def mean_weight_mask(model, name="dense0", th=0.05):
layer = model.get_layer(name=name)
layer_idx = get_layer_index(model, name=name)
if layer_idx == 0:
print("Not on the input layer")
return
layer_class = layer.__class__.__name__
if not (layer_class == 'Dense'):
print(" Please assign a dense or conv layer")
return model
weights = layer.get_weights()
weight = weights[0]
zeros_n = np.count_nonzero(weight)
print("Before pruning: nonzeros in weights:", zeros_n)
it = np.nditer(weight, flags=['multi_index'])
while not it.finished:
if np.abs(it[0])<th:
weight[it.multi_index] = 0.0
it.iternext()
zeros_n = np.count_nonzero(weight)
print("After Pruning: nonzeros in weights:", zeros_n)
weights[0] = weight
layer.set_weights(weights)
mask_layer = Masking(mask_value=0.0)
model = insert_layer(model, model.layers[layer_idx-1],mask_layer)
return model
def test_insert_layer():
# Create all model layers
input_1 = Input(shape=[7, 7, 1])
dense_1 = Dense(3)
dense_2 = Dense(3)
dense_3 = Dense(3)
dense_4 = Dense(1)
# Create the model
x = dense_1(input_1)
x = dense_2(x)
output_1 = dense_4(x)
model_1 = utils.clean_copy(Model(input_1, output_1))
# Create the expected modified model
x = dense_1(input_1)
x = dense_2(x)
x = dense_3(x)
output_2 = dense_4(x)
model_2_exp = utils.clean_copy(Model(input_1, output_2))
# Insert dense_3 before dense_4 in model_1
model_2 = operations.insert_layer(model_1, model_1.get_layer(dense_4.name),
dense_3)
# Compare the modified model with the expected modified model
assert compare_models(model_2, model_2_exp)