Keras: visualize the newly learned weights in t-sne

[vinayakumarr]

Has any body know how to visualize the newly learned weights in t-sne. I have used keras for lstm with numeric features and also for text classification. I want to plot the newly learned weights in t-sne

[RamaneekGill]

I haven't visualized weights in a while, however I do visualize my embeddings for most data. I imagine that it probably is the same thing. Just call the layer and get the weights and then apply manifold learning on it to reduce it to 2 dimensions and then visualize it with a scatter plot.

Resources I often fall back on to are:

• https://indico.io/blog/visualizing-with-t-sne/
• http://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
• https://github.com/oreillymedia/t-SNE-tutorial

Here is some code for visualization to get you started:

def plot_embedding(x, y):
    cm = plt.cm.get_cmap('RdYlGn')
    f = plt.figure(figsize=(13, 13))
    ax = plt.subplot(aspect='equal')
    sc = ax.scatter(x[:,0], x[:,1], lw=0, s=40, c=y, cmap=cm)
    plt.xlim(-25, 25)
    plt.ylim(-25, 25)
    ax.axis('off')
    ax.axis('tight')

    plt.show()

plot_embedding(transformed_weights, weight_labels)

Code to get you started for t-sne with sklearn

weights = model.get_layer(index={your layer index}).get_weights()
tsne = TSNE(n_components=2, random_state=random_seed, verbose=1)
transformed_weights = tsne.fit_transform(weights)

If this helps please close the issue :)

[AritzBi]

I'm trying to visualize the output of my last dense layer which output is 512. I've applied t-sne in order to reduce the dimensionality and visualize them. However, all the test samples have been place in a big cluster. Should I reduce the dimensionality with some other technique or it's just like that and I cannot do anything?

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[psgl]

@AritzBi
According to this article in distill, you might want to try different parameter settings.

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