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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.
np.random.seed(42)
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.cm.tab10
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})
plt.gca().add_artist(imagebox)
plt.axis("off")
plt.show()
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