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
