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I'm training a model as:

with tf.Graph().as_default():
 with tf.Session(config=tf.ConfigProto(allow_soft_placement = True)) as sess:
 K.set_session(sess)
 tf.train.create_global_step()
 #with tf.device('/gpu:0:'):
 m = GAReader.Model(nlayers, data.vocab_size, data.num_chars, W_init,
 nhidden, embed_dim, dropout, train_emb,
 char_dim, use_feat, gating_fn, words).build_network()
 m.compile(optimizer=tf.train.AdamOptimizer(0.01),
 loss=tf.keras.losses.categorical_crossentropy,
 metrics=[tf.keras.metrics.categorical_accuracy])
 tensorboard = TensorBoardCustom(log_dir="logs", sess=sess)
 m.fit_generator(generator=batch_loader_train, steps_per_epoch=len(batch_loader_train.batch_pool), epochs=100, callbacks=[tensorboard])

and I defined a custom callback extending the keras.callbacks.Tensorboard as:

class TensorBoardCustom(TensorBoard):

 def __init__(self, log_dir, sess, **kwargs):
 super(TensorBoardCustom, self).__init__(log_dir, **kwargs)
 self.sess = sess

 def on_batch_end(self, batch, logs=):
 summary = tf.summary.merge_all()
 writer = tf.summary.FileWriter(self.log_dir)
 s = self.sess.run(summary)
 writer.add_summary(s, batch)
 writer.close()
 super(TensorBoardCustom, self).on_batch_end(batch, logs)

and I'm adding a new summary as:

l_docin = tf.keras.layers.Input(shape=(None,))
with tf.name_scope('summaries'):
 table = tf.contrib.lookup.index_to_string_table_from_tensor(
 self.mapping_string, default_value="UNKNOWN")
 words = table.lookup(tf.cast(l_qin, tf.int64))
 text = tf.reduce_join(words, 1, separator=' ')
 tf.summary.text('text', text)

However, this is not working and I'm getting the following error:

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'input_2' with dtype float and shape [?,?]
 [[node input_2 = Placeholder[dtype=DT_FLOAT, shape=[?,?], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

Can someone explain why this is happening and how I can correct it? Is there a simpler/better way of adding custom summaries?