我的代码是：
从vis.visualization导入visualize_saliency
从vis.utils导入utils
从keras导入激活
从matplotlib导入pyplot作为plt
％matplotlib内联
class_idx = 0
indices = np.where(y_test[:, class_idx] == 1.)[0]
# pick some random input from here.
idx = indices[0]
# Utility to search for layer index by name.
# Alternatively we can specify this as -1 since it corresponds to the last layer.
layer_idx = utils.find_layer_idx(model, 'preds')
# Swap softmax with linear
model.layers[layer_idx].activation = activations.linear
model = utils.apply_modifications(model)
grads = visualize_saliency(model, layer_idx, filter_indices=class_idx,
seed_input=x_test[idx])
# Plot with 'jet' colormap to visualize as a heatmap.
plt.imshow(grads, cmap='jet')