def my_net(iters): def body(loss, x): with variable_scope.variable_scope("vs", use_resource=True): y = layers.Conv2D( 2, 1, use_bias=True, kernel_initializer=init_ops.ones_initializer(), name='conv1')(x) loss = math_ops.reduce_sum(y) optimizer = gradient_descent.GradientDescentOptimizer(0.1) train = optimizer.minimize(loss) with ops.control_dependencies([train]): return array_ops.identity(loss) loss = 0.0 return loops.repeat(iters, body, (loss), infeed_queue)
def my_net2(v): r = loops.repeat(7, body2, (v)) return r
def my_net1(v): r = loops.repeat(5, body1, (v)) return r
def my_net(v): r = loops.repeat(10, body, (v), infeed_queue) return r
def my_net(): v = constant_op.constant(0.0, shape=shape, dtype=np.float32) r = loops.repeat(5, body, [v], infeed_queue) return r
def my_net(v): r = loops.repeat(20, body, (v)) return r
def my_net(): v = constant_op.constant(0.0, shape=[4, 4], dtype=np.float32) r = loops.repeat(iters, body, (v), infeed_queue) return r
def my_net(): v1 = constant_op.constant(0.0, shape=[4, 4], dtype=np.float32) v2 = constant_op.constant(0.0, shape=[4, 4], dtype=np.float32) r = loops.repeat(5, body, [v1, v2], infeed_queue) return r