From 6b019002755c58365d5bc9412d40400b1ce91eeb Mon Sep 17 00:00:00 2001
From: Dillon Erb <585865+dte@users.noreply.github.com>
Date: Tue, 6 Aug 2019 11:03:26 -0400
Subject: [PATCH 1/2] Update README.md

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 README.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/README.md b/README.md
index 2fc2940..2ac1972 100755
--- a/README.md
+++ b/README.md
@@ -1,5 +1,8 @@
 # Paperspace Hyperparameter Tuning for solving CIFAR-100 with a convolutional neural network (CNN) built with Keras and TensorFlow, GPU backend
 
+[![Gradient](https://s3.amazonaws.com/ps.public.resources/img/gradient-badge-gradient-05.svg)](https://gradient.cloud)
+
+
 This project acts as both a tutorial and demo for using Paperspace Hyperparameter Tuning based on _Hyperopt_ with Keras, TensorFlow, and TensorBoard. Not only do we try to find the best hyperparameters for the given hyperspace, but we also represent the neural network architecture as hyperparameters that can be tuned. This serves to automate the process of searching for the best neural architecture configuration and hyperparameters.
 
 Here, we are meta-optimizing a neural net and its architecture using the CIFAR-100 dataset, a computer vision task with 100 fine labeled items. This code could be easily transferred to another vision dataset or even to another machine learning task.

From 4c3a8d082ccdba3371bb18d2bfe15c9456aedae7 Mon Sep 17 00:00:00 2001
From: Dillon Erb <585865+dte@users.noreply.github.com>
Date: Wed, 7 Aug 2019 11:29:41 -0400
Subject: [PATCH 2/2] Update README.md

---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 2ac1972..f697d70 100755
--- a/README.md
+++ b/README.md
@@ -1,6 +1,7 @@
 # Paperspace Hyperparameter Tuning for solving CIFAR-100 with a convolutional neural network (CNN) built with Keras and TensorFlow, GPU backend
 
-[![Gradient](https://s3.amazonaws.com/ps.public.resources/img/gradient-badge-gradient-05.svg)](https://gradient.cloud)
+[![Gradient](https://s3.amazonaws.com/ps.public.resources/img/gradient-badge-gradient-05-d-05-05.svg)](https://gradient.paperspace.com)
+
 
 
 This project acts as both a tutorial and demo for using Paperspace Hyperparameter Tuning based on _Hyperopt_ with Keras, TensorFlow, and TensorBoard. Not only do we try to find the best hyperparameters for the given hyperspace, but we also represent the neural network architecture as hyperparameters that can be tuned. This serves to automate the process of searching for the best neural architecture configuration and hyperparameters.