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Evaluating the Model Performance

Let us now view how well our autoencoder is trained and how good the reconstructed images are trained.

  • Use show_reconstructions function and pass stacked_ae and X_test as input argument. This displays 5 ground truth images and the corresponding reconstructed images.

    show_reconstructions(stacked_ae, X_test)
  • Let us view the rounded_accuracies of X_test using stacked_ae.evaluate.

    << your code comes here >>(X_test, X_test)
  • Let us view the class-wise clusters for the validation data as predicted by our model stacked_ae. Since we can't display multiple-dimensions, we shall do this by using TSNE dimensionality reduction.

    • We shall use predict of stacked_encoder on X_valid to get the compressed data of the validation data.

    • Use fit_transform of TSNE() to get the 2D representation of the compressed validation data and scale it data.

    • Now plot this data with colormaps for each class.

    Use the following code to do get the 2D representation of the compressed validation data.

    from sklearn.manifold import TSNE
    start = time.time()
    X_valid_compressed = stacked_encoder.predict(X_valid)
    tsne = TSNE()
    X_valid_2D = tsne.fit_transform(X_valid_compressed)
    X_valid_2D = (X_valid_2D - X_valid_2D.min()) / (X_valid_2D.max() - X_valid_2D.min())
    end = time.time()
    print("Time of execution:", round(end-start,2),"seconds")

    Use the following code to display the class-wise clusters.

    plt.figure(figsize=(10, 8))
    cmap =
    plt.scatter(X_valid_2D[:, 0], X_valid_2D[:, 1], c=y_valid, s=10, cmap=cmap)
    image_positions = np.array([[1., 1.]])
    for index, position in enumerate(X_valid_2D):
        dist = np.sum((position - image_positions) ** 2, axis=1)
        if np.min(dist) > 0.02: # if far enough from other images
            image_positions = np.r_[image_positions, [position]]
            imagebox = mpl.offsetbox.AnnotationBbox(
                mpl.offsetbox.OffsetImage(X_valid[index], cmap="binary"),
                position, bboxprops={"edgecolor": cmap(y_valid[index]), "lw": 2})

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