From 4d84fae3ef7688ccb0a5d91cf030f7db4091ec15 Mon Sep 17 00:00:00 2001 From: Ralph Date: Thu, 16 Apr 2026 13:22:32 -0400 Subject: [PATCH 01/19] Update run-tests.yml --- .github/workflows/run-tests.yml | 4 ---- 1 file changed, 4 deletions(-) diff --git a/.github/workflows/run-tests.yml b/.github/workflows/run-tests.yml index ab4f9a7..977439e 100644 --- a/.github/workflows/run-tests.yml +++ b/.github/workflows/run-tests.yml @@ -1,10 +1,6 @@ name: Run Tests on: - push: - branches: - - develop - - main pull_request: branches: - develop From 8c40a36d3b030943cf3ec6b543340f1b22ef023d Mon Sep 17 00:00:00 2001 From: Ralph Date: Thu, 16 Apr 2026 16:01:53 -0400 Subject: [PATCH 02/19] Changelog update and version bump --- CHANGELOG.md | 4 ++++ pyproject.toml | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 045ad9e..c17b21d 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,9 @@ # Changelog +## v0.1.5 + +- Removed redundant runs on pushes to develop / main in CI/CD. They already happen on PRs. + ## v0.1.4 (05/14/2026) - Added a copy method to Sequential class to make deep copies of a model instance. diff --git a/pyproject.toml b/pyproject.toml index c92779e..3f149e4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "phitodeep" -version = "0.1.4" +version = "0.1.5" description = "Deep learning framework built from scratch with numpy!" authors = ["Ralph Dugue"] license = "Apache License 2.0" From e5864c871ad76068c41e4d6ada6bc02f7fa9c017 Mon Sep 17 00:00:00 2001 From: Ralph Date: Thu, 16 Apr 2026 17:46:50 -0400 Subject: [PATCH 03/19] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index b5a763d..830aaf3 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ -# phitodeep +# Phito-Deep -Deep learning framework built from scratch with numpy! +Phito-Deep is a deep learning framework built from scratch with only numpy. This is being actively developed as part of my learning journey to becoming a machine learning engineer. I'm using it to better understand the underlying algorithms that power modern deep learning frameworks and architectures. ## Installation From bc8a0284a490e1a6fd7e1b3b2dc532972f496747 Mon Sep 17 00:00:00 2001 From: Ralph Date: Mon, 27 Apr 2026 02:08:30 -0400 Subject: [PATCH 04/19] Update publish-main.yml --- .github/workflows/publish-main.yml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/publish-main.yml b/.github/workflows/publish-main.yml index 29ef4f4..83dbc4d 100644 --- a/.github/workflows/publish-main.yml +++ b/.github/workflows/publish-main.yml @@ -26,9 +26,10 @@ jobs: run: poetry publish --build --no-interaction - name: Tag release + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} run: | VERSION=$(poetry version --short) git config user.name "github-actions[bot]" git config user.email "github-actions[bot]@users.noreply.github.com" - git tag "v$VERSION" - git push origin "v$VERSION" + gh release create "v$VERSION" --notes-file ./CHANGELOG.md From 116771ce7b681929aa9e0fb3de82e37e62448749 Mon Sep 17 00:00:00 2001 From: Ralph Date: Tue, 28 Apr 2026 23:26:42 -0400 Subject: [PATCH 05/19] Creaed learning rate schedulers module - Implemented inverse time decay. --- .gitignore | 4 +++- src/phitodeep/schedulers.py | 6 ++++++ 2 files changed, 9 insertions(+), 1 deletion(-) create mode 100644 src/phitodeep/schedulers.py diff --git a/.gitignore b/.gitignore index b07c663..448e508 100644 --- a/.gitignore +++ b/.gitignore @@ -139,4 +139,6 @@ dmypy.json **.Rhistory # MacOS -.DS_Store \ No newline at end of file +.DS_Store +./ +.gitignore diff --git a/src/phitodeep/schedulers.py b/src/phitodeep/schedulers.py new file mode 100644 index 0000000..88caa47 --- /dev/null +++ b/src/phitodeep/schedulers.py @@ -0,0 +1,6 @@ +import numpy as np + + +class AlphaScheduler: + def __ini__(self, alpha): + self.alpha = alpha From 26357624a9f6f18d6b6c60d8c054fcf1f0f7da8b Mon Sep 17 00:00:00 2001 From: Ralph Date: Fri, 1 May 2026 20:45:38 -0400 Subject: [PATCH 06/19] Added new activation functions - LeakyReLU - GELU - Swish --- src/phitodeep/layers/activation.py | 65 ++++++++++++++++++++++++++++++ src/phitodeep/schedulers.py | 14 ++++++- 2 files changed, 78 insertions(+), 1 deletion(-) diff --git a/src/phitodeep/layers/activation.py b/src/phitodeep/layers/activation.py index 40f26f8..297ce39 100644 --- a/src/phitodeep/layers/activation.py +++ b/src/phitodeep/layers/activation.py @@ -25,6 +25,71 @@ def copy(self): new_layer.cache = self.cache.copy() return new_layer +class LeakyReLu(Layer): + def __init__(self, alpha=0.01) -> None: + super().__init__("leaky_rely") + self.alpha = alpha + + def forward(self, X): + self.cache["X"] = X + return np.where(X > 0, X, self.alpha * X) + + def backward(self, dL_dZ): + X = self.cache["X"] + dL_dX = dL_dZ * np.where(X > 0, 1, self.alpha) + return dL_dX + + def copy(self): + new_layer = LeakyReLu() + new_layer.cache = self.cache.copy() + return new_layer + +class GELU(Layer): + def __init__(self) -> None: + super.__init__("gelu") + + def forward(self, X): + self.cache["X"] = X + constant = 0.044715 + inner = (np.sqrt(2.0 / np.pi) * (X + constant * X ** 3)) + t = Tanh().forward(inner) + self.cache["t"] = t + return 0.5 * X * (1 + t) + + def backward(self, dL_dz): + t = self.cache["t"] + X = self.cache["X"] + dL_dX = 0.5 * (1 + t) + dL_dX += 0.5 * x * (1 - t ** 2) * np.sqrt(2.0 / np.pi) + dL_dX *= (1 + 3 * constant * X ** 3) + dL_dX *= dL_dZ + return dl_dX + + def copy(self): + new_layer = GELU() + new_layer.cache = self.cache.copy() + return new_layer + +class Swish(Layer): + def __init__(self) -> None: + super().__init__("swish") + + def forward(self, X): + self.cache["X"] = X + Z = 1 / (1 + np.exp(-X)) + self.cache["Z"] = Z + return X * Z + + def backward(self, dL_dZ): + X = self.cache["X"] + Z = self.cache["Z"] + dL_dX = dL_dZ * (Z + X * Z * (1 - Z)) + return dL_dX + + def copy(self): + new_layer = Swish() + new_layer.cache = self.cache.copy() + return new_layer class Sigmoid(Layer): def __init__(self) -> None: diff --git a/src/phitodeep/schedulers.py b/src/phitodeep/schedulers.py index 88caa47..6e75ff3 100644 --- a/src/phitodeep/schedulers.py +++ b/src/phitodeep/schedulers.py @@ -2,5 +2,17 @@ class AlphaScheduler: - def __ini__(self, alpha): + def __ini__(self, name: str, alpha: np.float16) -> None: + self.name = name self.alpha = alpha + + def step(self, time_step: int) -> np.float16: + raise NotImplementedError(f"{self.name} must implement the step function") + +class InverseTimeDecay(AlphaScheduler): + def __init__(self, alpha: np.float16, decay_rate: np.float16): + super().__init__("InverseTimeDecay", alpha) + self.decay_rate = decay_rate + + def step(self, time_step: int) -> np.float16: + return self.alpha / (1 + self.decay_rate * time_step) From 2b5c40a256218d6c2465229d9e5e7ba9c30eb806 Mon Sep 17 00:00:00 2001 From: Ralph Date: Sun, 3 May 2026 22:50:15 -0400 Subject: [PATCH 07/19] Testing org notebook generation --- docs/mnist.ipynb | 2 +- docs/overview.ipynb | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) create mode 100644 docs/overview.ipynb diff --git a/docs/mnist.ipynb b/docs/mnist.ipynb index 160cfdb..4ab22fb 100644 --- a/docs/mnist.ipynb +++ b/docs/mnist.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Example usage\n", + "# MNIST Example\n", "A classic example of MNIST digit classification using `phitodeep`:\n", "## Imports" ] diff --git a/docs/overview.ipynb b/docs/overview.ipynb new file mode 100644 index 0000000..3fbdb67 --- /dev/null +++ b/docs/overview.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","id":"72ac8882-984e-40e9-9882-28762ff39355","metadata":{},"source":"\n\n\n**Author:** RALPH-RIG-2020\n\n"},{"cell_type":"markdown","id":"db10f3af-355c-40fc-bd02-cda230d10553","metadata":{},"source":["## Library Overview\n\n"]},{"cell_type":"markdown","id":"07135f60-8bb5-4202-9e3c-4a2d13bc873e","metadata":{},"source":["Phito-Deep is a deep learning framework developed by [me](https://ralphdugue.com/). It provides a sequential API for architecting deep neural networks. It has many logical defaults that are also easily customizable. It’s main purpose is to to beeducational, and will continuously be updated as I learn more about deep learning architectures and their components.\n\n"]},{"cell_type":"markdown","id":"0ab85be8-1684-4fc4-8aa7-8c3af3c68863","metadata":{},"source":["### Features\n\n"]},{"cell_type":"markdown","id":"9b1291f2-33a5-45d6-813e-e15000dbb639","metadata":{},"source":["\n| Name|Module|Description|Status|\n|---|---|---|---|\n| Dense|Base|Standard fully connected, feed forward layer.|Implemented|\n\n"]},{"cell_type":"markdown","id":"8b3effc2-bf39-46f8-9152-afb45693715b","metadata":{},"source":["### Initialization\n\n"]},{"cell_type":"markdown","id":"ea816142-d0f4-4649-a50f-3db0f529f9e1","metadata":{},"source":["There are currently two ways to initialize a model:\n\n- The \\`Sequential\\` class. This is the main class for impeachmenting all architectures.\n\n"]},{"cell_type":"code","execution_count":1,"id":"7965c170-b017-46ac-b2a4-4337272122ce","metadata":{},"outputs":[],"source":["from phitodeep.model import Sequential\n from phitodeep.layers.base import Dense, Flatten\n from phitodeep.layers.activation import Softmax, ReLu\n from phitodeep.loss import CategoricalCrossEntropy\n\n model = Sequential(\n Flatten(),\n Dense(784, 128),\n ReLu(),\n Dense(128, 64),\n ReLu(),\n Dense(64, 10),\n )\n model.add(Softmax())\n model.setoptimizer(\"adam\")\n model.setloss(CategoricalCrossEntropy())\n model.setbatchsize(64)\n\n model.summary()"]},{"cell_type":"markdown","id":"d7d229ff-a8e3-4f1c-bd04-b3d6cebe6d38","metadata":{},"source":["- The \\`SequentialBuilder\\` class. This just a wrapper around the model class to allow for a more fluent building experience.\n\n"]},{"cell_type":"code","execution_count":1,"id":"0111bcd8-3177-4727-a7b1-5389e818504c","metadata":{},"outputs":[],"source":["from phitodeep.model import Sequential\n from phitodeep.loss import CategoricalCrossEntropy\n\n model = (\n SequentialBuilder()\n .flatten()\n .dense(784, 128)\n .relu()\n .dense(128, 64)\n .relu()\n .dense(64, 10)\n .softmax()\n .optimizer(\"adam\")\n .loss(CategoricalCrossEntropy())\n .alpha(0.05)\n .epochs(5)\n .batch(64)\n .build()\n )\n\n model.summary()"]},{"cell_type":"markdown","id":"b212892e-98d9-48d4-abb4-8f3da4615cfd","metadata":{},"source":["If you run both cell blocks, you will see they generate similar models. The builder will save you time on imports, but the \\`Sequential\\` class is a bit more dynamic.\n\n"]},{"cell_type":"markdown","id":"66606c42-4ffb-4cc8-8f29-7a11347e1352","metadata":{},"source":["### Training\n\n"]}],"metadata":[["org"],null,null],"nbformat":4,"nbformat_minor":5} From 24e178c238d6d6bc44106655fe4ba64e953a51ff Mon Sep 17 00:00:00 2001 From: Ralph Date: Mon, 4 May 2026 19:47:07 -0400 Subject: [PATCH 08/19] Overview document --- docs/overview.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/overview.ipynb b/docs/overview.ipynb index 3fbdb67..7c7452a 100644 --- a/docs/overview.ipynb +++ b/docs/overview.ipynb @@ -1 +1 @@ -{"cells":[{"cell_type":"markdown","id":"72ac8882-984e-40e9-9882-28762ff39355","metadata":{},"source":"\n\n\n**Author:** RALPH-RIG-2020\n\n"},{"cell_type":"markdown","id":"db10f3af-355c-40fc-bd02-cda230d10553","metadata":{},"source":["## Library Overview\n\n"]},{"cell_type":"markdown","id":"07135f60-8bb5-4202-9e3c-4a2d13bc873e","metadata":{},"source":["Phito-Deep is a deep learning framework developed by [me](https://ralphdugue.com/). It provides a sequential API for architecting deep neural networks. It has many logical defaults that are also easily customizable. It’s main purpose is to to beeducational, and will continuously be updated as I learn more about deep learning architectures and their components.\n\n"]},{"cell_type":"markdown","id":"0ab85be8-1684-4fc4-8aa7-8c3af3c68863","metadata":{},"source":["### Features\n\n"]},{"cell_type":"markdown","id":"9b1291f2-33a5-45d6-813e-e15000dbb639","metadata":{},"source":["\n| Name|Module|Description|Status|\n|---|---|---|---|\n| Dense|Base|Standard fully connected, feed forward layer.|Implemented|\n\n"]},{"cell_type":"markdown","id":"8b3effc2-bf39-46f8-9152-afb45693715b","metadata":{},"source":["### Initialization\n\n"]},{"cell_type":"markdown","id":"ea816142-d0f4-4649-a50f-3db0f529f9e1","metadata":{},"source":["There are currently two ways to initialize a model:\n\n- The \\`Sequential\\` class. This is the main class for impeachmenting all architectures.\n\n"]},{"cell_type":"code","execution_count":1,"id":"7965c170-b017-46ac-b2a4-4337272122ce","metadata":{},"outputs":[],"source":["from phitodeep.model import Sequential\n from phitodeep.layers.base import Dense, Flatten\n from phitodeep.layers.activation import Softmax, ReLu\n from phitodeep.loss import CategoricalCrossEntropy\n\n model = Sequential(\n Flatten(),\n Dense(784, 128),\n ReLu(),\n Dense(128, 64),\n ReLu(),\n Dense(64, 10),\n )\n model.add(Softmax())\n model.setoptimizer(\"adam\")\n model.setloss(CategoricalCrossEntropy())\n model.setbatchsize(64)\n\n model.summary()"]},{"cell_type":"markdown","id":"d7d229ff-a8e3-4f1c-bd04-b3d6cebe6d38","metadata":{},"source":["- The \\`SequentialBuilder\\` class. This just a wrapper around the model class to allow for a more fluent building experience.\n\n"]},{"cell_type":"code","execution_count":1,"id":"0111bcd8-3177-4727-a7b1-5389e818504c","metadata":{},"outputs":[],"source":["from phitodeep.model import Sequential\n from phitodeep.loss import CategoricalCrossEntropy\n\n model = (\n SequentialBuilder()\n .flatten()\n .dense(784, 128)\n .relu()\n .dense(128, 64)\n .relu()\n .dense(64, 10)\n .softmax()\n .optimizer(\"adam\")\n .loss(CategoricalCrossEntropy())\n .alpha(0.05)\n .epochs(5)\n .batch(64)\n .build()\n )\n\n model.summary()"]},{"cell_type":"markdown","id":"b212892e-98d9-48d4-abb4-8f3da4615cfd","metadata":{},"source":["If you run both cell blocks, you will see they generate similar models. The builder will save you time on imports, but the \\`Sequential\\` class is a bit more dynamic.\n\n"]},{"cell_type":"markdown","id":"66606c42-4ffb-4cc8-8f29-7a11347e1352","metadata":{},"source":["### Training\n\n"]}],"metadata":[["org"],null,null],"nbformat":4,"nbformat_minor":5} +{"cells":[{"cell_type":"markdown","id":"d9c55d7b-a3e2-461e-aff4-58f844869560","metadata":{},"source":"\n\n\n**Author:** RALPH-RIG-2020\n\n"},{"cell_type":"markdown","id":"c734fd1f-bee2-41df-8916-29a75a4f20a8","metadata":{},"source":["## Library Overview\n\n"]},{"cell_type":"markdown","id":"bb5e3d1f-93b3-4b63-a7c7-f004b9df12d2","metadata":{},"source":["Phito-Deep is a deep learning framework developed by [me](https://ralphdugue.com/). It provides a sequential API for architecting deep neural networks. It has many logical defaults that are also easily customizable. It’s main purpose is to to beeducational, and will continuously be updated as I learn more about deep learning architectures and their components.\n\n"]},{"cell_type":"markdown","id":"0a6d6b9e-7de9-44e0-9b59-ad5e2aed2828","metadata":{},"source":["### Initialization\n\n"]},{"cell_type":"markdown","id":"d2d31b1b-a013-4e07-a62e-5b5e64f26ce3","metadata":{},"source":["There are currently two ways to initialize a model:\n\n- The \\`Sequential\\` class. This is the main class for impeachmenting all architectures.\n\n"]},{"cell_type":"code","execution_count":1,"id":"14d39522-0813-4207-af3a-d4ee43329a10","metadata":{},"outputs":[],"source":["from phitodeep.model import Sequential\nfrom phitodeep.layers.base import Dense, Flatten\nfrom phitodeep.layers.activation import Softmax, ReLu\nfrom phitodeep.loss import CategoricalCrossEntropy\n\nmodel = Sequential(\n Flatten(),\n Dense(784, 128),\n ReLu(),\n Dense(128, 64),\n ReLu(),\n Dense(64, 10),\n)\nmodel.add(Softmax())\nmodel.setoptimizer(\"adam\")\nmodel.setloss(CategoricalCrossEntropy())\nmodel.setbatchsize(64)\n\nmodel.summary()"]},{"cell_type":"markdown","id":"d86fb071-8bbc-44b3-ab18-97bf38ca1bdb","metadata":{},"source":["- The \\`SequentialBuilder\\` class. This just a wrapper around the model class to allow for a more fluent building experience.\n\n"]},{"cell_type":"code","execution_count":1,"id":"5f74749e-d692-4a34-8c91-ccbfb53e7e55","metadata":{},"outputs":[],"source":["from phitodeep.model import SequentialBuilder\nfrom phitodeep.loss import CategoricalCrossEntropy\n\nmodel = (\n SequentialBuilder()\n .flatten()\n .dense(784, 128)\n .relu()\n .dense(128, 64)\n .relu()\n .dense(64, 10)\n .softmax()\n .optimizer(\"adam\")\n .loss(CategoricalCrossEntropy())\n .alpha(0.05)\n .epochs(5)\n .batch(64)\n .build()\n)\n\nmodel.summary()"]},{"cell_type":"markdown","id":"7e3e9550-8742-4397-83f4-c2ab69a80a70","metadata":{},"source":["If you run both cell blocks, you will see they generate similar models. The builder will save you time on imports, but the \\`Sequential\\` class is a bit more dynamic.\n\n"]},{"cell_type":"markdown","id":"f93bbca1-e7af-4a09-b5d3-4988a0999c58","metadata":{},"source":["### Training\n\n"]},{"cell_type":"markdown","id":"e256e898-4fd3-4435-a366-2f1e4749c41e","metadata":{},"source":["Training a model pretty straight forward. You call the \\`train\\` function with your data, and it handles all forward and back propagation:\n\n"]},{"cell_type":"code","execution_count":1,"id":"95428ee7-8c59-4431-8c1a-5bac05f36ad4","metadata":{},"outputs":[],"source":["model.train(x_train, y_train, x_test, y_test)"]},{"cell_type":"markdown","id":"8ffbe18f-ff79-401a-b94e-2c25fba7e2ea","metadata":{},"source":["And of course predictions(no batch predictions yet):\n\n"]},{"cell_type":"code","execution_count":1,"id":"1e0048cb-fca4-40a1-a49b-a21ebd914132","metadata":{},"outputs":[],"source":["model.predict(features)"]}],"metadata":[["org"],null,null],"nbformat":4,"nbformat_minor":5} From 3ba60372b6976c851227f0b75acf12dd729a9f62 Mon Sep 17 00:00:00 2001 From: Ralph Date: Tue, 5 May 2026 11:26:07 -0400 Subject: [PATCH 09/19] Removing metadata from notebook --- .gitignore | 3 + CONDUCT.md => CODE_OF_CONDUCT.md | 0 docs/conduct.md | 4 +- docs/index.md | 1 + docs/overview.ipynb | 193 ++++++++++++++++++++++++++++++- 5 files changed, 198 insertions(+), 3 deletions(-) rename CONDUCT.md => CODE_OF_CONDUCT.md (100%) diff --git a/.gitignore b/.gitignore index 448e508..cd6514f 100644 --- a/.gitignore +++ b/.gitignore @@ -142,3 +142,6 @@ dmypy.json .DS_Store ./ .gitignore + +# Emacs +*.org diff --git a/CONDUCT.md b/CODE_OF_CONDUCT.md similarity index 100% rename from CONDUCT.md rename to CODE_OF_CONDUCT.md diff --git a/docs/conduct.md b/docs/conduct.md index 0568705..1c46998 100755 --- a/docs/conduct.md +++ b/docs/conduct.md @@ -1,2 +1,2 @@ -```{include} ../CONDUCT.md -``` \ No newline at end of file +```{include} ../CODE_OF_CONDUCT.md +``` diff --git a/docs/index.md b/docs/index.md index 81ad1f5..12f9280 100755 --- a/docs/index.md +++ b/docs/index.md @@ -5,6 +5,7 @@ :maxdepth: 1 :hidden: +overview.ipynb mnist.ipynb changelog.md contributing.md diff --git a/docs/overview.ipynb b/docs/overview.ipynb index 7c7452a..b4b4864 100644 --- a/docs/overview.ipynb +++ b/docs/overview.ipynb @@ -1 +1,192 @@ -{"cells":[{"cell_type":"markdown","id":"d9c55d7b-a3e2-461e-aff4-58f844869560","metadata":{},"source":"\n\n\n**Author:** RALPH-RIG-2020\n\n"},{"cell_type":"markdown","id":"c734fd1f-bee2-41df-8916-29a75a4f20a8","metadata":{},"source":["## Library Overview\n\n"]},{"cell_type":"markdown","id":"bb5e3d1f-93b3-4b63-a7c7-f004b9df12d2","metadata":{},"source":["Phito-Deep is a deep learning framework developed by [me](https://ralphdugue.com/). It provides a sequential API for architecting deep neural networks. It has many logical defaults that are also easily customizable. It’s main purpose is to to beeducational, and will continuously be updated as I learn more about deep learning architectures and their components.\n\n"]},{"cell_type":"markdown","id":"0a6d6b9e-7de9-44e0-9b59-ad5e2aed2828","metadata":{},"source":["### Initialization\n\n"]},{"cell_type":"markdown","id":"d2d31b1b-a013-4e07-a62e-5b5e64f26ce3","metadata":{},"source":["There are currently two ways to initialize a model:\n\n- The \\`Sequential\\` class. This is the main class for impeachmenting all architectures.\n\n"]},{"cell_type":"code","execution_count":1,"id":"14d39522-0813-4207-af3a-d4ee43329a10","metadata":{},"outputs":[],"source":["from phitodeep.model import Sequential\nfrom phitodeep.layers.base import Dense, Flatten\nfrom phitodeep.layers.activation import Softmax, ReLu\nfrom phitodeep.loss import CategoricalCrossEntropy\n\nmodel = Sequential(\n Flatten(),\n Dense(784, 128),\n ReLu(),\n Dense(128, 64),\n ReLu(),\n Dense(64, 10),\n)\nmodel.add(Softmax())\nmodel.setoptimizer(\"adam\")\nmodel.setloss(CategoricalCrossEntropy())\nmodel.setbatchsize(64)\n\nmodel.summary()"]},{"cell_type":"markdown","id":"d86fb071-8bbc-44b3-ab18-97bf38ca1bdb","metadata":{},"source":["- The \\`SequentialBuilder\\` class. This just a wrapper around the model class to allow for a more fluent building experience.\n\n"]},{"cell_type":"code","execution_count":1,"id":"5f74749e-d692-4a34-8c91-ccbfb53e7e55","metadata":{},"outputs":[],"source":["from phitodeep.model import SequentialBuilder\nfrom phitodeep.loss import CategoricalCrossEntropy\n\nmodel = (\n SequentialBuilder()\n .flatten()\n .dense(784, 128)\n .relu()\n .dense(128, 64)\n .relu()\n .dense(64, 10)\n .softmax()\n .optimizer(\"adam\")\n .loss(CategoricalCrossEntropy())\n .alpha(0.05)\n .epochs(5)\n .batch(64)\n .build()\n)\n\nmodel.summary()"]},{"cell_type":"markdown","id":"7e3e9550-8742-4397-83f4-c2ab69a80a70","metadata":{},"source":["If you run both cell blocks, you will see they generate similar models. The builder will save you time on imports, but the \\`Sequential\\` class is a bit more dynamic.\n\n"]},{"cell_type":"markdown","id":"f93bbca1-e7af-4a09-b5d3-4988a0999c58","metadata":{},"source":["### Training\n\n"]},{"cell_type":"markdown","id":"e256e898-4fd3-4435-a366-2f1e4749c41e","metadata":{},"source":["Training a model pretty straight forward. You call the \\`train\\` function with your data, and it handles all forward and back propagation:\n\n"]},{"cell_type":"code","execution_count":1,"id":"95428ee7-8c59-4431-8c1a-5bac05f36ad4","metadata":{},"outputs":[],"source":["model.train(x_train, y_train, x_test, y_test)"]},{"cell_type":"markdown","id":"8ffbe18f-ff79-401a-b94e-2c25fba7e2ea","metadata":{},"source":["And of course predictions(no batch predictions yet):\n\n"]},{"cell_type":"code","execution_count":1,"id":"1e0048cb-fca4-40a1-a49b-a21ebd914132","metadata":{},"outputs":[],"source":["model.predict(features)"]}],"metadata":[["org"],null,null],"nbformat":4,"nbformat_minor":5} +{ + "cells": [ + { + "cell_type": "markdown", + "id": "786625b6-b6d4-4cc2-8b21-e2d3812eb846", + "metadata": {}, + "source": [ + "## Library Overview\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "4f728224-99fe-4453-8a60-aeea9d7e70af", + "metadata": {}, + "source": [ + "Phito-Deep is a deep learning framework developed by [me](https://ralphdugue.com/). It provides a sequential API for architecting deep neural networks. It has many logical defaults that are also easily customizable. It’s main purpose is to to beeducational, and will continuously be updated as I learn more about deep learning architectures and their components.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "72a9882d-7136-4764-8056-d2449a407f12", + "metadata": {}, + "source": [ + "### Initialization\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "908b10eb-e533-4bf8-b2cc-7bec1fd54f49", + "metadata": {}, + "source": [ + "There are currently two ways to initialize a model:\n", + "\n", + "- The \\`Sequential\\` class. This is the main class for impeachmenting all architectures.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b9a394ff-e01c-47f0-a617-b4a1ce73d084", + "metadata": {}, + "outputs": [], + "source": [ + "from phitodeep.model import Sequential\n", + "from phitodeep.layers.base import Dense, Flatten\n", + "from phitodeep.layers.activation import Softmax, ReLu\n", + "from phitodeep.loss import CategoricalCrossEntropy\n", + "\n", + "model = Sequential(\n", + " Flatten(),\n", + " Dense(64, 128),\n", + " ReLu(),\n", + " Dense(128, 64),\n", + " ReLu(),\n", + " Dense(64, 10),\n", + ")\n", + "model.add(Softmax())\n", + "model.setoptimizer(\"adam\")\n", + "model.setloss(CategoricalCrossEntropy())\n", + "model.setbatchsize(4)\n", + "\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "a595b537-7d72-4003-a986-974c80514249", + "metadata": {}, + "source": [ + "- The \\`SequentialBuilder\\` class. This just a wrapper around the model class to allow for a more fluent building experience.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9e84c9db-6264-4b3f-b341-30c2161f316a", + "metadata": {}, + "outputs": [], + "source": [ + "from phitodeep.model import SequentialBuilder\n", + "from phitodeep.loss import CategoricalCrossEntropy\n", + "\n", + "model = (\n", + " SequentialBuilder()\n", + " .flatten()\n", + " .dense(64, 128)\n", + " .relu()\n", + " .dense(128, 64)\n", + " .relu()\n", + " .dense(64, 10)\n", + " .softmax()\n", + " .optimizer(\"adam\")\n", + " .loss(CategoricalCrossEntropy())\n", + " .alpha(0.05)\n", + " .epochs(5)\n", + " .batch(4)\n", + " .build()\n", + ")\n", + "\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "62649dfd-bf49-44db-9b53-d123ba8015d0", + "metadata": {}, + "source": [ + "If you run both cell blocks, you will see they generate similar models. The builder will save you time on imports, but the \\`Sequential\\` class is a bit more dynamic.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "87ba3bd0-81f5-442e-8890-ccfe4d368a66", + "metadata": {}, + "source": [ + "### Training\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "7f9a0b5b-93a5-4969-8b2f-52977ddc067f", + "metadata": {}, + "source": [ + "Training a model pretty straight forward. You call the \\`train\\` function with your data, and it handles all forward and back propagation:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4f284f8c-3220-43c5-99cd-587a8cefa9aa", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "rng = np.random.default_rng(42)\n", + "\n", + "X = rng.standard_normal((64, 8, 8)).astype(np.float32)\n", + "y = rng.integers(0, 10, 64)\n", + "\n", + "split = int(0.6 * X.shape[0])\n", + "x_train, y_train = X[:split], y[:split]\n", + "x_temp, y_temp = X[split:], y[split:]\n", + "\n", + "split = int(0.5 * x_temp.shape[0])\n", + "x_test, y_test = x_temp[:split], y_temp[:split]\n", + "x_val, y_val = x_temp[split:], y_temp[split:]\n", + "\n", + "\n", + "losses = model.train(x_train, y_train, x_test, y_test)" + ] + }, + { + "cell_type": "markdown", + "id": "17c2db3e-94c3-4aed-a121-294da3d869ff", + "metadata": {}, + "source": [ + "And of course predictions:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0dfb299d-c55e-4c3f-9260-2934f08f13ab", + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = model.predict(x_val)\n", + "accuracy = np.mean(np.argmax(y_pred, axis=1) == y_test)\n", + "print(f\"Test Accuracy: {accuracy:.4f} %\")" + ] + } + ], + "metadata": [ + [ + "org" + ], + null, + null + ], + "nbformat": 4, + "nbformat_minor": 5 +} From 38d3258935e35aa82caf998b8c3e60d10593889e Mon Sep 17 00:00:00 2001 From: Ralph Date: Wed, 6 May 2026 09:52:48 -0400 Subject: [PATCH 10/19] Overview document done. --- docs/index.md | 2 +- docs/overview.md | 84 ++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 85 insertions(+), 1 deletion(-) create mode 100644 docs/overview.md diff --git a/docs/index.md b/docs/index.md index 12f9280..09d2fb8 100755 --- a/docs/index.md +++ b/docs/index.md @@ -5,7 +5,7 @@ :maxdepth: 1 :hidden: -overview.ipynb +overview.md mnist.ipynb changelog.md contributing.md diff --git a/docs/overview.md b/docs/overview.md new file mode 100644 index 0000000..98c26be --- /dev/null +++ b/docs/overview.md @@ -0,0 +1,84 @@ +# Library Overview + +Phito-Deep is a deep learning framework developed by [me](https://ralphdugue.com/). It provides a sequential API for architecting deep neural networks. It has many logical defaults that are also easily customizable. It’s main purpose is to to beeducational, and will continuously be updated as I learn more about deep learning architectures and their components. + +## Initialization + +There are currently two ways to initialize a model: + +- The \`Sequential\` class. This is the main class for impeachmenting all architectures. +```python + from phitodeep.model import Sequential + from phitodeep.layers.base import Dense, Flatten + from phitodeep.layers.activation import Softmax, ReLu + from phitodeep.loss import CategoricalCrossEntropy + + model = Sequential( + Flatten(), + Dense(64, 128), + ReLu(), + Dense(128, 64), + ReLu(), + Dense(64, 10), + ) + model.add(Softmax()) + model.setoptimizer("adam") + model.setloss(CategoricalCrossEntropy()) + model.setbatchsize(4) + + model.summary() +`````` +- The \`SequentialBuilder\` class. This just a wrapper around the model class to allow for a more fluent building experience. +```python + from phitodeep.model import SequentialBuilder + from phitodeep.loss import CategoricalCrossEntropy + + model = ( + SequentialBuilder() + .flatten() + .dense(64, 128) + .relu() + .dense(128, 64) + .relu() + .dense(64, 10) + .softmax() + .optimizer("adam") + .loss(CategoricalCrossEntropy()) + .alpha(0.05) + .epochs(5) + .batch(4) + .build() + ) + + model.summary() +``` +If you run both cell blocks, you will see they generate similar models. The builder will save you time on imports, but the \`Sequential\` class is a bit more dynamic. + +## Training + +Training a model pretty straight forward. You call the \`train\` function with your data, and it handles all forward and back propagation: +```python + import numpy as np + + rng = np.random.default_rng(42) + + X = rng.standard_normal((64, 8, 8)).astype(np.float32) + y = rng.integers(0, 10, 64) + + split = int(0.6 * X.shape[0]) + x_train, y_train = X[:split], y[:split] + x_temp, y_temp = X[split:], y[split:] + + split = int(0.5 * x_temp.shape[0]) + x_test, y_test = x_temp[:split], y_temp[:split] + x_val, y_val = x_temp[split:], y_temp[split:] + + + losses = model.train(x_train, y_train, x_test, y_test) +`````` +And of course predictions: +```python + y_pred = model.predict(x_val) + accuracy = np.mean(np.argmax(y_pred, axis=1) == y_test) + print(f"Test Accuracy: {accuracy:.4f} %") +``` From e5489e83ca95d3a421747dcd29600309edcf7b1a Mon Sep 17 00:00:00 2001 From: Ralph Date: Wed, 6 May 2026 14:09:34 -0400 Subject: [PATCH 11/19] Prepping refactor --- CHANGELOG.md | 6 ++ docs/overview.ipynb | 192 -------------------------------------------- pyproject.toml | 2 +- 3 files changed, 7 insertions(+), 193 deletions(-) delete mode 100644 docs/overview.ipynb diff --git a/CHANGELOG.md b/CHANGELOG.md index c17b21d..2db456b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,11 @@ # Changelog +## v0.2.0 + +- Optimization module has been refactored. This is a breaking change. +- Added initialization base class, He initialization, and Xavier initialization. +- Documentation updated to include overview. + ## v0.1.5 - Removed redundant runs on pushes to develop / main in CI/CD. They already happen on PRs. diff --git a/docs/overview.ipynb b/docs/overview.ipynb deleted file mode 100644 index b4b4864..0000000 --- a/docs/overview.ipynb +++ /dev/null @@ -1,192 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "786625b6-b6d4-4cc2-8b21-e2d3812eb846", - "metadata": {}, - "source": [ - "## Library Overview\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "4f728224-99fe-4453-8a60-aeea9d7e70af", - "metadata": {}, - "source": [ - "Phito-Deep is a deep learning framework developed by [me](https://ralphdugue.com/). It provides a sequential API for architecting deep neural networks. It has many logical defaults that are also easily customizable. It’s main purpose is to to beeducational, and will continuously be updated as I learn more about deep learning architectures and their components.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "72a9882d-7136-4764-8056-d2449a407f12", - "metadata": {}, - "source": [ - "### Initialization\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "908b10eb-e533-4bf8-b2cc-7bec1fd54f49", - "metadata": {}, - "source": [ - "There are currently two ways to initialize a model:\n", - "\n", - "- The \\`Sequential\\` class. This is the main class for impeachmenting all architectures.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b9a394ff-e01c-47f0-a617-b4a1ce73d084", - "metadata": {}, - "outputs": [], - "source": [ - "from phitodeep.model import Sequential\n", - "from phitodeep.layers.base import Dense, Flatten\n", - "from phitodeep.layers.activation import Softmax, ReLu\n", - "from phitodeep.loss import CategoricalCrossEntropy\n", - "\n", - "model = Sequential(\n", - " Flatten(),\n", - " Dense(64, 128),\n", - " ReLu(),\n", - " Dense(128, 64),\n", - " ReLu(),\n", - " Dense(64, 10),\n", - ")\n", - "model.add(Softmax())\n", - "model.setoptimizer(\"adam\")\n", - "model.setloss(CategoricalCrossEntropy())\n", - "model.setbatchsize(4)\n", - "\n", - "model.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "a595b537-7d72-4003-a986-974c80514249", - "metadata": {}, - "source": [ - "- The \\`SequentialBuilder\\` class. This just a wrapper around the model class to allow for a more fluent building experience.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "9e84c9db-6264-4b3f-b341-30c2161f316a", - "metadata": {}, - "outputs": [], - "source": [ - "from phitodeep.model import SequentialBuilder\n", - "from phitodeep.loss import CategoricalCrossEntropy\n", - "\n", - "model = (\n", - " SequentialBuilder()\n", - " .flatten()\n", - " .dense(64, 128)\n", - " .relu()\n", - " .dense(128, 64)\n", - " .relu()\n", - " .dense(64, 10)\n", - " .softmax()\n", - " .optimizer(\"adam\")\n", - " .loss(CategoricalCrossEntropy())\n", - " .alpha(0.05)\n", - " .epochs(5)\n", - " .batch(4)\n", - " .build()\n", - ")\n", - "\n", - "model.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "62649dfd-bf49-44db-9b53-d123ba8015d0", - "metadata": {}, - "source": [ - "If you run both cell blocks, you will see they generate similar models. The builder will save you time on imports, but the \\`Sequential\\` class is a bit more dynamic.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "87ba3bd0-81f5-442e-8890-ccfe4d368a66", - "metadata": {}, - "source": [ - "### Training\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "7f9a0b5b-93a5-4969-8b2f-52977ddc067f", - "metadata": {}, - "source": [ - "Training a model pretty straight forward. You call the \\`train\\` function with your data, and it handles all forward and back propagation:\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "4f284f8c-3220-43c5-99cd-587a8cefa9aa", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "rng = np.random.default_rng(42)\n", - "\n", - "X = rng.standard_normal((64, 8, 8)).astype(np.float32)\n", - "y = rng.integers(0, 10, 64)\n", - "\n", - "split = int(0.6 * X.shape[0])\n", - "x_train, y_train = X[:split], y[:split]\n", - "x_temp, y_temp = X[split:], y[split:]\n", - "\n", - "split = int(0.5 * x_temp.shape[0])\n", - "x_test, y_test = x_temp[:split], y_temp[:split]\n", - "x_val, y_val = x_temp[split:], y_temp[split:]\n", - "\n", - "\n", - "losses = model.train(x_train, y_train, x_test, y_test)" - ] - }, - { - "cell_type": "markdown", - "id": "17c2db3e-94c3-4aed-a121-294da3d869ff", - "metadata": {}, - "source": [ - "And of course predictions:\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "0dfb299d-c55e-4c3f-9260-2934f08f13ab", - "metadata": {}, - "outputs": [], - "source": [ - "y_pred = model.predict(x_val)\n", - "accuracy = np.mean(np.argmax(y_pred, axis=1) == y_test)\n", - "print(f\"Test Accuracy: {accuracy:.4f} %\")" - ] - } - ], - "metadata": [ - [ - "org" - ], - null, - null - ], - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/pyproject.toml b/pyproject.toml index 3f149e4..e07bafd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "phitodeep" -version = "0.1.5" +version = "0.2.0" description = "Deep learning framework built from scratch with numpy!" authors = ["Ralph Dugue"] license = "Apache License 2.0" From 5f062f4e6150d206a7f194bc6ae97ee5f95df09d Mon Sep 17 00:00:00 2001 From: Ralph Date: Thu, 7 May 2026 23:01:39 -0400 Subject: [PATCH 12/19] Aded full batch training --- src/phitodeep/model.py | 33 +++++------- src/phitodeep/optimization/__init__.py | 0 src/phitodeep/optimization/initializations.py | 0 .../optimizers.py} | 38 ------------- .../{ => optimization}/schedulers.py | 0 src/phitodeep/optimization/training.py | 53 +++++++++++++++++++ tests/test_model.py | 29 +++++----- 7 files changed, 78 insertions(+), 75 deletions(-) create mode 100644 src/phitodeep/optimization/__init__.py create mode 100644 src/phitodeep/optimization/initializations.py rename src/phitodeep/{optimization.py => optimization/optimizers.py} (57%) rename src/phitodeep/{ => optimization}/schedulers.py (100%) create mode 100644 src/phitodeep/optimization/training.py diff --git a/src/phitodeep/model.py b/src/phitodeep/model.py index 80f6e09..be6f9f5 100644 --- a/src/phitodeep/model.py +++ b/src/phitodeep/model.py @@ -1,5 +1,6 @@ from . import loss as ls -from . import optimization +from .optimization.training import train_loop +from .optimization import optimizers as o from .layers import activation as a from .layers import base as b @@ -9,7 +10,7 @@ def __init__( self, *layers, alpha=0.01, - optimizer="adam", + optimizer: o.Optimizer=o.Adam(), batch_size=1, epochs=1000, loss_class=ls.MeanSquaredError(), @@ -27,7 +28,7 @@ def __init__( """ self.layers = list(layers) self.alpha = alpha - self.optimizer = optimizer + self.optimizer_type = optimizer self.batch_size = batch_size self.epochs = epochs self.loss_class = loss_class @@ -36,8 +37,8 @@ def add(self, layer) -> None: """Add a layer to the network.""" self.layers.append(layer) - def setoptimizer(self, name): - self.optimizer = name + def setoptimizer(self, optimizer): + self.optimizer_type = optimizer def setbatchsize(self, num): self.batch_size = num @@ -58,21 +59,13 @@ def train(self, X, y, X_test, y_test): Returns: list: A list of tuples containing the training and test losses for each epoch. """ - match self.optimizer: - case "sgd": - optimizer = optimization.SGD(alpha=self.alpha) - case "adam": - optimizer = optimization.Adam(alpha=self.alpha) - case _: - raise ValueError(f"{self.optimizer} is not a valid optimizer.") - - losses = optimization.train_loop( + losses = train_loop( model=self, X=X, y=y, X_test=X_test, y_test=y_test, - optimizer=optimizer, + optimizer=self.optimizer_type, loss_class=self.loss_class, batch_size=self.batch_size, epochs=self.epochs, @@ -152,7 +145,7 @@ def copy(self): return Sequential( *[layer.copy() for layer in self.layers], alpha=self.alpha, - optimizer=self.optimizer, + optimizer=self.optimizer_type, batch_size=self.batch_size, epochs=self.epochs, loss_class=self.loss_class, @@ -165,7 +158,7 @@ class SequentialBuilder: def __init__(self): self.layers = [] self.alpha_value = 1 - self.optimizer_name = "sgd" + self.optimizer_type = o.Adam() self.batch_size = 1 self.epochs_value = 1000 self.loss_class = ls.MeanSquaredError() @@ -205,9 +198,9 @@ def elu(self, alpha_activation=1.0): self.layers.append(a.ELU(alpha_activation)) return self - def optimizer(self, name): + def optimizer(self, optimizer): """Set the optimizer.""" - self.optimizer_name = name + self.optimizer_type = optimizer return self def batch(self, num): @@ -235,7 +228,7 @@ def build(self): return Sequential( *[layer.copy() for layer in self.layers], alpha=self.alpha_value, - optimizer=self.optimizer_name, + optimizer=self.optimizer_type, batch_size=self.batch_size, epochs=self.epochs_value, loss_class=self.loss_class, diff --git a/src/phitodeep/optimization/__init__.py b/src/phitodeep/optimization/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/phitodeep/optimization/initializations.py b/src/phitodeep/optimization/initializations.py new file mode 100644 index 0000000..e69de29 diff --git a/src/phitodeep/optimization.py b/src/phitodeep/optimization/optimizers.py similarity index 57% rename from src/phitodeep/optimization.py rename to src/phitodeep/optimization/optimizers.py index cabfadb..1d5de69 100644 --- a/src/phitodeep/optimization.py +++ b/src/phitodeep/optimization/optimizers.py @@ -46,41 +46,3 @@ def step(self, layers): param = getattr(layer, param_name) param -= self.alpha * m_hat / (np.sqrt(v_hat) + self.epsilon) - - -def train_loop( - model, X, y, X_test, y_test, loss_class, optimizer, epochs=1000, batch_size=1 -): - - losses = [] - rng = np.random.default_rng() - - for epoch in range(epochs): - for _ in range(len(X) // batch_size): - indices = rng.integers(0, len(X), batch_size) - X_batch = X[indices] - y_batch = y[indices] - - # Forward pass - y_pred = model.predict(X_batch) - loss = loss_class.loss_func(y_pred, y_batch) - - # Compute gradient of loss w.r.t. predictions (dy) - dy = loss_class.loss_gradient(y_pred, y_batch) - - # Backward pass with gradient of loss - model.backward(dy) - optimizer.step(model.layers) - - y_pred = model.predict(X) - loss = loss_class.loss_func(y_pred, y) - - y_pred_test = model.predict(X_test) - loss_test = loss_class.loss_func(y_pred_test, y_test) - - losses.append((loss, loss_test)) - - if epoch % 10 == 0: - print(f"Epoch {epoch}, Loss: {loss:.4f}, Test Loss: {loss_test:.4f}") - - return losses diff --git a/src/phitodeep/schedulers.py b/src/phitodeep/optimization/schedulers.py similarity index 100% rename from src/phitodeep/schedulers.py rename to src/phitodeep/optimization/schedulers.py diff --git a/src/phitodeep/optimization/training.py b/src/phitodeep/optimization/training.py new file mode 100644 index 0000000..2562aaf --- /dev/null +++ b/src/phitodeep/optimization/training.py @@ -0,0 +1,53 @@ +import numpy as np +from .optimizers import Optimizer + + +def train_loop( + model, X, y, X_test, y_test, loss_class, optimizer, epochs=1000, batch_size=None +): + + losses = [] + rng = np.random.default_rng() + + for epoch in range(epochs): + if batch_size is not None: + for _ in range(len(X) // batch_size): + indices = rng.integers(0, len(X), batch_size) + X_batch = X[indices] + y_batch = y[indices] + + # Forward pass + y_pred = model.predict(X_batch) + loss = loss_class.loss_func(y_pred, y_batch) + + # Compute gradient of loss w.r.t. predictions (dy) + dy = loss_class.loss_gradient(y_pred, y_batch) + + # Backward pass with gradient of loss + model.backward(dy) + optimizer.step(model.layers) + else: + indices = rng.integers(0, len(X), len(X)) + # Forward pass + y_pred = model.predict(X[indices]) + loss = loss_class.loss_func(y_pred, y[indices]) + + # Compute gradient of loss w.r.t. predictions (dy) + dy = loss_class.loss_gradient(y_pred, y[indices]) + + # Backward pass with gradient of loss + model.backward(dy) + optimizer.step(model.layers) + + y_pred = model.predict(X) + loss = loss_class.loss_func(y_pred, y) + + y_pred_test = model.predict(X_test) + loss_test = loss_class.loss_func(y_pred_test, y_test) + + losses.append((loss, loss_test)) + + if epoch % 10 == 0: + print(f"Epoch {epoch}, Loss: {loss:.4f}, Test Loss: {loss_test:.4f}") + + return losses diff --git a/tests/test_model.py b/tests/test_model.py index 80a4d6b..cc23043 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -5,6 +5,7 @@ from phitodeep import model as m from phitodeep.layers import activation as a from phitodeep.layers import base as b +from phitodeep.optimization import optimizers as o # --------------------------------------------------------------------------- # Fixtures @@ -41,7 +42,7 @@ def trainable_model(): b.Dense(8, 2), a.Softmax(), loss_class=ls.CategoricalCrossEntropy(), - optimizer="adam", + optimizer=o.Adam(), epochs=3, batch_size=4, ) @@ -57,7 +58,7 @@ class TestSequential: def test_default_optimizer_is_adam(self): model = m.Sequential() - assert model.optimizer == "adam" + assert isinstance(model.optimizer_type, o.Adam) def test_default_alpha(self): model = m.Sequential() @@ -116,8 +117,8 @@ def test_add_multiple_layers(self, simple_model): # --- setoptimizer / setbatchsize / setloss --- def test_setoptimizer_updates_attribute(self, simple_model): - simple_model.setoptimizer("sgd") - assert simple_model.optimizer == "sgd" + simple_model.setoptimizer(o.SGD()) + assert isinstance(simple_model.optimizer_type, o.SGD) def test_setbatchsize_updates_attribute(self, simple_model): simple_model.setbatchsize(64) @@ -189,7 +190,7 @@ def test_train_losses_are_finite(self, trainable_model, small_data): assert np.isfinite(train_loss) assert np.isfinite(test_loss) - @pytest.mark.parametrize("optimizer", ["sgd", "adam"]) + @pytest.mark.parametrize("optimizer", [o.SGD(), o.Adam()]) def test_train_works_with_both_optimizers(self, small_data, optimizer): X, y = small_data model = m.Sequential( @@ -205,12 +206,6 @@ def test_train_works_with_both_optimizers(self, small_data, optimizer): losses = model.train(X, y, X, y) assert len(losses) == 2 - def test_train_raises_on_invalid_optimizer(self, trainable_model, small_data): - X, y = small_data - trainable_model.setoptimizer("invalid_opt") - with pytest.raises(ValueError, match="invalid_opt"): - trainable_model.train(X, y, X, y) - # --- copy --- def test_copy_returns_sequential_instance(self, simple_model): @@ -245,7 +240,7 @@ def test_copy_preserves_alpha(self, simple_model): def test_copy_preserves_optimizer(self, simple_model): copy = simple_model.copy() - assert copy.optimizer == simple_model.optimizer + assert copy.optimizer_type == simple_model.optimizer_type def test_copy_preserves_batch_size(self, simple_model): copy = simple_model.copy() @@ -313,8 +308,8 @@ def test_elu_sets_alpha_activation(self): # --- hyperparameter methods --- def test_optimizer_sets_correctly(self): - model = m.SequentialBuilder().optimizer("sgd").build() - assert model.optimizer == "sgd" + model = m.SequentialBuilder().optimizer(o.SGD()).build() + assert isinstance(model.optimizer_type, o.SGD) def test_alpha_sets_correctly(self): model = m.SequentialBuilder().alpha(0.001).build() @@ -344,7 +339,7 @@ def test_all_builder_methods_return_self(self): assert builder.tanh() is builder assert builder.softmax() is builder assert builder.elu() is builder - assert builder.optimizer("adam") is builder + assert builder.optimizer(o.Adam()) is builder assert builder.alpha(0.01) is builder assert builder.batch(32) is builder assert builder.epochs(100) is builder @@ -386,14 +381,14 @@ def test_full_chain_hyperparams(self): m.SequentialBuilder() .dense(4, 2) .softmax() - .optimizer("adam") + .optimizer(o.Adam()) .alpha(0.001) .batch(64) .epochs(10) .loss(cce) .build() ) - assert model.optimizer == "adam" + assert isinstance(model.optimizer_type, o.Adam) assert model.alpha == 0.001 assert model.batch_size == 64 assert model.epochs == 10 From e91005c9f44b21358c6a8fda68d8861ba138e340 Mon Sep 17 00:00:00 2001 From: Ralph Date: Sun, 10 May 2026 01:26:06 -0400 Subject: [PATCH 13/19] Switcing project manager to UV --- pyproject.toml | 29 +- uv.lock | 2450 ++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 2468 insertions(+), 11 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a/.github/workflows/publish-develop.yml b/.github/workflows/publish-develop.yml index a47f028..e789569 100644 --- a/.github/workflows/publish-develop.yml +++ b/.github/workflows/publish-develop.yml @@ -7,21 +7,16 @@ jobs: publish: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - name: Checkout code + uses: actions/checkout@v4 - - name: Set up Python 3.12 - uses: actions/setup-python@v5 - with: - python-version: "3.12" + - name: Install UV + run: curl -LsSf https://astral.sh/uv/install.sh | sh - - name: Install Poetry via pipx - run: pipx install poetry - - - name: Configure TestPyPI repository - run: poetry config repositories.testpypi https://test.pypi.org/legacy/ - - - name: Build and publish to TestPyPI + - name: Build and Publish to TestPyPI env: - POETRY_HTTP_BASIC_TESTPYPI_USERNAME: __token__ - POETRY_HTTP_BASIC_TESTPYPI_PASSWORD: ${{ secrets.TEST_PYPI_TOKEN }} - run: poetry publish --build --no-interaction -r testpypi + UV_PUBLISH_USERNAME: __token__ + UV_PUBLISH_TOKEN: ${{ secrets.TEST_PYPI_TOKEN }} + run: | + uv build + uv publish --index testpypi diff --git a/.github/workflows/publish-main.yml b/.github/workflows/publish-main.yml index 83dbc4d..10865ee 100644 --- a/.github/workflows/publish-main.yml +++ b/.github/workflows/publish-main.yml @@ -9,21 +9,19 @@ jobs: permissions: contents: write # required to push git tags steps: - - uses: actions/checkout@v4 + - name: Checkout code + uses: actions/checkout@v4 - - name: Set up Python 3.12 - uses: actions/setup-python@v5 - with: - python-version: "3.12" + - name: Install UV + run: curl -LsSf https://astral.sh/uv/install.sh | sh - - name: Install Poetry via pipx - run: pipx install poetry - - - name: Build and publish to PyPI + - name: Build and Publish env: - POETRY_HTTP_BASIC_PYPI_USERNAME: __token__ - POETRY_HTTP_BASIC_PYPI_PASSWORD: ${{ secrets.PYPI_TOKEN }} - run: poetry publish --build --no-interaction + UV_PUBLISH_USERNAME: __token__ + UV_PUBLISH_TOKEN: ${{ secrets.PYPI_TOKEN }} + run: | + uv build + uv publish - name: Tag release env: diff --git a/.github/workflows/run-tests.yml b/.github/workflows/run-tests.yml index 977439e..a6d521a 100644 --- a/.github/workflows/run-tests.yml +++ b/.github/workflows/run-tests.yml @@ -1,29 +1,22 @@ name: Run Tests on: - pull_request: + pull_requests: branches: + - 'release/**' - develop - main jobs: test: runs-on: ubuntu-latest - steps: - name: Checkout code uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: "3.12" - - - name: Install dependencies - run: | - pipx install poetry - poetry install + - name: Install UV + run: curl -LsSf https://astral.sh/uv/install.sh | sh - name: Run tests run: | - poetry run pytest + uv run pytest From cdad1e1635876f3d7cf1871f50dc3a0551e1795b Mon Sep 17 00:00:00 2001 From: Ralph Date: Sun, 10 May 2026 19:15:39 -0400 Subject: [PATCH 15/19] Iniialization implemented --- src/phitodeep/layers/activation.py | 16 ++++---- src/phitodeep/layers/base.py | 25 ++++++++---- src/phitodeep/optimization/initialization.py | 38 +++++++++++++++++++ src/phitodeep/optimization/initializations.py | 0 src/phitodeep/optimization/schedulers.py | 2 +- 5 files changed, 64 insertions(+), 17 deletions(-) create mode 100644 src/phitodeep/optimization/initialization.py delete mode 100644 src/phitodeep/optimization/initializations.py diff --git a/src/phitodeep/layers/activation.py b/src/phitodeep/layers/activation.py index 297ce39..018d4c5 100644 --- a/src/phitodeep/layers/activation.py +++ b/src/phitodeep/layers/activation.py @@ -5,7 +5,7 @@ class ReLu(Layer): def __init__(self) -> None: - super().__init__("relu") + super().__init__("relu", None) def forward(self, X): self.cache["X"] = X @@ -27,7 +27,7 @@ def copy(self): class LeakyReLu(Layer): def __init__(self, alpha=0.01) -> None: - super().__init__("leaky_rely") + super().__init__("leaky_rely", None) self.alpha = alpha def forward(self, X): @@ -46,7 +46,7 @@ def copy(self): class GELU(Layer): def __init__(self) -> None: - super.__init__("gelu") + super.__init__("gelu", None) def forward(self, X): self.cache["X"] = X @@ -72,7 +72,7 @@ def copy(self): class Swish(Layer): def __init__(self) -> None: - super().__init__("swish") + super().__init__("swish", None) def forward(self, X): self.cache["X"] = X @@ -93,7 +93,7 @@ def copy(self): class Sigmoid(Layer): def __init__(self) -> None: - super().__init__("sigmoid") + super().__init__("sigmoid", None) def forward(self, X): self.cache["X"] = X @@ -117,7 +117,7 @@ def copy(self): class Tanh(Layer): def __init__(self) -> None: - super().__init__("tanh") + super().__init__("tanh", None) def forward(self, X): self.cache["X"] = X @@ -143,7 +143,7 @@ def copy(self): class Softmax(Layer): def __init__(self) -> None: - super().__init__("softmax") + super().__init__("softmax", None) def forward(self, X): self.cache["X"] = X @@ -173,7 +173,7 @@ def copy(self): class ELU(Layer): def __init__(self, alpha=1.0) -> None: - super().__init__("elu") + super().__init__("elu", None) self.alpha_activation = alpha def forward(self, X): diff --git a/src/phitodeep/layers/base.py b/src/phitodeep/layers/base.py index 7c8c958..9250a4c 100644 --- a/src/phitodeep/layers/base.py +++ b/src/phitodeep/layers/base.py @@ -1,17 +1,20 @@ import numpy as np +from ..optimization.initialization import Initializer, InitType, He + class Layer: """ Base class for all layers in the network. """ - def __init__(self, name) -> None: + def __init__(self, name: str, initializer: Initializer) -> None: self.name = name self.cache = {} self.grads = {} + self.initializer = initializer - def forward(self, X): + def forward(self, X: np.ndarray): raise NotImplementedError(f"Block '{self.name}' must implement forward method") def backward(self, dL_dZ): @@ -36,9 +39,9 @@ class Flatten(Layer): """ def __init__(self): - super().__init__("flatten") + super().__init__("flatten", He()) - def forward(self, X): + def forward(self, X: np.ndarray): """ X: (batch_size, ...) -> (batch_size, ...) """ @@ -63,16 +66,22 @@ class Dense(Layer): Fully connected layer. """ - def __init__(self, input_size, output_size): - super().__init__("dense") + def __init__(self, input_size: int, output_size: int, initializer: Initializer=He()): + super().__init__("dense", initializer) self.grads = {} self.input_size = input_size self.output_size = output_size + # Initialize weights and biases - self.W = np.random.randn(input_size, output_size) * np.sqrt(2.0 / input_size) + rng = np.random.default_rng() + if initializer.init_type == InitType.NORMAL: + weights = rng.normal(size=(input_size, output_size)) + else: + weights = rng.uniform(size=(input_size, output_size)) + self.W = weights * initializer.get_scale(weights) self.b = np.zeros(output_size) - def forward(self, X): + def forward(self, X: np.ndarray): """ X: (batch_size, input_size) -> (batch_size, output_size) """ diff --git a/src/phitodeep/optimization/initialization.py b/src/phitodeep/optimization/initialization.py new file mode 100644 index 0000000..1802a2d --- /dev/null +++ b/src/phitodeep/optimization/initialization.py @@ -0,0 +1,38 @@ +from enum import Enum +import numpy as np + + +class InitType(Enum): + NORMAL = 1 + UNIFORM = 2 + +class Initializer: + def __init__(self, name: str, init_type: InitType): + self.name = name + self.init_type = init_type + + def get_scale(self, weights: np.ndarray) -> float: + raise NotImplementedError( + f"{self.name} initialization must implement get_weights." + ) + +class Xavier(Initializer): + def __init__(self, init_type: InitType=InitType.UNIFORM): + super().__init__("Xavier", init_type) + + def get_scale(self, weights: np.ndarray) -> float: + divident = 2 if self.init_type == InitType.NORMAL else 6 + fan_in = weights.shape[0] + fan_out = weights.shape[1] + + return np.sqrt(divident / (fan_in + fan_out)) + +class He(Initializer): + def __init__(self, init_type: InitType=InitType.UNIFORM): + super().__init__("He", init_type) + + def get_scale(self, weights: np.ndarray) -> float: + divident = 2 if self.init_type == InitType.NORMAL else 6 + fan_in = weights.shape[0] + + return np.sqrt(divident / fan_in) diff --git a/src/phitodeep/optimization/initializations.py b/src/phitodeep/optimization/initializations.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/phitodeep/optimization/schedulers.py b/src/phitodeep/optimization/schedulers.py index 6e75ff3..f71878c 100644 --- a/src/phitodeep/optimization/schedulers.py +++ b/src/phitodeep/optimization/schedulers.py @@ -10,7 +10,7 @@ def step(self, time_step: int) -> np.float16: raise NotImplementedError(f"{self.name} must implement the step function") class InverseTimeDecay(AlphaScheduler): - def __init__(self, alpha: np.float16, decay_rate: np.float16): + def __init__(self, alpha: np.float16, decay_rate: np.float16=0.01): super().__init__("InverseTimeDecay", alpha) self.decay_rate = decay_rate From 2f79ac6ad5f2d25d29880697b54f71761c8f33df Mon Sep 17 00:00:00 2001 From: Ralph Date: Sun, 10 May 2026 19:20:01 -0400 Subject: [PATCH 16/19] Iniialization implemented --- .github/workflows/run-tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/run-tests.yml b/.github/workflows/run-tests.yml index a6d521a..146ba45 100644 --- a/.github/workflows/run-tests.yml +++ b/.github/workflows/run-tests.yml @@ -1,7 +1,7 @@ name: Run Tests on: - pull_requests: + pull_request: branches: - 'release/**' - develop From 7083c4546293bd933cfcbc4564648041c98b3f33 Mon Sep 17 00:00:00 2001 From: Ralph Date: Mon, 11 May 2026 20:39:53 -0400 Subject: [PATCH 17/19] Compleed optimization for v0.2.0 --- .readthedocs.yml | 10 +- CHANGELOG.md | 25 +- CONTRIBUTING.md | 19 +- README.md | 23 +- docs/mnist.ipynb | 388 +- poetry.lock | 3766 ------------------ pyproject.toml | 3 +- src/phitodeep/model.py | 9 +- src/phitodeep/optimization/initialization.py | 4 +- src/phitodeep/optimization/optimizers.py | 5 + tests/test_model.py | 2 +- uv.lock | 590 +++ 12 files changed, 848 insertions(+), 3996 deletions(-) delete mode 100644 poetry.lock diff --git a/.readthedocs.yml b/.readthedocs.yml index 3e1b7d0..e0e218b 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -7,16 +7,16 @@ version: 2 # Set the OS, Python version and other tools you might need build: - os: ubuntu-22.04 + os: ubuntu-24.04 tools: python: "3.12" jobs: post_create_environment: - # Install poetry - # https://python-poetry.org/docs/#installing-manually - - pip install poetry + # Install UV + # https://docs.astral.sh/uv/getting-started/installation/#__tabbed_1_1 + - curl -LsSf https://astral.sh/uv/install.sh | sh post_install: - - VIRTUAL_ENV=$READTHEDOCS_VIRTUALENV_PATH poetry install --all-groups + - VIRTUAL_ENV=$READTHEDOCS_VIRTUALENV_PATH uv sync # Build documentation in the "docs/" directory with Sphinx sphinx: diff --git a/CHANGELOG.md b/CHANGELOG.md index 2db456b..3503c80 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,29 +4,6 @@ - Optimization module has been refactored. This is a breaking change. - Added initialization base class, He initialization, and Xavier initialization. +- Added "full-batch" training by setting `batch_size` model parameter to `None`. - Documentation updated to include overview. -## v0.1.5 - -- Removed redundant runs on pushes to develop / main in CI/CD. They already happen on PRs. - -## v0.1.4 (05/14/2026) - -- Added a copy method to Sequential class to make deep copies of a model instance. -- Added tests for Sequential class and builder, and added a test runner to CI/CD. - -## v0.1.3 (05/07/2026) - -- Removed datasets and pillow as package dependencies and moved to dev dependencies - -## v0.1.2 (05/07/2026) - -- Fixing CI publishing issue - -## v0.1.1 (05/07/2026) - -- Setup CI/CD with GitHub Actions - -## v0.1.0 (05/04/2026) - -- First release of `phitodeep`! diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 1ef45c1..80f598e 100755 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,4 +1,4 @@ -# Contributing +# Contributing to Phito-Deep Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given. @@ -42,20 +42,11 @@ If you are proposing a feature: Ready to contribute? Here's how to set up `phitodeep` for local development. -1. Download a copy of `phitodeep` locally. +1. Clone the repository from GitHub. -2. Create and activate a conda environment for `phitodeep`: +2. You will need to install [UV](https://docs.astral.sh/uv/getting-started/installation/). It is a modern Python environment and project management tool. You will grow to love it, trust me. - ```console - $ conda create -n phitodeep python=3.12 - $ conda activate phitodeep - ``` - -3. Install `phitodeep` using `poetry`: - - ```console - $ poetry install - ``` +3. Simply run `uv run pytest` in the root directory of your cloned repository and it should automatically create your virtual environment with all the necessary dependencies, followed by running the project test suite. 4. Use `git` (or similar) to create a branch for local development and make your changes: @@ -65,7 +56,7 @@ Ready to contribute? Here's how to set up `phitodeep` for local development. 5. When you're done making changes, check that your changes conform to any code formatting requirements and pass any tests. -6. Commit your changes and open a pull request. +6. Commit your changes and open a pull request against the current release branch in development. ## Pull Request Guidelines diff --git a/README.md b/README.md index 830aaf3..1c16a9b 100644 --- a/README.md +++ b/README.md @@ -14,8 +14,10 @@ MNIST quickstart: import numpy as np from datasets import load_dataset -import phitodeep.loss as loss -import phitodeep.model as m +from phitodeep.model import SequentialBuilder +from phitodeep.loss import CategoricalCrossEntropy +from phitodeep.optimization.optimizers import Adam +from phitodeep.optimization.initialization import Xavier, InitType train_dataset = load_dataset("ylecun/mnist", split="train") test_dataset = load_dataset("ylecun/mnist", split="test") @@ -32,24 +34,25 @@ y_test = np.array(y_test) print(X_train.shape, y_train.shape) model = ( - m.SequentialBuilder() + SequentialBuilder() .flatten() .dense(784, 128) .relu() - .dense(128, 10) + .dense(128, 64, Xavier(InitType.NORMAL)) + .relu() + .dense(64, 10, Xavier(InitType.NORMAL)) .softmax() - .optimizer("adam") - .loss(loss.CategoricalCrossEntropy()) - .alpha(0.001) - .epochs(300) - .batch(32) + .optimizer(Adam()) + .loss(CategoricalCrossEntropy()) + .alpha(0.05) + .epochs(5) + .batch(64) .build() ) model.summary() model.train(X_train, y_train, X_test, y_test) - ``` ## Contributing diff --git a/docs/mnist.ipynb b/docs/mnist.ipynb index 4ab22fb..5afaef4 100644 --- a/docs/mnist.ipynb +++ b/docs/mnist.ipynb @@ -1,172 +1,222 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MNIST Example\n", - "A classic example of MNIST digit classification using `phitodeep`:\n", - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from datasets import load_dataset\n", - "\n", - "from phitodeep.layers import base, activation\n", - "import phitodeep.loss as loss\n", - "import phitodeep.model as m\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load MNIST dataset\n", - "Using the `datasets` library from Hugging Face, we can easily load the MNIST dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train_dataset = load_dataset(\"ylecun/mnist\", split=\"train\")\n", - "test_dataset = load_dataset(\"ylecun/mnist\", split=\"test\")\n", - "\n", - "X_train = train_dataset[\"image\"]\n", - "y_train = train_dataset[\"label\"]\n", - "X_test = test_dataset[\"image\"]\n", - "y_test = test_dataset[\"label\"]\n", - "\n", - "X_train = np.array(X_train).astype(np.float32) / 255.0\n", - "y_train = np.array(y_train)\n", - "X_test = np.array(X_test).astype(np.float32) / 255.0\n", - "y_test = np.array(y_test)\n", - "print(X_train.shape, y_train.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model Definition\n", - "The following two cells show the different ways to initialize the model. Through the `Sequential` class directly, or by using the builder class:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = m.Sequential(\n", - " base.Flatten(),\n", - " base.Dense(784, 128),\n", - " activation.ReLu(),\n", - " base.Dense(128, 64),\n", - " activation.ReLu(),\n", - " base.Dense(64, 10),\n", - ")\n", - "model.add(activation.Softmax())\n", - "model.setoptimizer(\"adam\")\n", - "model.setloss(loss.CategoricalCrossEntropy())\n", - "model.setbatchsize(64)\n", - "\n", - "model.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = (\n", - " m.SequentialBuilder()\n", - " .flatten()\n", - " .dense(784, 128)\n", - " .relu()\n", - " .dense(128, 64)\n", - " .relu()\n", - " .dense(64, 10)\n", - " .softmax()\n", - " .optimizer(\"adam\")\n", - " .loss(loss.CategoricalCrossEntropy())\n", - " .alpha(0.05)\n", - " .epochs(5)\n", - " .batch(64)\n", - " .build()\n", - ")\n", - "\n", - "model.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Training \n", - "First we check prediction accuracy before training, then we train the model for 5 epochs, and finally check the accuracy again to see the improvement." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y_pred = model.predict(X_test)\n", - "accuracy = np.mean(np.argmax(y_pred, axis=1) == y_test) * 100\n", - "print(f\"Test Accuracy: {accuracy:.4f} %\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "losses = model.train(X_train, y_train, X_test, y_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y_pred = model.predict(X_test)\n", - "accuracy = np.mean(np.argmax(y_pred, axis=1) == y_test) * 100\n", - "print(f\"Test Accuracy: {accuracy:.4f} %\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MNIST Example\n", + "A classic example of MNIST digit classification using `phitodeep`:\n", + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from datasets import load_dataset\n", + "\n", + "from phitodeep.model import SequentialBuilder\n", + "from phitodeep.loss import CategoricalCrossEntropy\n", + "from phitodeep.optimization.optimizers import Adam\n", + "from phitodeep.optimization.initialization import Xavier, InitType\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load MNIST dataset\n", + "Using the `datasets` library from Hugging Face, we can easily load the MNIST dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" + ] }, - "nbformat": 4, - "nbformat_minor": 4 + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(60000, 28, 28) (60000,)\n" + ] + } + ], + "source": [ + "train_dataset = load_dataset(\"ylecun/mnist\", split=\"train\")\n", + "test_dataset = load_dataset(\"ylecun/mnist\", split=\"test\")\n", + "\n", + "X_train = train_dataset[\"image\"]\n", + "y_train = train_dataset[\"label\"]\n", + "X_test = test_dataset[\"image\"]\n", + "y_test = test_dataset[\"label\"]\n", + "\n", + "X_train = np.array(X_train).astype(np.float32) / 255.0\n", + "y_train = np.array(y_train)\n", + "X_test = np.array(X_test).astype(np.float32) / 255.0\n", + "y_test = np.array(y_test)\n", + "print(X_train.shape, y_train.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Definition" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model Summary:\n", + "------------------------------------------------------------\n", + "Optimizer: Adam | Learning Rate: 0.05 | Batch Size: 64 \n", + "Epochs: 5 | Loss: CategoricalCrossEntropy\n", + "------------------------------------------------------------\n", + "Layer 0: FLATTEN \n", + "Layer 1: DENSE | Input: 784 Output: 128 \n", + "Layer 2: RELU \n", + "Layer 3: DENSE | Input: 128 Output: 64 \n", + "Layer 4: RELU \n", + "Layer 5: DENSE | Input: 64 Output: 10 \n", + "Layer 6: SOFTMAX \n", + "------------------------------------------------------------\n" + ] + } + ], + "source": [ + "model = (\n", + " SequentialBuilder()\n", + " .flatten()\n", + " .dense(784, 128, Xavier(InitType.NORMAL))\n", + " .relu()\n", + " .dense(128, 64, Xavier(InitType.NORMAL))\n", + " .relu()\n", + " .dense(64, 10, Xavier(InitType.NORMAL))\n", + " .softmax()\n", + " .optimizer(Adam())\n", + " .loss(CategoricalCrossEntropy())\n", + " .alpha(0.05)\n", + " .epochs(5)\n", + " .batch(64)\n", + " .build()\n", + ")\n", + "\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training \n", + "First we check prediction accuracy before training, then we train the model for 5 epochs, and finally check the accuracy again to see the improvement." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Accuracy: 9.8000 %\n" + ] + } + ], + "source": [ + "y_pred = model.predict(X_test)\n", + "accuracy = np.mean(np.argmax(y_pred, axis=1) == y_test) * 100\n", + "print(f\"Test Accuracy: {accuracy:.4f} %\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0, Loss: 0.1890, Test Loss: 0.1935\n", + "Training complete.\n", + "------------------------------------------------------------\n", + "Starting Training Loss: 0.1890 | Starting Test Loss: 0.1935\n", + "Final Training Loss: 0.1077 | Final Test Loss: 0.1588\n", + "Training Loss Improvement: 0.0813 | Test Loss Improvement: 0.0347\n", + "------------------------------------------------------------\n" + ] + } + ], + "source": [ + "losses = model.train(X_train, y_train, X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Accuracy: 96.1000 %\n" + ] + } + ], + "source": [ + "y_pred = model.predict(X_test)\n", + "accuracy = np.mean(np.argmax(y_pred, axis=1) == y_test) * 100\n", + "print(f\"Test Accuracy: {accuracy:.4f} %\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.4" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index 1857e1a..0000000 --- a/poetry.lock +++ /dev/null @@ -1,3766 +0,0 @@ -# This file is automatically @generated by Poetry 2.3.3 and should not be changed by hand. - 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{self.optimizer_type.name} | Learning Rate: {self.alpha} | Batch Size: {self.batch_size} \nEpochs: {self.epochs} | Loss: {self.loss_class.name}" ) print("-" * 60) for i, layer in enumerate(self.layers): @@ -168,9 +169,9 @@ def flatten(self): self.layers.append(b.Flatten()) return self - def dense(self, input_size, output_size): + def dense(self, input_size, output_size, initializer=He()): """Add a Dense layer.""" - self.layers.append(b.Dense(input_size, output_size)) + self.layers.append(b.Dense(input_size, output_size, initializer)) return self def relu(self): diff --git a/src/phitodeep/optimization/initialization.py b/src/phitodeep/optimization/initialization.py index 1802a2d..d74ee84 100644 --- a/src/phitodeep/optimization/initialization.py +++ b/src/phitodeep/optimization/initialization.py @@ -17,7 +17,7 @@ def get_scale(self, weights: np.ndarray) -> float: ) class Xavier(Initializer): - def __init__(self, init_type: InitType=InitType.UNIFORM): + def __init__(self, init_type: 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2196503, upload-time = "2025-11-01T21:15:53.565Z" }, +] + [[package]] name = "xxhash" version = "3.7.0" From 42dd3a79cef78c6f53cafba8f4a188aa7e69821d Mon Sep 17 00:00:00 2001 From: Ralph Date: Tue, 12 May 2026 00:51:45 -0400 Subject: [PATCH 18/19] Fixng version tagging command --- .github/workflows/publish-main.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/publish-main.yml b/.github/workflows/publish-main.yml index 10865ee..2d29e3e 100644 --- a/.github/workflows/publish-main.yml +++ b/.github/workflows/publish-main.yml @@ -27,7 +27,7 @@ jobs: env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} run: | - VERSION=$(poetry version --short) + VERSION=$(uv version --short) git config user.name "github-actions[bot]" git config user.email "github-actions[bot]@users.noreply.github.com" gh release create "v$VERSION" --notes-file ./CHANGELOG.md From 2e9833b2b19977a63d34925d9e4d43e4fd4b18dd Mon Sep 17 00:00:00 2001 From: Ralph Date: Tue, 12 May 2026 01:12:21 -0400 Subject: [PATCH 19/19] Fixng publishing --- .github/workflows/publish-develop.yml | 1 - .github/workflows/publish-main.yml | 1 - 2 files changed, 2 deletions(-) diff --git a/.github/workflows/publish-develop.yml b/.github/workflows/publish-develop.yml index e789569..0c745c2 100644 --- a/.github/workflows/publish-develop.yml +++ b/.github/workflows/publish-develop.yml @@ -15,7 +15,6 @@ jobs: - name: Build and Publish to TestPyPI env: - UV_PUBLISH_USERNAME: __token__ UV_PUBLISH_TOKEN: ${{ secrets.TEST_PYPI_TOKEN }} run: | uv build diff --git a/.github/workflows/publish-main.yml b/.github/workflows/publish-main.yml index 2d29e3e..8bc7a2e 100644 --- a/.github/workflows/publish-main.yml +++ b/.github/workflows/publish-main.yml @@ -17,7 +17,6 @@ jobs: - name: Build and Publish env: - UV_PUBLISH_USERNAME: __token__ UV_PUBLISH_TOKEN: ${{ secrets.PYPI_TOKEN }} run: | uv build