Progress

Author: vanpelt

keras-audio/audio.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [

keras-audio/audio_utilities.py

@@ -12,7 +12,6 @@
 import math
 import matplotlib
 import subprocess
-matplotlib.use("Agg")
 
 # Author: Brian K. Vogel
 # [email protected]

keras-autoencoder/conditional_vae.py

@@ -9,6 +9,7 @@
 import plotly.graph_objs as go
 import plotly.plotly as py
 from sklearn import manifold
+from sklearn.decomposition import PCA
 import matplotlib.pyplot as plt
 import pdb
 import os
@@ -136,7 +137,8 @@ def on_epoch_end(self, epoch, logs):
         else:
             latent = encoder.predict([X_test[tsne_idx], y_test[tsne_idx]])
         # Compute t-SNE embedding of latent space
-        tsne = manifold.TSNE(n_components=2, init='pca', random_state=0)
+        # tsne = manifold.TSNE(n_components=2, init='pca', random_state=0)
+        tsne = PCA(n_components=2)
         X = tsne.fit_transform(latent)
         X_tsne = X
         trace = go.Scatter(x=list(X_tsne[:, 0]), y=list(X_tsne[:, 1]),

keras-cnn/convolution.ipynb

@@ -71,7 +71,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,