From f8d2c1f8ef7b48a42077c24ddb879dea557b6e82 Mon Sep 17 00:00:00 2001
From: Charles Martin <cpm@charlesmartin.com.au>
Date: Mon, 18 Mar 2019 21:37:35 +0100
Subject: [PATCH 1/2] fixed small error in sampling procedure in MDN-2D
notebook
---
notebooks/MDN-2D-spiral-prediction.ipynb | 34 +++++-------------------
1 file changed, 7 insertions(+), 27 deletions(-)
diff --git a/notebooks/MDN-2D-spiral-prediction.ipynb b/notebooks/MDN-2D-spiral-prediction.ipynb
index 677eeb1..07e2e97 100644
--- a/notebooks/MDN-2D-spiral-prediction.ipynb
+++ b/notebooks/MDN-2D-spiral-prediction.ipynb
@@ -142,26 +142,6 @@
"plt.show()"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Sampling Functions\n",
- "\n",
- "The MDN model outputs parameters of a mixture model---a list of means (mu), variances (sigma), and weights (pi).\n",
- "\n",
- "The MDN package contains some functions to split up these parameters and sample from the normal distributions that they form.\n",
- "\n",
- "We use \n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -191,12 +171,12 @@
"# To find points on the graph, we need to sample from each distribution.\n",
"\n",
"# Split up the mixture parameters (for future fun)\n",
- "mus = np.apply_along_axis((lambda a: a[:N_MIXES*OUTPUT_DIMS]),1, y_test)\n",
- "sigs = np.apply_along_axis((lambda a: a[N_MIXES*OUTPUT_DIMS:2*N_MIXES*OUTPUT_DIMS]),1, y_test)\n",
- "pis = np.apply_along_axis((lambda a: softmax(a[-N_MIXES:])),1, y_test)\n",
+ "mus = np.apply_along_axis((lambda a: a[:N_MIXES*OUTPUT_DIMS]), 1, y_test)\n",
+ "sigs = np.apply_along_axis((lambda a: a[N_MIXES*OUTPUT_DIMS:2*N_MIXES*OUTPUT_DIMS]), 1, y_test)\n",
+ "pis = np.apply_along_axis((lambda a: mdn.softmax(a[-N_MIXES:])), 1, y_test)\n",
"\n",
"# Sample from the predicted distributions\n",
- "y_samples = np.apply_along_axis(sample_from_output, 1, y_test, N_MIXES,OUTPUT_DIMS,temp=1.0)"
+ "y_samples = np.apply_along_axis(mdn.sample_from_output, 1, y_test, OUTPUT_DIMS, N_MIXES, temp=1.0, sigma_temp=1.0)"
]
},
{
@@ -254,9 +234,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "venv",
"language": "python",
- "name": "python3"
+ "name": "venv"
},
"language_info": {
"codemirror_mode": {
@@ -268,7 +248,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.5"
+ "version": "3.6.8"
}
},
"nbformat": 4,
From a99f9128bcc52b797eca57899126b2dc329c5733 Mon Sep 17 00:00:00 2001
From: Charles Martin <cpm@charlesmartin.com.au>
Date: Tue, 19 Mar 2019 22:22:33 +0100
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 c1d84e3..283a2e5 100644
--- a/README.md
+++ b/README.md
@@ -17,11 +17,12 @@ Two important functions are provided for training and prediction:
## Installation
-You clone or download this repository and then install via `python setup.py install`, or copy the `mdn` folder into your own project.
+This project requires Python 3.6+. You can clone or download this repository and then install via `python setup.py install`, or copy the `mdn` folder into your own project.
You can easily install this package directly from Github via `pip` like so:
pip install git+git://github.com/cpmpercussion/keras-mdn-layer.git#egg=keras-mdn-layer
+
And finally, import the `mdn` module in Python: `import mdn`