From 6d73bc59983ceee76edb6f588f830c0fd492af55 Mon Sep 17 00:00:00 2001 From: ASEM000 Date: Mon, 1 Apr 2024 16:26:59 +0900 Subject: [PATCH] Update convert_keras.ipynb --- docs/notebooks/convert_keras.ipynb | 286 +++++++++++++++-------------- 1 file changed, 146 insertions(+), 140 deletions(-) diff --git a/docs/notebooks/convert_keras.ipynb b/docs/notebooks/convert_keras.ipynb index 5807c55..7d586b0 100644 --- a/docs/notebooks/convert_keras.ipynb +++ b/docs/notebooks/convert_keras.ipynb @@ -23,7 +23,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Imports" + "## Imports" ] }, { @@ -40,15 +40,14 @@ "import jax.random as jr\n", "import keras\n", "import serket as sk\n", - "import jax\n", - "import matplotlib.pyplot as plt" + "import jax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Conversion layer" + "## `serket` -> `keras` conversion" ] }, { @@ -64,35 +63,40 @@ " return False\n", "\n", "\n", - "class Serket2Keras(keras.Layer):\n", - " def __init__(self, layer, name=None):\n", - " \"\"\"Converts a serket layer to a keras layer\"\"\"\n", - " super().__init__(name=name)\n", - " # extract the leaves from the serket layer\n", - " # here leaves of a masked layer are the trainable variables\n", - " # and treedef is the tree structure of the layer\n", - " leaves, treedef = jax.tree_util.tree_flatten(sk.tree_mask(layer))\n", - " self.treedef = treedef\n", - " for leaf in leaves:\n", - " variable = keras.Variable(initializer=leaf, trainable=is_trainable(leaf))\n", - " self._track_variable(variable)\n", - " # mark the layer as built\n", - " self.built = True\n", + "def serket_to_keras(layer: sk.TreeClass) -> keras.Layer:\n", + " leaves, treedef = jax.tree_util.tree_flatten(sk.tree_mask(layer))\n", "\n", - " def call(self, x):\n", - " \"\"\"Applies the layer to the input\"\"\"\n", - " # convert the keras variables to jax arrays to be used in serket\n", - " leaves = jtu.tree_map(jnp.array, self.trainable_variables)\n", - " # unflatten the layer with the updated leaves\n", - " layer = jtu.tree_unflatten(self.treedef, leaves)\n", - " # apply the layer after unmasking it\n", - " return sk.tree_unmask(layer)(x)\n", + " class SerketToKeras(keras.Layer):\n", + " def __init__(self, layer, name=None):\n", + " \"\"\"Converts a serket layer to a keras layer\"\"\"\n", + " super().__init__(name=name)\n", + " # extract the leaves from the serket layer\n", + " # here leaves of a masked layer are the trainable variables\n", + " # and treedef is the tree structure of the layer\n", + " for leaf in leaves:\n", + " variable = keras.Variable(\n", + " initializer=leaf, trainable=is_trainable(leaf)\n", + " )\n", + " self._track_variable(variable)\n", + " # mark the layer as built\n", + " self.built = True\n", "\n", - " @property\n", - " def model(self):\n", - " leaves = jax.tree_map(jnp.array, self.trainable_variables)\n", - " layer = jax.tree_util.tree_unflatten(self.treedef, leaves)\n", - " return sk.tree_unmask(layer)" + " def call(self, x):\n", + " \"\"\"Applies the layer to the input\"\"\"\n", + " # convert the keras variables to jax arrays to be used in serket\n", + " leaves = jtu.tree_map(jnp.array, self.trainable_variables)\n", + " # unflatten the layer with the updated leaves\n", + " layer = jtu.tree_unflatten(treedef, leaves)\n", + " # apply the layer after unmasking it\n", + " return sk.tree_unmask(layer)(x)\n", + "\n", + " @property\n", + " def model(self):\n", + " leaves = jax.tree_map(jnp.array, self.trainable_variables)\n", + " layer = jax.tree_util.tree_unflatten(treedef, leaves)\n", + " return sk.tree_unmask(layer)\n", + "\n", + " return SerketToKeras(layer)" ] }, { @@ -138,211 +142,211 @@ "output_type": "stream", "text": [ "Epoch 1/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 11.4385\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 1.3688\n", "Epoch 2/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 451us/step - loss: 2.3039\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 482us/step - loss: 0.0929\n", "Epoch 3/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 416us/step - loss: 2.5281\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 395us/step - loss: 0.2363\n", "Epoch 4/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 367us/step - loss: 0.8995\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 389us/step - loss: 0.1136\n", "Epoch 5/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 473us/step - loss: 1.5571\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 587us/step - loss: 0.0141\n", "Epoch 6/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 486us/step - loss: 0.7577\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 378us/step - loss: 0.0579\n", "Epoch 7/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 398us/step - loss: 0.2899\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 468us/step - loss: 0.0498\n", "Epoch 8/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 344us/step - 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"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 379us/step - loss: 0.1101\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 438us/step - loss: 0.0031\n", "Epoch 13/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 375us/step - loss: 0.0924\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 477us/step - loss: 0.0056\n", "Epoch 14/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 411us/step - loss: 0.0785\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 382us/step - loss: 0.0042\n", "Epoch 15/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 395us/step - loss: 0.0810\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 431us/step - 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loss: 2.3416e-04\n", "Epoch 76/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 374us/step - loss: 0.0023\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 445us/step - loss: 2.5766e-04\n", "Epoch 77/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 351us/step - loss: 0.0023\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 413us/step - loss: 2.0533e-04\n", "Epoch 78/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 406us/step - loss: 0.0024\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 524us/step - loss: 2.7161e-04\n", "Epoch 79/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 400us/step - 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loss: 2.0246e-04\n", "Epoch 83/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 369us/step - loss: 0.0022\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 977us/step - loss: 2.0976e-04\n", "Epoch 84/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 413us/step - loss: 0.0024\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 768us/step - loss: 2.0156e-04\n", "Epoch 85/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 395us/step - loss: 0.0022\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 608us/step - loss: 1.8208e-04\n", "Epoch 86/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 341us/step - 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loss: 1.5132e-04\n", "Epoch 90/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 411us/step - loss: 0.0016 \n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 445us/step - loss: 1.6035e-04\n", "Epoch 91/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 366us/step - loss: 0.0021\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 494us/step - loss: 1.5763e-04\n", "Epoch 92/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 432us/step - loss: 0.0020\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 428us/step - loss: 1.4480e-04\n", "Epoch 93/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 483us/step - loss: 0.0017\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 676us/step - loss: 1.6287e-04\n", "Epoch 94/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 762us/step - loss: 0.0023\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 766us/step - loss: 1.5020e-04\n", "Epoch 95/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 639us/step - loss: 0.0017\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 446us/step - loss: 1.5299e-04\n", "Epoch 96/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 478us/step - loss: 0.0020\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 438us/step - loss: 1.7338e-04\n", "Epoch 97/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 582us/step - loss: 0.0020\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 641us/step - loss: 1.5630e-04\n", "Epoch 98/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 652us/step - loss: 0.0017\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 412us/step - loss: 1.3388e-04\n", "Epoch 99/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 759us/step - loss: 0.0017\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 604us/step - loss: 1.4354e-04\n", "Epoch 100/100\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 580us/step - loss: 0.0019\n" + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 797us/step - loss: 1.8138e-04\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -354,11 +358,12 @@ "sk_model = sk.Sequential(\n", " Linear(1, 20, key=jr.PRNGKey(0)),\n", " jax.nn.tanh,\n", - " Linear(20, 15, key=jr.PRNGKey(1)),\n", + " Linear(20, 20, key=jr.PRNGKey(1)),\n", + " jax.nn.tanh,\n", ")\n", "\n", "# use serket with keras model\n", - "model = keras.Sequential([Serket2Keras(sk_model, name=\"serket\"), keras.layers.Dense(1)])\n", + "model = keras.Sequential([serket_to_keras(sk_model), keras.layers.Dense(1)])\n", "\n", "\n", "model.compile(\n", @@ -391,16 +396,17 @@ " Linear(\n", " in_features=1, \n", " out_features=20, \n", - " weight=f32[1,20](μ=-0.25, σ=0.94, ∈[-1.76,1.72]), \n", - " bias=f32[20](μ=-0.05, σ=0.91, ∈[-2.14,1.75])\n", + " weight=f32[1,20](μ=-0.19, σ=0.92, ∈[-1.65,1.91]), \n", + " bias=f32[20](μ=-0.03, σ=0.95, ∈[-2.12,1.93])\n", " ), \n", " jit(tanh(x)), \n", " Linear(\n", " in_features=20, \n", - " out_features=15, \n", - " weight=f32[20,15](μ=0.01, σ=0.94, ∈[-2.31,2.37]), \n", - " bias=f32[15](μ=0.09, σ=1.16, ∈[-1.60,2.25])\n", - " )\n", + " out_features=20, \n", + " weight=f32[20,20](μ=-0.04, σ=0.96, ∈[-2.73,2.65]), \n", + " bias=f32[20](μ=0.37, σ=0.82, ∈[-1.04,1.99])\n", + " ), \n", + " jit(tanh(x))\n", " )\n", ")" ]