from dagbldr.datasets import fetch_fer
from dagbldr.utils import convert_to_one_hot
from dagbldr.utils import load_checkpoint, interpolate_between_points, make_gif
parser = argparse.ArgumentParser()
parser.add_argument("saved_functions_file",
help="Saved pickle file from vae training")
parser.add_argument("--seed", "-s",
help="random seed for path calculation",
action="store", default=1979, type=int)
args = parser.parse_args()
if not os.path.exists(args.saved_functions_file):
raise ValueError("Please provide a valid path for saved pickle file!")
checkpoint_dict = load_checkpoint(args.saved_functions_file)
encode_function = checkpoint_dict["encode_function"]
decode_function = checkpoint_dict["decode_function"]
predict_function = checkpoint_dict["predict_function"]
fer = fetch_fer()
data = fer["data"]
valid_indices = fer["valid_indices"]
valid_data = data[valid_indices]
mean_norm = fer["mean0"]
pca_tf = fer["pca_matrix"]
X = valid_data - mean_norm
X = np.dot(X, pca_tf.T)
y = fer["target"][valid_indices]
n_classes = len(set(y))
random_state = np.random.RandomState(args.seed)
t = time.time()
else:
t = index
plt.savefig("lines_%i.png" % t)
def delta(x):
return np.hstack((x[1:, 0][:, None], x[1:, 1:] - x[:-1, 1:]))
def undelta(x):
agg = np.cumsum(x[:, 1:], axis=0)
return np.hstack((x[:, 0][:, None], agg))
model_path = sys.argv[1]
checkpoint = load_checkpoint(model_path)
predict_function = checkpoint.checkpoint_dict["predict_function"]
cost_function = checkpoint.checkpoint_dict["cost_function"]
iamondb = fetch_iamondb()
X = iamondb["data"]
X_offset = [delta(x) for x in X]
X = X_offset
Xt = [x[:, 1:] for x in X]
X_len = np.array([len(x) for x in Xt]).sum()
X_mean = np.array([x.sum() for x in Xt]).sum() / X_len
X_sqr = np.array([(x**2).sum() for x in Xt]).sum() / X_len
X_std = np.sqrt(X_sqr - X_mean ** 2)
def normalize(x):
else:
t = index
plt.savefig("lines_%i.png" % t)
def delta(x):
return np.hstack((x[1:, 0][:, None], x[1:, 1:] - x[:-1, 1:]))
def undelta(x):
agg = np.cumsum(x[:, 1:], axis=0)
return np.hstack((x[:, 0][:, None], agg))
model_path = sys.argv[1]
checkpoint = load_checkpoint(model_path)
predict_function = checkpoint.checkpoint_dict["predict_function"]
cost_function = checkpoint.checkpoint_dict["cost_function"]
iamondb = fetch_iamondb()
X = iamondb["data"]
X_offset = [delta(x) for x in X]
X = X_offset
Xt = [x[:, 1:] for x in X]
X_len = np.array([len(x) for x in Xt]).sum()
X_mean = np.array([x.sum() for x in Xt]).sum() / X_len
X_sqr = np.array([(x**2).sum() for x in Xt]).sum() / X_len
X_std = np.sqrt(X_sqr - X_mean**2)
def normalize(x):
