From 5c402a0297942b6f3e0787fba1e938a8423f767e Mon Sep 17 00:00:00 2001 From: Daniele Nerini Date: Wed, 1 Nov 2023 14:29:03 +0100 Subject: [PATCH 1/3] Add option to use batch normalization (#38) --- mlpp_lib/models.py | 36 +++++++++++++++++++++++++----------- 1 file changed, 25 insertions(+), 11 deletions(-) diff --git a/mlpp_lib/models.py b/mlpp_lib/models.py index 5dd09e9..7bb3142 100644 --- a/mlpp_lib/models.py +++ b/mlpp_lib/models.py @@ -32,6 +32,7 @@ def call(self, inputs): def _build_fcn_block( inputs, hidden_layers, + batchnorm, activations, dropout, mc_dropout, @@ -40,7 +41,10 @@ def _build_fcn_block( ): x = inputs for i, units in enumerate(hidden_layers): - x = Dense(units, activation=activations[i], name=f"dense_{idx}_{i}")(x) + x = Dense(units, name=f"dense_{idx}_{i}")(x) + if batchnorm: + x = BatchNormalization()(x) + x = Activation(activations[i])(x) if i < len(dropout) and 0.0 < dropout[i] < 1.0: if mc_dropout: x = MonteCarloDropout(dropout[i], name=f"mc_dropout_{idx}_{i}")(x) @@ -82,6 +86,7 @@ def fully_connected_network( input_shape: tuple[int], output_size: int, hidden_layers: list, + batchnorm: bool = False, activations: Optional[Union[str, list[str]]] = "relu", dropout: Optional[Union[float, list[float]]] = None, mc_dropout: bool = False, @@ -101,6 +106,8 @@ def fully_connected_network( hidden_layers: list[int] List that is used to define the fully connected block. Each element creates a Dense layer with the corresponding units. + batchnorm: bool + Use batch normalization. Default is False. activations: str or list[str] (Optional) Activation function(s) for the Dense layer(s). See https://keras.io/api/layers/activations/#relu-function. If a string is passed, the same activation is used for all layers. Default is `relu`. @@ -150,6 +157,7 @@ def fully_connected_network( x = _build_fcn_block( inputs, hidden_layers, + batchnorm, activations, dropout, mc_dropout, @@ -165,6 +173,7 @@ def fully_connected_multibranch_network( input_shape: tuple[int], output_size: int, hidden_layers: list, + batchnorm: bool = False, activations: Optional[Union[str, list[str]]] = "relu", dropout: Optional[Union[float, list[float]]] = None, mc_dropout: bool = False, @@ -184,6 +193,8 @@ def fully_connected_multibranch_network( hidden_layers: list[int] List that is used to define the fully connected block. Each element creates a Dense layer with the corresponding units. + batchnorm: bool + Use batch normalization. Default is False. activations: str or list[str] (Optional) Activation function(s) for the Dense layer(s). See https://keras.io/api/layers/activations/#relu-function. If a string is passed, the same activation is used for all layers. Default is `relu`. @@ -242,6 +253,7 @@ def fully_connected_multibranch_network( x = _build_fcn_block( inputs, hidden_layers, + batchnorm, activations, dropout, mc_dropout, @@ -263,6 +275,7 @@ def deep_cross_network( input_shape: tuple[int], output_size: int, hidden_layers: list, + batchnorm: bool = True, activations: Optional[Union[str, list[str]]] = "relu", dropout: Optional[Union[float, list[float]]] = None, mc_dropout: bool = False, @@ -282,6 +295,8 @@ def deep_cross_network( hidden_layers: list[int] List that is used to define the fully connected block. Each element creates a Dense layer with the corresponding units. + batchnorm: bool + Use batch normalization. Default is True. activations: str or list[str] (Optional) Activation function(s) for the Dense layer(s). See https://keras.io/api/layers/activations/#relu-function. If a string is passed, the same activation is used for all layers. Default is `relu`. @@ -338,16 +353,15 @@ def deep_cross_network( # deep part deep = inputs - for i, u in enumerate(hidden_layers): - deep = Dense(u)(deep) - deep = BatchNormalization()(deep) - deep = Activation(activations[i])(deep) - if i < len(dropout) and 0.0 < dropout[i] < 1.0: - if mc_dropout: - deep = MonteCarloDropout(dropout[i])(deep) - else: - deep = Dropout(dropout[i])(deep) - # deep = tf.keras.Model(inputs=inputs, outputs=deep, name="deepblock") + deep = _build_fcn_block( + deep, + hidden_layers, + batchnorm, + activations, + dropout, + mc_dropout, + skip_connection=False, + ) # merge merge = tf.keras.layers.Concatenate()([cross, deep]) From 9decd1db19e934dff7557ba946c5247dad95d53a Mon Sep 17 00:00:00 2001 From: ned Date: Wed, 1 Nov 2023 15:03:46 +0100 Subject: [PATCH 2/3] Bump up version --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 28ad90f..79bd128 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "mlpp-lib" -version = "0.4.2" +version = "0.5.0" description = "Collection of methods for ML-based postprocessing of weather forecasts." authors = ["Daniele Nerini "] include = ["LICENSE", "README.md"] From 783916b9e765831a5bd6cb2dd9181f61c1309cd6 Mon Sep 17 00:00:00 2001 From: Daniele Nerini Date: Mon, 27 Nov 2023 10:24:08 +0100 Subject: [PATCH 3/3] Serialize custom layer and test with mlflow (#39) --- mlpp_lib/models.py | 14 +- poetry.lock | 1623 ++++++++++++++++++++++++++------------ pyproject.toml | 3 +- tests/test_save_model.py | 36 +- 4 files changed, 1186 insertions(+), 490 deletions(-) diff --git a/mlpp_lib/models.py b/mlpp_lib/models.py index 7bb3142..e756b8b 100644 --- a/mlpp_lib/models.py +++ b/mlpp_lib/models.py @@ -1,6 +1,8 @@ -import tensorflow as tf +import logging from typing import Optional, Union, Any +import numpy as np +import tensorflow as tf from tensorflow.keras.layers import ( Add, Dense, @@ -8,14 +10,11 @@ BatchNormalization, Activation, ) - from tensorflow.keras import Model, initializers from mlpp_lib.physical_layers import * from mlpp_lib.probabilistic_layers import * -import numpy as np - try: import tcn # type: ignore except ImportError: @@ -24,6 +23,10 @@ TCN_IMPORTED = True +_LOGGER = logging.getLogger(__name__) + + +@tf.keras.utils.register_keras_serializable() class MonteCarloDropout(Dropout): def call(self, inputs): return super().call(inputs, training=True) @@ -39,6 +42,9 @@ def _build_fcn_block( skip_connection, idx=0, ): + if mc_dropout and dropout is None: + _LOGGER.warning("dropout=None, hence I will ignore mc_dropout=True") + x = inputs for i, units in enumerate(hidden_layers): x = Dense(units, name=f"dense_{idx}_{i}")(x) diff --git a/poetry.lock b/poetry.lock index 307d514..37c667f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,20 +1,42 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. 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"sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"}, ] [package.extras] -docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] [extras] extended = ["numba", "tensorflow", "zarr"] @@ -1799,4 +2456,4 @@ tensorflow-cpu = ["tensorflow-cpu"] [metadata] lock-version = "2.0" python-versions = ">=3.9, <3.11" -content-hash = "9cce460b750325558ffb24a1d41aa331c1781c8231327dcd28c92485bff85091" +content-hash = "41877e4ee5d60174c62336ef24536fab4e2db1e201f096c72a64bedda93a49e9" diff --git a/pyproject.toml b/pyproject.toml index 79bd128..501048e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,10 +24,11 @@ tensorflow = ["tensorflow"] tensorflow-cpu = ["tensorflow-cpu"] extended = ["zarr", "numba", "tensorflow"] -[tool.poetry.dev-dependencies] +[tool.poetry.group.dev.dependencies] tensorflow = "~2.9.3" pytest = "^7" black = "^22.1.0" +mlflow = "<2" [build-system] requires = ["poetry-core>=1.0.8"] diff --git a/tests/test_save_model.py b/tests/test_save_model.py index e0a68c3..a2235a2 100644 --- a/tests/test_save_model.py +++ b/tests/test_save_model.py @@ -9,7 +9,7 @@ from mlpp_lib import models from mlpp_lib import losses, metrics from mlpp_lib import probabilistic_layers -from mlpp_lib.utils import get_loss, get_metric +from mlpp_lib.utils import get_loss, get_metric, get_optimizer def _belongs_here(obj, module): @@ -63,7 +63,11 @@ def test_save_model(save_format, loss, prob_layer, tmp_path): save_traces = False model = models.fully_connected_network( - (5,), 2, hidden_layers=[3], probabilistic_layer=prob_layer + (5,), + 2, + hidden_layers=[3], + probabilistic_layer=prob_layer, + mc_dropout=True, ) assert isinstance(model.from_config(model.get_config()), Functional) loss = get_loss(loss) @@ -105,3 +109,31 @@ def test_save_model(save_format, loss, prob_layer, tmp_path): model = tf.keras.models.load_model(tmp_path, compile=False) assert isinstance(model, Functional) np.testing.assert_allclose(model(input_arr).mean(), outputs) + + +def test_save_model_mlflow(tmp_path): + """Test model save/load""" + pytest.importorskip("mlflow") + + import mlflow + + mlflow_uri = f"file://{tmp_path.absolute()}/mlruns" + mlflow.set_tracking_uri(mlflow_uri) + + model = models.fully_connected_network( + (5,), 2, hidden_layers=[3, 3], dropout=0.5, mc_dropout=True + ) + optimizer = get_optimizer("Adam") + model.compile(optimizer=optimizer, loss=None, metrics=None) + custom_objects = tf.keras.layers.serialize(model) + + model_info = mlflow.keras.log_model( + model, + "model_save", + custom_objects=custom_objects, + save_format="h5", + ) + + tf.keras.backend.clear_session() + model = mlflow.keras.load_model(model_info.model_uri) + assert isinstance(model, Functional)