def cli(train_data_nc, train_step):
with nc.Dataset(train_data_nc, "r") as src:
inputs = src["inputs"][:]
model = torsk.load_model(train_data_nc.parent, prefix="idx0-")
model.params.debug = True
model.forward(inputs)
def test_numpy_save_load(tmpdir):
params_string = """{
"input_shape": [10, 10],
"input_map_specs": [
{"type":"pixels", "size":[10, 10], "input_scale":3}],
"reservoir_representation": "dense",
"spectral_radius" : 2.0,
"density": 1e-1,
"train_length": 800,
"pred_length": 300,
"transient_length": 100,
"train_method": "pinv_svd",
"dtype": "float64",
"backend": "numpy",
"debug": false,
"imed_loss": true,
"timing_depth": 1
}
"""
params_json = tmpdir.join("params.json")
with open(params_json, "w") as dst:
dst.write(params_string)
params = torsk.Params(params_json)
model = NumpyESN(params)
inputs = bh.random.uniform(size=[2, 10, 10]).astype(bh.float64)
state = bh.random.uniform(size=[100]).astype(bh.float64)
_, out1 = model.forward(inputs, state)
torsk.save_model(tmpdir, model)
model = torsk.load_model(str(tmpdir))
_, out2 = model.forward(inputs, state)
assert bh.all(out1 == out2)
def generate_offline_esn_pred(inputs_batch,
labels_batch,
pred_labels_batch,
outdir,
hidden_size=512):
model = torsk.load_model(outdir)
params = esn_params(hidden_size)
params.train_length = 200
esn_error = []
print("Generating ESN predictions")
for inputs, labels, pred_labels in zip(inputs_batch, labels_batch,
pred_labels_batch):
zero_state = np.zeros(model.esn_cell.hidden_size)
_, states = model.forward(inputs, zero_state, states_only=True)
pred, _ = model.predict(labels[-1],
states[-1],
nr_predictions=params.pred_length)
err = np.abs(pred - pred_labels)
esn_error.append(err.squeeze())
esn_error = np.mean(esn_error, axis=0)
np.save(outdir / "esn_error.npy", esn_error)
np.save(outdir / "esn_pred.npy", pred)
np.save(outdir / "esn_lbls.npy", pred_labels)
inputs = inputs_batch[0]
labels = labels_batch[0]
pred_labels = pred_labels_batch[0]
zero_state = np.zeros(model.esn_cell.hidden_size)
_, states = model.forward(inputs, zero_state, states_only=True)
pred, _ = model.predict(labels[-1],
states[-1],
nr_predictions=params.pred_length)
return esn_error, pred, pred_labels
if params.backend == "numpy":
logger.info("Running with NUMPY backend")
from torsk.data.numpy_dataset import NumpyImageDataset as ImageDataset
from torsk.models.numpy_esn import NumpyESN as ESN
else:
logger.info("Running with TORCH backend")
from torsk.data.torch_dataset import TorchImageDataset as ImageDataset
from torsk.models.torch_esn import TorchESN as ESN
npzpath = pathlib.Path(
"/mnt/data/torsk_experiments/aguhlas_SSH_3daymean_x1050:1550_y700:1000.npz"
)
images = np.load(npzpath)["SSH"][:]
images = resample2d_sequence(images, params.input_shape)
dataset = ImageDataset(images, params, scale_images=True)
prefix = "idx0"
if outdir.joinpath(f"{prefix}-model.pkl").exists():
logger.info("Restoring model ...")
model = torsk.load_model(outdir, prefix=prefix)
else:
logger.info("Building model ...")
model = ESN(params)
logger.info("Training + predicting ...")
model, outputs, pred_labels = torsk.train_predict_esn(model,
dataset,
outdir,
steps=1000,
step_length=1)