khaledmustafa91
hello i take this code and i run it on colab when i run it i got these message TypeError: An op outside of the function building code is being passed a Graph tensor i doesn't change any thing in model part code and i noticed that **sv.should_stop** change from false to true after this line : if init: sess.run(model.emb_assign, {model.word_embeddings_placeholder:glove}) because it changed it enter the loop and break without any training ``` with model.graph.as_default(): config = tf.compat.v1.ConfigProto() config.gpu_options.allow_growth = True sv = tf.compat.v1.train.Supervisor(logdir=Params.logdir, save_model_secs=0, global_step = model.global_step, init_op = model.init_op) print("\n" + str(sv.should_stop()) + "\n") with sv.managed_session(config = config) as sess: print("\n before sess Here \n") **if init: sess.run(model.emb_assign, {model.word_embeddings_placeholder:glove})** print("\n after sess Here \n") print("\n" + str(sv.should_stop()) + "\n") print("\n" + str(init) +"\n") for epoch in range(1, Params.num_epochs+1): **if sv.should_stop():** print("\n break \n") break for step in tqdm(range(model.num_batch), total = model.num_batch, ncols=70, leave=False, unit='b'): sess.run(model.train_op) if step % Params.save_steps == 0: print("\n Global step = " + str(mode.global_step) + "\n") gs = sess.run(model.global_step) sv.saver.save(sess, Params.logdir + '/model_epoch_%d_step_%d'%(gs//model.num_batch, gs%model.num_batch)) sample = np.random.choice(dev_ind, Params.batch_size) feed_dict = {data: devdata[i][sample] for i,data in enumerate(model.data)} index, dev_loss = sess.run([model.output_index, model.mean_loss], feed_dict = feed_dict) F1, EM = 0.0, 0.0 print("\n before batch") for batch in range(Params.batch_size): print("\n inside batch") f1, em = f1_and_EM(index[batch], devdata[8][sample][batch], devdata[0][sample][batch], dict_) F1 += f1 EM += em print("\n after batch") F1 /= float(Params.batch_size) EM /= float(Params.batch_size) sess.run(model.metric_assign,{model.F1_placeholder: F1, model.EM_placeholder: EM, model.dev_loss_placeholder: dev_loss}) print("\nDev_loss: {}\nDev_Exact_match: {}\nDev_F1_score: {}".format(dev_loss,EM,F1)) ``` **the error :** TypeError: An op outside of the function building code is being passed a "Graph" tensor. It is possible to have Graph tensors leak out of the function building context by including a tf.init_scope in your function building code. For example, the following function will fail: @tf.function def has_init_scope(): my_constant = tf.constant(1.) with tf.init_scope(): added = my_constant * 2 The graph tensor has name: global_step:0