From f6211b088f60be8f19fd0481c3a5b7d1e9578399 Mon Sep 17 00:00:00 2001 From: jburnim Date: Tue, 13 Feb 2024 11:37:00 -0800 Subject: [PATCH] Update tf.optimizers -> tf_keras.optimizers in STS example notebook. PiperOrigin-RevId: 606694059 --- ...ies_Atmospheric_CO2_and_Electricity_Demand.ipynb | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/tensorflow_probability/examples/jupyter_notebooks/Structural_Time_Series_Modeling_Case_Studies_Atmospheric_CO2_and_Electricity_Demand.ipynb b/tensorflow_probability/examples/jupyter_notebooks/Structural_Time_Series_Modeling_Case_Studies_Atmospheric_CO2_and_Electricity_Demand.ipynb index ba6aa756a9..55b3f93d12 100644 --- a/tensorflow_probability/examples/jupyter_notebooks/Structural_Time_Series_Modeling_Case_Studies_Atmospheric_CO2_and_Electricity_Demand.ipynb +++ b/tensorflow_probability/examples/jupyter_notebooks/Structural_Time_Series_Modeling_Case_Studies_Atmospheric_CO2_and_Electricity_Demand.ipynb @@ -85,7 +85,6 @@ "#@title Import and set ups{ display-mode: \"form\" }\n", "\n", "%matplotlib inline\n", - "import matplotlib as mpl\n", "from matplotlib import pylab as plt\n", "import matplotlib.dates as mdates\n", "import seaborn as sns\n", @@ -94,12 +93,10 @@ "\n", "import numpy as np\n", "import tensorflow.compat.v2 as tf\n", + "import tf_keras\n", "import tensorflow_probability as tfp\n", "\n", - "from tensorflow_probability import distributions as tfd\n", - "from tensorflow_probability import sts\n", - "\n", - "tf.enable_v2_behavior()" + "from tensorflow_probability import sts" ] }, { @@ -296,7 +293,6 @@ "\n", " fig=plt.figure(figsize=(12, 6))\n", " ax = fig.add_subplot(1,1,1)\n", - " num_timesteps = one_step_mean.shape[-1]\n", " ax.plot(dates, observed_time_series, label=\"observed time series\", color=c1)\n", " ax.plot(dates, one_step_mean, label=\"one-step prediction\", color=c2)\n", " ax.fill_between(dates,\n", @@ -504,7 +500,7 @@ " target_log_prob_fn=co2_model.joint_distribution(\n", " observed_time_series=co2_by_month_training_data).log_prob,\n", " surrogate_posterior=variational_posteriors,\n", - " optimizer=tf.optimizers.Adam(learning_rate=0.1),\n", + " optimizer=tf_keras.optimizers.Adam(learning_rate=0.1),\n", " num_steps=num_variational_steps,\n", " jit_compile=True)\n", "\n", @@ -918,7 +914,7 @@ " target_log_prob_fn=demand_model.joint_distribution(\n", " observed_time_series=demand_training_data).log_prob,\n", " surrogate_posterior=variational_posteriors,\n", - " optimizer=tf.optimizers.Adam(learning_rate=0.1),\n", + " optimizer=tf_keras.optimizers.Adam(learning_rate=0.1),\n", " num_steps=num_variational_steps,\n", " jit_compile=True)\n", "plt.plot(elbo_loss_curve)\n", @@ -1245,6 +1241,7 @@ "metadata": { "colab": { "collapsed_sections": [ + "uiR4-VOt9NFX", "5BVYddeJg-An" ], "name": "Structural Time Series Modeling Case Studies Atmospheric CO2 and Electricity Demand",