"tr_pure_cost_mb": [], "error_tr": [], "error_vl": [],
"error_tr_mb": [], "error_vl_mb": [], "code_cost_mb": [],
"best_epoch": 0, "best_mb": 0}
# tag
if aes_in == [] and aes_out == []:
tag = "sup"
elif aes_in != [] and aes_out == []:
tag = "sup + in"
elif aes_in == [] and aes_out != []:
tag = "sup + out"
elif aes_in != [] and aes_out != []:
tag = "sup + in + out"
tag += ", data: " + faceset + " " + id_data
# First evaluation on valid
error_mn, _ = evaluate_model(list_minibatchs_vl, eval_fn)
vl_error_begin = np.mean(error_mn)
shutil.copy(inspect.stack()[0][1], fold_exp)
l_ch_tr_vl = []
for ch in l_ch_tr:
with open(ch, 'r') as f:
l_samples = pkl.load(f)
l_ch_tr_vl.append(l_samples)
stop = False
while i < max_epochs:
stop = (i == max_epochs - 1)
stats = train_one_epoch_chuncks(
train_updates, eval_fn, l_ch_tr_vl,
l_in, l_out, l_sup, l_code, list_minibatchs_vl,
eval_fn = get_eval_fn(model)
# Unit test
unit_test(fold_exp+"/unit_imgs/", w, h, path_mean_shap, eval_fn, ds)
# Perf mean shape.
# TRAIN
tr_path = "../../inout/data/face/" + faceset + "_data/ch_tr_1800_0_0_0.pkl"
print "TRAIN EVAL:"
with open(tr_path, 'r') as f:
tr_data = pkl.load(f)
list_minibatchs_train = split_data_to_minibatchs_eval(
{"x": tr_data["x"], "y": tr_data['y']}, ts_batch_size)
cdf_ms, cdf0_1_ms, auc_ms = evaluate_mean_shape(path_mean_shap, l_infos,
w, h, y=tr_data['y'],
save_ims=False)
# Evaluation
error, output = evaluate_model(list_minibatchs_train, eval_fn)
print "Error (MSE) model train data", np.mean(error)
# VALID
vl_path = "../../inout/data/face/" + faceset + "_data/valid.pkl"
print "VALID EVAL:"
with open(vl_path, 'r') as f:
vl_data = pkl.load(f)
list_minibatchs_valid = split_data_to_minibatchs_eval(
{"x": vl_data["x"], "y": vl_data['y']}, ts_batch_size)
cdf_ms, cdf0_1_ms, auc_ms = evaluate_mean_shape(path_mean_shap, l_infos,
w, h, y=vl_data['y'],
save_ims=False)
# Evaluation
error, output = evaluate_model(list_minibatchs_valid, eval_fn)
print "Error (MSE) model valid data", error
# TEST
rng = np.random.RandomState(23455)
input = T.fmatrix("x_input")
for l in layers_infos:
l["W"], l["b"], l["rng"] = None, None, rng
model = ModelMLP(layers_infos, input, dropout=dropout)
model.set_params_vals(fold_exp + "/model.pkl")
# with open("best_model_keras.pkl", 'r') as f:
# keras_p = pkl.load(f)
# for param, param_vl in zip(model.params, keras_p):
# param.set_value(param_vl)
# cdf_ms, cdf0_1_ms, auc_ms = evaluate_mean_shape(
# path_mean_shap, l_infos, w, h, save_ims=False)
# Evaluation
eval_fn = get_eval_fn(model)
error, output = evaluate_model(list_minibatchs_vl, eval_fn)
# Mean shape
with open(path_mean_shap, 'r') as f:
mean_shape = pkl.load(f)
test_mean_shape_y = y * 0.
nbr_test = y.shape[0]
for i in xrange(nbr_test):
test_mean_shape_y[i] = mean_shape
# Reproject the mean shape.
x = np.arange(0, border_x, dx)
test_mean_shape_y_reproj = ro.reproject_shape(bboxesT, test_mean_shape_y,
nfids)
test_mean_shape_y = ro.back_to_original_size_shapes(
set_y=test_mean_shape_y_reproj,
bbox=bboxesT,
