def test_integration(decorrelate, fft, inceptionv1): obj = objectives.neuron("mixed3a_pre_relu", 0) param_f = lambda: param.image(16, decorrelate=decorrelate, fft=fft) rendering = render.render_vis( inceptionv1, obj, param_f=param_f, thresholds=(1, 2), verbose=False, transforms=[], ) start_image = rendering[0] end_image = rendering[-1] objective_f = objectives.neuron("mixed3a", 177) param_f = lambda: param.image(64, decorrelate=decorrelate, fft=fft) rendering = render.render_vis( inceptionv1, objective_f, param_f, verbose=False, thresholds=(0, 64), use_fixed_seed=True, ) start_image, end_image = rendering assert (start_image != end_image).any()
def test_integration(decorrelate, fft): obj = objectives.neuron("mixed3a_pre_relu", 0) param_f = lambda: param.image(16, decorrelate=decorrelate, fft=fft) rendering = render.render_vis(model, obj, param_f=param_f, thresholds=(1, 2), verbose=False, transforms=[]) start_image = rendering[0] end_image = rendering[-1] assert (start_image != end_image).any()
def visualization(learning_rate, neuron, channel, contrast, NRO_IMG, SAVE_P): LEARNING_RATE = learning_rate optimizer = tf.train.AdamOptimizer(LEARNING_RATE) obj = objectives.neuron(neuron, channel) imgs = render.render_vis(model, obj, optimizer=optimizer, transforms=[], param_f=lambda: param.image(256, fft=True, decorrelate=True, init_val=NRO_IMG), # 256 es el tamanio de la imagen thresholds=(0,2), verbose=False) # Note that we're doubling the image scale to make artifacts more obvious plt.figure() plt.imshow(imgs[0][0]) plt.axis('off') contraste = contrast # Mover este numero hasta ver algo razonable plt.imshow(contraste*(imgs[1][0]-imgs[0][0]) + 0.5) plt.savefig(SAVE_P, bbox_inches='tight')
def test_integration_any_channels(): inceptionv1 = InceptionV1() objectives_f = [ objectives.deepdream("mixed4a_pre_relu"), objectives.channel("mixed4a_pre_relu", 360), objectives.neuron("mixed3a", 177) ] params_f = [ lambda: param.grayscale_image_rgb(128), lambda: arbitrary_channels_to_rgb(128, channels=10) ] for objective_f in objectives_f: for param_f in params_f: rendering = render.render_vis( inceptionv1, objective_f, param_f, verbose=False, thresholds=(0, 64), use_fixed_seed=True, ) start_image, end_image = rendering assert (start_image != end_image).any()
def test_neuron(inceptionv1): objective = objectives.neuron("mixed4a_pre_relu", 42) assert_gradient_ascent(objective, inceptionv1)