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bartzbeielsteinbartzbeielstein
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preparing generaal models
1 parent df78d88 commit b4d1409

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Lines changed: 707 additions & 14 deletions

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‎pyproject.toml‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
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[project]
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name = "spotPython"
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version = "0.6.4"
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version = "0.6.5"
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authors = [
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{ name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
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]

‎src/spotPython/data/light_hyper_dict.json‎

Lines changed: 83 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -1,4 +1,85 @@
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{
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{ "NetLinearBase":
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{
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"l1": {
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"type": "int",
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"default": 3,
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"transform": "transform_power_2_int",
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"lower": 3,
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"upper": 8},
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"epochs": {
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"type": "int",
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"default": 4,
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"transform": "transform_power_2_int",
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"lower": 4,
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"upper": 9},
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"batch_size": {
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"type": "int",
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"default": 4,
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"transform": "transform_power_2_int",
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"lower": 1,
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"upper": 4},
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"act_fn": {
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"levels": ["Sigmoid",
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"Tanh",
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"ReLU",
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"LeakyReLU",
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"ELU",
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"Swish"],
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"type": "factor",
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"default": "ReLU",
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"transform": "None",
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"class_name": "spotPython.torch.activation",
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"core_model_parameter_type": "instance()",
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"lower": 0,
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"upper": 5},
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"optimizer": {
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"levels": ["Adadelta",
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"Adagrad",
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"Adam",
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"AdamW",
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"SparseAdam",
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"Adamax",
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"ASGD",
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"NAdam",
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"RAdam",
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"RMSprop",
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"Rprop",
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"SGD"],
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"type": "factor",
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"default": "SGD",
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"transform": "None",
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"class_name": "torch.optim",
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"core_model_parameter_type": "str",
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"lower": 0,
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"upper": 11},
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"dropout_prob": {
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"type": "float",
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"default": 0.01,
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"transform": "None",
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"lower": 0.0,
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"upper": 0.25},
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"lr_mult": {
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"type": "float",
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"default": 1.0,
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"transform": "None",
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"lower": 0.1,
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"upper": 10.0},
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"patience": {
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"type": "int",
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"default": 2,
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"transform": "transform_power_2_int",
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"lower": 2,
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"upper": 6
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},
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"initialization": {
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"levels": ["Default", "Kaiming", "Xavier"],
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"type": "factor",
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"default": "Default",
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"transform": "None",
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"core_model_parameter_type": "str",
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"lower": 0,
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"upper": 2}
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},
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"NetLightBase":
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{
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"l1": {
@@ -79,7 +160,7 @@
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"transform": "None",
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"core_model_parameter_type": "str",
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"lower": 0,
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"upper": 2}
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"upper": 2}
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},
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"LitModel":
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{

‎src/spotPython/fun/hyperlight.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -17,7 +17,7 @@
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class HyperLight:
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"""
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Hyperparameter Tuning for Lightning 2.
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Hyperparameter Tuning for Lightning.
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Args:
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seed (int): seed for the random number generator. See Numpy Random Sampling.

‎src/spotPython/light/cifar10datamodule.py‎

Lines changed: 18 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -29,8 +29,8 @@ def __init__(self, batch_size: int, data_dir: str = "./data", num_workers: int =
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def prepare_data(self) -> None:
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"""Prepares the data for use."""
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# download
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CIFAR10(self.data_dir, train=True, download=True)
33-
CIFAR10(self.data_dir, train=False, download=True)
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CIFAR10(root=self.data_dir, train=True, download=True)
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CIFAR10(root=self.data_dir, train=False, download=True)
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def setup(self, stage: Optional[str] = None) -> None:
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"""
@@ -42,14 +42,21 @@ def setup(self, stage: Optional[str] = None) -> None:
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"""
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# Assign train/val datasets for use in dataloaders
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if stage == "fit" or stage is None:
45-
transform = transforms.Compose([transforms.ToTensor()])
46-
cifar_full = CIFAR10(self.data_dir, train=True, transform=transform)
47-
self.data_train, self.data_val = random_split(cifar_full, [45000, 5000])
45+
transform = transforms.Compose(
46+
[transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
47+
)
48+
data_full = CIFAR10(root=self.data_dir, train=True, transform=transform)
49+
# self.data_train, self.data_val = random_split(daata_full, [45000, 5000])
50+
test_abs = int(len(data_full) * 0.6)
51+
print("test_abs", test_abs)
52+
self.data_train, self.data_val = random_split(data_full, [test_abs, len(data_full) - test_abs])
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4954
# Assign test dataset for use in dataloader(s)
5055
if stage == "test" or stage is None:
51-
transform = transforms.Compose([transforms.ToTensor()])
52-
self.data_test = CIFAR10(self.data_dir, train=False, transform=transform)
56+
transform = transforms.Compose(
57+
[transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
58+
)
59+
self.data_test = CIFAR10(root=self.data_dir, train=False, transform=transform)
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5461
def train_dataloader(self) -> DataLoader:
5562
"""
@@ -59,7 +66,8 @@ def train_dataloader(self) -> DataLoader:
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DataLoader: The training dataloader.
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"""
62-
return DataLoader(self.data_train, batch_size=self.batch_size, num_workers=self.num_workers)
69+
print("self.batch_size", self.batch_size)
70+
return DataLoader(self.data_train, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers)
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6472
def val_dataloader(self) -> DataLoader:
6573
"""
@@ -70,7 +78,7 @@ def val_dataloader(self) -> DataLoader:
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"""
73-
return DataLoader(self.data_val, batch_size=self.batch_size, num_workers=self.num_workers)
81+
return DataLoader(self.data_val, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers)
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7583
def test_dataloader(self) -> DataLoader:
7684
"""
@@ -81,4 +89,4 @@ def test_dataloader(self) -> DataLoader:
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"""
84-
return DataLoader(self.data_test, batch_size=self.batch_size, num_workers=self.num_workers)
92+
return DataLoader(self.data_test, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers)

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