# ================================================== print("Entering into graph\n") with tf.Graph().as_default(): session_conf = tf.ConfigProto( allow_soft_placement=FLAGS.allow_soft_placement, log_device_placement=FLAGS.log_device_placement) sess = tf.Session(config=session_conf) print("Entering into session\n") with sess.as_default(): model = Model( num_classes=y_train.shape[1], vocab_size=len(vocab_processor.vocabulary_), embedding_size = embeddings.shape[1], max_length = FLAGS.max_doc_length, vocab_proc = vocab_processor, filter_sizes=list(map(int, FLAGS.filter_sizes.split(","))), num_filters=FLAGS.num_filters, l2_reg_lambda=FLAGS.l2_reg_lambda) print("Defined Model\n") # Define Training procedure global_step = tf.Variable(0, name="global_step", trainable=False) optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate) grads_and_vars = optimizer.compute_gradients(model.loss) # ,tf.trainable_variables() def ClipIfNotNone(grad): if grad is None: print("NAN----------------------------------------------------") return grad
# Training # ================================================== print("Entering into graph\n") with tf.Graph().as_default(): session_conf = tf.ConfigProto( allow_soft_placement=FLAGS.allow_soft_placement, log_device_placement=FLAGS.log_device_placement) sess = tf.Session(config=session_conf) print("Entering into session\n") with sess.as_default(): model = Model( #sequence_length=q_train.shape[1], num_classes=y_train.shape[1], #vocab_size=len(vocab_processor.vocabulary_), embedding_size=q_train.shape[1], filter_sizes=list(map(int, FLAGS.filter_sizes.split(","))), num_filters=FLAGS.num_filters, l2_reg_lambda=FLAGS.l2_reg_lambda) # Output directory for models and summaries timestamp = str(int(time.time())) out_dir = os.path.abspath(os.path.join("../../runs", timestamp)) #os.path.curdir,"runs", timestamp print("Writing to {}\n".format(out_dir)) print("Defined Model\n") # Define Training procedure global_step = tf.Variable(0, name="global_step", trainable=False) optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate) grads_and_vars = optimizer.compute_gradients(model.loss, tf.trainable_variables()) train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)