我会提前感谢任何形式的帮助。我正在使用 tensorflow 1.15 版，在运行以下代码后，我从优化器那里得到错误，他无法获得任何变量来优化。
在此代码之前，有关数据的所有预处理都已完成。现在我正在训练模型，首先我defined placeholders在运行时获取值。之后对这些占位符应用一些操作来计算positive energyandnegative energy并计算KBC loss. 当我将其提供KBC loss给优化器时，我收到错误“ValueError：没有要优化的变量”。
为什么在将值提供给占位符之前我会收到此错误。
请帮助解决这个问题。我将不胜感激任何帮助。
谢谢
with tf.name_scope('input'):
# Relation
r_input = tf.placeholder(dtype=tf.float32, shape=[None, 100],name="r_input")
r_neg_input = tf.placeholder(dtype=tf.float32, shape=[None, 100],name="r_input")
h_pos_txt_input = tf.placeholder(dtype=tf.float32, shape=[None, 100], name="h_pos_txt_input")
h_neg_txt_input = tf.placeholder(dtype=tf.float32, shape=[None, 100], name="h_neg_txt_input")
t_pos_txt_input = tf.placeholder(dtype=tf.float32, shape=[None, 100], name="t_pos_txt_input")
t_neg_txt_input = tf.placeholder(dtype=tf.float32, shape=[None, 100], name="t_neg_txt_input")
with tf.name_scope('cosine'):
# Energy calculation for postive and negative triples
pos_energy = tf.reduce_sum(abs(h_pos_txt_input + r_input - t_pos_txt_input), 1, keep_dims = True, name="pos_energy")
negative_energy = tf.reduce_sum(abs(h_neg_txt_input + r_neg_input - t_neg_txt_input), 1, keep_dims = True, name="negative_energy")
kbc_loss = tf.maximum(pos_energy - negative_energy + margin, 0) ##Margin defined at 2nd cell
tf.summary.histogram("loss", kbc_loss)
optimizer = tf.train.AdamOptimizer(initial_learning_rate).minimize(kbc_loss)
在此之后我收到错误
ValueError Traceback (most recent call last)
<ipython-input-43-64eddec80472> in <module>
----> 1 optimizer = tf.train.AdamOptimizer(initial_learning_rate).minimize(kbc_loss) #initial LR defined in 2nd cell
~\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow_core\python\training\optimizer.py in minimize(self, loss, global_step, var_list, gate_gradients, aggregation_method, colocate_gradients_with_ops, name, grad_loss)
401 aggregation_method=aggregation_method,
402 colocate_gradients_with_ops=colocate_gradients_with_ops,
--> 403 grad_loss=grad_loss)
404
405 vars_with_grad = [v for g, v in grads_and_vars if g is not None]
~\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow_core\python\training\optimizer.py in compute_gradients(self, loss, var_list, gate_gradients, aggregation_method, colocate_gradients_with_ops, grad_loss)
504 processors = [_get_processor(v) for v in var_list]
505 if not var_list:
--> 506 raise ValueError("No variables to optimize.")
507 var_refs = [p.target() for p in processors]
508 grads = gradients.gradients(
ValueError: No variables to optimize.
剩余代码如下
summary_op = tf.summary.merge_all()
saver = tf.train.Saver()
log_file = open(log_file,"w") #log file defined in 2nd cell
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as sess:
sess.run(tf.global_variables_initializer())
# Load pre-trained weights if available
if os.path.isfile(best_valid_model_meta_file): #best_valid_model_meta_file defined in 2nd cell
print("restore the weights",checkpoint_best_valid_dir) #checkpoint_best_valid_dir defined in 2nd cell
saver = tf.train.import_meta_graph(best_valid_model_meta_file) #best_valid_model_meta_file defined in 2nd cell
saver.restore(sess, tf.train.latest_checkpoint(checkpoint_best_valid_dir)) #checkpoint_best_valid_dir defined in 2nd cell
else:
print("no weights to load :(")
writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())
initial_valid_loss = 100
for epoch in range(training_epochs): #training_epochs defined in 2nd cell
gc.collect()
np.random.shuffle(training_data)
training_loss = 0.
total_batch = int(len(training_data) / batch_size) #batch_size defined in 2nd cell
for i in range(total_batch):
batch_loss = 0
start = i * batch_size #batch_size defined in 2nd cell
end = (i + 1) * batch_size
h_data_txt, r_data, t_data_txt, \
t_neg_data_txt, h_neg_data_txt, r_neg_data= get_batch_with_neg_heads_and_neg_tails_relation_multimodal(
training_data, triples_set, entity_list, start,
end, entity_embeddings_txt, relation_embeddings, r_head, head_r, r_tail, tail_r)
_, loss, summary = sess.run(
[optimizer, kbc_loss, summary_op],
feed_dict={r_input: r_data,
r_neg_input: r_neg_data,
h_pos_txt_input: h_data_txt,
t_pos_txt_input: t_data_txt,
t_neg_txt_input: t_neg_data_txt,
h_neg_txt_input: h_neg_data_txt
})
batch_loss = np.sum(loss)/batch_size #batch_size defined in 2nd cell
training_loss += batch_loss
writer.add_summary(summary, epoch * total_batch + i)
training_loss = training_loss / total_batch
val_loss = sess.run([kbc_loss],
feed_dict={r_input: r_data_valid,
r_neg_input: r_neg_data_valid,
h_pos_txt_input: h_data_valid_txt,
t_pos_txt_input: t_data_valid_txt,
t_neg_txt_input: t_neg_data_valid_txt,
h_neg_txt_input: h_neg_data_valid_txt
})
val_score = np.sum(val_loss) / len(valid_data)
print("Epoch:", (epoch + 1), "loss=", str(round(training_loss, 4)), "val_loss", str(round(val_score, 4)))
if val_score < initial_valid_loss :
saver.save(sess, model_weights_best_valid_file) #model_weights_best_valid_file defined in 2nd cell
log_file.write("save model best validation loss: " + str(initial_valid_loss) + "==>" + str(val_score) + "\n")
print("save model valid loss: ", str(initial_valid_loss), "==>", str(val_score))
initial_valid_loss = val_score
saver.save(sess, model_current_weights_file) #model_current_weights_file defined in 2nd cell
log_file.write("Epoch:\t" + str(epoch + 1) + "\tloss:\t" + str(round(training_loss, 5)) + "\tval_loss:\t" + str(
round(val_score, 5)) + "\n")
log_file.flush()