def deeper_conv_block(conv_layer, kernel_size, weighted=True):
filter_shape = (kernel_size,) * 2
n_filters = conv_layer.filters
weight = np.zeros((n_filters, n_filters) + filter_shape)
center = tuple(map(lambda x: int((x - 1) / 2), filter_shape))
for i in range(n_filters):
filter_weight = np.zeros((n_filters,) + filter_shape)
index = (i,) + center
filter_weight[index] = 1
weight[i, ...] = filter_weight
bias = np.zeros(n_filters)
new_conv_layer = StubConv(conv_layer.filters, n_filters, kernel_size=kernel_size)
bn = StubBatchNormalization(n_filters)
if weighted:
new_conv_layer.set_weights((add_noise(weight, np.array([0, 1])), add_noise(bias, np.array([0, 1]))))
new_weights = [add_noise(np.ones(n_filters, dtype=np.float32), np.array([0, 1])),
add_noise(np.zeros(n_filters, dtype=np.float32), np.array([0, 1])),
add_noise(np.zeros(n_filters, dtype=np.float32), np.array([0, 1])),
add_noise(np.ones(n_filters, dtype=np.float32), np.array([0, 1]))]
bn.set_weights(new_weights)
return [StubReLU(),
new_conv_layer,
bn]
def wider_bn(layer, start_dim, total_dim, n_add, weighted=True):
if not weighted:
return StubBatchNormalization(layer.num_features + n_add)
weights = layer.get_weights()
new_weights = [add_noise(np.ones(n_add, dtype=np.float32), np.array([0, 1])),
add_noise(np.zeros(n_add, dtype=np.float32), np.array([0, 1])),
add_noise(np.zeros(n_add, dtype=np.float32), np.array([0, 1])),
add_noise(np.ones(n_add, dtype=np.float32), np.array([0, 1]))]
student_w = tuple()
for weight, new_weight in zip(weights, new_weights):
temp_w = weight.copy()
temp_w = np.concatenate((temp_w[:start_dim], new_weight, temp_w[start_dim:total_dim]))
student_w += (temp_w,)
new_layer = StubBatchNormalization(layer.num_features + n_add)
new_layer.set_weights(student_w)
return new_layer