Introduction to flax

IntroductionToFlax.ipynb

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    "colab": {
      "name": "IntroductionToFlax.ipynb",
      "provenance": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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    "accelerator": "GPU",
    "gpuClass": "standard"
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "In8vQyZEZV9H"
      },
      "outputs": [],
      "source": [
        "pip install optax flax"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from typing import Sequence, Callable\n",
        "from flax import linen as nn\n",
        "import jax.numpy as jnp\n",
        "import jax\n",
        "import numpy as np\n",
        "import optax\n",
        "import copy\n",
        "from sklearn.datasets import load_diabetes, make_moons\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "8qZuNyCNZXEA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining a MLP"
      ],
      "metadata": {
        "id": "0ZDCu1yG6mGO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MLP(nn.Module):\n",
        "  features: Sequence[int]\n",
        "\n",
        "  @nn.compact\n",
        "  def __call__(self, x):\n",
        "    for feat in self.features[:-1]:\n",
        "      x = nn.relu(nn.Dense(feat)(x))\n",
        "    x = nn.Dense(self.features[-1])(x)\n",
        "    return x"
      ],
      "metadata": {
        "id": "eORYktYchcTh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "seed = 8964\n",
        "rng_key = jax.random.PRNGKey(seed)\n",
        "rng_keys = jax.random.split(rng_key)\n",
        "\n",
        "model = MLP([10, 8, 8, 1])\n",
        "batch = jnp.ones((32, 10))\n",
        "params = model.init(rng_keys[0], batch)\n",
        "output = model.apply(params, batch)"
      ],
      "metadata": {
        "id": "yl8Ia-YHiIK8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Optax related"
      ],
      "metadata": {
        "id": "V8vZVbw86sg1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "learning_rate = 1e-2\n",
        "tx = optax.adam(learning_rate)\n",
        "opt_state = tx.init(params)"
      ],
      "metadata": {
        "id": "0pPkoWmUlxTd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@jax.jit\n",
        "def train_step(params, opt_state, x, y):\n",
        "    def loss(params):\n",
        "        pred_y = model.apply(params, x)\n",
        "        return jnp.mean(jnp.square(pred_y - y))\n",
        "    grad_fn = jax.value_and_grad(loss)\n",
        "    value, grad = grad_fn(params)\n",
        "    updates, state = tx.update(grad, opt_state)\n",
        "    params = optax.apply_updates(params, updates)\n",
        "    return params, opt_state\n",
        "\n",
        "@jax.jit\n",
        "def eval_step(params, x, y):\n",
        "    pred_y = model.apply(params, x)\n",
        "    return jnp.mean(jnp.square(pred_y - y))"
      ],
      "metadata": {
        "id": "2mkWu5IYjRYB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train"
      ],
      "metadata": {
        "id": "MtGmmd4d6v6r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 1000\n",
        "\n",
        "x, y = load_diabetes(return_X_y=True)\n",
        "y = y.reshape(-1,1)\n",
        "\n",
        "x_train = x[:int(len(x)*0.8)]\n",
        "y_train = y[:int(len(y)*0.8)]\n",
        "x_test = x[int(len(x)*0.8):]\n",
        "y_test = y[int(len(y)*0.8):]\n",
        "\n",
        "print(x.shape, y.shape)\n",
        "\n",
        "loss = 1e10\n",
        "for epoch in range(0, num_epochs):\n",
        "    # Run an optimization step over a training batch\n",
        "    params, opt_state = train_step(params, opt_state, x_train, y_train)\n",
        "    loss_local = eval_step(params, x_test, y_test)\n",
        "    if epoch % 100 == 0:\n",
        "        print('Epoch %d' % epoch)\n",
        "        print('Loss: %.3f' % loss_local)\n",
        "    if loss_local < loss:\n",
        "        loss = loss_local\n",
        "        save_params = copy.copy(params)\n",
        "        save_state = copy.copy(opt_state)"
      ],
      "metadata": {
        "id": "vVLCtTlzjxZB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Normalizing flow example"
      ],
      "metadata": {
        "id": "lKJaN7GN6YrL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install flowMC"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWgdxz-VqPTK",
        "outputId": "0feb446d-6bd2-4f62-8db0-e777ea7d4c6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting flowMC\n",
            "  Downloading flowMC-0.0.1-py3-none-any.whl (13 kB)\n",
            "Requirement already satisfied: jax in /usr/local/lib/python3.7/dist-packages (from flowMC) (0.3.8)\n",
            "Requirement already satisfied: flax in /usr/local/lib/python3.7/dist-packages (from flowMC) (0.5.2)\n",
            "Requirement already satisfied: jaxlib in /usr/local/lib/python3.7/dist-packages (from flowMC) (0.3.7+cuda11.cudnn805)\n",
            "Requirement already satisfied: rich~=11.1 in /usr/local/lib/python3.7/dist-packages (from flax->flowMC) (11.2.0)\n",
            "Requirement already satisfied: PyYAML>=5.4.1 in /usr/local/lib/python3.7/dist-packages (from flax->flowMC) (6.0)\n",
            "Requirement already satisfied: typing-extensions>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from flax->flowMC) (4.1.1)\n",
            "Requirement already satisfied: msgpack in /usr/local/lib/python3.7/dist-packages (from flax->flowMC) (1.0.4)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from flax->flowMC) (3.2.2)\n",
            "Requirement already satisfied: optax in /usr/local/lib/python3.7/dist-packages (from flax->flowMC) (0.1.2)\n",
            "Requirement already satisfied: numpy>=1.12 in /usr/local/lib/python3.7/dist-packages (from flax->flowMC) (1.21.6)\n",
            "Requirement already satisfied: scipy>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from jax->flowMC) (1.4.1)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from jax->flowMC) (1.1.0)\n",
            "Requirement already satisfied: opt-einsum in /usr/local/lib/python3.7/dist-packages (from jax->flowMC) (3.3.0)\n",
            "Requirement already satisfied: colorama<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from rich~=11.1->flax->flowMC) (0.4.5)\n",
            "Requirement already satisfied: commonmark<0.10.0,>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from rich~=11.1->flax->flowMC) (0.9.1)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.6.0 in /usr/local/lib/python3.7/dist-packages (from rich~=11.1->flax->flowMC) (2.6.1)\n",
            "Requirement already satisfied: flatbuffers<3.0,>=1.12 in /usr/local/lib/python3.7/dist-packages (from jaxlib->flowMC) (2.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->flax->flowMC) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->flax->flowMC) (1.4.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->flax->flowMC) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->flax->flowMC) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->flax->flowMC) (1.15.0)\n",
            "Requirement already satisfied: chex>=0.0.4 in /usr/local/lib/python3.7/dist-packages (from optax->flax->flowMC) (0.1.3)\n",
            "Requirement already satisfied: dm-tree>=0.1.5 in /usr/local/lib/python3.7/dist-packages (from chex>=0.0.4->optax->flax->flowMC) (0.1.7)\n",
            "Requirement already satisfied: toolz>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from chex>=0.0.4->optax->flax->flowMC) (0.11.2)\n",
            "Installing collected packages: flowMC\n",
            "Successfully installed flowMC-0.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from flowMC.nfmodel.realNVP import RealNVP\n",
        "import jax\n",
        "import jax.numpy as jnp                # JAX NumPy\n",
        "import jax.random as random            # JAX random\n",
        "from jax.scipy.stats import multivariate_normal\n",
        "\n",
        "from flax import linen as nn           # The Linen API\n",
        "from flax.training import train_state  # Useful dataclass to keep train state\n",
        "import optax                           # Optimizers\n",
        "\n",
        "from sklearn.datasets import make_moons"
      ],
      "metadata": {
        "id": "u2xUc_iosBjq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "model = RealNVP(10, 2, 64, 1)\n",
        "data = make_moons(n_samples=10000, noise=0.05)\n",
        "key1, rng, init_rng = jax.random.split(jax.random.PRNGKey(0),3)\n",
        "\n",
        "def create_train_state(rng, learning_rate, momentum):\n",
        "    params = model.init(rng, jnp.ones((1,2)))['params']\n",
        "    tx = optax.adam(learning_rate, momentum)\n",
        "    return train_state.TrainState.create(apply_fn=model.apply, params=params, tx=tx)\n",
        "\n",
        "\n",
        "learning_rate = 0.01\n",
        "momentum = 0.9\n",
        "\n",
        "state = create_train_state(init_rng, learning_rate, momentum)\n",
        "\n",
        "@jax.jit\n",
        "def train_step(state, batch):\n",
        "    def loss(params):\n",
        "        y, log_det = model.apply({'params': params},batch)\n",
        "        mean = jnp.zeros((batch.shape[0],2))\n",
        "        cov = jnp.repeat(jnp.eye(2)[None,:],batch.shape[0],axis=0)\n",
        "        log_det = log_det + multivariate_normal.logpdf(y,mean,cov)\n",
        "        return -jnp.mean(log_det)\n",
        "    grad_fn = jax.value_and_grad(loss)\n",
        "    value, grad = grad_fn(state.params)\n",
        "    state = state.apply_gradients(grads=grad)\n",
        "    return state\n",
        "\n",
        "@jax.jit\n",
        "def eval_step(params, batch):\n",
        "    y, log_det = model.apply({'params': params},batch)\n",
        "    mean = jnp.zeros((batch.shape[0],2))\n",
        "    cov = jnp.repeat(jnp.eye(2)[None,:],batch.shape[0],axis=0)\n",
        "    log_det = log_det + multivariate_normal.logpdf(y,mean,cov)\n",
        "    return -jnp.mean(log_det)\n",
        "\n",
        "def train_epoch(state, train_ds, batch_size, epoch, rng):\n",
        "  \"\"\"Train for a single epoch.\"\"\"\n",
        "  train_ds_size = len(train_ds)\n",
        "  steps_per_epoch = train_ds_size // batch_size\n",
        "\n",
        "  perms = jax.random.permutation(rng, train_ds_size)\n",
        "  perms = perms[:steps_per_epoch * batch_size]  # skip incomplete batch\n",
        "  perms = perms.reshape((steps_per_epoch, batch_size))\n",
        "  for perm in perms:\n",
        "    batch = train_ds[perm, ...]\n",
        "    state = train_step(state, batch)\n",
        "\n",
        "  return state\n",
        "\n",
        "num_epochs = 5000\n",
        "batch_size = 10000\n",
        "\n",
        "for epoch in range(1, num_epochs + 1):\n",
        "    print('Epoch %d' % epoch)\n",
        "    # Use a separate PRNG key to permute image data during shuffling\n",
        "    rng, input_rng = jax.random.split(rng)\n",
        "    # Run an optimization step over a training batch\n",
        "    state = train_epoch(state, data[0], batch_size, epoch, input_rng)\n",
        "    if epoch % 100 == 0:\n",
        "      print('Loss: %.3f' % eval_step(state.params, data[0]))\n",
        "\n",
        "mean = jnp.zeros((batch_size,2))\n",
        "cov = jnp.repeat(jnp.eye(2)[None,:],batch_size,axis=0)\n",
        "gaussian = random.multivariate_normal(key1, mean, cov)\n",
        "samples = model.apply({'params': state.params},gaussian,method=model.inverse)\n"
      ],
      "metadata": {
        "id": "3HA08BUDsFzU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(10,10))\n",
        "plt.scatter(data[0][:,0], data[0][:,1], s=0.1, c='r',label='data')\n",
        "plt.scatter(samples[0][:,0], samples[0][:,1], s=0.1, c='b',label='NF') # x-y is flipped in this configuration\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "e2OAqUbZsJFX",
        "outputId": "aa9c4ec8-34d7-40ef-98af-7986950c69e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "l0cFGUgcsZES"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}