Update tutorial notebooks

Author: stefanradev93

docs/source/tutorial_notebooks/Covid19_Initial_Posterior_Estimation.ipynb

@@ -36,17 +36,6 @@
     "import bayesflow.diagnostics as diag"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "racial-james",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "contemporary-arthritis",

docs/source/tutorial_notebooks/Intro_Amortized_Posterior_Estimation.ipynb

@@ -45,7 +45,7 @@
    ],
    "source": [
     "sys.path.append(os.path.abspath(os.path.join('../../..')))\n",
-    "from bayesflow.forward_inference import Prior, Simulator, GenerativeModel\n",
+    "from bayesflow.simulation import Prior, Simulator, GenerativeModel\n",
     "from bayesflow.networks import InvertibleNetwork\n",
     "from bayesflow.amortized_inference import AmortizedPosterior\n",
     "from bayesflow.trainers import Trainer\n",
@@ -460,10 +460,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "inference_net = InvertibleNetwork({\n",
-    "    'n_params' : 6,\n",
-    "    'n_coupling_layers' : 8,\n",
-    "})"
+    "inference_net = InvertibleNetwork(num_params=6, num_coupling_layers=8)"
    ]
   },
   {
@@ -505,7 +502,7 @@
     "sim_mean = np.mean(data['sim_data'])\n",
     "sim_std = np.std(data['sim_data'])\n",
     "\n",
-    "def preprocessing(forward_dict):\n",
+    "def configure_input(forward_dict):\n",
     "    \"\"\"Configures dictionary of prior draws and simulated data into BayesFlow format.\"\"\"\n",
     "    \n",
     "    out_dict = {}\n",
@@ -560,9 +557,7 @@
     "trainer = Trainer(\n",
     "    amortizer=amortizer,\n",
     "    generative_model=model,\n",
-    "    configurator=preprocessing,\n",
-    "    learning_rate=PiecewiseConstantDecay([100000, 150000], [0.001, 0.0001, 0.00001]),\n",
-    "    optional_stopping=False,\n",
+    "    configurator=configure_input\n",
     ")"
    ]
   },
@@ -610,7 +605,7 @@
    "id": "178a1127",
    "metadata": {},
    "source": [
-    "Now, we can train our BayesFlow architecture using online learning:"
+    "Now, we can train our BayesFlow architecture using online learning and showcasing a custom optimizer."
    ]
   },
   {
@@ -3421,7 +3416,9 @@
     }
    ],
    "source": [
-    "losses = trainer.train_online(epochs=200, iterations_per_epoch=1000, batch_size=32)"
+    "learning_rate = PiecewiseConstantDecay([100000, 150000], [0.001, 0.0001, 0.00001])\n",
+    "optimizer = tf.keras.optimizers.Adam(learning_rate)\n",
+    "losses = trainer.train_online(epochs=200, iterations_per_epoch=1000, batch_size=32, optimizer=optimizer)"
    ]
   },
   {
@@ -3706,35 +3703,11 @@
     "ax.legend()\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "69c64757",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "IPython.notebook.save_notebook()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%%javascript\n",
-    "IPython.notebook.save_notebook()"
-   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -3748,7 +3721,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.13"
   },
   "toc": {
    "base_numbering": "1",