def create_graph()

in tf-ner-poc/src/main/python/normalizer/normalizer.py [0:0]

• 64 lines of code
• 6 McCabe index (conditional complexity)

def create_graph(mode, batch_size, encoder_nchars, max_target_length, decoder_nchars):

    # Hyper  parameters
    encoder_char_dim = 100
    num_units = 256

    batch_size_ph = tf.placeholder_with_default(batch_size, shape=(), name="batch_size")

    # Encoder
    encoder_char_ids_ph = tf.placeholder(tf.int32, shape=[None, None], name="encoder_char_ids")
    encoder_lengths_ph = tf.placeholder(tf.int32, shape=[None], name="encoder_lengths")

    encoder_embedding_weights = tf.get_variable(name="char_embeddings", dtype=tf.float32,
                        shape=[encoder_nchars, encoder_char_dim])

    encoder_emb_inp = tf.nn.embedding_lookup(encoder_embedding_weights, encoder_char_ids_ph)

    if "TRAIN" == mode:
        encoder_emb_inp = tf.nn.dropout(encoder_emb_inp, 0.7)

    encoder_emb_inp = tf.transpose(encoder_emb_inp, perm=[1, 0, 2])

    encoder_cell = tf.nn.rnn_cell.LSTMCell(num_units)
    initial_state = encoder_cell.zero_state(batch_size_ph, dtype=tf.float32)

    encoder_outputs, encoder_state = tf.nn.dynamic_rnn(
        encoder_cell, encoder_emb_inp, initial_state=initial_state,
        sequence_length=encoder_lengths_ph,
        time_major=True, swap_memory=True)

    # Decoder
    decoder_char_ids_ph = tf.placeholder(tf.int32, shape=[None, None], name="decoder_char_ids")
    decoder_lengths = tf.placeholder(tf.int32, shape=[None], name="decoder_lengths")

    # decoder output (decoder_input shifted to the left by one)

    decoder_char_dim = 100
    decoder_embedding_weights = tf.get_variable(name="decoder_char_embeddings", dtype=tf.float32,
                                             shape=[decoder_nchars, decoder_char_dim])

    projection_layer = tf.layers.Dense(units=decoder_nchars, use_bias=True) # To predict one output char at a time ...

    attention_states = tf.transpose(encoder_outputs, [1, 0, 2])

    attention_mechanism = tf.contrib.seq2seq.LuongAttention(
        num_units, attention_states,
        memory_sequence_length=encoder_lengths_ph)

    decoder_cell = tf.nn.rnn_cell.LSTMCell(num_units)

    decoder_cell = tf.contrib.seq2seq.AttentionWrapper(decoder_cell, attention_mechanism,
        attention_layer_size=num_units)

    # decoder_initial_state = encoder_state
    decoder_initial_state = decoder_cell.zero_state(dtype=tf.float32, batch_size=batch_size_ph)

    if "TRAIN" == mode:

        decoder_input = tf.pad(decoder_char_ids_ph, tf.constant([[0,0], [1,0]]),
                               'CONSTANT', constant_values=(decoder_nchars-2))

        decoder_emb_inp = tf.nn.embedding_lookup(decoder_embedding_weights, decoder_input)
        decoder_emb_inp = tf.transpose(decoder_emb_inp, perm=[1, 0, 2])

        helper = tf.contrib.seq2seq.TrainingHelper(
            decoder_emb_inp, [max_target_length for _ in range(batch_size)], time_major=True)


        decoder = tf.contrib.seq2seq.BasicDecoder(decoder_cell, helper,
                                                  decoder_initial_state, output_layer=projection_layer)

        outputs, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder, output_time_major=True, swap_memory=True )

        logits = outputs.rnn_output
        train_prediction = outputs.sample_id

        decoder_output = tf.pad(tf.transpose(decoder_char_ids_ph, perm=[1, 0]), tf.constant([[0,1], [0,0]]),
                                'CONSTANT', constant_values=(decoder_nchars-1))

        crossent = tf.nn.sparse_softmax_cross_entropy_with_logits(
            labels=decoder_output, logits=logits, name="crossent")

        loss = tf.reduce_sum(crossent * tf.to_float(decoder_lengths)) / (batch_size * max_target_length)

        # Optimizer
        # TODO: Tutorial suggest to swap to SGD for alter iterations
        # optimizer = tf.train.AdamOptimizer()
        optimizer = tf.train.RMSPropOptimizer(learning_rate=0.001)
        gradients, v = zip(*optimizer.compute_gradients(loss))
        gradients, _ = tf.clip_by_global_norm(gradients, 10.0)
        optimize = optimizer.apply_gradients(zip(gradients, v))

        return encoder_char_ids_ph, encoder_lengths_ph, decoder_char_ids_ph, decoder_lengths, optimize, train_prediction, outputs

    if "EVAL" == mode:
        helperE = tf.contrib.seq2seq.GreedyEmbeddingHelper(
            decoder_embedding_weights,
            tf.fill([batch_size_ph], decoder_nchars-2), decoder_nchars-1)
        decoderE = tf.contrib.seq2seq.BasicDecoder(
            decoder_cell, helperE, decoder_initial_state,
            output_layer=projection_layer)
        outputs, _, _ = tf.contrib.seq2seq.dynamic_decode(decoderE, maximum_iterations=20)

        translations = tf.identity(outputs.sample_id, name="decode")

        return encoder_char_ids_ph, encoder_lengths_ph, translations