reload(alex_net_utils)
np.random.seed(7) # Set the random seed for reproducibility
if __name__ == "__main__":
plt.ion()
# 1. Build the model
# ---------------------------------------------------------------------
K.set_image_dim_ordering('th')
print("Building Contour Integration Model...")
# Gaussian Multiplicative Model
contour_integration_model = cont_int_models.build_contour_integration_model(
"masked_multiplicative",
"trained_models/AlexNet/alexnet_weights.h5",
weights_type='enhance',
n=25,
)
# contour_integration_model.summary()
# Define callback functions to get activations of L1 convolutional layer &
# L2 contour integration layer
l1_activations_cb = alex_net_utils.get_activation_cb(
contour_integration_model, 1)
l2_activations_cb = alex_net_utils.get_activation_cb(
contour_integration_model, 2)
# 2. Extract the neural data we would like to match
# ---------------------------------------------------------------------
with open('.//neuro_data//Li2006.pickle', 'rb') as handle:
data = pickle.load(handle)
if __name__ == "__main__":
plt.ion()
K.clear_session()
K.set_image_dim_ordering('th')
# --------------------------
tgt_filter_idx = 10
# Build Contour Integration Model
# -------------------------------
print("Building Contour Integration Model...")
# Multiplicative Model
contour_integration_model = cont_int_models.build_contour_integration_model(
"multiplicative",
"trained_models/AlexNet/alexnet_weights.h5",
n=25,
activation='relu')
# contour_integration_model.summary()
# Define callback functions to get activations of L1 convolutional layer &
# L2 contour integration layer
l1_activations_cb = alex_net_utils.get_activation_cb(
contour_integration_model, 1)
l2_activations_cb = alex_net_utils.get_activation_cb(
contour_integration_model, 2)
# Store the start weights & bias for comparison later
start_weights, start_bias = contour_integration_model.layers[
2].get_weights()
return f
if __name__ == "__main__":
plt.ion()
K.clear_session()
# 1. Load/Make the model
# ----------------------
K.set_image_dim_ordering('th')
print("Building Contour Integration Model...")
# Gaussian Multiplicative Model
contour_integration_model = cont_int_models.build_contour_integration_model(
"gaussian_multiplicative",
"trained_models/AlexNet/alexnet_weights.h5",
weights_type='enhance_and_suppress',
n=25,
sigma=6.0)
# contour_integration_model.summary()
# Define callback functions to get activations of L1 convolutional layer &
# L2 contour integration layer
l1_activations_cb = alex_net_utils.get_activation_cb(
contour_integration_model, 1)
l2_activations_cb = alex_net_utils.get_activation_cb(
contour_integration_model, 2)
# --------------------------------------------------------------------------------------------
# Vertical Contours
# --------------------------------------------------------------------------------------------
tgt_filter_index = 10