From 3f5b5fe3f32c19d2314a3f25ca20858b7e55adb0 Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Fri, 20 Jul 2018 13:41:13 +0200 Subject: [PATCH] sync cont. learner --- .../simple_approach/Continous_Learner.ipynb | 32 ++++++++++++++++++- 1 file changed, 31 insertions(+), 1 deletion(-) diff --git a/Project/simple_approach/Continous_Learner.ipynb b/Project/simple_approach/Continous_Learner.ipynb index d561cc8..ba45709 100644 --- a/Project/simple_approach/Continous_Learner.ipynb +++ b/Project/simple_approach/Continous_Learner.ipynb @@ -145,7 +145,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "31e69854333f4c599b037b6c27f30f20", + "model_id": "ef207b9276fc4a84b46053e7d979f2a2", "version_major": 2, "version_minor": 0 }, @@ -692,6 +692,36 @@ "sdm" ] }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'NoneType' object has no attribute 'pipeline'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnamed_steps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'vectorizer'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"I am sad\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'pipeline'" + ] + } + ], + "source": [ + "v = pm.pipeline.named_steps['vectorizer'].transform([\"I am sad\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pm.pipeline.named_steps['keras_model'].predict([v])" + ] + }, { "cell_type": "code", "execution_count": null,