def get_conv_dense_model():
graph = Graph((32, 32, 3), False)
output_node_id = 0
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubConv2d(3, 3, 3), output_node_id)
output_node_id = graph.add_layer(StubBatchNormalization2d(3),
output_node_id)
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubConv2d(3, 3, 3), output_node_id)
output_node_id = graph.add_layer(StubBatchNormalization2d(3),
output_node_id)
output_node_id = graph.add_layer(StubFlatten(), output_node_id)
output_node_id = graph.add_layer(
StubDropout2d(Constant.DENSE_DROPOUT_RATE), output_node_id)
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(
StubDense(graph.node_list[output_node_id].shape[0], 5), output_node_id)
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubDense(5, 5), output_node_id)
graph.add_layer(StubSoftmax(), output_node_id)
graph.produce_model().set_weight_to_graph()
return graph
def test_wider_bn():
bn_layer = StubBatchNormalization2d(3)
bn_layer.set_weights([
np.ones(3, dtype=np.float32),
np.zeros(3, dtype=np.float32),
np.zeros(3, dtype=np.float32),
np.ones(3, dtype=np.float32)
])
new_bn_layer = wider_bn(bn_layer, 1, 3, 4)
assert new_bn_layer.get_weights()[0].shape[0] == 7