f"training_data/logs/{exp_folder}/{exp}/{name_str}")
res, naeval, corr = uasp.evaluate_all(hsm, input_val_processed,
output_val, output_val_mask)
print(f"MultiEval_val: {str(np.mean(corr))}")
res, naeval, corr = uasp.evaluate_all(hsm, input_tr_processed,
output_tr, output_tr_mask)
print(f"MultiEval_tr: {str(np.mean(corr))}")
hsm.save_model(
f"./training_data/models/{exp_folder}/{exp}/{name}__{i}.ndnmod")
with open("./training_data/experiments.txt", "a+") as f:
f.write(f"{exp_folder}/{exp}/{name}\n")
