From b92afc95de17d1d6c043e28b147b458942036488 Mon Sep 17 00:00:00 2001 From: stefanradev93 Date: Wed, 23 Oct 2024 19:39:18 -0400 Subject: [PATCH] Adapt examples --- README.md | 4 +- ...ynb => Bayesian_Experimental_Design.ipynb} | 4 +- examples/TwoMoons_ConsistencyModel.ipynb | 704 ------------------ examples/TwoMoons_StarterNotebook.ipynb | 190 +++-- 4 files changed, 95 insertions(+), 807 deletions(-) rename examples/{michaelis_menten_BED_tutorial.ipynb => Bayesian_Experimental_Design.ipynb} (99%) delete mode 100644 examples/TwoMoons_ConsistencyModel.ipynb diff --git a/README.md b/README.md index 17433280..0ea14988 100644 --- a/README.md +++ b/README.md @@ -87,8 +87,8 @@ conda env create --file environment.yaml --name bayesflow Check out some of our walk-through notebooks below. We are actively working on porting all notebooks to the new interface so more will be available soon! -1. [Two moons toy example](examples/TwoMoons_FlowMatching.ipynb) -2. [Bayesian experimental design (BED)](examples/michaelis_menten_BED_tutorial.ipynb) +1. [Two moons starter toy example](examples/TwoMoons_StarterNotebook.ipynb) +2. [Bayesian experimental design (BED)](examples/Bayesian_Experimental_Design.ipynb) 3. Coming soon... ## Documentation \& Help diff --git a/examples/michaelis_menten_BED_tutorial.ipynb b/examples/Bayesian_Experimental_Design.ipynb similarity index 99% rename from examples/michaelis_menten_BED_tutorial.ipynb rename to examples/Bayesian_Experimental_Design.ipynb index a2a13cd9..af875fbc 100644 --- a/examples/michaelis_menten_BED_tutorial.ipynb +++ b/examples/Bayesian_Experimental_Design.ipynb @@ -752,7 +752,7 @@ ], "metadata": { "kernelspec": { - "display_name": "bf-torch", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -766,7 +766,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/examples/TwoMoons_ConsistencyModel.ipynb b/examples/TwoMoons_ConsistencyModel.ipynb deleted file mode 100644 index 7b1efa7c..00000000 --- a/examples/TwoMoons_ConsistencyModel.ipynb +++ /dev/null @@ -1,704 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "009b6adf", - "metadata": {}, - "source": [ - "# Consistency Models for Posterior Estimation\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d5f88a59", - "metadata": { - "ExecuteTime": { - "end_time": "2024-09-23T14:39:46.551814Z", - "start_time": "2024-09-23T14:39:46.032170Z" - } - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import seaborn as sns\n", - "\n", - "# ensure the backend is set\n", - "import os\n", - "if \"KERAS_BACKEND\" not in os.environ:\n", - " # set this to \"torch\", \"tensorflow\", or \"jax\"\n", - " os.environ[\"KERAS_BACKEND\"] = \"jax\"\n", - "\n", - "import keras\n", - "\n", - "# for bayesflow devs: this ensures that the latest dev version can be found\n", - "import sys\n", - "sys.path.append('../')\n", - "\n", - "import bayesflow as bf" - ] - }, - { - "cell_type": "markdown", - "id": "eadaf793-ab63-4f69-b962-178e343ca21b", - "metadata": {}, - "source": [ - "In this notebook, we use Consistency Models (CMs) as a plug-in replacement to obtain posterior samples with fewer sampling steps.\n", - "\n", - "CMs can be trained in two ways: First, they can be used to _distill_ an existing score-based diffusion model, thereby massively decreasing the sampling time at the expense of an additional training phase. Second, they can be trained from scratch using a procedure named _Consistency Training_. For now, we only support the latter.\n" - ] - }, - { - "cell_type": "markdown", - "id": "6286c800-460a-4881-87d8-c3aca7aeec70", - "metadata": {}, - "source": [ - "## Background\n" - ] - }, - { - "cell_type": "markdown", - "id": "fdff817a-6321-4af0-9d41-7ec80097f93b", - "metadata": {}, - "source": [ - "Consistency Models [1] leverage some nice properties of score-based diffusion to enable few-step sampling. Score-based diffusion initially relied on a stochastic differential equation (SDE) for sampling, but there is also a ordinary (non-stochastic) differential equation (ODE) has the same _marginal_ distribution at each time step $t$ [2]. This means that even though SDE and ODE produce different paths from the noise distribution to the target distribution, the resulting distributions when looking at many paths at time $t$ is the same. The ODE is also called Probability Flow ODE.\n" - ] - }, - { - "cell_type": "markdown", - "id": "4a2e996d-355a-4fab-8347-728e563c6014", - "metadata": {}, - "source": [ - "CMs now leverage the fact that there is no randomness in the ODE formulation. That means, if you start at a certain point in the latent space, you will always take the same path and always end up at the same point in the data space. The same is true for every point on the path: if you integrate to get to time $t=0$, you will end up at the same point as well. In short: for each path, there is exactly one corresponding point in latent space (at $t=T$) and one corresponding point in data space (at $t=0$). The goal of CMs is now the following: each point at a time point $t$ belongs to exactly one path, and we want to predict where this path will end up at $t=0$. The function that does this is called the _consistency function_ $f$. If we have the correct function for all $t\\in(0,T]$, we can just sample from the latent distribution ($t=T$) and use $f$ to directly map to the corresponding point at $t=0$, which is in the target distribution. So for sampling from the target distribution, we avoid any integration and only need one evaluation of the consistency function. In practice, the one-step sampling does not work very well. Instead, we leverage a multi-step sampling method where we call $f$ multiple times. Please check out the [1] for more background on this sampling procedure.\n" - ] - }, - { - "cell_type": "markdown", - "id": "25023294-3096-4ebc-83a6-a372208e0504", - "metadata": {}, - "source": [ - "When only reading the above you might wonder why we also learn the mapping to $t=0$ of all intermediate time steps $t\\in[0, T]$, and not only for $t=T$. The main answer is that for efficient training, we do not want to actually compute the two associated points explicitly. Doing so would require to do a precise integration at training time, which is often not feasible as it is too computationally costly. Learning all time steps opens up the possibility for a different training approach where we can avoid this.\n" - ] - }, - { - "cell_type": "markdown", - "id": "9e6eb121-f89f-4268-9a0d-6fa733c758ff", - "metadata": {}, - "source": [ - "The details of this become a bit more complicated, and we advise you to take a look at [1] if you are interested in a more thorough and mathematical discussion. Here we will give a rough description of the underlying concepts.\n" - ] - }, - { - "cell_type": "markdown", - "id": "9b201899-b946-4432-bcac-106b9d580d32", - "metadata": {}, - "source": [ - "First, we know that at $t=0$, it holds that $f(\\theta,t=0)=\\theta$, as $\\theta$ is part of the path that ends at $\\theta$. This _boundary condition_ serves as an \"anchor\" for our training, this is the information that the network knows at the start of the training procedure (we encode it with a time-dependent skip-connection, so the network is forced to be the identity function at $t=0$).\n" - ] - }, - { - "cell_type": "markdown", - "id": "a11f7ac7-9c11-49c1-b29c-3f0d44c4bf49", - "metadata": {}, - "source": [ - "For training, we now somehow have to propagate this information to the rest of the part. The basic idea for this is simple. We just take a point $\\theta_1$ closer to the data distribution (smaller time $t_1$) and integrate for a small time step $dt$ to a point $\\theta_2$ on the same path that is closer to the latent distribution (larger time $t_2=t_1+dt$). As we know that for $t=0$ our network provides the correct output for our path, we want to propagate the information from smaller times to larger times. Our training goal is to move the output of $f(\\theta_2, t=t_2)$ towards the output of $f(\\theta_1, t=t_1)$. How to choose $\\theta_1$, $t_1$ and $dt$ is an empirical question, see the [1] for some thoughts on what works well.\n" - ] - }, - { - "cell_type": "markdown", - "id": "73ff5ed5-dbd0-423b-b4f4-8d209947ed87", - "metadata": {}, - "source": [ - "In the case of _distillation_, we start with a trained score-based diffusion model. We can use it to integrate the Probability Flow ODE to get from $\\theta_1$ to $\\theta_2$. If we do not have such a model, it seems as if we were stuck. We do not know which points lie on the same path, so we do not know which outputs to make similar. Fortunately, it turns out that there is an _unbiased approximator_ that, if averaged over many samples (check out the paper for the exact description), will also give us the correct score. If we use this approximator instead of the score model, and use only a single Euler step to move along the path, we get an algorithm similar to the one described for distillation. It is called Consistency Training (CT) and allows us to train a consistency model using only _samples_ from the data distribution. The algorithm for this was improved a lot in [3], and we have incorporated those improvements into our implementation.\n" - ] - }, - { - "cell_type": "markdown", - "id": "283e8fea-9a36-4f8a-80f5-19e2fe98bd09", - "metadata": {}, - "source": [ - "We have made several approximations to get to a standalone Consistency Training algorithm. As a consequence, the introduced hyperparameters and their choice unfortunately becomes somewhat unintuitive. We have to rely on empirical observations and heuristics to see what works. This was done in [4], we encourage you to use the values provided there as starting points. If you happen to find hyperparameters that work significantly better, please let us know (e.g., by opening an issue or sending an email). This will help others to find the correct region in the hyperparameter space.\n" - ] - }, - { - "cell_type": "markdown", - "id": "8da3535f-0354-40a4-991f-845c33ff75b8", - "metadata": {}, - "source": [ - "To make this work for Bayesian inverse problems in simulation-based inference, we can make the whole process conditional on some quantity $x$, so we can produce conditional distributions as well. Below, you can see a conceptual visualization of posterior estimation with CMs.\n" - ] - }, - { - "cell_type": "markdown", - "id": "6c105be4-c490-4100-9502-60dd7405cfb4", - "metadata": {}, - "source": [ - "![Visualization of the way consistency models map from the path to the end point in the data distribution. Depicts the concepts described in the main text.](https://arxiv.org/html/2312.05440v2/extracted/5435837/figures/cmpe_main.png)\n" - ] - }, - { - "cell_type": "markdown", - "id": "baafb8fd-5b14-4ddf-a6dd-8272f706deaa", - "metadata": {}, - "source": [ - "### References\n", - "\n", - "[1] Song, Y., Dhariwal, P., Chen, M., & Sutskever, I. (2023). Consistency Models. _arXiv preprint_. [https://doi.org/10.48550/arXiv.2303.01469](https://doi.org/10.48550/arXiv.2303.01469)\n", - "\n", - "[2] Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., & Poole, B. (2021). Score-Based Generative Modeling through Stochastic Differential Equations. In _International Conference on Learning Representations_. [https://openreview.net/forum?id=PxTIG12RRHS](https://openreview.net/forum?id=PxTIG12RRHS)\n", - "\n", - "[3] Song, Y., & Dhariwal, P. (2023). Improved Techniques for Training Consistency Models. _arXiv preprint_. [https://doi.org/10.48550/arXiv.2310.14189](https://doi.org/10.48550/arXiv.2310.14189)\n", - "\n", - "[4] Schmitt, M., Pratz, V., Köthe, U., Bürkner, P.-C., & Radev, S. T. (2024). Consistency Models for Scalable and Fast Simulation-Based Inference. _arXiv preprint_. [https://doi.org/10.48550/arXiv.2312.05440](https://doi.org/10.48550/arXiv.2312.05440)\n" - ] - }, - { - "cell_type": "markdown", - "id": "c63b26ba", - "metadata": {}, - "source": [ - "## Simulator: Two Moons\n" - ] - }, - { - "cell_type": "markdown", - "id": "9525ffd7", - "metadata": {}, - "source": [ - "We will use the Concistency Model as a plug-in replacement for Flow Matching. Check out the tutorial \"Two moons toy example with flow matching\" for more details on the simulator and setting.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4b89c861527c13b8", - "metadata": { - "ExecuteTime": { - "end_time": "2024-09-23T14:39:46.747091Z", - "start_time": "2024-09-23T14:39:46.744830Z" - } - }, - "outputs": [], - "source": [ - "simulator = bf.benchmarks.simulators.TwoMoons()" - ] - }, - { - "cell_type": "markdown", - "id": "f6e1eb5777c59eba", - "metadata": {}, - "source": [ - "We generate some data to see what the simulator does:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e6218e61d529e357", - "metadata": { - "ExecuteTime": { - "end_time": "2024-09-23T14:39:46.798575Z", - "start_time": "2024-09-23T14:39:46.790581Z" - } - }, - "outputs": [], - "source": [ - "# generate 64 random draws from the joint distribution p(r, alpha, theta, x)\n", - "sample_data = simulator.sample((64,))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "46174ccb0167026c", - "metadata": { - "ExecuteTime": { - "end_time": "2024-09-23T14:39:46.854911Z", - "start_time": "2024-09-23T14:39:46.852129Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Type of sample_data:\n", - "\t \n", - "Keys of sample_data:\n", - "\t dict_keys(['parameters', 'observables'])\n", - "Types of sample_data values:\n", - "\t {'parameters': , 'observables': }\n", - "Shapes of sample_data values:\n", - "\t {'parameters': (64, 2), 'observables': (64, 2)}\n" - ] - } - ], - "source": [ - "print(\"Type of sample_data:\\n\\t\", type(sample_data))\n", - "print(\"Keys of sample_data:\\n\\t\", sample_data.keys())\n", - "print(\"Types of sample_data values:\\n\\t\", {k: type(v) for k, v in sample_data.items()})\n", - "print(\"Shapes of sample_data values:\\n\\t\", {k: v.shape for k, v in sample_data.items()})" - ] - }, - { - "cell_type": "markdown", - "id": "fee88fcfd7a373b0", - "metadata": {}, - "source": [ - "## Data Adapter\n", - "\n", - "The next step is to tell BayesFlow how to deal with the simulated variables. You may also think of this as informing BayesFlow about the data flow, i.e., which variables go into which network.\n", - "\n", - "For this example, we want to learn the posterior distribution $p(\\theta\\,|\\,x)$, so we **infer** $\\theta$, **conditioning** on $x$. In the output from the last command, we see that the simulator provides $\\theta$ as `\"parameters\"` and $x$ as `\"observables\"`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c9637c576d4ad4e5", - "metadata": { - "ExecuteTime": { - "end_time": "2024-09-23T14:39:46.905081Z", - "start_time": "2024-09-23T14:39:46.903091Z" - } - }, - "outputs": [], - "source": [ - "data_adapter = bf.ContinuousApproximator.build_data_adapter(\n", - " inference_variables=[\"parameters\"],\n", - " inference_conditions=[\"observables\"],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "254e287b2bccdad", - "metadata": {}, - "source": [ - "## Dataset\n", - "\n", - "For this example, we will sample our training data ahead of time and use offline training with a `bf.datasets.OfflineDataset`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "39cb5a1c9824246f", - "metadata": { - "ExecuteTime": { - "end_time": "2024-09-23T14:39:46.950573Z", - "start_time": "2024-09-23T14:39:46.948624Z" - } - }, - "outputs": [], - "source": [ - "batch_size = 64\n", - "num_training_batches = 512\n", - "num_validation_batches = 128" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9dee7252ef99affa", - "metadata": { - "ExecuteTime": { - "end_time": "2024-09-23T14:39:53.268860Z", - "start_time": "2024-09-23T14:39:46.994697Z" - } - }, - "outputs": [], - "source": [ - "training_samples = simulator.sample((num_training_batches * batch_size,))\n", - "validation_samples = simulator.sample((num_validation_batches * batch_size,))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "51045bbed88cb5c2", - "metadata": { - "ExecuteTime": { - "end_time": "2024-09-23T14:39:53.281170Z", - "start_time": "2024-09-23T14:39:53.275921Z" - } - }, - "outputs": [], - "source": [ - "training_dataset = bf.datasets.OfflineDataset(training_samples, batch_size=batch_size, data_adapter=data_adapter)\n", - "validation_dataset = bf.datasets.OfflineDataset(validation_samples, batch_size=batch_size, data_adapter=data_adapter)" - ] - }, - { - "cell_type": "markdown", - "id": "2d4c6eb0", - "metadata": {}, - "source": [ - "## Training a neural network to approximate all posteriors\n", - "\n", - "The next step is to set up the neural network that will approximate the posterior $p(\\theta\\,|\\,x)$.\n", - "\n", - "Consistency models use _scheduling functions_ to adjust some of the hyperparameters, for example the time discretization during training. Consequently, we have to specify the total number of training steps (_gradient updates_) before the start of the training.\n", - "For offline training with a given number of epochs, we can calculate it as below:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "516ac3c4-b66f-4cf0-a443-b00705a6ace5", - "metadata": {}, - "outputs": [], - "source": [ - "epochs = 30\n", - "total_steps = epochs * num_training_batches" - ] - }, - { - "cell_type": "markdown", - "id": "2c7980ec-5623-43e3-847e-16a5b6eb8777", - "metadata": {}, - "source": [ - "Apart from the usual parameters like learning rate and batch size, CMs come with a number of different hyperparameters. Unfortunately, they can heavily interact, so they can be hard to tune. The main hyperparameters are:\n", - "\n", - "- Maximum time `max_time`: This also serves as the standard deviation of the latent distribution. You can experiment with this, values from 10-200 seem to work well. In any case, it should be larger than the standard deviation of the target distribution.\n", - "- Minimum/maximum number of discretization steps during training `s0`/`s1`: The effect of those is hard to grasp. 10 works well for `s0`. Intuitively, increasing `s1` along with the number of epochs should lead to better result, but in practice we sometimes observe a breakdown for high values of `s1`. This seems to be problem-dependent, so just try it out.\n", - "- `sigma2` modifies the time-dependency of the skip connection. Its effect on the training is unclear, we recommend leaving it at 1.0 or setting it to the approximate variance of the target distribution.\n", - "- Smallest time value `eps` ($t=\\epsilon$ is used instead of $t=0$ for numerical reasons): No large effect in our experiments, as long as it is kept small enough. Probably not worth tuning.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "09206e6f", - "metadata": { - "ExecuteTime": { - "end_time": "2024-09-23T14:39:53.339590Z", - "start_time": "2024-09-23T14:39:53.319852Z" - } - }, - "outputs": [], - "source": [ - "# Compute the empirical variance of the draws from the prior θ ~ p(θ)\n", - "sigma2 = keras.ops.var(training_samples[\"parameters\"], axis=0, keepdims=True)\n", - "\n", - "inference_network = bf.networks.ConsistencyModel(\n", - " subnet=\"mlp\",\n", - " subnet_kwargs=dict(\n", - " depth=6,\n", - " width=256,\n", - " ),\n", - " total_steps = total_steps,\n", - " max_time=10, # works well for this task\n", - " sigma2=sigma2, # pass the empirical variance to the network\n", - " # the remaining hyperparameters (s0, s1, eps) are the default values\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "851e522f", - "metadata": {}, - "source": [ - "This inference network is just a general consistency model architecture, not yet adapted to the specific inference task at hand (i.e., posterior appproximation). To achieve this adaptation, we combine the network with our data adapter, which together form an `approximator`. In this case, we need a `ContinuousApproximator` since the target we want to approximate is the posterior of the _continuous_ parameter vector $\\theta$.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "96ca6ffa", - "metadata": { - "ExecuteTime": { - "end_time": "2024-09-23T14:39:53.371691Z", - "start_time": "2024-09-23T14:39:53.369375Z" - } - }, - "outputs": [], - "source": [ - "approximator = bf.ContinuousApproximator(\n", - " inference_network=inference_network,\n", - " data_adapter=data_adapter,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "566264eadc76c2c", - "metadata": {}, - "source": [ - "### Optimizer and Learning Rate\n", - "\n", - "We use an Adam optimizer with [cosine decay](https://keras.io/api/optimizers/learning_rate_schedules/cosine_decay/) to decrease the learning rate towards zero over the training time.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "e8d7e053", - "metadata": { - "ExecuteTime": { - "end_time": "2024-09-23T14:39:53.433012Z", - "start_time": "2024-09-23T14:39:53.415903Z" - } - }, - "outputs": [], - "source": [ - "initial_learning_rate = 5e-4\n", - "scheduled_lr = keras.optimizers.schedules.CosineDecay(\n", - " initial_learning_rate,\n", - " total_steps,\n", - ")\n", - "\n", - "optimizer = keras.optimizers.Adam(learning_rate=scheduled_lr)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "51808fcd560489ac", - "metadata": { - "ExecuteTime": { - "end_time": "2024-09-23T14:39:53.476089Z", - "start_time": "2024-09-23T14:39:53.466001Z" - } - }, - "outputs": [], - "source": [ - "approximator.compile(optimizer=optimizer)" - ] - }, - { - "cell_type": "markdown", - "id": "708b1303", - "metadata": {}, - "source": [ - "### Training\n", - "\n", - "We are ready to train our deep posterior approximator on the two moons example. We pass the dataset object to the `fit` method and watch as bayesflow trains. This notebook is being executed on a consumer-grade CPU and training is still reasonably fast. If you have a GPU available, training will be even faster, especially for larger networks.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "0f496bda", - "metadata": { - "ExecuteTime": { - "end_time": "2024-09-23T14:42:36.067393Z", - "start_time": "2024-09-23T14:39:53.513436Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:bayesflow:Fitting on dataset instance of OfflineDataset.\n", - "INFO:bayesflow:Building on a test batch.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 6ms/step - loss: 0.4117 - loss/inference_loss: 0.4117 - val_loss: 0.3387 - val_loss/inference_loss: 0.3387\n", - "Epoch 2/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3568 - loss/inference_loss: 0.3568 - val_loss: 0.3603 - val_loss/inference_loss: 0.3603\n", - "Epoch 3/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3503 - loss/inference_loss: 0.3503 - val_loss: 0.2898 - val_loss/inference_loss: 0.2898\n", - "Epoch 4/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3362 - loss/inference_loss: 0.3362 - val_loss: 0.4429 - val_loss/inference_loss: 0.4429\n", - "Epoch 5/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3343 - loss/inference_loss: 0.3343 - val_loss: 0.3929 - val_loss/inference_loss: 0.3929\n", - "Epoch 6/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3311 - loss/inference_loss: 0.3311 - val_loss: 0.2825 - val_loss/inference_loss: 0.2825\n", - "Epoch 7/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3264 - loss/inference_loss: 0.3264 - val_loss: 0.3029 - val_loss/inference_loss: 0.3029\n", - "Epoch 8/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3222 - loss/inference_loss: 0.3222 - val_loss: 0.3447 - val_loss/inference_loss: 0.3447\n", - "Epoch 9/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3222 - loss/inference_loss: 0.3222 - val_loss: 0.3209 - val_loss/inference_loss: 0.3209\n", - "Epoch 10/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3141 - loss/inference_loss: 0.3141 - val_loss: 0.2195 - val_loss/inference_loss: 0.2195\n", - "Epoch 11/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3123 - loss/inference_loss: 0.3123 - val_loss: 0.3043 - val_loss/inference_loss: 0.3043\n", - "Epoch 12/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3121 - loss/inference_loss: 0.3121 - val_loss: 0.3225 - val_loss/inference_loss: 0.3225\n", - "Epoch 13/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3089 - loss/inference_loss: 0.3089 - val_loss: 0.2082 - val_loss/inference_loss: 0.2082\n", - "Epoch 14/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3028 - loss/inference_loss: 0.3028 - val_loss: 0.2394 - val_loss/inference_loss: 0.2394\n", - "Epoch 15/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.2996 - loss/inference_loss: 0.2996 - val_loss: 0.3735 - val_loss/inference_loss: 0.3735\n", - "Epoch 16/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.2949 - loss/inference_loss: 0.2949 - val_loss: 0.2624 - val_loss/inference_loss: 0.2624\n", - "Epoch 17/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.2956 - loss/inference_loss: 0.2956 - val_loss: 0.3925 - val_loss/inference_loss: 0.3925\n", - "Epoch 18/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.2904 - loss/inference_loss: 0.2904 - val_loss: 0.2991 - val_loss/inference_loss: 0.2991\n", - "Epoch 19/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.2953 - loss/inference_loss: 0.2953 - val_loss: 0.2517 - val_loss/inference_loss: 0.2517\n", - "Epoch 20/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.2867 - loss/inference_loss: 0.2867 - val_loss: 0.3187 - val_loss/inference_loss: 0.3187\n", - "Epoch 21/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.2880 - loss/inference_loss: 0.2880 - val_loss: 0.3218 - val_loss/inference_loss: 0.3218\n", - "Epoch 22/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.2819 - loss/inference_loss: 0.2819 - val_loss: 0.2689 - val_loss/inference_loss: 0.2689\n", - "Epoch 23/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.2775 - loss/inference_loss: 0.2775 - val_loss: 0.2354 - val_loss/inference_loss: 0.2354\n", - "Epoch 24/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.2848 - loss/inference_loss: 0.2848 - val_loss: 0.2992 - val_loss/inference_loss: 0.2992\n", - "Epoch 25/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.2699 - loss/inference_loss: 0.2699 - val_loss: 0.1976 - val_loss/inference_loss: 0.1976\n", - "Epoch 26/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.2760 - loss/inference_loss: 0.2760 - val_loss: 0.3003 - val_loss/inference_loss: 0.3003\n", - "Epoch 27/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.2774 - loss/inference_loss: 0.2774 - val_loss: 0.3333 - val_loss/inference_loss: 0.3333\n", - "Epoch 28/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.2745 - loss/inference_loss: 0.2745 - val_loss: 0.2938 - val_loss/inference_loss: 0.2938\n", - "Epoch 29/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.2795 - loss/inference_loss: 0.2795 - val_loss: 0.2968 - val_loss/inference_loss: 0.2968\n", - "Epoch 30/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.2720 - loss/inference_loss: 0.2720 - val_loss: 0.2105 - val_loss/inference_loss: 0.2105\n" - ] - } - ], - "source": [ - "history = approximator.fit(\n", - " epochs=epochs,\n", - " dataset=training_dataset,\n", - " validation_data=validation_dataset,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "b90a6062", - "metadata": {}, - "source": [ - "## Validation\n" - ] - }, - { - "cell_type": "markdown", - "id": "ca62b21d", - "metadata": {}, - "source": [ - "### Two Moons Posterior\n", - "\n", - "By design, the two moons posterior at point $x = (0, 0)$ should resemble two crescent moons, hence the name. Below, we plot the corresponding posterior samples.\n", - "\n", - "These results suggest that our consistency model posterior estimation setup can approximate the target posterior well. You can achieve an even better fit if you use online training, more epochs, or better hyperparameters. We won't do that here because this tutorial shall only illustrate the basic setup for consistency models in amortized inference with bayesflow.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "8562caeb", - "metadata": { - "ExecuteTime": { - "end_time": "2024-09-23T14:42:38.584554Z", - "start_time": "2024-09-23T14:42:36.076923Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(-0.4, 0.4)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Set the number of posterior draws you want to get\n", - "num_samples = 3000\n", - "\n", - "# Obtain samples from amortized posterior\n", - "conditions = {\"observables\": np.array([[0.0, 0.0]]).astype(\"float32\")}\n", - "samples_at_origin = approximator.sample(conditions=conditions, num_samples=num_samples)[\"parameters\"]\n", - "\n", - "# Prepare figure\n", - "f, axes = plt.subplots(1, figsize=(6, 6))\n", - "\n", - "# Plot samples\n", - "axes.scatter(samples_at_origin[0, :, 0], samples_at_origin[0, :, 1], color=\"#153c7a\", alpha=0.75, s=0.5)\n", - "sns.despine(ax=axes)\n", - "axes.set_title(r\"Posterior samples at origin $x=(0, 0)$\")\n", - "axes.grid(alpha=0.3)\n", - "axes.set_aspect(\"equal\", adjustable=\"box\")\n", - "axes.set_xlim([-0.4, 0.4])\n", - "axes.set_ylim([-0.4, 0.4])" - ] - }, - { - "cell_type": "markdown", - "id": "01821d24", - "metadata": {}, - "source": [ - "The posterior looks as we have expected in this case. However, in general, we do not know how the posterior is supposed to look like for any specific dataset. As such, we need diagnostics that validate the correctness of the inferred posterior. One such diagnostic is simulation-based calibration (SBC), which we can compute essentially for free due to amortization. For more details on SBC and diagnostic plots, see:\n", - "\n", - "1. Talts, S., Betancourt, M., Simpson, D., Vehtari, A., & Gelman, A. (2018). Validating Bayesian inference algorithms with simulation-based calibration. _arXiv preprint_.\n", - "2. Säilynoja, T., Bürkner, P. C., & Vehtari, A. (2022). Graphical test for discrete uniformity and its applications in goodness-of-fit evaluation and multiple sample comparison. _Statistics and Computing_.\n" - ] - }, - { - "cell_type": "markdown", - "id": "cb38d0c8", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "bayesflow", - "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": "3.11.9" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": true, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": true, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "165px" - }, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/TwoMoons_StarterNotebook.ipynb b/examples/TwoMoons_StarterNotebook.ipynb index 8151755e..299c3094 100644 --- a/examples/TwoMoons_StarterNotebook.ipynb +++ b/examples/TwoMoons_StarterNotebook.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 15, "id": "d5f88a59", "metadata": { "ExecuteTime": { @@ -18,21 +18,13 @@ "start_time": "2024-09-23T14:39:46.032170Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn as sns\n", "\n", - "# ensure the backend is set\n", + "# Ensure the backend is set\n", "import os\n", "if \"KERAS_BACKEND\" not in os.environ:\n", " # set this to \"torch\", \"tensorflow\", or \"jax\"\n", @@ -40,7 +32,7 @@ "\n", "import keras\n", "\n", - "# for BayesFlow devs: this ensures that the latest dev version can be found\n", + "# For BayesFlow devs: this ensures that the latest dev version can be found\n", "import sys\n", "sys.path.append('../')\n", "\n", @@ -90,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 16, "id": "f761b142a0e1da66", "metadata": { "ExecuteTime": { @@ -122,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 17, "id": "4b89c861527c13b8", "metadata": { "ExecuteTime": { @@ -145,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 18, "id": "e6218e61d529e357", "metadata": { "ExecuteTime": { @@ -161,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 19, "id": "46174ccb0167026c", "metadata": { "ExecuteTime": { @@ -211,12 +203,12 @@ "\n", "For this example, we want to learn the posterior distribution $p(\\theta | x)$, so we **infer** $\\theta$, **conditioning** on $x$.\n", "\n", - "First, we rename the raw simulator outputs so that trhe neural networks know how interpret them: the $\\theta$ vectors becomes the variables to be inferred (i.e., `inference_variables`) and the $\\x$ vector is designated as the variables to use as conditions (i.e., `inference_conditions`). " + "First, we rename the raw simulator outputs so that trhe neural networks know how interpret them: the $\\theta$ vectors becomes the variables to be inferred (i.e., `inference_variables`) and the $x$ vector is designated as the variables to use as conditions (i.e., `inference_conditions`). " ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "id": "c9637c576d4ad4e5", "metadata": { "ExecuteTime": { @@ -248,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 21, "id": "39cb5a1c9824246f", "metadata": { "ExecuteTime": { @@ -267,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 22, "id": "9dee7252ef99affa", "metadata": { "ExecuteTime": { @@ -283,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 23, "id": "51045bbed88cb5c2", "metadata": { "ExecuteTime": { @@ -324,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 24, "id": "09206e6f", "metadata": { "ExecuteTime": { @@ -336,7 +328,7 @@ "source": [ "inference_network = bf.networks.FlowMatching(\n", " subnet=\"mlp\", \n", - " subnet_kwargs={\"depth\": 6, \"width\": 256}\n", + " subnet_kwargs={\"widths\": (256,)*6} # use an inner network with 6 hidden layers of 256 units\n", ")" ] }, @@ -350,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 25, "id": "96ca6ffa", "metadata": { "ExecuteTime": { @@ -377,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 26, "id": "e8d7e053", "metadata": { "ExecuteTime": { @@ -399,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 27, "id": "51808fcd560489ac", "metadata": { "ExecuteTime": { @@ -424,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 28, "id": "0f496bda", "metadata": { "ExecuteTime": { @@ -446,67 +438,67 @@ "output_type": "stream", "text": [ "Epoch 1/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - loss: 0.4254 - loss/inference_loss: 0.4254 - val_loss: 0.4950 - val_loss/inference_loss: 0.4950\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - loss: 0.4225 - loss/inference_loss: 0.4225 - val_loss: 0.3897 - val_loss/inference_loss: 0.3897\n", "Epoch 2/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.3796 - loss/inference_loss: 0.3796 - val_loss: 0.3187 - val_loss/inference_loss: 0.3187\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.3764 - loss/inference_loss: 0.3764 - val_loss: 0.2469 - val_loss/inference_loss: 0.2469\n", "Epoch 3/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - loss: 0.3694 - loss/inference_loss: 0.3694 - val_loss: 0.3467 - val_loss/inference_loss: 0.3467\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.3680 - loss/inference_loss: 0.3680 - val_loss: 0.3456 - val_loss/inference_loss: 0.3456\n", "Epoch 4/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - loss: 0.3581 - loss/inference_loss: 0.3581 - val_loss: 0.4014 - val_loss/inference_loss: 0.4014\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.3596 - loss/inference_loss: 0.3596 - val_loss: 0.3564 - val_loss/inference_loss: 0.3564\n", "Epoch 5/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - loss: 0.3489 - loss/inference_loss: 0.3489 - val_loss: 0.3101 - val_loss/inference_loss: 0.3101\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.3558 - loss/inference_loss: 0.3558 - val_loss: 0.3258 - val_loss/inference_loss: 0.3258\n", "Epoch 6/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - loss: 0.3515 - loss/inference_loss: 0.3515 - val_loss: 0.3398 - val_loss/inference_loss: 0.3398\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - loss: 0.3507 - loss/inference_loss: 0.3507 - val_loss: 0.2755 - val_loss/inference_loss: 0.2755\n", "Epoch 7/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - loss: 0.3528 - loss/inference_loss: 0.3528 - val_loss: 0.3643 - val_loss/inference_loss: 0.3643\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.3450 - loss/inference_loss: 0.3450 - val_loss: 0.3038 - val_loss/inference_loss: 0.3038\n", "Epoch 8/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3402 - loss/inference_loss: 0.3402 - val_loss: 0.2596 - val_loss/inference_loss: 0.2596\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.3391 - loss/inference_loss: 0.3391 - val_loss: 0.2291 - val_loss/inference_loss: 0.2291\n", "Epoch 9/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3398 - loss/inference_loss: 0.3398 - val_loss: 0.4423 - val_loss/inference_loss: 0.4423\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.3451 - loss/inference_loss: 0.3451 - val_loss: 0.3416 - val_loss/inference_loss: 0.3416\n", "Epoch 10/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3418 - loss/inference_loss: 0.3418 - val_loss: 0.3876 - val_loss/inference_loss: 0.3876\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.3408 - loss/inference_loss: 0.3408 - val_loss: 0.2305 - val_loss/inference_loss: 0.2305\n", "Epoch 11/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3410 - loss/inference_loss: 0.3410 - val_loss: 0.2288 - val_loss/inference_loss: 0.2288\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.3306 - loss/inference_loss: 0.3306 - val_loss: 0.3630 - val_loss/inference_loss: 0.3630\n", "Epoch 12/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3311 - loss/inference_loss: 0.3311 - val_loss: 0.2649 - val_loss/inference_loss: 0.2649\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.3383 - loss/inference_loss: 0.3383 - val_loss: 0.4263 - val_loss/inference_loss: 0.4263\n", "Epoch 13/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3270 - loss/inference_loss: 0.3270 - val_loss: 0.3067 - val_loss/inference_loss: 0.3067\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.3296 - loss/inference_loss: 0.3296 - val_loss: 0.3179 - val_loss/inference_loss: 0.3179\n", "Epoch 14/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - loss: 0.3285 - loss/inference_loss: 0.3285 - val_loss: 0.4922 - val_loss/inference_loss: 0.4922\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.3309 - loss/inference_loss: 0.3309 - val_loss: 0.6036 - val_loss/inference_loss: 0.6036\n", "Epoch 15/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - loss: 0.3292 - loss/inference_loss: 0.3292 - val_loss: 0.2729 - val_loss/inference_loss: 0.2729\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.3280 - loss/inference_loss: 0.3280 - val_loss: 0.3043 - val_loss/inference_loss: 0.3043\n", "Epoch 16/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3288 - loss/inference_loss: 0.3288 - val_loss: 0.4003 - val_loss/inference_loss: 0.4003\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.3322 - loss/inference_loss: 0.3322 - val_loss: 0.2144 - val_loss/inference_loss: 0.2144\n", "Epoch 17/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3322 - loss/inference_loss: 0.3322 - val_loss: 0.2052 - val_loss/inference_loss: 0.2052\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.3170 - loss/inference_loss: 0.3170 - val_loss: 0.3984 - val_loss/inference_loss: 0.3984\n", "Epoch 18/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3218 - loss/inference_loss: 0.3218 - val_loss: 0.3458 - val_loss/inference_loss: 0.3458\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.3236 - loss/inference_loss: 0.3236 - val_loss: 0.3907 - val_loss/inference_loss: 0.3907\n", "Epoch 19/30\n", - "\u001b[1m512/512\u001b[0m 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6ms/step - loss: 0.3220 - loss/inference_loss: 0.3220 - val_loss: 0.3217 - val_loss/inference_loss: 0.3217\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.3175 - loss/inference_loss: 0.3175 - val_loss: 0.3143 - val_loss/inference_loss: 0.3143\n", "Epoch 22/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3233 - loss/inference_loss: 0.3233 - val_loss: 0.3606 - val_loss/inference_loss: 0.3606\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.3202 - loss/inference_loss: 0.3202 - val_loss: 0.2706 - val_loss/inference_loss: 0.2706\n", "Epoch 23/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3169 - loss/inference_loss: 0.3169 - val_loss: 0.2697 - val_loss/inference_loss: 0.2697\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.3138 - loss/inference_loss: 0.3138 - val_loss: 0.3042 - val_loss/inference_loss: 0.3042\n", "Epoch 24/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3141 - loss/inference_loss: 0.3141 - val_loss: 0.2131 - val_loss/inference_loss: 0.2131\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.3097 - loss/inference_loss: 0.3097 - val_loss: 0.2372 - val_loss/inference_loss: 0.2372\n", "Epoch 25/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3126 - loss/inference_loss: 0.3126 - val_loss: 0.3206 - val_loss/inference_loss: 0.3206\n", + "\u001b[1m512/512\u001b[0m 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3ms/step - loss: 0.3126 - loss/inference_loss: 0.3126 - val_loss: 0.3095 - val_loss/inference_loss: 0.3095\n", "Epoch 28/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3095 - loss/inference_loss: 0.3095 - val_loss: 0.3211 - val_loss/inference_loss: 0.3211\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.3100 - loss/inference_loss: 0.3100 - val_loss: 0.3371 - val_loss/inference_loss: 0.3371\n", "Epoch 29/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3117 - loss/inference_loss: 0.3117 - val_loss: 0.2739 - val_loss/inference_loss: 0.2739\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.3066 - loss/inference_loss: 0.3066 - val_loss: 0.2807 - val_loss/inference_loss: 0.2807\n", "Epoch 30/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.3107 - loss/inference_loss: 0.3107 - val_loss: 0.2902 - val_loss/inference_loss: 0.2902\n", - "CPU times: user 2min 37s, sys: 5.22 s, total: 2min 42s\n", - "Wall time: 1min 26s\n" + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.3071 - loss/inference_loss: 0.3071 - val_loss: 0.3870 - val_loss/inference_loss: 0.3870\n", + "CPU times: total: 8.27 s\n", + "Wall time: 55.5 s\n" ] } ], @@ -590,7 +582,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 29, "id": "d53a41b8", "metadata": {}, "outputs": [], @@ -600,7 +592,7 @@ "\n", "inference_network = bf.networks.ConsistencyModel(\n", " subnet=\"mlp\",\n", - " subnet_kwargs={\"depth\": 6, \"width\": 256},\n", + " subnet_kwargs={\"widths\": (256,)*6},\n", " total_steps=total_steps,\n", " max_time=10,\n", " sigma2=sigma2,\n", @@ -623,7 +615,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 30, "id": "d1bc228a", "metadata": {}, "outputs": [], @@ -640,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 31, "id": "41c4599f", "metadata": {}, "outputs": [], @@ -658,7 +650,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 32, "id": "c3c1a812", "metadata": {}, "outputs": [ @@ -675,67 +667,67 @@ "output_type": "stream", "text": [ "Epoch 1/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - loss: 0.3980 - loss/inference_loss: 0.3980 - val_loss: 0.4073 - val_loss/inference_loss: 0.4073\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - loss: 0.3919 - loss/inference_loss: 0.3919 - val_loss: 0.2735 - val_loss/inference_loss: 0.2735\n", "Epoch 2/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 8ms/step - loss: 0.3501 - loss/inference_loss: 0.3501 - val_loss: 0.2820 - val_loss/inference_loss: 0.2820\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.3469 - loss/inference_loss: 0.3469 - val_loss: 0.3136 - val_loss/inference_loss: 0.3136\n", "Epoch 3/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - loss: 0.3351 - loss/inference_loss: 0.3351 - val_loss: 0.2432 - val_loss/inference_loss: 0.2432\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.3342 - loss/inference_loss: 0.3342 - val_loss: 0.3803 - val_loss/inference_loss: 0.3803\n", "Epoch 4/30\n", - "\u001b[1m512/512\u001b[0m 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- loss: 0.3180 - loss/inference_loss: 0.3180 - val_loss: 0.3263 - val_loss/inference_loss: 0.3263\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.3218 - loss/inference_loss: 0.3218 - val_loss: 0.4023 - val_loss/inference_loss: 0.4023\n", "Epoch 7/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - loss: 0.3177 - loss/inference_loss: 0.3177 - val_loss: 0.2487 - val_loss/inference_loss: 0.2487\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.3107 - loss/inference_loss: 0.3107 - val_loss: 0.1864 - val_loss/inference_loss: 0.1864\n", "Epoch 8/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - loss: 0.3114 - loss/inference_loss: 0.3114 - val_loss: 0.2381 - val_loss/inference_loss: 0.2381\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.3154 - loss/inference_loss: 0.3154 - val_loss: 0.2744 - val_loss/inference_loss: 0.2744\n", "Epoch 9/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - loss: 0.3089 - loss/inference_loss: 0.3089 - val_loss: 0.2677 - val_loss/inference_loss: 0.2677\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.3116 - loss/inference_loss: 0.3116 - val_loss: 0.2823 - val_loss/inference_loss: 0.2823\n", "Epoch 10/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - loss: 0.3089 - loss/inference_loss: 0.3089 - val_loss: 0.3307 - val_loss/inference_loss: 0.3307\n", + "\u001b[1m512/512\u001b[0m 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4ms/step - loss: 0.3019 - loss/inference_loss: 0.3019 - val_loss: 0.1924 - val_loss/inference_loss: 0.1924\n", "Epoch 13/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - loss: 0.3012 - loss/inference_loss: 0.3012 - val_loss: 0.2679 - val_loss/inference_loss: 0.2679\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.2984 - loss/inference_loss: 0.2984 - val_loss: 0.3208 - val_loss/inference_loss: 0.3208\n", "Epoch 14/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - loss: 0.3009 - loss/inference_loss: 0.3009 - val_loss: 0.2093 - val_loss/inference_loss: 0.2093\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.2991 - loss/inference_loss: 0.2991 - val_loss: 0.2844 - val_loss/inference_loss: 0.2844\n", "Epoch 15/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - loss: 0.2975 - loss/inference_loss: 0.2975 - val_loss: 0.2663 - val_loss/inference_loss: 0.2663\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.2912 - loss/inference_loss: 0.2912 - val_loss: 0.2385 - val_loss/inference_loss: 0.2385\n", "Epoch 16/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - loss: 0.2949 - loss/inference_loss: 0.2949 - val_loss: 0.2288 - val_loss/inference_loss: 0.2288\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.2955 - loss/inference_loss: 0.2955 - val_loss: 0.1594 - val_loss/inference_loss: 0.1594\n", "Epoch 17/30\n", - "\u001b[1m512/512\u001b[0m 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val_loss/inference_loss: 0.1906\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.2834 - loss/inference_loss: 0.2834 - val_loss: 0.2057 - val_loss/inference_loss: 0.2057\n", "Epoch 22/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - loss: 0.2882 - loss/inference_loss: 0.2882 - val_loss: 0.2277 - val_loss/inference_loss: 0.2277\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.2812 - loss/inference_loss: 0.2812 - val_loss: 0.2432 - val_loss/inference_loss: 0.2432\n", "Epoch 23/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - loss: 0.2857 - loss/inference_loss: 0.2857 - val_loss: 0.1923 - val_loss/inference_loss: 0.1923\n", + "\u001b[1m512/512\u001b[0m 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4ms/step - loss: 0.2859 - loss/inference_loss: 0.2859 - val_loss: 0.2324 - val_loss/inference_loss: 0.2324\n", "Epoch 26/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - loss: 0.2812 - loss/inference_loss: 0.2812 - val_loss: 0.2629 - val_loss/inference_loss: 0.2629\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.2804 - loss/inference_loss: 0.2804 - val_loss: 0.3024 - val_loss/inference_loss: 0.3024\n", "Epoch 27/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - loss: 0.2806 - loss/inference_loss: 0.2806 - val_loss: 0.3617 - val_loss/inference_loss: 0.3617\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.2836 - loss/inference_loss: 0.2836 - val_loss: 0.1902 - val_loss/inference_loss: 0.1902\n", "Epoch 28/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - loss: 0.2853 - loss/inference_loss: 0.2853 - val_loss: 0.2874 - val_loss/inference_loss: 0.2874\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.2814 - loss/inference_loss: 0.2814 - val_loss: 0.1490 - val_loss/inference_loss: 0.1490\n", "Epoch 29/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - loss: 0.2754 - loss/inference_loss: 0.2754 - val_loss: 0.2088 - val_loss/inference_loss: 0.2088\n", + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.2762 - loss/inference_loss: 0.2762 - val_loss: 0.2249 - val_loss/inference_loss: 0.2249\n", "Epoch 30/30\n", - "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - loss: 0.2849 - loss/inference_loss: 0.2849 - val_loss: 0.2632 - val_loss/inference_loss: 0.2632\n", - "CPU times: user 5min 50s, sys: 1min, total: 6min 50s\n", - "Wall time: 2min 15s\n" + "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.2805 - loss/inference_loss: 0.2805 - val_loss: 0.1796 - val_loss/inference_loss: 0.1796\n", + "CPU times: total: 6.89 s\n", + "Wall time: 1min 7s\n" ] } ], @@ -770,13 +762,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 33, "id": "073bcd0b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -857,7 +849,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.5" }, "toc": { "base_numbering": 1,