a18932cb658c012808dd78bbd7f5bf1fe712e6c9,examples/neural_style_transfer.py,,,#,236

Before Change

    x = x.reshape((1, 3, img_width, img_height))
    return np.array(f_grads([x])).flatten().astype("float64")

f_loss = K.function([combination_image], [loss])
def eval_loss(x):
    x = x.reshape((1, 3, img_width, img_height))
    return f_loss([x])[0].astype("float64")

After Change

x = np.random.uniform(0, 255, (1, 3, img_width, img_height))
for i in range(10):
    print("Start of iteration", i)
    start_time = time.time()
    x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(),
                                     fprime=evaluator.grads, maxfun=20)
    print("Current loss value:", min_val)
    // save current generated image
    img = deprocess_image(x.reshape((3, img_width, img_height)))
    fname = result_prefix + "_at_iteration_%d.png" % i
    imsave(fname, img)
    end_time = time.time()
    print("Image saved as", fname)
    print("Iteration %d completed in %ds" % (i, end_time - start_time))

Instances