import unittest import paddle.v2.fluid.layers as layers import paddle.v2.fluid.core as core from paddle.v2.fluid.framework import g_startup_program, g_main_program from paddle.v2.fluid.executor import Executor from paddle.v2.fluid.backward import append_backward_ops import numpy class ConditionalBlock(unittest.TestCase): def test_forward(self): data = layers.data(name='X', shape=[1], dtype='float32') data.stop_gradient = False cond = layers.ConditionalBlock(inputs=[data]) out = layers.create_tensor(dtype='float32') with cond.block(): hidden = layers.fc(input=data, size=10) layers.assign(hidden, out) cpu = core.CPUPlace() exe = Executor(cpu) exe.run(g_startup_program) x = numpy.random.random(size=(10, 1)).astype('float32') outs = exe.run(feed={'X': x}, fetch_list=[out])[0] print outs loss = layers.mean(x=out) append_backward_ops(loss=loss) outs = exe.run( feed={'X': x}, fetch_list=[g_main_program.block(0).var(data.name + "@GRAD")])[0] print outs if __name__ == '__main__': unittest.main()