slight update to nsynth autoencoder

timsainb · May 14, 2019 · 37d653e · 37d653e
1 parent 4186131
commit 37d653e
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Showing 2 changed files with 15 additions and 7 deletions.
diff --git a/8.0-NSYNTH-iterator.ipynb b/8.0-NSYNTH-iterator.ipynb
@@ -546,7 +546,7 @@
    },
    "source": [
     "### test how fast we can iterate over the dataset\n",
-    "This value is around 15 iterations/second on my computer locally with 10 parallel"
+    "This value is around 15 iterations/second on my computer locally with 10 parallel threads. It might be slower on colab or other free resources. "
    ]
   },
   {
@@ -565,10 +565,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2019-05-14T04:28:46.836Z"
+     "end_time": "2019-05-14T04:29:59.782683Z",
+     "start_time": "2019-05-14T04:28:46.857524Z"
     }
    },
    "outputs": [
@@ -585,6 +586,13 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
     }
    ],
    "source": [

diff --git a/9.0-seq2seq-NSYNTH.ipynb b/9.0-seq2seq-NSYNTH.ipynb
@@ -851,14 +851,14 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2019-05-14T04:30:02.723Z"
+     "start_time": "2019-05-14T04:35:59.784Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fdf9e13e24d0480ba691e07c577ff0e5",
+       "model_id": "356f8c66ed5a436b850e1664c8e81a32",
        "version_major": 2,
        "version_minor": 0
       },
@@ -881,9 +881,9 @@
     "    # test on holdout\n",
     "    loss = []\n",
     "    for batch, test_x in tqdm(\n",
-    "        zip(range(n_batches_test), dset.test_dataset), total=n_batches_test\n",
+    "        zip(range(n_batches_test), dset.dataset_test), total=n_batches_test\n",
     "    ):\n",
-    "        loss.append(model.compute_loss(train_x))\n",
+    "        loss.append(model.compute_loss(test_x['spectrogram']))\n",
     "    losses.loc[len(losses)] = np.mean(loss, axis=0)\n",
     "    # plot results\n",
     "    display.clear_output()\n",