Integrating bayesflow-trained likelihoods into HSSM (NLE via ONNX)¶
This notebook covers the bayesflow version of the ONNX-based NLE integration workflow in HSSM. Once you've trained a bayesflow ContinuousApproximator (NLE) or RatioApproximator (NRE), you can export it to a single ONNX file and hand the path to HSSM. From HSSM's side, the file is consumed identically to a LAN export or an sbi export — same loglik="file.onnx" gesture, no library-specific glue.
Two paths into HSSM exist, side by side:
| Path | Source | Mechanism | When to use |
|---|---|---|---|
loglik="file.onnx" |
sbi or bayesflow | ONNX file, framework-agnostic | Portability, sharing trained surrogates |
loglik=<jax_callable> |
bayesflow (this tutorial's sibling) | In-memory JAX callable | Fast iteration during model development |
See bayesflow_lre_integration.ipynb for the JAX-callable path. This notebook covers the ONNX path.
Part 1 — Setup¶
Critical: KERAS_BACKEND=torch must be set before importing keras or bayesflow. torch.onnx.export cannot trace a JAX-backed Keras model. On Apple silicon also pin KERAS_TORCH_DEVICE=cpu to avoid the orthogonal initializer's missing MPS op.
Note on the two ONNX-related setup lines below (jax_enable_x64 and the jaxonnxruntime strict-mode relax): these are the same workarounds that HSSM PR #964 plans to auto-handle inside hssm.distribution_utils.onnx2jax. Until that PR lands on main, this tutorial sets them explicitly so it runs standalone. Once #964 merges, both lines can be deleted — HSSM will take care of them on import.
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import os
os.environ["KERAS_BACKEND"] = "torch"
os.environ["KERAS_TORCH_DEVICE"] = "cpu"
import jax
# x64 BEFORE other JAX-touching imports; ONNX graphs from torch.onnx.export
# carry int64 shape/index tensors that get silently truncated under JAX's
# default int32 mode, producing wrong log-prob values inside HSSM.
jax.config.update("jax_enable_x64", True)
# Relax jaxonnxruntime's default strict mode on Reshape shape arguments —
# torch.onnx.export emits them as Constant nodes, not initializers, which the
# strict default rejects. Safe because the shapes are genuinely constant.
# HSSM PR #964 sets this automatically inside hssm.distribution_utils.onnx2jax;
# until that PR lands on main, we set it here.
from jaxonnxruntime import config as _jaxonnx_config
_jaxonnx_config.update("jaxort_only_allow_initializers_as_static_args", False)
import tempfile # noqa: F401 — available for the Part 4 override example
from pathlib import Path
import arviz as az
import bayesflow as bf
import keras
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from bayesflow.datasets import OfflineDataset
from bayesflow.networks.inference.coupling.transforms import AffineTransform
from lanfactory.onnx import transform_bayesflow_to_onnx
from ssms.basic_simulators.simulator import simulator
import hssm
print("keras backend:", keras.backend.backend())
print("bayesflow: ", bf.__version__)
print("hssm: ", hssm.__version__)
import os
os.environ["KERAS_BACKEND"] = "torch"
os.environ["KERAS_TORCH_DEVICE"] = "cpu"
import jax
# x64 BEFORE other JAX-touching imports; ONNX graphs from torch.onnx.export
# carry int64 shape/index tensors that get silently truncated under JAX's
# default int32 mode, producing wrong log-prob values inside HSSM.
jax.config.update("jax_enable_x64", True)
# Relax jaxonnxruntime's default strict mode on Reshape shape arguments —
# torch.onnx.export emits them as Constant nodes, not initializers, which the
# strict default rejects. Safe because the shapes are genuinely constant.
# HSSM PR #964 sets this automatically inside hssm.distribution_utils.onnx2jax;
# until that PR lands on main, we set it here.
from jaxonnxruntime import config as _jaxonnx_config
_jaxonnx_config.update("jaxort_only_allow_initializers_as_static_args", False)
import tempfile # noqa: F401 — available for the Part 4 override example
from pathlib import Path
import arviz as az
import bayesflow as bf
import keras
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from bayesflow.datasets import OfflineDataset
from bayesflow.networks.inference.coupling.transforms import AffineTransform
from lanfactory.onnx import transform_bayesflow_to_onnx
from ssms.basic_simulators.simulator import simulator
import hssm
print("keras backend:", keras.backend.backend())
print("bayesflow: ", bf.__version__)
print("hssm: ", hssm.__version__)
INFO:bayesflow:Using backend 'torch'
WARNING:bayesflow:
When using torch backend, we need to disable autograd by default to avoid excessive memory usage. Use
with torch.enable_grad():
...
in contexts where you need gradients (e.g. custom training loops).
INFO:2026-06-30 02:19:50,875:jax._src.xla_bridge:925: Unable to initialize backend 'rocm': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig'
INFO:jax._src.xla_bridge:Unable to initialize backend 'rocm': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig'
INFO:2026-06-30 02:19:50,876:jax._src.xla_bridge:925: Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: dlopen(libtpu.so, 0x0001): tried: 'libtpu.so' (no such file), '/System/Volumes/Preboot/Cryptexes/OSlibtpu.so' (no such file), '/Users/afengler/.local/share/uv/python/cpython-3.12.13-macos-aarch64-none/lib/libtpu.so' (no such file), '/usr/lib/libtpu.so' (no such file, not in dyld cache), 'libtpu.so' (no such file), '/usr/local/lib/libtpu.so' (no such file), '/usr/lib/libtpu.so' (no such file, not in dyld cache)
INFO:jax._src.xla_bridge:Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: dlopen(libtpu.so, 0x0001): tried: 'libtpu.so' (no such file), '/System/Volumes/Preboot/Cryptexes/OSlibtpu.so' (no such file), '/Users/afengler/.local/share/uv/python/cpython-3.12.13-macos-aarch64-none/lib/libtpu.so' (no such file), '/usr/lib/libtpu.so' (no such file, not in dyld cache), 'libtpu.so' (no such file), '/usr/local/lib/libtpu.so' (no such file), '/usr/lib/libtpu.so' (no such file, not in dyld cache)
mlflow not available mlflow not available keras backend: torch bayesflow: 2.0.12 hssm: 0.3.0
Part 2 — Simulate observed DDM data¶
We use ssm-simulators for ground-truth DDM samples at a known parameter vector. HSSM consumes a DataFrame with rt and response columns.
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DDM_PARAM_NAMES = ["v", "a", "z", "t"]
DDM_PARAM_LOW = np.array([-2.0, 0.6, 0.3, 0.1], dtype=np.float32)
DDM_PARAM_HIGH = np.array([2.0, 1.8, 0.7, 0.5], dtype=np.float32)
TRUE_THETA = np.array([0.5, 1.2, 0.5, 0.25], dtype=np.float32)
N_OBS = 500
out = simulator(theta=TRUE_THETA[None, :], model="ddm", n_samples=N_OBS)
obs_data = pd.DataFrame(
{
"rt": out["rts"].squeeze().astype(np.float32),
"response": out["choices"].squeeze().astype(np.float32),
}
)
obs_data.head()
DDM_PARAM_NAMES = ["v", "a", "z", "t"]
DDM_PARAM_LOW = np.array([-2.0, 0.6, 0.3, 0.1], dtype=np.float32)
DDM_PARAM_HIGH = np.array([2.0, 1.8, 0.7, 0.5], dtype=np.float32)
TRUE_THETA = np.array([0.5, 1.2, 0.5, 0.25], dtype=np.float32)
N_OBS = 500
out = simulator(theta=TRUE_THETA[None, :], model="ddm", n_samples=N_OBS)
obs_data = pd.DataFrame(
{
"rt": out["rts"].squeeze().astype(np.float32),
"response": out["choices"].squeeze().astype(np.float32),
}
)
obs_data.head()
Out[2]:
| rt | response | |
|---|---|---|
| 0 | 0.982721 | 1.0 |
| 1 | 0.417203 | 1.0 |
| 2 | 1.533354 | 1.0 |
| 3 | 0.721513 | 1.0 |
| 4 | 1.038754 | 1.0 |
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fig, ax = plt.subplots(1, 1, figsize=(8, 4))
for choice, label in [(1.0, "choice +1"), (-1.0, "choice -1")]:
rts = obs_data.loc[obs_data["response"] == choice, "rt"]
ax.hist(rts, bins=40, alpha=0.5, label=label)
ax.set_xlabel("reaction time")
ax.set_ylabel("count")
ax.legend()
ax.set_title(f"Observed DDM data at θ={dict(zip(DDM_PARAM_NAMES, TRUE_THETA))}")
plt.tight_layout()
fig, ax = plt.subplots(1, 1, figsize=(8, 4))
for choice, label in [(1.0, "choice +1"), (-1.0, "choice -1")]:
rts = obs_data.loc[obs_data["response"] == choice, "rt"]
ax.hist(rts, bins=40, alpha=0.5, label=label)
ax.set_xlabel("reaction time")
ax.set_ylabel("count")
ax.legend()
ax.set_title(f"Observed DDM data at θ={dict(zip(DDM_PARAM_NAMES, TRUE_THETA))}")
plt.tight_layout()
Part 3 — Train a bayesflow CouplingFlow NLE on DDM simulations¶
The CouplingFlow setup below is the v1 ONNX-friendly configuration documented in LANfactory's exporting_bayesflow_models.md. Three opinionated choices that aren't bayesflow's defaults:
permutation=None—FixedPermutationuseskeras.ops.take, which exports asaten::ravel, unsupported in ONNX opset 17/20.transform=AffineTransform(clamp=False)(explicit instance) — defaultclamp=Trueemitsaten::asinh. Also,bf.networks.CouplingFlow(..., transform_kwargs={"clamp": False})silently drops the kwarg (upstream bug); pass an instance.activation="silu"— default"hard_silu"exports as a single fusedHardSwishop that jaxonnxruntime can't run. Real SiLU decomposes toSigmoid+Mul.
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keras.utils.set_random_seed(0)
rng = np.random.default_rng(0)
N_TRAIN = 20_000
theta_train = rng.uniform(
DDM_PARAM_LOW, DDM_PARAM_HIGH, size=(N_TRAIN, len(DDM_PARAM_NAMES))
).astype(np.float32)
# One trial per (θ_i) — NLE convention: each row is (θ_i, x_i).
x_train = np.empty((N_TRAIN, 2), dtype=np.float32)
for i, th in enumerate(theta_train):
out = simulator(theta=th[None, :], model="ddm", n_samples=1)
x_train[i, 0] = out["rts"].squeeze()
x_train[i, 1] = out["choices"].squeeze()
print("theta_train:", theta_train.shape, " x_train:", x_train.shape)
keras.utils.set_random_seed(0)
rng = np.random.default_rng(0)
N_TRAIN = 20_000
theta_train = rng.uniform(
DDM_PARAM_LOW, DDM_PARAM_HIGH, size=(N_TRAIN, len(DDM_PARAM_NAMES))
).astype(np.float32)
# One trial per (θ_i) — NLE convention: each row is (θ_i, x_i).
x_train = np.empty((N_TRAIN, 2), dtype=np.float32)
for i, th in enumerate(theta_train):
out = simulator(theta=th[None, :], model="ddm", n_samples=1)
x_train[i, 0] = out["rts"].squeeze()
x_train[i, 1] = out["choices"].squeeze()
print("theta_train:", theta_train.shape, " x_train:", x_train.shape)
theta_train: (20000, 4) x_train: (20000, 2)
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approximator = bf.ContinuousApproximator(
inference_network=bf.networks.CouplingFlow(
depth=6,
subnet_kwargs={"widths": (64, 64), "activation": "silu", "dropout": None},
permutation=None,
use_actnorm=False,
transform=AffineTransform(clamp=False),
),
standardize="inference_variables", # standardize x (rt, choice)
)
approximator.build(
{
"inference_variables": (None, 2),
"inference_conditions": (None, len(DDM_PARAM_NAMES)),
}
)
approximator.compile(optimizer=keras.optimizers.Adam(learning_rate=5e-4))
dataset = OfflineDataset(
data={
"inference_variables": x_train,
"inference_conditions": theta_train,
},
batch_size=256,
# MUST be identity for ONNX export; we use only the in-network
# Standardize layer
adapter=None,
)
history = approximator.fit(dataset=dataset, epochs=50, verbose=1)
approximator = bf.ContinuousApproximator(
inference_network=bf.networks.CouplingFlow(
depth=6,
subnet_kwargs={"widths": (64, 64), "activation": "silu", "dropout": None},
permutation=None,
use_actnorm=False,
transform=AffineTransform(clamp=False),
),
standardize="inference_variables", # standardize x (rt, choice)
)
approximator.build(
{
"inference_variables": (None, 2),
"inference_conditions": (None, len(DDM_PARAM_NAMES)),
}
)
approximator.compile(optimizer=keras.optimizers.Adam(learning_rate=5e-4))
dataset = OfflineDataset(
data={
"inference_variables": x_train,
"inference_conditions": theta_train,
},
batch_size=256,
# MUST be identity for ONNX export; we use only the in-network
# Standardize layer
adapter=None,
)
history = approximator.fit(dataset=dataset, epochs=50, verbose=1)
INFO:bayesflow:Fitting on dataset instance of OfflineDataset.
Epoch 1/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 2s 30ms/step - loss: 2.8379
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79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: 2.5370
Epoch 2/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: 2.1744
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 2.1412
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79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 2.0115
Epoch 3/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: 1.6383
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Epoch 4/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: 0.3161
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79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 0.0671
Epoch 5/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.1445
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59/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.5125
65/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.3076
71/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.7669
77/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.6692
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -0.4400
Epoch 6/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.8034
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.8240
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.7373
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.6786
29/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.1635
36/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.7098
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.7449
50/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.5054
57/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.5954
63/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.9356
69/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.1505
76/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.1277
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -0.6517
Epoch 7/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.1054
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.1545
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.7551
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.2269
29/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.6361
36/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.9255
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.7063
50/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.8959
57/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.9685
64/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.1100
70/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.0312
77/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.2058
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -0.6657
Epoch 8/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.3139
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.4764
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.4888
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.5885
29/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.4532
36/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.7178
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.4414
49/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.0527
55/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.4749
62/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.5716
69/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.0574
76/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.5485
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -1.3710
Epoch 9/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.7062
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.8188
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.6492
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.8714
29/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.8420
36/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.8533
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.9349
49/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.7635
56/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.8056
62/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.9915
68/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.8863
75/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.9399
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -1.7278
Epoch 10/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.5847
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.7893
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.9685
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.0308
29/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.0592
36/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.0506
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.6710
50/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 13.7024
57/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 6.5340
64/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 1.9147
71/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 1.0224
78/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.8516
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 2.5190
Epoch 11/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: 0.9661
7/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.5003
13/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.6064
19/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0810
25/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0529
32/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.2814
39/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.3683
46/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0565
52/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.2320
58/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0401
65/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.0491
72/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.1023
79/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.0665
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 0.2326
Epoch 12/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: 5.5112e-05
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.3060
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.2194
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.3883
28/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.1956
34/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.1659
41/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.4288
48/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.2480
54/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.2144
61/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.4319
68/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.1360
75/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.0098
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -0.1101
Epoch 13/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.0739
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.4508
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.3168
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.2703
29/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.1367
36/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.7035
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.1959
50/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.6241
57/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.6640
63/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.4801
70/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.4425
77/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.2184
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -0.4160
Epoch 14/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.5108
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.5364
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.7174
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.6121
29/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.7871
36/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.6571
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.4535
50/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.5948
57/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.3256
64/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.1951
71/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.5922
78/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.6192
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -0.5086
Epoch 15/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.3510
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.8377
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.8193
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.7614
28/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.6645
35/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.8075
42/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.6542
49/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.7891
56/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.9011
63/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.5118
70/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.7325
77/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.9858
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -0.7220
Epoch 16/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.8100
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.6231
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.9226
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.8337
29/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.8147
36/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.6370
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.0321
50/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.5259
57/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.5410
64/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.5995
71/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.4912
78/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.9533
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -0.6521
Epoch 17/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.6455
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.7714
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.0713
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.8458
29/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.7782
35/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.8773
42/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.0052
49/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.9745
55/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.7848
61/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.2250
67/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.0244
74/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.9716
76/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.2613
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: -0.9131
Epoch 18/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.4123
7/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.1247
14/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.0882
21/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.7738
28/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.9513
35/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.9372
42/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.9815
49/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.2735
55/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.2489
62/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.0241
68/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.6993
75/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.0051
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -1.0121
Epoch 19/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.9974
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.1341
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.2219
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.1893
29/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.2658
36/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 5.9710
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 62.4526
50/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 5.9028
57/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 1.0789
63/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 1.9358
69/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 2.0992
76/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 1.5693
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 3.8129
Epoch 20/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: 1.2499
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 1.2311
14/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.9486
21/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.9913
28/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.8295
35/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.8507
42/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.7838
49/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.6775
56/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.6869
63/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.6849
69/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.6453
75/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.4224
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 0.8355
Epoch 21/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: 0.6940
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.7217
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.5625
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.4139
29/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.4734
36/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.4720
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.4020
50/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0034
56/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.3743
63/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.3527
70/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.2065
76/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.3377
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 0.4489
Epoch 22/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.1627
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.2227
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.2184
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.2576
29/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0773
36/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.3266
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.2805
50/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.2347
57/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.2553
64/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.1548
70/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.1113
77/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -6.3896e-04
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 0.1922
Epoch 23/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: 0.0770
7/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.1202
14/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.1239
21/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.1913
28/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0095
35/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.0106
42/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0011
49/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -8.8604e-04
56/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.1291
63/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.1843
70/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.0094
77/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.1416
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -0.0129
Epoch 24/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: 0.0036
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.0415
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.1443
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.0916
29/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.3095
36/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.2047
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.0961
50/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.2787
56/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.2841
62/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.3195
68/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.3464
74/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.2614
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -0.1932
Epoch 25/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.2818
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.2586
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.3095
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.1283
28/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.4247
35/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.4222
42/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.4832
48/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.4202
55/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.4006
62/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.4063
68/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.4569
75/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.3583
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -0.3526
Epoch 26/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.5164
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.5443
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.4611
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.3659
29/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.5708
36/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.4427
42/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.4472
48/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.5117
54/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.4561
60/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.5640
66/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.3740
72/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.6197
78/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.7065
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -0.4950
Epoch 27/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.5442
7/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.4702
13/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.6610
19/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.6540
25/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.5223
31/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.8475
37/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.6326
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.6792
49/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.6462
55/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.5332
61/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.7055
67/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.7954
73/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.8323
79/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.8492
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: -0.6291
Epoch 28/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.6735
7/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.6677
13/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.6325
19/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.5760
25/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.8413
31/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.9308
37/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.7069
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.7114
49/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.0270
55/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.6907
61/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.7413
67/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.8297
73/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.8168
79/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.8485
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: -0.7642
Epoch 29/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - loss: -0.8637
7/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.8201
13/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.8159
19/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.9760
25/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.9445
31/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.9270
37/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.0213
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.8536
49/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.9961
55/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.0975
61/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.6206
67/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.9980
73/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.9166
79/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.9895
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: -0.9011
Epoch 30/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - loss: -1.1019
7/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.0322
13/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.0321
19/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.9773
25/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.8853
31/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.9310
38/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.0021
45/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.9198
51/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.1058
58/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.0877
65/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.0088
71/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.1554
77/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.0535
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: -1.0430
Epoch 31/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - loss: -1.0207
7/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.0521
13/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.1446
19/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.1752
25/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.0910
31/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.2400
37/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.1877
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.1639
49/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.3520
55/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.1997
61/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.3884
67/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.1541
73/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.3974
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: -1.1955
Epoch 32/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.1474
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.3832
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.5001
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.2600
29/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.3987
36/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.3226
42/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.3721
48/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.4392
54/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.4056
61/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.5285
68/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.4164
75/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.5523
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -1.3552
Epoch 33/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.5110
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.2211
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.5089
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.4895
29/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.6779
36/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.4850
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.4911
50/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.7902
57/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.5280
64/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.5306
71/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.3485
77/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.4834
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -1.5237
Epoch 34/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - loss: -1.5788
7/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.5891
13/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.8331
19/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.6705
26/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.6309
33/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.7496
40/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.5338
46/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.7853
53/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.6385
60/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.6248
66/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.5999
73/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.7087
79/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.8852
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -1.6996
Epoch 35/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - loss: -1.8357
7/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.8454
13/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.9020
19/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.8157
25/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.8811
31/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.7117
37/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.8680
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.9533
49/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.9127
55/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.7818
61/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.8413
67/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.8314
73/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.8411
79/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.9307
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: -1.8721
Epoch 36/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.0006
7/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.9373
13/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.9494
19/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.0302
25/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.0637
31/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.9097
37/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.8482
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.8936
49/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.9583
55/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.4254
61/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.4739
68/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.6233
74/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.0998
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: -1.7277
Epoch 37/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.8994
7/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.1749
14/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.0579
21/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.9965
28/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.0094
35/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.0576
42/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.8938
49/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.1810
56/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.0945
63/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.0974
69/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.1060
75/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.5416
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -2.1145
Epoch 38/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - loss: -2.1214
7/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.2290
13/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.1181
19/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.2700
25/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.3903
31/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.2368
37/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.2554
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.4040
49/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.0896
55/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.3799
61/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.3425
68/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.2670
75/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.5099
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: -2.3101
Epoch 39/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.5619
7/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.1712
13/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.6453
20/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.1858
26/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.4423
33/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.6691
40/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.3797
47/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.4254
53/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.4017
60/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.4210
67/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.6905
74/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.1221
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: -2.3396
Epoch 40/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.2710
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.9414
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.3393
21/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.6588
27/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.4031
34/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.3610
41/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.0482
47/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.2521
53/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.3619
59/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.3133
65/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.4601
71/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.3218
77/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -1.0527
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: -1.8660
Epoch 41/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - loss: -2.3440
7/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.4087
13/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.3712
19/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.6600
26/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.6419
33/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.4796
39/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.4311
45/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.5641
51/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.4266
57/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.6215
63/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.6302
70/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.6181
77/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.6238
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: -2.5102
Epoch 42/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.6963
7/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.7689
13/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.6303
19/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.5743
25/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.7826
31/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.7389
38/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.3858
44/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.3244
50/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.6695
56/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.8677
62/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.6320
68/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.5155
74/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -2.4966
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: -2.4133
Epoch 43/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: 2.2960
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.6791
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -1.5272
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.2385
29/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -2.1680
35/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.5411
42/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 13.6757
49/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 9.6865
56/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 4.1649
63/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 3.3732
70/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 2.9153
77/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 1.4015
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 3.7798
Epoch 44/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: 1.6029
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 1.4142
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 1.4581
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.9306
29/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 1.1093
36/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.9757
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 1.2047
50/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.6594
57/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 1.5844
64/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.5464
71/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.6440
78/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.3049
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 0.8979
Epoch 45/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: 0.6055
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.8648
15/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.0650
22/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.3127
29/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.5841
36/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.1857
43/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.1360
50/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.2193
57/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.1337
64/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.0327
71/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0092
78/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.0532
79/79 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 0.0454
Epoch 46/50
1/79 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: -0.2000
8/79 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: -0.2780
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Epoch 47/50
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Epoch 48/50
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Epoch 49/50
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Epoch 50/50
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Part 4 — Export the trained approximator to ONNX¶
One call. The exporter raises clearly if any v1 constraint is violated (wrong KERAS_BACKEND, non-identity adapter, missing inference_network).
Where to write the file. The cell below sets ARTIFACT_DIR to ~/bayesflow_onnx_tutorial/ — outside the HSSM repo, so re-running the notebook doesn't leave artifacts in your working tree. Change it to any path you want to keep the trained ONNX around for downstream work; set it to tempfile.mkdtemp() for an ephemeral demo run.
In [6]:
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# User-configurable: where the .onnx file lands. Default is outside the HSSM
# repo so notebook re-runs don't pollute the working tree.
# Override examples:
# ARTIFACT_DIR = Path("/path/to/my/project/onnx") # keep nearby
# ARTIFACT_DIR = Path(tempfile.mkdtemp()) # ephemeral
ARTIFACT_DIR = Path.home() / "bayesflow_onnx_tutorial"
ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
onnx_path = ARTIFACT_DIR / "ddm_nle.onnx"
transform_bayesflow_to_onnx(
approximator,
str(onnx_path),
mode="nle",
example_theta_dim=len(DDM_PARAM_NAMES),
example_x_dim=2,
)
print(f"wrote {onnx_path} ({onnx_path.stat().st_size:,} bytes)")
# User-configurable: where the .onnx file lands. Default is outside the HSSM
# repo so notebook re-runs don't pollute the working tree.
# Override examples:
# ARTIFACT_DIR = Path("/path/to/my/project/onnx") # keep nearby
# ARTIFACT_DIR = Path(tempfile.mkdtemp()) # ephemeral
ARTIFACT_DIR = Path.home() / "bayesflow_onnx_tutorial"
ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
onnx_path = ARTIFACT_DIR / "ddm_nle.onnx"
transform_bayesflow_to_onnx(
approximator,
str(onnx_path),
mode="nle",
example_theta_dim=len(DDM_PARAM_NAMES),
example_x_dim=2,
)
print(f"wrote {onnx_path} ({onnx_path.stat().st_size:,} bytes)")
/Users/afengler/Projects/proj_hssmspine/HSSMSpine/repos/HSSM/.venv/lib/python3.12/site-packages/keras/src/backend/common/variables.py:634: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! d = int(d) /Users/afengler/Projects/proj_hssmspine/HSSMSpine/repos/HSSM/.venv/lib/python3.12/site-packages/keras/src/backend/torch/numpy.py:1898: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! if dim % indices_or_sections != 0: /Users/afengler/Projects/proj_hssmspine/HSSMSpine/repos/HSSM/.venv/lib/python3.12/site-packages/keras/src/backend/torch/numpy.py:1912: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! if dim == 0 and isinstance(indices_or_sections, int):
wrote /Users/afengler/bayesflow_onnx_tutorial/ddm_nle.onnx (298,339 bytes)
Part 5 — Hand the ONNX to HSSM¶
The user gesture is identical to the sbi path and the LAN-MLP path. HSSM detects the .onnx extension, loads it via jaxonnxruntime, vmaps over trials, and wires it into a PyMC Distribution.
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model_nle = hssm.HSSM(
data=obs_data,
model="ddm",
loglik_kind="approx_differentiable",
loglik=str(onnx_path),
p_outlier=0,
)
model_nle
model_nle = hssm.HSSM(
data=obs_data,
model="ddm",
loglik_kind="approx_differentiable",
loglik=str(onnx_path),
p_outlier=0,
)
model_nle
INFO:jaxonnxruntime.call_onnx:Start tracing the jax_func model to get some static info
INFO:/Users/afengler/Projects/proj_hssmspine/HSSMSpine/repos/HSSM/.venv/lib/python3.12/site-packages/pytensor/link/c/lazylinker_c.py:Compiling new CVM
INFO:/Users/afengler/Projects/proj_hssmspine/HSSMSpine/repos/HSSM/.venv/lib/python3.12/site-packages/pytensor/link/c/lazylinker_c.py:New version 0.31
Model initialized successfully.
INFO:hssm:Model initialized successfully.
Out[7]:
Hierarchical Sequential Sampling Model
Model: ddm
Response variable: rt,response
Likelihood: approx_differentiable
Observations: 500
Parameters:
v:
Prior: Uniform(lower: -3.0, upper: 3.0)
Explicit bounds: (-3.0, 3.0)
a:
Prior: Uniform(lower: 0.3, upper: 2.5)
Explicit bounds: (0.3, 2.5)
z:
Prior: Uniform(lower: 0.0, upper: 1.0)
Explicit bounds: (0.0, 1.0)
t:
Prior: HalfNormal(sigma: 2.0)
Explicit bounds: (0.0, 2.0)
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idata_nle = model_nle.sample(
sampler="numpyro",
draws=1000,
tune=1000,
chains=2,
target_accept=0.9,
mp_ctx="spawn",
)
idata_nle = model_nle.sample(
sampler="numpyro",
draws=1000,
tune=1000,
chains=2,
target_accept=0.9,
mp_ctx="spawn",
)
Using default initvals.
INFO:hssm:Using default initvals.
/Users/afengler/Projects/proj_hssmspine/HSSMSpine/repos/HSSM/.venv/lib/python3.12/site-packages/pymc/sampling/jax.py:475: UserWarning: There are not enough devices to run parallel chains: expected 2 but got 1. Chains will be drawn sequentially. If you are running MCMC in CPU, consider using `numpyro.set_host_device_count(2)` at the beginning of your program. You can double-check how many devices are available in your system using `jax.local_device_count()`. pmap_numpyro = MCMC(
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INFO:pymc.stats.convergence:We recommend running at least 4 chains for robust computation of convergence diagnostics
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In [9]:
Copied!
summary_nle = az.summary(idata_nle, var_names=DDM_PARAM_NAMES)
summary_nle
summary_nle = az.summary(idata_nle, var_names=DDM_PARAM_NAMES)
summary_nle
Out[9]:
| mean | sd | hdi_3% | hdi_97% | mcse_mean | mcse_sd | ess_bulk | ess_tail | r_hat | |
|---|---|---|---|---|---|---|---|---|---|
| v | 0.249 | 0.043 | 0.163 | 0.321 | 0.001 | 0.001 | 1153.0 | 1111.0 | 1.0 |
| a | 1.371 | 0.033 | 1.309 | 1.432 | 0.001 | 0.001 | 863.0 | 928.0 | 1.0 |
| z | 0.639 | 0.023 | 0.596 | 0.681 | 0.001 | 0.001 | 932.0 | 926.0 | 1.0 |
| t | 0.318 | 0.016 | 0.292 | 0.344 | 0.001 | 0.000 | 783.0 | 800.0 | 1.0 |
In [10]:
Copied!
az.plot_trace(idata_nle, var_names=DDM_PARAM_NAMES)
plt.tight_layout()
az.plot_trace(idata_nle, var_names=DDM_PARAM_NAMES)
plt.tight_layout()
Part 6 — Ground-truth posterior via HSSM's analytical DDM¶
DDM has a closed-form likelihood (Navarro & Fuss). Comparing the bayesflow-NLE posterior to this gives a fairness check: any drift comes from the neural approximation, not from the inference machinery.
In [11]:
Copied!
model_analytical = hssm.HSSM(
data=obs_data,
model="ddm",
p_outlier=0,
# default loglik_kind here is the analytical Navarro & Fuss path
)
idata_analytical = model_analytical.sample(
sampler="numpyro",
draws=1000,
tune=1000,
chains=2,
target_accept=0.9,
mp_ctx="spawn",
)
summary_analytical = az.summary(idata_analytical, var_names=DDM_PARAM_NAMES)
summary_analytical
model_analytical = hssm.HSSM(
data=obs_data,
model="ddm",
p_outlier=0,
# default loglik_kind here is the analytical Navarro & Fuss path
)
idata_analytical = model_analytical.sample(
sampler="numpyro",
draws=1000,
tune=1000,
chains=2,
target_accept=0.9,
mp_ctx="spawn",
)
summary_analytical = az.summary(idata_analytical, var_names=DDM_PARAM_NAMES)
summary_analytical
Model initialized successfully.
INFO:hssm:Model initialized successfully.
Using default initvals.
INFO:hssm:Using default initvals.
/Users/afengler/Projects/proj_hssmspine/HSSMSpine/repos/HSSM/.venv/lib/python3.12/site-packages/pymc/sampling/jax.py:475: UserWarning: There are not enough devices to run parallel chains: expected 2 but got 1. Chains will be drawn sequentially. If you are running MCMC in CPU, consider using `numpyro.set_host_device_count(2)` at the beginning of your program. You can double-check how many devices are available in your system using `jax.local_device_count()`. pmap_numpyro = MCMC(
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INFO:pymc.stats.convergence:We recommend running at least 4 chains for robust computation of convergence diagnostics
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Out[11]:
| mean | sd | hdi_3% | hdi_97% | mcse_mean | mcse_sd | ess_bulk | ess_tail | r_hat | |
|---|---|---|---|---|---|---|---|---|---|
| v | 0.557 | 0.051 | 0.461 | 0.653 | 0.002 | 0.001 | 1063.0 | 1026.0 | 1.01 |
| a | 1.243 | 0.029 | 1.193 | 1.300 | 0.001 | 0.001 | 1031.0 | 1203.0 | 1.00 |
| z | 0.500 | 0.017 | 0.470 | 0.532 | 0.001 | 0.000 | 986.0 | 1166.0 | 1.00 |
| t | 0.216 | 0.018 | 0.184 | 0.252 | 0.001 | 0.000 | 898.0 | 990.0 | 1.00 |
Part 7 — Posterior comparison: bayesflow NLE vs analytical¶
Overlay marginals and mark the true values. If the bayesflow NLE was trained well, the two posterior densities should agree closely.
In [12]:
Copied!
fig, axes = plt.subplots(1, len(DDM_PARAM_NAMES), figsize=(16, 4))
for ax, name, true_val in zip(axes, DDM_PARAM_NAMES, TRUE_THETA):
az.plot_kde(
idata_analytical.posterior[name].values.flatten(),
plot_kwargs={"color": "black", "linestyle": "--", "label": "analytical"},
ax=ax,
)
az.plot_kde(
idata_nle.posterior[name].values.flatten(),
plot_kwargs={"color": "tab:blue", "label": "bayesflow NLE"},
ax=ax,
)
ax.axvline(true_val, color="red", lw=1, label="truth")
ax.set_title(name)
ax.legend(fontsize=8)
plt.tight_layout()
fig, axes = plt.subplots(1, len(DDM_PARAM_NAMES), figsize=(16, 4))
for ax, name, true_val in zip(axes, DDM_PARAM_NAMES, TRUE_THETA):
az.plot_kde(
idata_analytical.posterior[name].values.flatten(),
plot_kwargs={"color": "black", "linestyle": "--", "label": "analytical"},
ax=ax,
)
az.plot_kde(
idata_nle.posterior[name].values.flatten(),
plot_kwargs={"color": "tab:blue", "label": "bayesflow NLE"},
ax=ax,
)
ax.axvline(true_val, color="red", lw=1, label="truth")
ax.set_title(name)
ax.legend(fontsize=8)
plt.tight_layout()
Summary¶
- User gesture is the same as sbi or LAN-MLP:
hssm.HSSM(loglik="file.onnx", loglik_kind="approx_differentiable"). HSSM doesn't need to know which framework trained the surrogate. - The v1 constraints on the bayesflow side (CouplingFlow with
permutation=None, explicitAffineTransform(clamp=False),silunothard_siluactivation, identity adapter,KERAS_BACKEND=torchat export time) are all enforced or documented bylanfactory.onnx.transform_bayesflow_to_onnx. The exporter raises clearly when a constraint is violated. - For an in-memory JAX-callable alternative (no ONNX file, faster iteration during model development), see
bayesflow_lre_integration.ipynb.
Out of v1 scope (tracked as future work):
- MNLE-style discrete + continuous mixed observations
- Non-identity bayesflow Adapters (would require either baking the tensor-able subset into the ONNX graph, or shipping the adapter spec alongside the ONNX file)
- Transformer / attention summary networks (LayerNorm + dynamic-shape ops)
- FlowMatching / DiffusionModel / ConsistencyModel inference networks (
log_probrequires ODE integration)