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)