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Chapter 5
for act in layer_activations:
# In gradient ascent, we'll want to maximize this value
# so our image increasingly "excites" the layer
loss = tf.math.reduce_mean(act)
# Normalize by the number of units in the layer
loss /= np.prod(act.shape)
total_loss += loss
return total_loss
Now let's run the gradient ascent:
img = tf.Variable(img)
steps = 400
for step in range(steps):
with tf.GradientTape() as tape:
activations = forward(img)
loss = calc_loss(activations)
gradients = tape.gradient(loss, img)
# Normalize the gradients
gradients /= gradients.numpy().std() + 1e-8
# Update our image by directly adding the gradients
img.assign_add(gradients)
if step % 50 == 0:
clear_output()
print ("Step %d, loss %f" % (step, loss))
show(deprocess(img.numpy()))
plt.show()
# Let's see the result
clear_output()
show(deprocess(img.numpy()))
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