要构建测试用例代码，我只想将张量传递给图，然后让模型决定是乘以2一次还是两次。该决定基于布尔值的评估。这是代码：
class what_to_do():
def __init__(self):
self.conditionally_determined_A = None
self.conditionally_determined_B = None
self.truth_values = tf.placeholder(shape=[None, 1],
dtype=tf.bool)
self.input = tf.placeholder(shape=[None, 1],
dtype=tf.float32)
# The question is, can this statement evaluate the conditional and then direct the
# implementation of the model's components.
_ = tf.cond(tf.constant(True),
lambda: self.real_conditional(),
lambda: self.fake_condition())
self.input_A = tf.Variable(initial_value=self.conditionally_determined_A,
trainable=False,
validate_shape=False)
self.const_A = tf.constant([2.])
self.operation_A1 = tf.multiply(self.input_A, self.const_A)
self.input_B = tf.Variable(initial_value=self.conditionally_determined_B,
trainable=False,
validate_shape=False)
self.const_B = tf.constant([2.])
self.operation_B1 = tf.multiply(self.input_B, self.const_B)
self.output = self.operation_B1
# These functions will serve as the condition coordinators for the model
def real_conditional(self):
print('in loop')
self.conditionally_determined_B = self.input
self.conditionally_determined_A = None
return 1
def fake_condition(self):
print('not in loop')
self.conditionally_determined_B = None
self.conditionally_determined_A = self.input
return 0
tf.reset_default_graph()
model = what_to_do()
data_set = np.array([[2,True], [2,True], [2,True], [2,False], [2,False], [2,False], [2,False]])
i, t = np.split(ary=data_set,
indices_or_sections=2,
axis=-1)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
output = sess.run(fetches=[model.output],
feed_dict={model.input_A:i, model.truth_values:t})
尝试获取tf.cond（）处理张量时遇到麻烦。它抱怨等级等（也许是另一个问题）。
我对代码所做的只是将条件定义为True或False，从而执行适当的功能。如果为True，则图形应从input_B开始，而不用担心input_A。
是否需要设置tf.cond（）来动态处理图形？