diff --git a/examples/TwoMoons_Bimodal_Posterior.ipynb b/examples/TwoMoons_Bimodal_Posterior.ipynb
index 352095b8c..0cf95d4fe 100644
--- a/examples/TwoMoons_Bimodal_Posterior.ipynb
+++ b/examples/TwoMoons_Bimodal_Posterior.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "d5f88a59",
    "metadata": {},
    "outputs": [
@@ -32,7 +32,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "c:\\Users\\radevs\\Desktop\\Projects\\BayesFlow\\examples\\..\\bayesflow\\simulation.py:28: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
+      "C:\\Users\\radevs\\Desktop\\Projects\\BayesFlow\\examples\\..\\bayesflow\\simulation.py:28: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
       "  from tqdm.autonotebook import tqdm\n"
      ]
     }
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "0b9a9817",
    "metadata": {},
    "outputs": [
@@ -112,496 +112,54 @@
    "id": "2d4c6eb0",
    "metadata": {},
    "source": [
-    "## Inference Network and Amortizer <a class=\"anchor\" id=\"inference_network_and\"></a>\n",
-    "We will use a neural spline flow (https://arxiv.org/abs/1906.04032) for modeling the posterior, as these are specialized for locally weird posteriors. By default, some weight regularization and dropout will be applied during training. These can be modified through the `coupling_settings` keyword of the `InvertibleNetwork`."
+    "## Inference Network and Amortizer <a class=\"anchor\" id=\"inference_network_and\"></a>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
    "id": "e8d7e053",
    "metadata": {},
    "outputs": [],
    "source": [
-    "offline_data = benchmark.generative_model(1000)\n",
+    "num_sims = 1000\n",
+    "batch_size = 32\n",
+    "offline_data = benchmark.generative_model(num_sims)\n",
     "\n",
     "data = {\n",
     "    \"parameters\": dict(theta=offline_data[\"prior_draws\"]),\n",
     "    \"observables\": dict(x=offline_data[\"sim_data\"])\n",
     "}\n",
-    "dataset = OfflineDataset(data, batch_size=8, batches_per_epoch=1000//8)"
+    "# TODO - remove batches_per_epoch from user interface\n",
+    "dataset = OfflineDataset(data, batch_size=batch_size, batches_per_epoch=num_sims//batch_size)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 26,
    "id": "b1c98fbd",
    "metadata": {},
    "outputs": [],
    "source": [
-    "inference_net = CouplingFlow.all_in_one(target_dim=2, coupling_layers=4)\n",
+    "epochs = 250\n",
+    "\n",
+    "inference_net = CouplingFlow(depth=4, permutation=\"orthogonal\")\n",
     "\n",
     "amortizer = AmortizedPosterior(inference_net)\n",
     "\n",
-    "epochs = 200\n",
     "lr = keras.optimizers.schedules.CosineDecay(5e-4, decay_steps=int(epochs * dataset.batches_per_epoch))\n",
-    "# TODO: Experiments with clipnorm\n",
+    "\n",
     "optimizer = keras.optimizers.AdamW(lr, weight_decay=1e-3)\n",
+    "\n",
     "amortizer.compile(optimizer=optimizer)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "ecc11536",
+   "execution_count": null,
+   "id": "0f496bda",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/200\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\radevs\\AppData\\Local\\anaconda3\\envs\\bf\\Lib\\site-packages\\keras\\src\\layers\\layer.py:1295: UserWarning: Layer 'amortized_posterior_1' looks like it has unbuilt state, but Keras is not able to trace the layer `call()` in order to build it automatically. Possible causes:\n",
-      "1. The `call()` method of your layer may be crashing. Try to `__call__()` the layer eagerly on some test input first to see if it works. E.g. `x = np.random.random((3, 4)); y = layer(x)`\n",
-      "2. If the `call()` method is correct, then you may need to implement the `def build(self, input_shape)` method on your layer. It should create all variables used by the layer (e.g. by calling `layer.build()` on all its children layers).\n",
-      "Exception encountered: ''Exception encountered when calling CouplingFlow.call().\n",
-      "\n",
-      "\u001b[1mgot an unexpected keyword argument 'kwargs'\u001b[0m\n",
-      "\n",
-      "Arguments received by CouplingFlow.call():\n",
-      "  • args=('tf.Tensor(shape=(None, 2), dtype=float32)', 'tf.Tensor(shape=(None, 2), dtype=float32)')\n",
-      "  • kwargs={'kwargs': {}}''\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From c:\\Users\\radevs\\AppData\\Local\\anaconda3\\envs\\bf\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\core.py:184: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From c:\\Users\\radevs\\AppData\\Local\\anaconda3\\envs\\bf\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\core.py:184: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 23ms/step\n",
-      "Epoch 2/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 22ms/step\n",
-      "Epoch 3/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 22ms/step\n",
-      "Epoch 4/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 22ms/step\n",
-      "Epoch 5/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 22ms/step\n",
-      "Epoch 6/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 23ms/step\n",
-      "Epoch 7/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 23ms/step\n",
-      "Epoch 8/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 23ms/step\n",
-      "Epoch 9/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 22ms/step\n",
-      "Epoch 10/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 22ms/step\n",
-      "Epoch 11/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 23ms/step\n",
-      "Epoch 12/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 23ms/step\n",
-      "Epoch 13/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 23ms/step\n",
-      "Epoch 14/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 23ms/step\n",
-      "Epoch 15/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 16/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 17/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 18/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 19/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step\n",
-      "Epoch 20/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 21/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 22/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 23/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 24/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 25/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 26/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 27/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 28/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 29/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 30/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 31/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 32/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 33/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 34/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 35/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 36/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 37/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 38/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 39/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 40/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 41/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 42/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 43/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 44/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 45/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 46/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 47/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 48/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 49/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 50/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 51/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 52/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 53/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 54/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 55/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 56/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 57/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 58/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 59/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 60/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 61/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 62/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 63/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 64/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 65/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 66/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 67/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 68/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 69/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 70/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 71/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 72/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 73/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 74/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 75/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 76/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 77/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 78/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 79/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 80/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 81/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 82/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 83/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 84/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 85/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 86/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 87/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 88/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 89/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 90/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 91/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 92/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 93/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 94/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 95/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 96/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 97/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 98/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 99/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 100/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 101/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 102/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 103/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 104/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 105/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 106/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 107/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 108/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 109/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 110/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 111/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 112/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 27ms/step\n",
-      "Epoch 113/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 114/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 115/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 116/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 117/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 118/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 119/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 120/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 121/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 122/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 123/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 124/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 125/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 126/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 127/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 128/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 129/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 130/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 131/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 132/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 133/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 134/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 135/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 136/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 137/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 138/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 139/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 140/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 141/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 142/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 143/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 144/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 145/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 146/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step\n",
-      "Epoch 147/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 148/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 149/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 150/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 151/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 152/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 153/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 154/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 155/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 156/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 157/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 158/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 159/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 160/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 161/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 162/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 163/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 164/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 165/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step\n",
-      "Epoch 166/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 167/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 168/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 169/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 170/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 171/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 172/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 173/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 174/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 175/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step\n",
-      "Epoch 176/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 177/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 178/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 179/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step\n",
-      "Epoch 180/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step\n",
-      "Epoch 181/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 28ms/step\n",
-      "Epoch 182/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step\n",
-      "Epoch 183/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step\n",
-      "Epoch 184/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step\n",
-      "Epoch 185/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 186/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 187/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 188/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 189/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 190/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 191/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 192/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 193/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 194/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 195/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step\n",
-      "Epoch 196/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step\n",
-      "Epoch 197/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 198/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step\n",
-      "Epoch 199/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step\n",
-      "Epoch 200/200\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 28ms/step\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "h = amortizer.fit(dataset, epochs=epochs)"
    ]
@@ -620,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "f76289b3",
    "metadata": {},
    "outputs": [],
@@ -640,13 +198,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 24,
    "id": "065384db",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x400 with 2 Axes>"
       ]
@@ -665,7 +223,7 @@
     "# Obtain amortized samples\n",
     "obs_data = np.zeros((num_draws, 2)).astype(np.float32)\n",
     "# TODO - replace with amortizer interface\n",
-    "samples_at_origin = inference_net.sample(num_draws, obs_data)\n",
+    "samples_at_origin = inference_net.sample(num_samples=num_draws, conditions=obs_data)\n",
     "\n",
     "# Plot samples\n",
     "axarr[0].scatter(samples_at_origin[:, 0], samples_at_origin[:, 1], color=\"maroon\", alpha=0.75, s=0.5)\n",
@@ -681,7 +239,7 @@
     "obs_data_rep = np.zeros((10000, 2)).astype(np.float32)\n",
     "params = np.c_[x.flatten(), y.flatten()]\n",
     "# TODO - replace with amortizer interface\n",
-    "lpdf = amortizer.log_prob(params, obs_data_rep)\n",
+    "lpdf = inference_net.log_prob(params, obs_data_rep)\n",
     "\n",
     "# # Plot the density map using nearest-neighbor interpolation\n",
     "axarr[1].pcolormesh(x, y, np.exp(lpdf).reshape(100, 100), cmap=cm.hot)\n",