updated links (#6)

* updated several notebooks for json errors * updated colab and json
timsainb · May 14, 2019 · e341fd6 · e341fd6
1 parent 698862e
commit e341fd6
Show file tree

Hide file tree

Showing 2 changed files with 2 additions and 2 deletions.
diff --git a/4.0-seq2seq-fashion-mnist.ipynb b/4.0-seq2seq-fashion-mnist.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/timsainb/tensorflow2-generative-models/blob/master/6.0-VAE-GAN-fashion-mnist.ipynb)\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/timsainb/tensorflow2-generative-models/blob/master/4.0-seq2seq-fashion-mnist.ipynb)\n",
     "\n",
     "## Seq2Seq Autoencoder (without attention)  \n",
     "\n",

diff --git a/readme.md b/readme.md
@@ -45,7 +45,7 @@ GAIA is an autoencoder trained to learn convex latent representations by adversa
 
 ## Other Notebooks:
 
-### Seq2Seq Autoencoder (without attention) (Fasion MNIST: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/timsainb/tensorflow2-generative-models/blob/master/6.0-VAE-GAN-fashion-mnist.ipynb) | NSYNTH: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timsainb/tensorflow2-generative-models/blob/master/9.0-seq2seq-NSYNTH.ipynb))
+### Seq2Seq Autoencoder (without attention) (Fasion MNIST: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timsainb/tensorflow2-generative-models/blob/master/4.0-seq2seq-fashion-mnist.ipynb) | NSYNTH: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timsainb/tensorflow2-generative-models/blob/master/9.0-seq2seq-NSYNTH.ipynb))
 
 Seq2Seq models use recurrent neural network cells (like LSTMs) to better capture sequential organization in data. This implementation uses Convolutional Layers as input to the LSTM cells, and a single Bidirectional LSTM layer.