diff --git a/.gitignore b/.gitignore
index 67be387..c8f4c07 100755
--- a/.gitignore
+++ b/.gitignore
@@ -3,6 +3,7 @@
notebooks/test_notebooks/data/
+
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
diff --git a/docs/overrides/main.html b/docs/overrides/main.html
index 00e0bce..5983209 100644
--- a/docs/overrides/main.html
+++ b/docs/overrides/main.html
@@ -8,7 +8,7 @@
Navigate the site here!
- v0.4.3 is out!
+ v0.4.4 is out!
diff --git a/lanfactory/__init__.py b/lanfactory/__init__.py
index 6e23957..fd8f4a1 100755
--- a/lanfactory/__init__.py
+++ b/lanfactory/__init__.py
@@ -1,4 +1,4 @@
-__version__ = "0.4.3"
+__version__ = "0.4.4"
from . import config
from . import trainers
diff --git a/lanfactory/trainers/jax_mlp.py b/lanfactory/trainers/jax_mlp.py
index 85abd20..b16069d 100755
--- a/lanfactory/trainers/jax_mlp.py
+++ b/lanfactory/trainers/jax_mlp.py
@@ -72,6 +72,7 @@ class MLPJax(nn.Module):
The output type of the model during training.
"""
+ network_type_dict: dict = frozendict({"logprob": "lan", "logits": "cpn"})
layer_sizes: Sequence[int] = (100, 90, 80, 1)
activations: Sequence[str] = ("tanh", "tanh", "tanh", "linear")
train: bool = True
@@ -81,9 +82,10 @@ class MLPJax(nn.Module):
activations_dict = frozendict(
{"relu": nn.relu, "tanh": nn.tanh, "sigmoid": nn.sigmoid}
)
+ # network_type: Optional[str] = "none"
# Define network type
- network_type = "lan" if train_output_type == "logprob" else "cpn"
+ # network_type = "lan" if train_output_type == "logprob" else "cpn"
def setup(self):
"""Setup function for the JaxMLP class.
@@ -99,7 +101,7 @@ def setup(self):
]
# Identification
- # self.network_type = "lan" if self.train_output_type == "logprob" else "cpn"
+ self.network_type = self.network_type_dict[self.train_output_type]
def __call__(self, inputs):
"""Call function for the JaxMLP class.
@@ -126,11 +128,14 @@ def __call__(self, inputs):
else:
x = self.activation_funs[i](x)
- if not self.train and self.train_output_type == "logprob":
+ if (not self.train) and (self.train_output_type == "logprob"):
+ print("passing through identity")
x = x # just for pedagogy
- elif not self.train and self.train_output_type == "logits":
+ elif (not self.train) and (self.train_output_type == "logits"):
+ print("passing through transform")
x = -jnp.log((1 + jnp.exp(-x)))
elif not self.train:
+ print("passing through identity 2")
x = x # just for pedagogy
return x
diff --git a/notebooks/test_notebooks/test_jax_network.ipynb b/notebooks/test_notebooks/test_jax_network.ipynb
index b49daf5..b02708d 100644
--- a/notebooks/test_notebooks/test_jax_network.ipynb
+++ b/notebooks/test_notebooks/test_jax_network.ipynb
@@ -4,20 +4,7 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [
- {
- "ename": "ImportError",
- "evalue": "cannot import name 'onnx' from partially initialized module 'lanfactory' (most likely due to a circular import) (/users/afengler/data/software/miniconda3/envs/lan_pipe/lib/python3.10/site-packages/lanfactory/__init__.py)",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mssms\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mlanfactory\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mos\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n",
- "File \u001b[0;32m~/data/software/miniconda3/envs/lan_pipe/lib/python3.10/site-packages/lanfactory/__init__.py:6\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m \u001b[39mimport\u001b[39;00m trainers\n\u001b[1;32m 5\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m \u001b[39mimport\u001b[39;00m utils\n\u001b[0;32m----> 6\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m \u001b[39mimport\u001b[39;00m onnx\n\u001b[1;32m 8\u001b[0m __all__ \u001b[39m=\u001b[39m [\u001b[39m\"\u001b[39m\u001b[39mconfig\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mtrainers\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mutils\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39monnx\u001b[39m\u001b[39m\"\u001b[39m]\n",
- "\u001b[0;31mImportError\u001b[0m: cannot import name 'onnx' from partially initialized module 'lanfactory' (most likely due to a circular import) (/users/afengler/data/software/miniconda3/envs/lan_pipe/lib/python3.10/site-packages/lanfactory/__init__.py)"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import ssms\n",
"import lanfactory\n",
diff --git a/notebooks/test_notebooks/test_jax_network_cpn.ipynb b/notebooks/test_notebooks/test_jax_network_cpn.ipynb
new file mode 100644
index 0000000..6b58662
--- /dev/null
+++ b/notebooks/test_notebooks/test_jax_network_cpn.ipynb
@@ -0,0 +1,3573 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import ssms\n",
+ "import lanfactory\n",
+ "import os\n",
+ "import numpy as np\n",
+ "from copy import deepcopy\n",
+ "import torch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "MODEL = \"ddm\"\n",
+ "RUN_SIMS = False"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Initialize the generator config (for MLP LANs)\n",
+ "generator_config = deepcopy(ssms.config.data_generator_config[\"lan\"])\n",
+ "# Specify generative model (one from the list of included models mentioned above)\n",
+ "generator_config[\"dgp_list\"] = MODEL\n",
+ "# Specify number of parameter sets to simulate\n",
+ "generator_config[\"n_parameter_sets\"] = 256\n",
+ "# Specify how many samples a simulation run should entail\n",
+ "generator_config[\"n_samples\"] = 2000\n",
+ "# Specify folder in which to save generated data\n",
+ "generator_config[\"output_folder\"] = \"data/lan_mlp/\"\n",
+ "\n",
+ "# Make model config dict\n",
+ "model_config = ssms.config.model_config[MODEL]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'name': 'ddm',\n",
+ " 'params': ['v', 'a', 'z', 't'],\n",
+ " 'param_bounds': [[-3.0, 0.3, 0.1, 0.0], [3.0, 2.5, 0.9, 2.0]],\n",
+ " 'boundary': ,\n",
+ " 'n_params': 4,\n",
+ " 'default_params': [0.0, 1.0, 0.5, 0.001],\n",
+ " 'hddm_include': ['z'],\n",
+ " 'nchoices': 2}"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model_config"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "generator_config[\"output_folder\"] = (\n",
+ " \"data/lan_mlp/\"\n",
+ " + generator_config[\"dgp_list\"]\n",
+ " + \"/\"\n",
+ " + str(generator_config[\"n_samples\"])\n",
+ " + \"_\"\n",
+ " + str(generator_config[\"n_training_samples_by_parameter_set\"])\n",
+ " + \"/\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if RUN_SIMS:\n",
+ " n_datafiles = 20\n",
+ " for i in range(n_datafiles):\n",
+ " print(\"Datafile: \", i)\n",
+ " my_dataset_generator = ssms.dataset_generators.lan_mlp.data_generator(\n",
+ " generator_config=generator_config, model_config=model_config\n",
+ " )\n",
+ " training_data = my_dataset_generator.generate_data_training_uniform(save=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pickle\n",
+ "\n",
+ "my_data = pickle.load(\n",
+ " open(\n",
+ " \"data/lan_mlp/ddm/2000_1000//training_data_802269f0685a11ee8748ac1f6bfea5a4.pickle\",\n",
+ " \"rb\",\n",
+ " )\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(250,)"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "my_data[\"choice_p\"].shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(250, 4)"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "my_data[\"thetas\"].shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Network config: \n",
+ "{'layer_sizes': [100, 100, 1], 'activations': ['tanh', 'tanh', 'linear'], 'train_output_type': 'logprob'}\n",
+ "Train config: \n",
+ "{'cpu_batch_size': 128, 'gpu_batch_size': 256, 'n_epochs': 5, 'optimizer': 'adam', 'learning_rate': 0.002, 'lr_scheduler': 'reduce_on_plateau', 'lr_scheduler_params': {}, 'weight_decay': 0.0, 'loss': 'huber', 'save_history': True}\n"
+ ]
+ }
+ ],
+ "source": [
+ "network_config = lanfactory.config.network_configs.network_config_mlp\n",
+ "\n",
+ "print(\"Network config: \")\n",
+ "print(network_config)\n",
+ "\n",
+ "train_config = lanfactory.config.network_configs.train_config_mlp\n",
+ "\n",
+ "print(\"Train config: \")\n",
+ "print(train_config)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "network_config[\"train_output_type\"] = \"logits\"\n",
+ "\n",
+ "\n",
+ "train_config[\"loss\"] = \"bcelogit\"\n",
+ "train_config[\"cpu_batch_size\"] = 128\n",
+ "train_config[\"gpu_batch_size\"] = 128\n",
+ "train_config[\"n_epochs\"] = 20"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "folder_ = \"data/lan_mlp/\" + MODEL + \"/2000_1000/\"\n",
+ "file_list_ = [folder_ + file_ for file_ in os.listdir(folder_)]\n",
+ "\n",
+ "# Training dataset\n",
+ "jax_training_dataset = lanfactory.trainers.DatasetTorch(\n",
+ " file_ids=file_list_,\n",
+ " batch_size=train_config[\"gpu_batch_size\"]\n",
+ " if torch.cuda.is_available()\n",
+ " else train_config[\"cpu_batch_size\"],\n",
+ " label_lower_bound=np.log(1e-10),\n",
+ " features_key=\"thetas\",\n",
+ " label_key=\"choice_p\",\n",
+ " out_framework=\"jax\",\n",
+ ")\n",
+ "\n",
+ "jax_training_dataloader = torch.utils.data.DataLoader(\n",
+ " jax_training_dataset, shuffle=True, batch_size=None, num_workers=1, pin_memory=True\n",
+ ")\n",
+ "\n",
+ "# Validation dataset\n",
+ "jax_validation_dataset = lanfactory.trainers.DatasetTorch(\n",
+ " file_ids=file_list_,\n",
+ " batch_size=train_config[\"gpu_batch_size\"]\n",
+ " if torch.cuda.is_available()\n",
+ " else train_config[\"cpu_batch_size\"],\n",
+ " label_lower_bound=np.log(1e-10),\n",
+ " features_key=\"thetas\",\n",
+ " label_key=\"choice_p\",\n",
+ " out_framework=\"jax\",\n",
+ ")\n",
+ "\n",
+ "jax_validation_dataloader = torch.utils.data.DataLoader(\n",
+ " jax_validation_dataset,\n",
+ " shuffle=True,\n",
+ " batch_size=None,\n",
+ " num_workers=1,\n",
+ " pin_memory=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor([[ 1.3875, 1.0988, 0.4319, 0.6890],\n",
+ " [ 2.0641, 0.5735, 0.4064, 1.0609],\n",
+ " [ 0.3673, 2.4499, 0.1387, 0.5495],\n",
+ " [ 0.1586, 2.4325, 0.8431, 1.9932],\n",
+ " [-1.2530, 1.1292, 0.2656, 1.3690],\n",
+ " [ 2.1943, 0.4761, 0.5051, 0.8956],\n",
+ " [-2.8107, 1.7185, 0.7278, 1.5208],\n",
+ " [ 1.4085, 1.4228, 0.4704, 0.1973],\n",
+ " [-2.6501, 0.5449, 0.3855, 0.1303],\n",
+ " [-1.1308, 1.1600, 0.1699, 1.9684],\n",
+ " [ 2.5065, 1.1481, 0.4963, 0.8414],\n",
+ " [-0.9240, 1.7089, 0.7574, 1.8790],\n",
+ " [-1.8158, 0.8894, 0.3338, 0.1655],\n",
+ " [ 1.2889, 2.0544, 0.5561, 1.9970],\n",
+ " [ 0.9539, 1.7233, 0.5906, 0.2917],\n",
+ " [ 1.1837, 1.6261, 0.7342, 1.5239],\n",
+ " [ 2.2922, 0.8479, 0.2296, 0.1975],\n",
+ " [ 2.3063, 0.6019, 0.5261, 0.1864],\n",
+ " [ 2.7554, 1.2701, 0.2686, 0.5694],\n",
+ " [ 2.6340, 1.3958, 0.3809, 0.9869],\n",
+ " [ 0.8128, 2.0258, 0.3488, 0.0488],\n",
+ " [ 0.9539, 1.7233, 0.5906, 0.2917],\n",
+ " [ 2.7758, 0.5842, 0.4133, 1.5215],\n",
+ " [-2.2586, 0.3381, 0.6819, 0.5807],\n",
+ " [ 2.3908, 0.8323, 0.1184, 0.3139],\n",
+ " [ 0.6654, 0.7986, 0.6528, 1.5458],\n",
+ " [-2.2881, 0.7703, 0.6878, 0.0779],\n",
+ " [ 2.7758, 0.5842, 0.4133, 1.5215],\n",
+ " [-0.8794, 0.6326, 0.5838, 0.0351],\n",
+ " [-0.5542, 1.9819, 0.8946, 0.0785],\n",
+ " [ 0.7437, 1.0027, 0.2901, 0.5856],\n",
+ " [-2.4393, 1.3407, 0.5083, 1.3016],\n",
+ " [ 2.6340, 1.3958, 0.3809, 0.9869],\n",
+ " [ 2.0965, 0.8170, 0.1695, 0.0117],\n",
+ " [-2.5668, 0.4322, 0.7148, 1.1451],\n",
+ " [ 1.0704, 2.0681, 0.3131, 1.5057],\n",
+ " [ 0.4478, 1.3764, 0.4466, 1.7641],\n",
+ " [ 2.2935, 2.1882, 0.2009, 0.8839],\n",
+ " [-0.2078, 1.3746, 0.4476, 0.8415],\n",
+ " [-0.3896, 1.0292, 0.1759, 1.7376],\n",
+ " [-0.6599, 2.2791, 0.8697, 0.6236],\n",
+ " [-1.6129, 2.4090, 0.8165, 1.0484],\n",
+ " [-1.4501, 1.8850, 0.8890, 0.1004],\n",
+ " [-0.5837, 1.6132, 0.1289, 1.9553],\n",
+ " [ 1.2048, 0.5776, 0.5247, 1.6208],\n",
+ " [-2.5801, 1.6950, 0.8453, 1.7339],\n",
+ " [ 2.0965, 0.8170, 0.1695, 0.0117],\n",
+ " [ 0.2167, 2.2391, 0.8035, 1.7274],\n",
+ " [ 2.5577, 0.3971, 0.1984, 1.1467],\n",
+ " [ 1.0930, 2.0644, 0.3615, 0.1800],\n",
+ " [ 1.8516, 1.5569, 0.3288, 1.3727],\n",
+ " [ 2.8785, 0.5808, 0.8485, 1.0557],\n",
+ " [-0.7410, 1.8908, 0.6553, 1.1936],\n",
+ " [ 1.8942, 0.6125, 0.7274, 0.9967],\n",
+ " [-0.4275, 2.0055, 0.6584, 0.1120],\n",
+ " [-0.1997, 1.7774, 0.3159, 1.6888],\n",
+ " [ 1.9827, 0.9276, 0.7918, 1.5781],\n",
+ " [ 1.8312, 1.7687, 0.4100, 1.2013],\n",
+ " [-1.5843, 1.0479, 0.3429, 0.0696],\n",
+ " [ 2.8169, 1.3649, 0.2887, 1.9693],\n",
+ " [-2.9593, 2.4280, 0.8278, 1.8518],\n",
+ " [ 2.8583, 1.3794, 0.1319, 1.4388],\n",
+ " [ 2.3063, 0.6019, 0.5261, 0.1864],\n",
+ " [ 2.4349, 1.1920, 0.1859, 0.9601],\n",
+ " [ 0.3636, 1.2342, 0.6493, 0.1102],\n",
+ " [-1.4894, 1.4995, 0.2184, 0.6781],\n",
+ " [-1.8408, 0.6882, 0.5383, 1.9635],\n",
+ " [-0.0890, 1.7026, 0.4538, 0.7120],\n",
+ " [-0.7431, 2.1162, 0.8039, 1.4059],\n",
+ " [ 0.3673, 2.4499, 0.1387, 0.5495],\n",
+ " [ 0.1796, 1.8180, 0.6482, 0.1462],\n",
+ " [ 2.7758, 0.5842, 0.4133, 1.5215],\n",
+ " [-0.4040, 1.9994, 0.2359, 0.5288],\n",
+ " [-1.0479, 0.5696, 0.4889, 0.6260],\n",
+ " [-0.1055, 0.4207, 0.2265, 1.0189],\n",
+ " [-1.9433, 2.0773, 0.6072, 1.3275],\n",
+ " [-0.5431, 1.3278, 0.7334, 1.4458],\n",
+ " [-2.4260, 0.7019, 0.4338, 1.8502],\n",
+ " [-0.1997, 1.7774, 0.3159, 1.6888],\n",
+ " [ 2.3744, 1.4798, 0.2579, 0.8686],\n",
+ " [-0.4445, 1.9301, 0.3538, 1.6293],\n",
+ " [ 1.2889, 2.0544, 0.5561, 1.9970],\n",
+ " [ 1.4661, 1.2125, 0.7066, 0.2156],\n",
+ " [-1.8296, 0.8701, 0.5311, 1.9409],\n",
+ " [-1.9433, 2.0773, 0.6072, 1.3275],\n",
+ " [ 1.2454, 1.1718, 0.2020, 0.4617],\n",
+ " [-2.2607, 2.1367, 0.6355, 1.1657],\n",
+ " [ 0.9132, 1.5293, 0.6625, 0.9570],\n",
+ " [-0.5487, 1.8166, 0.6880, 1.9263],\n",
+ " [ 1.0465, 0.7443, 0.4817, 1.4546],\n",
+ " [-1.3106, 0.5323, 0.4335, 0.4437],\n",
+ " [ 1.1837, 1.6261, 0.7342, 1.5239],\n",
+ " [ 1.4661, 1.2125, 0.7066, 0.2156],\n",
+ " [ 0.0515, 1.9203, 0.8981, 0.0347],\n",
+ " [ 1.8096, 2.4884, 0.1699, 1.9607],\n",
+ " [-1.2877, 1.0378, 0.4701, 0.4296],\n",
+ " [ 1.9040, 1.6969, 0.1759, 1.8537],\n",
+ " [ 0.5895, 0.6994, 0.1222, 0.9950],\n",
+ " [ 2.7543, 0.5636, 0.5107, 1.8221],\n",
+ " [ 2.6839, 0.5573, 0.3767, 1.8638],\n",
+ " [ 2.6866, 0.3448, 0.6766, 0.1069],\n",
+ " [ 2.2935, 2.1882, 0.2009, 0.8839],\n",
+ " [ 1.8312, 1.7687, 0.4100, 1.2013],\n",
+ " [ 0.1796, 1.8180, 0.6482, 0.1462],\n",
+ " [ 1.2510, 0.6432, 0.3902, 0.3313],\n",
+ " [-0.5558, 2.3053, 0.3952, 1.4417],\n",
+ " [ 2.7758, 0.5842, 0.4133, 1.5215],\n",
+ " [-2.5746, 0.6799, 0.6293, 0.6250],\n",
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+ ]
+ }
+ ],
+ "source": [
+ "cnt = 0\n",
+ "for xb, yb in jax_training_dataloader:\n",
+ " print(xb)\n",
+ " print(yb)\n",
+ " cnt += 1\n",
+ " if cnt > 10:\n",
+ " break"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# LOAD NETWORK\n",
+ "jax_net = lanfactory.trainers.MLPJaxFactory(network_config=network_config, train=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "jax_trainer = lanfactory.trainers.ModelTrainerJaxMLP(\n",
+ " train_config=train_config,\n",
+ " model=jax_net,\n",
+ " train_dl=jax_training_dataloader,\n",
+ " valid_dl=jax_validation_dataloader,\n",
+ " pin_memory=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found folder: data\n",
+ "Moving on...\n",
+ "Found folder: data/trained_model\n",
+ "Moving on...\n",
+ "Found folder: data/trained_model/jax\n",
+ "Moving on...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
+ "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mafengler\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
+ ]
+ },
+ {
+ "data": {
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+ "Tracking run with wandb version 0.15.12"
+ ],
+ "text/plain": [
+ ""
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+ "data": {
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+ "data": {
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+ "metadata": {},
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+ "name": "stdout",
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+ "text": [
+ "Epoch: 0 of 20\n",
+ "Training - Step: 0 of 20 - Loss: 0.66841096\n",
+ "Epoch 0/20 time: 2.480642557144165s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.3640096\n",
+ "Epoch 0/20 time: 0.711876392364502s\n",
+ "Epoch: 0 / 20, test_loss: 0.3763638436794281\n",
+ "Epoch: 1 of 20\n",
+ "Training - Step: 0 of 20 - Loss: 0.42096427\n",
+ "Epoch 1/20 time: 0.21535420417785645s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.32862508\n",
+ "Epoch 1/20 time: 0.2097616195678711s\n",
+ "Epoch: 1 / 20, test_loss: 0.36423781514167786\n",
+ "Epoch: 2 of 20\n",
+ "Training - Step: 0 of 20 - Loss: 0.32760912\n",
+ "Epoch 2/20 time: 0.2090158462524414s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.36396658\n",
+ "Epoch 2/20 time: 0.21240592002868652s\n",
+ "Epoch: 2 / 20, test_loss: 0.34704074263572693\n",
+ "Epoch: 3 of 20\n",
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+ "Epoch 3/20 time: 0.21966028213500977s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.33193588\n",
+ "Epoch 3/20 time: 0.21724534034729004s\n",
+ "Epoch: 3 / 20, test_loss: 0.33903735876083374\n",
+ "Epoch: 4 of 20\n",
+ "Training - Step: 0 of 20 - Loss: 0.32506475\n",
+ "Epoch 4/20 time: 0.2142655849456787s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.31381655\n",
+ "Epoch 4/20 time: 0.21552109718322754s\n",
+ "Epoch: 4 / 20, test_loss: 0.32855862379074097\n",
+ "Epoch: 5 of 20\n",
+ "Training - Step: 0 of 20 - Loss: 0.3243287\n",
+ "Epoch 5/20 time: 0.21051621437072754s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.2995989\n",
+ "Epoch 5/20 time: 0.21210789680480957s\n",
+ "Epoch: 5 / 20, test_loss: 0.32827991247177124\n",
+ "Epoch: 6 of 20\n",
+ "Training - Step: 0 of 20 - Loss: 0.34983116\n",
+ "Epoch 6/20 time: 0.2115771770477295s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.32285076\n",
+ "Epoch 6/20 time: 0.21551299095153809s\n",
+ "Epoch: 6 / 20, test_loss: 0.3344951272010803\n",
+ "Epoch: 7 of 20\n",
+ "Training - Step: 0 of 20 - Loss: 0.32843897\n",
+ "Epoch 7/20 time: 0.20916152000427246s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.31496266\n",
+ "Epoch 7/20 time: 0.21837782859802246s\n",
+ "Epoch: 7 / 20, test_loss: 0.3221416473388672\n",
+ "Epoch: 8 of 20\n",
+ "Training - Step: 0 of 20 - Loss: 0.31329334\n",
+ "Epoch 8/20 time: 0.20964837074279785s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.35225874\n",
+ "Epoch 8/20 time: 0.2106645107269287s\n",
+ "Epoch: 8 / 20, test_loss: 0.33055952191352844\n",
+ "Epoch: 9 of 20\n",
+ "Training - Step: 0 of 20 - Loss: 0.33820286\n",
+ "Epoch 9/20 time: 0.21038174629211426s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.35980263\n",
+ "Epoch 9/20 time: 0.21074843406677246s\n",
+ "Epoch: 9 / 20, test_loss: 0.32842135429382324\n",
+ "Epoch: 10 of 20\n",
+ "Training - Step: 0 of 20 - Loss: 0.29986778\n",
+ "Epoch 10/20 time: 0.21071910858154297s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.29436737\n",
+ "Epoch 10/20 time: 0.20849275588989258s\n",
+ "Epoch: 10 / 20, test_loss: 0.334464967250824\n",
+ "Epoch: 11 of 20\n",
+ "Training - Step: 0 of 20 - Loss: 0.30182517\n",
+ "Epoch 11/20 time: 0.2122347354888916s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.3220601\n",
+ "Epoch 11/20 time: 0.209702730178833s\n",
+ "Epoch: 11 / 20, test_loss: 0.3255159556865692\n",
+ "Epoch: 12 of 20\n",
+ "Training - Step: 0 of 20 - Loss: 0.30407035\n",
+ "Epoch 12/20 time: 0.20887398719787598s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.30918664\n",
+ "Epoch 12/20 time: 0.21337151527404785s\n",
+ "Epoch: 12 / 20, test_loss: 0.32516297698020935\n",
+ "Epoch: 13 of 20\n",
+ "Training - Step: 0 of 20 - Loss: 0.3260459\n",
+ "Epoch 13/20 time: 0.21240592002868652s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.33377397\n",
+ "Epoch 13/20 time: 0.2139298915863037s\n",
+ "Epoch: 13 / 20, test_loss: 0.3291628956794739\n",
+ "Epoch: 14 of 20\n",
+ "Training - Step: 0 of 20 - Loss: 0.2986664\n",
+ "Epoch 14/20 time: 0.21326541900634766s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.35728014\n",
+ "Epoch 14/20 time: 0.20975446701049805s\n",
+ "Epoch: 14 / 20, test_loss: 0.3265935778617859\n",
+ "Epoch: 15 of 20\n",
+ "Training - Step: 0 of 20 - Loss: 0.34128895\n",
+ "Epoch 15/20 time: 0.2152864933013916s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.3017817\n",
+ "Epoch 15/20 time: 0.21021294593811035s\n",
+ "Epoch: 15 / 20, test_loss: 0.3323461413383484\n",
+ "Epoch: 16 of 20\n",
+ "Training - Step: 0 of 20 - Loss: 0.3511687\n",
+ "Epoch 16/20 time: 0.21351981163024902s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.3242878\n",
+ "Epoch 16/20 time: 0.21068191528320312s\n",
+ "Epoch: 16 / 20, test_loss: 0.3287012279033661\n",
+ "Epoch: 17 of 20\n",
+ "Training - Step: 0 of 20 - Loss: 0.31763965\n",
+ "Epoch 17/20 time: 0.21467208862304688s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.31449765\n",
+ "Epoch 17/20 time: 0.21251487731933594s\n",
+ "Epoch: 17 / 20, test_loss: 0.32999876141548157\n",
+ "Epoch: 18 of 20\n",
+ "Training - Step: 0 of 20 - Loss: 0.3263458\n",
+ "Epoch 18/20 time: 0.21041607856750488s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.33029902\n",
+ "Epoch 18/20 time: 0.21444082260131836s\n",
+ "Epoch: 18 / 20, test_loss: 0.3336031436920166\n",
+ "Epoch: 19 of 20\n",
+ "Training - Step: 0 of 20 - Loss: 0.30876464\n",
+ "Epoch 19/20 time: 0.21229982376098633s\n",
+ "Validation - Step: 0 of 20 - Loss: 0.33319035\n",
+ "Epoch 19/20 time: 0.2130897045135498s\n",
+ "Epoch: 19 / 20, test_loss: 0.3296617865562439\n",
+ "Saving training history to: data/trained_model/jax//test_run_notebook_cpn_ddm__jax_training_history.csv\n",
+ "Saving model parameters to: data/trained_model/jax//test_run_notebook_cpn_ddm__train_state.jax\n",
+ "Saving training config to: data/trained_model/jax//test_run_notebook_cpn_ddm__train_config.pickle\n",
+ "Saving training data details to: data/trained_model/jax//test_run_notebook_cpn_ddm__data_details.pickle\n"
+ ]
+ }
+ ],
+ "source": [
+ "train_state = jax_trainer.train_and_evaluate(\n",
+ " output_folder=\"data/trained_model/jax/\",\n",
+ " output_file_id=MODEL,\n",
+ " run_id=\"test_run_notebook\",\n",
+ " wandb_on=True,\n",
+ " wandb_project_id=\"test_run_notebook\",\n",
+ " save_data_details=True,\n",
+ " verbose=1,\n",
+ " save_all=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'layer_sizes': [100, 100, 1], 'activations': ['tanh', 'tanh', 'linear'], 'train_output_type': 'logits'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(network_config)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Loaded Net\n",
+ "jax_infer = lanfactory.trainers.MLPJaxFactory(\n",
+ " network_config=network_config,\n",
+ " train=False,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "passing through transform\n"
+ ]
+ }
+ ],
+ "source": [
+ "my_state = jax_infer.load_state_from_file(\n",
+ " file_path=\"data/trained_model/jax/test_run_notebook_cpn_ddm__train_state.jax\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "passing through transform\n"
+ ]
+ }
+ ],
+ "source": [
+ "forward_pass, forward_pass_jitted = jax_infer.make_forward_partial(\n",
+ " seed=42,\n",
+ " input_dim=model_config[\"n_params\"] + 2,\n",
+ " state=\"data/trained_model/jax/test_run_notebook_cpn_ddm__train_state.jax\",\n",
+ " add_jitted=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-3.0\n",
+ "-2.877551020408163\n",
+ "-2.7551020408163267\n",
+ "-2.63265306122449\n",
+ "-2.510204081632653\n",
+ "-2.387755102040816\n",
+ "-2.2653061224489797\n",
+ "-2.142857142857143\n",
+ "-2.020408163265306\n",
+ "-1.8979591836734695\n",
+ "-1.7755102040816326\n",
+ "-1.653061224489796\n",
+ "-1.5306122448979593\n",
+ "-1.4081632653061225\n",
+ "-1.2857142857142858\n",
+ "-1.163265306122449\n",
+ "-1.0408163265306123\n",
+ "-0.9183673469387754\n",
+ "-0.795918367346939\n",
+ "-0.6734693877551021\n",
+ "-0.5510204081632653\n",
+ "-0.4285714285714288\n",
+ "-0.30612244897959195\n",
+ "-0.18367346938775508\n",
+ "-0.06122448979591866\n",
+ "0.06122448979591821\n",
+ "0.18367346938775508\n",
+ "0.30612244897959195\n",
+ "0.4285714285714284\n",
+ "0.5510204081632653\n",
+ "0.6734693877551021\n",
+ "0.7959183673469385\n",
+ "0.9183673469387754\n",
+ "1.0408163265306118\n",
+ "1.1632653061224492\n",
+ "1.2857142857142856\n",
+ "1.408163265306122\n",
+ "1.5306122448979593\n",
+ "1.6530612244897958\n",
+ "1.7755102040816322\n",
+ "1.8979591836734695\n",
+ "2.020408163265306\n",
+ "2.1428571428571423\n",
+ "2.2653061224489797\n",
+ "2.387755102040816\n",
+ "2.5102040816326525\n",
+ "2.63265306122449\n",
+ "2.7551020408163263\n",
+ "2.8775510204081627\n",
+ "3.0\n",
+ "passing through transform\n"
+ ]
+ }
+ ],
+ "source": [
+ "import jax.numpy as jnp\n",
+ "import numpy as np\n",
+ "\n",
+ "# Test parameters:\n",
+ "v, a, z, t, theta = 0.5, 1.5, 0.5, 0.3, 0.3\n",
+ "v = np.linspace(-3, 3, 50)\n",
+ "\n",
+ "# Comparison simulator run\n",
+ "choice_p_list = []\n",
+ "for v_tmp in v:\n",
+ " print(v_tmp)\n",
+ " sim_out = ssms.basic_simulators.simulator.simulator(\n",
+ " model=MODEL, theta=[v_tmp, a, z, t, theta], n_samples=2000\n",
+ " )\n",
+ " choice_p_list.append(\n",
+ " np.sum(sim_out[\"choices\"] == 1.0) / sim_out[\"choices\"].shape[0]\n",
+ " )\n",
+ "\n",
+ "\n",
+ "# Make input matric\n",
+ "input_mat = jnp.zeros((50, 4))\n",
+ "input_mat = input_mat.at[:, 0].set(jnp.array(v))\n",
+ "input_mat = input_mat.at[:, 1].set(jnp.ones(50) * a)\n",
+ "input_mat = input_mat.at[:, 2].set(jnp.ones(50) * z)\n",
+ "input_mat = input_mat.at[:, 3].set(jnp.ones(50) * t)\n",
+ "\n",
+ "net_out = forward_pass_jitted(input_mat)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "ename": "",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details."
+ ]
+ }
+ ],
+ "source": [
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "plt.plot(choice_p_list)\n",
+ "plt.plot(np.exp(net_out))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "lan_pipe",
+ "language": "python",
+ "name": "lan_pipe"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.13"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/test_notebooks/wandb/latest-run b/notebooks/test_notebooks/wandb/latest-run
new file mode 120000
index 0000000..826ef0e
--- /dev/null
+++ b/notebooks/test_notebooks/wandb/latest-run
@@ -0,0 +1 @@
+run-20231011_145002-mmbsz7jl
\ No newline at end of file
diff --git a/notebooks/test_notebooks/wandb/run-20231011_134526-vm73hbo3/files/conda-environment.yaml b/notebooks/test_notebooks/wandb/run-20231011_134526-vm73hbo3/files/conda-environment.yaml
new file mode 100644
index 0000000..19647f9
--- /dev/null
+++ b/notebooks/test_notebooks/wandb/run-20231011_134526-vm73hbo3/files/conda-environment.yaml
@@ -0,0 +1,200 @@
+name: lan_pipe
+channels:
+ - defaults
+dependencies:
+ - _libgcc_mutex=0.1=main
+ - _openmp_mutex=5.1=1_gnu
+ - bzip2=1.0.8=h7b6447c_0
+ - ca-certificates=2023.08.22=h06a4308_0
+ - ld_impl_linux-64=2.38=h1181459_1
+ - libffi=3.4.4=h6a678d5_0
+ - libgcc-ng=11.2.0=h1234567_1
+ - libgomp=11.2.0=h1234567_1
+ - libstdcxx-ng=11.2.0=h1234567_1
+ - libuuid=1.41.5=h5eee18b_0
+ - ncurses=6.4=h6a678d5_0
+ - openssl=3.0.11=h7f8727e_2
+ - pip=23.2.1=py310h06a4308_0
+ - python=3.10.13=h955ad1f_0
+ - readline=8.2=h5eee18b_0
+ - setuptools=68.0.0=py310h06a4308_0
+ - sqlite=3.41.2=h5eee18b_0
+ - tk=8.6.12=h1ccaba5_0
+ - wheel=0.41.2=py310h06a4308_0
+ - xz=5.4.2=h5eee18b_0
+ - zlib=1.2.13=h5eee18b_0
+ - pip:
+ - absl-py==2.0.0
+ - anyio==4.0.0
+ - appdirs==1.4.4
+ - argon2-cffi==23.1.0
+ - argon2-cffi-bindings==21.2.0
+ - arrow==1.3.0
+ - asttokens==2.4.0
+ - async-lru==2.0.4
+ - attrs==23.1.0
+ - babel==2.13.0
+ - backcall==0.2.0
+ - beautifulsoup4==4.12.2
+ - black==23.9.1
+ - bleach==6.1.0
+ - certifi==2022.12.7
+ - cffi==1.16.0
+ - charset-normalizer==2.1.1
+ - chex==0.1.83
+ - click==8.1.7
+ - comm==0.1.4
+ - contourpy==1.1.1
+ - cycler==0.12.1
+ - cython==3.0.3
+ - debugpy==1.8.0
+ - decorator==5.1.1
+ - defusedxml==0.7.1
+ - docker-pycreds==0.4.0
+ - etils==1.5.0
+ - exceptiongroup==1.1.3
+ - executing==2.0.0
+ - fastjsonschema==2.18.1
+ - filelock==3.9.0
+ - flax==0.7.4
+ - fonttools==4.43.1
+ - fqdn==1.5.1
+ - frozendict==2.3.8
+ - fsspec==2023.9.2
+ - gitdb==4.0.10
+ - gitpython==3.1.37
+ - idna==3.4
+ - importlib-resources==6.1.0
+ - ipykernel==6.25.2
+ - ipython==8.16.1
+ - ipython-genutils==0.2.0
+ - ipywidgets==8.1.1
+ - isoduration==20.11.0
+ - jax==0.4.18
+ - jaxlib==0.4.18+cuda11.cudnn86
+ - jedi==0.19.1
+ - jinja2==3.1.2
+ - joblib==1.3.2
+ - json5==0.9.14
+ - jsonpointer==2.4
+ - jsonschema==4.19.1
+ - jsonschema-specifications==2023.7.1
+ - jupyter==1.0.0
+ - jupyter-client==8.3.1
+ - jupyter-console==6.6.3
+ - jupyter-core==5.3.2
+ - jupyter-events==0.7.0
+ - jupyter-lsp==2.2.0
+ - jupyter-server==2.7.3
+ - jupyter-server-terminals==0.4.4
+ - jupyterlab==4.0.6
+ - jupyterlab-pygments==0.2.2
+ - jupyterlab-server==2.25.0
+ - jupyterlab-widgets==3.0.9
+ - kiwisolver==1.4.5
+ - lanfactory==0.4.4
+ - markdown-it-py==3.0.0
+ - markupsafe==2.1.2
+ - matplotlib==3.8.0
+ - matplotlib-inline==0.1.6
+ - mdurl==0.1.2
+ - mistune==3.0.2
+ - ml-dtypes==0.3.1
+ - mpmath==1.3.0
+ - msgpack==1.0.7
+ - mypy-extensions==1.0.0
+ - nbclient==0.8.0
+ - nbconvert==7.9.2
+ - nbformat==5.9.2
+ - nest-asyncio==1.5.8
+ - networkx==3.0
+ - notebook==7.0.4
+ - notebook-shim==0.2.3
+ - numpy==1.26.0
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new file mode 100644
index 0000000..1478ff7
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+_wandb:
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diff --git a/notebooks/test_notebooks/wandb/run-20231011_134526-vm73hbo3/files/requirements.txt b/notebooks/test_notebooks/wandb/run-20231011_134526-vm73hbo3/files/requirements.txt
new file mode 100644
index 0000000..8b7bf0d
--- /dev/null
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diff --git a/notebooks/test_notebooks/wandb/run-20231011_134526-vm73hbo3/files/wandb-metadata.json b/notebooks/test_notebooks/wandb/run-20231011_134526-vm73hbo3/files/wandb-metadata.json
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index 0000000..ee811e0
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diff --git a/notebooks/test_notebooks/wandb/run-20231011_134526-vm73hbo3/files/wandb-summary.json b/notebooks/test_notebooks/wandb/run-20231011_134526-vm73hbo3/files/wandb-summary.json
new file mode 100644
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diff --git a/notebooks/test_notebooks/wandb/run-20231011_134926-1uvi8w8t/files/conda-environment.yaml b/notebooks/test_notebooks/wandb/run-20231011_134926-1uvi8w8t/files/conda-environment.yaml
new file mode 100644
index 0000000..19647f9
--- /dev/null
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new file mode 100644
index 0000000..a333ab8
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diff --git a/notebooks/test_notebooks/wandb/run-20231011_134926-1uvi8w8t/files/requirements.txt b/notebooks/test_notebooks/wandb/run-20231011_134926-1uvi8w8t/files/requirements.txt
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diff --git a/notebooks/test_notebooks/wandb/run-20231011_142733-x13a51j1/files/conda-environment.yaml b/notebooks/test_notebooks/wandb/run-20231011_142733-x13a51j1/files/conda-environment.yaml
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index 0000000..19647f9
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diff --git a/notebooks/test_notebooks/wandb/run-20231011_142733-x13a51j1/files/requirements.txt b/notebooks/test_notebooks/wandb/run-20231011_142733-x13a51j1/files/requirements.txt
new file mode 100644
index 0000000..8b7bf0d
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diff --git a/notebooks/test_notebooks/wandb/run-20231011_142838-fnp4y0fc/files/conda-environment.yaml b/notebooks/test_notebooks/wandb/run-20231011_142838-fnp4y0fc/files/conda-environment.yaml
new file mode 100644
index 0000000..19647f9
--- /dev/null
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diff --git a/notebooks/test_notebooks/wandb/run-20231011_145002-mmbsz7jl/files/conda-environment.yaml b/notebooks/test_notebooks/wandb/run-20231011_145002-mmbsz7jl/files/conda-environment.yaml
new file mode 100644
index 0000000..19647f9
--- /dev/null
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index 0000000..4f64460
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diff --git a/notebooks/test_notebooks/wandb/run-20231011_145002-mmbsz7jl/files/wandb-metadata.json b/notebooks/test_notebooks/wandb/run-20231011_145002-mmbsz7jl/files/wandb-metadata.json
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diff --git a/notebooks/test_notebooks/wandb/run-20231011_145002-mmbsz7jl/files/wandb-summary.json b/notebooks/test_notebooks/wandb/run-20231011_145002-mmbsz7jl/files/wandb-summary.json
new file mode 100644
index 0000000..af5d6ad
--- /dev/null
+++ b/notebooks/test_notebooks/wandb/run-20231011_145002-mmbsz7jl/files/wandb-summary.json
@@ -0,0 +1 @@
+{"loss": 0.30876463651657104, "_timestamp": 1697050229.4732537, "_runtime": 26.426885843276978, "_step": 381}
\ No newline at end of file
diff --git a/notebooks/test_notebooks/wandb/run-20231011_145002-mmbsz7jl/run-mmbsz7jl.wandb b/notebooks/test_notebooks/wandb/run-20231011_145002-mmbsz7jl/run-mmbsz7jl.wandb
new file mode 100644
index 0000000..5638ca3
Binary files /dev/null and b/notebooks/test_notebooks/wandb/run-20231011_145002-mmbsz7jl/run-mmbsz7jl.wandb differ
diff --git a/pyproject.toml b/pyproject.toml
index 371c7a9..62802ec 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,7 @@ requires = ["setuptools", "wheel"]
[project]
name= "lanfactory"
-version= "0.4.3"
+version= "0.4.4"
authors= [{name = "Alexander Fenger", email = "alexander_fengler@brown.edu"}]
description= "Package with convenience functions to train LANs"
readme = "README.md"
diff --git a/setup.py b/setup.py
index 9e6ebab..1140641 100755
--- a/setup.py
+++ b/setup.py
@@ -6,6 +6,6 @@
"lanfactory.config",
"lanfactory.trainers",
"lanfactory.utils",
- "lanfactory.onnx"
+ "lanfactory.onnx",
],
)