class1_neural_network.py

import time
import collections
import logging
import json
import weakref

import numpy
import pandas

from .hyperparameters import HyperparameterDefaults

from .encodable_sequences import EncodableSequences
from .amino_acid import available_vector_encodings, vector_encoding_length
from .regression_target import to_ic50, from_ic50
from .common import random_peptides, amino_acid_distribution
from .custom_loss import CUSTOM_LOSSES


class Class1NeuralNetwork(object):
    """
    Low level class I predictor consisting of a single neural network.
    
    Both single allele and pan-allele prediction are supported, but pan-allele
    is in development and not yet well performing.
    
    Users will generally use Class1AffinityPredictor, which gives a higher-level
    interface and supports ensembles.
    """

    network_hyperparameter_defaults = HyperparameterDefaults(
        kmer_size=15,
        peptide_amino_acid_encoding="BLOSUM62",
        embedding_input_dim=21,
        embedding_output_dim=8,
        allele_dense_layer_sizes=[],
        peptide_dense_layer_sizes=[],
        peptide_allele_merge_method="multiply",
        peptide_allele_merge_activation="",
        layer_sizes=[32],
        dense_layer_l1_regularization=0.001,
        dense_layer_l2_regularization=0.0,
        activation="tanh",
        init="glorot_uniform",
        output_activation="sigmoid",
        dropout_probability=0.0,
        batch_normalization=False,
        embedding_init_method="glorot_uniform",
        locally_connected_layers=[
            {
                "filters": 8,
                "activation": "tanh",
                "kernel_size": 3
            }
        ],
    )
    """
    Hyperparameters (and their default values) that affect the neural network
    architecture.
    """

    compile_hyperparameter_defaults = HyperparameterDefaults(
        loss="custom:mse_with_inequalities",
        optimizer="rmsprop",
        learning_rate=None,
    )
    """
    Loss and optimizer hyperparameters. Any values supported by keras may be
    used.
    """

    input_encoding_hyperparameter_defaults = HyperparameterDefaults(
        left_edge=4,
        right_edge=4)
    """
    Number of amino acid residues that are given fixed positions on the each
    side in the variable length encoding.
    """

    fit_hyperparameter_defaults = HyperparameterDefaults(
        max_epochs=500,
        validation_split=0.1,
        early_stopping=True,
        minibatch_size=128,
        random_negative_rate=0.2,
        random_negative_constant=25,
        random_negative_affinity_min=20000.0,
        random_negative_affinity_max=50000.0,
        random_negative_match_distribution=True,
        random_negative_distribution_smoothing=0.0)
    """
    Hyperparameters for neural network training.
    """

    early_stopping_hyperparameter_defaults = HyperparameterDefaults(
        patience=20,
    )
    """
    Hyperparameters for early stopping.
    """

    miscelaneous_hyperparameter_defaults = HyperparameterDefaults(
        train_data={'subset': 'all'},
    )
    """
    Miscelaneous hyperaparameters. These parameters are not used by this class
    but may be interpreted by other code.
    """

    hyperparameter_defaults = network_hyperparameter_defaults.extend(
        compile_hyperparameter_defaults).extend(
        input_encoding_hyperparameter_defaults).extend(
        fit_hyperparameter_defaults).extend(
        early_stopping_hyperparameter_defaults).extend(
        miscelaneous_hyperparameter_defaults
    )
    """
    Combined set of all supported hyperparameters and their default values.
    """

    # Hyperparameter renames.
    # These are updated from time to time as new versions are developed. It
    # provides a primitive way to allow new code to work with models trained
    # using older code.
    # None indicates the hyperparameter has been dropped.
    hyperparameter_renames = {
        "use_embedding": None,
        "pseudosequence_use_embedding": None,
        "monitor": None,
        "min_delta": None,
        "verbose": None,
        "mode": None,
        "take_best_epoch": None,
    }

    @classmethod
    def apply_hyperparameter_renames(cls, hyperparameters):
        """
        Handle hyperparameter renames.

        Parameters
        ----------
        hyperparameters : dict

        Returns
        -------
        dict : updated hyperparameters

        """
        for (from_name, to_name) in cls.hyperparameter_renames.items():
            if from_name in hyperparameters:
                value = hyperparameters.pop(from_name)
                if to_name:
                    hyperparameters[to_name] = value
        return hyperparameters


    def __init__(self, **hyperparameters):
        self.hyperparameters = self.hyperparameter_defaults.with_defaults(
            self.apply_hyperparameter_renames(hyperparameters))

        self._network = None
        self.network_json = None
        self.network_weights = None
        self.network_weights_loader = None

        self.loss_history = None
        self.fit_seconds = None
        self.fit_num_points = None

        self.prediction_cache = weakref.WeakKeyDictionary()

    KERAS_MODELS_CACHE = {}
    """
    Process-wide keras model cache, a map from: architecture JSON string to
    (Keras model, existing network weights)
    """

    @classmethod
    def clear_model_cache(klass):
        """
        Clear the Keras model cache.
        """
        klass.KERAS_MODELS_CACHE.clear()

    @classmethod
    def borrow_cached_network(klass, network_json, network_weights):
        """
        Return a keras Model with the specified architecture and weights.
        As an optimization, when possible this will reuse architectures from a
        process-wide cache.

        The returned object is "borrowed" in the sense that its weights can
        change later after subsequent calls to this method from other objects.

        If you're using this from a parallel implementation you'll need to
        hold a lock while using the returned object.

        Parameters
        ----------
        network_json : string of JSON
        network_weights : list of numpy.array

        Returns
        -------
        keras.models.Model
        """
        assert network_weights is not None
        key = klass.keras_network_cache_key(network_json)
        if key not in klass.KERAS_MODELS_CACHE:
            # Cache miss.
            import keras.models
            network = keras.models.model_from_json(network_json)
            existing_weights = None
        else:
            # Cache hit.
            (network, existing_weights) = klass.KERAS_MODELS_CACHE[key]
        if existing_weights is not network_weights:
            network.set_weights(network_weights)
            klass.KERAS_MODELS_CACHE[key] = (network, network_weights)


        # As an added safety check we overwrite the fit method on the returned
        # model to throw an error if it is called.
        def throw(*args, **kwargs):
            raise NotImplementedError("Do not call fit on cached model.")

        network.fit = throw
        return network

    def network(self, borrow=False):
        """
        Return the keras model associated with this predictor.

        Parameters
        ----------
        borrow : bool
            Whether to return a cached model if possible. See
            borrow_cached_network for details

        Returns
        -------
        keras.models.Model
        """
        if self._network is None and self.network_json is not None:
            self.load_weights()
            if borrow:
                return self.borrow_cached_network(
                    self.network_json,
                    self.network_weights)
            else:
                import keras.models
                self._network = keras.models.model_from_json(self.network_json)
                if self.network_weights is not None:
                    self._network.set_weights(self.network_weights)
                self.network_json = None
                self.network_weights = None
        return self._network

    def update_network_description(self):
        if self._network is not None:
            self.network_json = self._network.to_json()
            self.network_weights = self._network.get_weights()

    @staticmethod
    def keras_network_cache_key(network_json):
        # As an optimization, we remove anything about regularization as these
        # do not affect predictions.
        def drop_properties(d):
            if 'kernel_regularizer' in d:
                del d['kernel_regularizer']
            return d

        description = json.loads(
            network_json,
            object_hook=drop_properties)
        return json.dumps(description)

    def get_config(self):
        """
        serialize to a dict all attributes except model weights
        
        Returns
        -------
        dict
        """
        self.update_network_description()
        result = dict(self.__dict__)
        result['_network'] = None
        result['network_weights'] = None
        result['network_weights_loader'] = None
        result['prediction_cache'] = None
        return result

    @classmethod
    def from_config(cls, config, weights=None, weights_loader=None):
        """
        deserialize from a dict returned by get_config().
        
        Parameters
        ----------
        config : dict
        weights : list of array, optional
            Network weights to restore
        weights_loader : callable, optional
            Function to call (no arguments) to load weights when needed

        Returns
        -------
        Class1NeuralNetwork
        """
        config = dict(config)
        instance = cls(**config.pop('hyperparameters'))
        assert all(hasattr(instance, key) for key in config), config.keys()
        instance.__dict__.update(config)
        instance.network_weights = weights
        instance.network_weights_loader = weights_loader
        instance.prediction_cache = weakref.WeakKeyDictionary()
        return instance

    def load_weights(self):
        """
        Load weights by evaluating self.network_weights_loader, if needed.

        After calling this, self.network_weights_loader will be None and
        self.network_weights will be the weights list, if available.
        """
        if self.network_weights_loader:
            self.network_weights = self.network_weights_loader()
            self.network_weights_loader = None

    def get_weights(self):
        """
        Get the network weights
        
        Returns
        -------
        list of numpy.array giving weights for each layer
        or None if there is no network
        """
        self.update_network_description()
        self.load_weights()
        return self.network_weights

    def __getstate__(self):
        """
        serialize to a dict. Model weights are included. For pickle support.
        
        Returns
        -------
        dict

        """
        self.update_network_description()
        self.load_weights()
        result = dict(self.__dict__)
        result['_network'] = None
        result['prediction_cache'] = None
        return result

    def peptides_to_network_input(self, peptides):
        """
        Encode peptides to the fixed-length encoding expected by the neural
        network (which depends on the architecture).
        
        Parameters
        ----------
        peptides : EncodableSequences or list of string

        Returns
        -------
        numpy.array
        """
        encoder = EncodableSequences.create(peptides)
        if (self.hyperparameters['peptide_amino_acid_encoding'] == "embedding"):
            encoded = encoder.variable_length_to_fixed_length_categorical(
                max_length=self.hyperparameters['kmer_size'],
                **self.input_encoding_hyperparameter_defaults.subselect(
                    self.hyperparameters))
        elif (
                self.hyperparameters['peptide_amino_acid_encoding'] in
                    available_vector_encodings()):
            encoded = encoder.variable_length_to_fixed_length_vector_encoding(
                self.hyperparameters['peptide_amino_acid_encoding'],
                max_length=self.hyperparameters['kmer_size'],
                **self.input_encoding_hyperparameter_defaults.subselect(
                    self.hyperparameters))
        else:
            raise ValueError("Unsupported peptide_amino_acid_encoding: %s" %
                             self.hyperparameters['peptide_amino_acid_encoding'])
        assert len(encoded) == len(peptides)
        return encoded

    @property
    def supported_peptide_lengths(self):
        """
        (minimum, maximum) lengths of peptides supported, inclusive.
        
        Returns
        -------
        (int, int) tuple

        """
        return (
            self.hyperparameters['left_edge'] +
            self.hyperparameters['right_edge'],
        self.hyperparameters['kmer_size'])

    def allele_encoding_to_network_input(self, allele_encoding):
        """
        Encode alleles to the fixed-length encoding expected by the neural
        network (which depends on the architecture).

        Parameters
        ----------
        allele_encoding : AlleleEncoding

        Returns
        -------
        numpy.array
        """
        return allele_encoding.fixed_length_vector_encoded_sequences("BLOSUM62")

    def fit(
            self,
            peptides,
            affinities,
            allele_encoding=None,
            inequalities=None,
            sample_weights=None,
            shuffle_permutation=None,
            verbose=1,
            progress_preamble="",
            progress_print_interval=5.0):
        """
        Fit the neural network.
        
        Parameters
        ----------
        peptides : EncodableSequences or list of string
        
        affinities : list of float
            nM affinities. Must be same length of as peptides.
        
        allele_encoding : AlleleEncoding, optional
            If not specified, the model will be a single-allele predictor.

        inequalities : list of string, each element one of ">", "<", or "=".
            Inequalities to use for fitting. Same length as affinities.
            Each element must be one of ">", "<", or "=". For example, a ">"
            will train on y_pred > y_true for that element in the training set.
            Requires using a custom losses that support inequalities (e.g.
            mse_with_ineqalities).
            If None all inequalities are taken to be "=".
            
        sample_weights : list of float, optional
            If not specified, all samples (including random negatives added
            during training) will have equal weight. If specified, the random
            negatives will be assigned weight=1.0.

        shuffle_permutation : list of int, optional
            Permutation (integer list) of same length as peptides and affinities
            If None, then a random permutation will be generated.

        verbose : int
            Keras verbosity level

        progress_preamble : string
            Optional string of information to include in each progress update

        progress_print_interval : float
            How often (in seconds) to print progress update. Set to None to
            disable.
        """

        self.fit_num_points = len(peptides)

        encodable_peptides = EncodableSequences.create(peptides)
        peptide_encoding = self.peptides_to_network_input(encodable_peptides)

        length_counts = (
            pandas.Series(encodable_peptides.sequences)
            .str.len().value_counts().to_dict())

        num_random_negative = {}
        for length in range(8, 16):
            num_random_negative[length] = int(
                length_counts.get(length, 0) *
                self.hyperparameters['random_negative_rate'] +
                self.hyperparameters['random_negative_constant'])
        num_random_negative = pandas.Series(num_random_negative)
        logging.info("Random negative counts per length:\n%s" % (
            str(num_random_negative.to_dict())))

        aa_distribution = None
        if self.hyperparameters['random_negative_match_distribution']:
            aa_distribution = amino_acid_distribution(
                encodable_peptides.sequences,
                smoothing=self.hyperparameters[
                    'random_negative_distribution_smoothing'])
            logging.info(
                "Using amino acid distribution for random negative:\n%s" % (
                str(aa_distribution.to_dict())))

        y_values = from_ic50(affinities)
        assert numpy.isnan(y_values).sum() == 0, numpy.isnan(y_values).sum()
        if inequalities is not None:
            # Reverse inequalities because from_ic50() flips the direction
            # (i.e. lower affinity results in higher y values).
            adjusted_inequalities = pandas.Series(inequalities).map({
                "=": "=",
                ">": "<",
                "<": ">",
            }).values
        else:
            adjusted_inequalities = numpy.tile("=", len(y_values))
        if len(adjusted_inequalities) != len(y_values):
            raise ValueError("Inequalities and y_values must have same length")

        x_dict_without_random_negatives = {
            'peptide': peptide_encoding,
        }
        allele_encoding_dims = None
        if allele_encoding is not None:
            allele_encoding_input = self.allele_encoding_to_network_input(
                allele_encoding)
            allele_encoding_dims = allele_encoding_input.shape[1:]
            x_dict_without_random_negatives['allele'] = allele_encoding_input

        # Shuffle y_values and the contents of x_dict_without_random_negatives
        # This ensures different data is used for the test set for early stopping
        # when multiple models are trained.
        if shuffle_permutation is None:
            shuffle_permutation = numpy.random.permutation(len(y_values))
        y_values = y_values[shuffle_permutation]
        peptide_encoding = peptide_encoding[shuffle_permutation]
        adjusted_inequalities = adjusted_inequalities[shuffle_permutation]
        for key in x_dict_without_random_negatives:
            x_dict_without_random_negatives[key] = (
                x_dict_without_random_negatives[key][shuffle_permutation])
        if sample_weights is not None:
            sample_weights = sample_weights[shuffle_permutation]

        if self.hyperparameters['loss'].startswith("custom:"):
            # Using a custom loss that supports inequalities
            try:
                custom_loss = CUSTOM_LOSSES[
                    self.hyperparameters['loss'].replace("custom:", "")
                ]
            except KeyError:
                raise ValueError(
                    "No such custom loss function: %s. Supported losses are: %s" % (
                        self.hyperparameters['loss'],
                        ", ".join([
                            "custom:" + loss_name for loss_name in CUSTOM_LOSSES
                        ])))
            loss_name_or_function = custom_loss.loss
            loss_supports_inequalities = custom_loss.supports_inequalities
            loss_encode_y_function = custom_loss.encode_y
        else:
            # Using a regular keras loss. No inequalities supported.
            loss_name_or_function = self.hyperparameters['loss']
            loss_supports_inequalities = False
            loss_encode_y_function = None

        if not loss_supports_inequalities and (
                any(inequality != "=" for inequality in adjusted_inequalities)):
            raise ValueError("Loss %s does not support inequalities" % (
                loss_name_or_function))

        if self.network() is None:
            self._network = self.make_network(
                allele_encoding_dims=allele_encoding_dims,
                **self.network_hyperparameter_defaults.subselect(
                    self.hyperparameters))
            self.network().compile(
                loss=loss_name_or_function,
                optimizer=self.hyperparameters['optimizer'])

        if self.hyperparameters['learning_rate'] is not None:
            from keras import backend as K
            K.set_value(
                self.network().optimizer.lr,
                self.hyperparameters['learning_rate'])

        if loss_supports_inequalities:
            # Do not sample negative affinities: just use an inequality.
            random_negative_ic50 = self.hyperparameters['random_negative_affinity_min']
            random_negative_target = from_ic50(random_negative_ic50)

            y_dict_with_random_negatives = {
                "output": numpy.concatenate([
                    numpy.tile(
                        random_negative_target, int(num_random_negative.sum())),
                    y_values,
                ]),
            }
            # Note: we are using "<" here not ">" because the inequalities are
            # now in target-space (0-1) not affinity-space.
            adjusted_inequalities_with_random_negatives = (
                ["<"] * int(num_random_negative.sum()) +
                list(adjusted_inequalities))
        else:
            # Randomly sample random negative affinities
            y_dict_with_random_negatives = {
                "output": numpy.concatenate([
                    from_ic50(
                        numpy.random.uniform(
                            self.hyperparameters[
                                'random_negative_affinity_min'],
                            self.hyperparameters[
                                'random_negative_affinity_max'],
                            int(num_random_negative.sum()))),
                    y_values,
                ]),
            }
        if sample_weights is not None:
            sample_weights_with_random_negatives = numpy.concatenate([
                numpy.ones(int(num_random_negative.sum())),
                sample_weights])
        else:
            sample_weights_with_random_negatives = None

        if loss_encode_y_function is not None:
            y_dict_with_random_negatives['output'] = loss_encode_y_function(
                y_dict_with_random_negatives['output'],
                adjusted_inequalities_with_random_negatives)

        val_losses = []
        min_val_loss_iteration = None
        min_val_loss = None

        self.loss_history = collections.defaultdict(list)
        start = time.time()
        last_progress_print = None
        x_dict_with_random_negatives = {}
        for i in range(self.hyperparameters['max_epochs']):
            random_negative_peptides_list = []
            for (length, count) in num_random_negative.iteritems():
                random_negative_peptides_list.extend(
                    random_peptides(
                        count,
                        length=length,
                        distribution=aa_distribution))
            random_negative_peptides = EncodableSequences.create(
                random_negative_peptides_list)
            random_negative_peptides_encoding = (
                self.peptides_to_network_input(random_negative_peptides))

            if not x_dict_with_random_negatives:
                if len(random_negative_peptides) > 0:
                    x_dict_with_random_negatives["peptide"] = numpy.concatenate([
                        random_negative_peptides_encoding,
                        peptide_encoding,
                    ])
                    if 'allele' in x_dict_without_random_negatives:
                        x_dict_with_random_negatives['allele'] = numpy.concatenate([
                            x_dict_without_random_negatives['allele'][
                                numpy.random.choice(
                                    x_dict_without_random_negatives[
                                        'allele'].shape[0],
                                    size=len(random_negative_peptides_list))],
                            x_dict_without_random_negatives['allele']
                        ])
                else:
                    x_dict_with_random_negatives = (
                        x_dict_without_random_negatives)
            else:
                # Update x_dict_with_random_negatives in place.
                # This is more memory efficient than recreating it as above.
                if len(random_negative_peptides) > 0:
                    x_dict_with_random_negatives["peptide"][:len(random_negative_peptides)] = (
                        random_negative_peptides_encoding
                    )
                    if 'allele' in x_dict_with_random_negatives:
                        x_dict_with_random_negatives['allele'][:len(random_negative_peptides)] = (
                            x_dict_with_random_negatives['allele'][
                                len(random_negative_peptides) + numpy.random.choice(
                                    x_dict_with_random_negatives['allele'].shape[0] -
                                    len(random_negative_peptides),
                                    size=len(random_negative_peptides))
                            ]
                        )

            fit_history = self.network().fit(
                x_dict_with_random_negatives,
                y_dict_with_random_negatives,
                shuffle=True,
                batch_size=self.hyperparameters['minibatch_size'],
                verbose=verbose,
                epochs=1,
                validation_split=self.hyperparameters['validation_split'],
                sample_weight=sample_weights_with_random_negatives)

            for (key, value) in fit_history.history.items():
                self.loss_history[key].extend(value)

            # Print progress no more often than once every few seconds.
            if progress_print_interval is not None and (
                    not last_progress_print or (
                        time.time() - last_progress_print
                        > progress_print_interval)):
                print((progress_preamble + " " +
                       "Epoch %3d / %3d: loss=%g. "
                       "Min val loss (%s) at epoch %s" % (
                           i,
                           self.hyperparameters['max_epochs'],
                           self.loss_history['loss'][-1],
                           str(min_val_loss),
                           min_val_loss_iteration)).strip())
                last_progress_print = time.time()

            if self.hyperparameters['validation_split']:
                val_loss = self.loss_history['val_loss'][-1]
                val_losses.append(val_loss)

                if min_val_loss is None or val_loss <= min_val_loss:
                    min_val_loss = val_loss
                    min_val_loss_iteration = i

                if self.hyperparameters['early_stopping']:
                    threshold = (
                        min_val_loss_iteration +
                        self.hyperparameters['patience'])
                    if i > threshold:
                        if progress_print_interval is not None:
                            print((progress_preamble + " " +
                                "Stopping at epoch %3d / %3d: loss=%g. "
                                "Min val loss (%s) at epoch %s" % (
                                    i,
                                    self.hyperparameters['max_epochs'],
                                    self.loss_history['loss'][-1],
                                    str(min_val_loss),
                                    min_val_loss_iteration)).strip())
                        break
        self.fit_seconds = time.time() - start

    def predict(self, peptides, allele_encoding=None, batch_size=4096):
        """
        Predict affinities.

        If peptides are specified as EncodableSequences, then the predictions
        will be cached for this predictor as long as the EncodableSequences object
        remains in memory. The cache is keyed in the object identity of the
        EncodableSequences, not the sequences themselves.

        Parameters
        ----------
        peptides : EncodableSequences or list of string
        
        allele_encoding : AlleleEncoding, optional
            Only required when this model is a pan-allele model

        batch_size : int
            batch_size passed to Keras

        Returns
        -------
        numpy.array of nM affinity predictions 
        """
        use_cache = (
            allele_encoding is None and
            isinstance(peptides, EncodableSequences))
        if use_cache and peptides in self.prediction_cache:
            return self.prediction_cache[peptides].copy()

        x_dict = {
            'peptide': self.peptides_to_network_input(peptides)
        }
        if allele_encoding is not None:
            allele_input = self.allele_encoding_to_network_input(allele_encoding)
            x_dict['allele'] = allele_input

        network = self.network(borrow=True)
        raw_predictions = network.predict(x_dict, batch_size=batch_size)
        predictions = numpy.array(raw_predictions, dtype = "float64")[:,0]
        result = to_ic50(predictions)
        if use_cache:
            self.prediction_cache[peptides] = result
        return result

    @staticmethod
    def make_network(
            allele_encoding_dims,
            kmer_size,
            peptide_amino_acid_encoding,
            embedding_input_dim,
            embedding_output_dim,
            allele_dense_layer_sizes,
            peptide_dense_layer_sizes,
            peptide_allele_merge_method,
            peptide_allele_merge_activation,
            layer_sizes,
            dense_layer_l1_regularization,
            dense_layer_l2_regularization,
            activation,
            init,
            output_activation,
            dropout_probability,
            batch_normalization,
            embedding_init_method,
            locally_connected_layers):
        """
        Helper function to make a keras network for class1 affinity prediction.
        """

        # We import keras here to avoid tensorflow debug output, etc. unless we
        # are actually about to use Keras.

        from keras.layers import Input
        import keras.layers
        from keras.layers.core import Dense, Flatten, Dropout
        from keras.layers.embeddings import Embedding
        from keras.layers.normalization import BatchNormalization

        if peptide_amino_acid_encoding == "embedding":
            peptide_input = Input(
                shape=(kmer_size,), dtype='int32', name='peptide')
            current_layer = Embedding(
                input_dim=embedding_input_dim,
                output_dim=embedding_output_dim,
                input_length=kmer_size,
                embeddings_initializer=embedding_init_method,
                name="peptide_embedding")(peptide_input)
        else:
            peptide_input = Input(
                shape=(
                    kmer_size,
                    vector_encoding_length(peptide_amino_acid_encoding)),
                dtype='float32',
                name='peptide')
            current_layer = peptide_input

        inputs = [peptide_input]

        kernel_regularizer = None
        l1 = dense_layer_l1_regularization
        l2 = dense_layer_l2_regularization
        if l1 > 0 or l2 > 0:
            kernel_regularizer = keras.regularizers.l1_l2(l1, l2)

        for (i, locally_connected_params) in enumerate(locally_connected_layers):
            current_layer = keras.layers.LocallyConnected1D(
                name="lc_%d" % i,
                **locally_connected_params)(current_layer)

        current_layer = Flatten(name="flattened_0")(current_layer)

        for (i, layer_size) in enumerate(peptide_dense_layer_sizes):
            current_layer = Dense(
                layer_size,
                name="peptide_dense_%d" % i,
                kernel_regularizer=kernel_regularizer,
                activation=activation)(current_layer)

        if batch_normalization:
            current_layer = BatchNormalization(name="batch_norm_early")(
                current_layer)

        if dropout_probability:
            current_layer = Dropout(dropout_probability, name="dropout_early")(
                current_layer)

        if allele_encoding_dims:
            allele_input = Input(
                shape=allele_encoding_dims,
                dtype='float32',
                name='allele')
            inputs.append(allele_input)
            allele_embedding_layer = Flatten(name="allele_flat")(allele_input)

            for (i, layer_size) in enumerate(allele_dense_layer_sizes):
                allele_embedding_layer = Dense(
                    layer_size,
                    name="allele_dense_%d" % i,
                    kernel_regularizer=kernel_regularizer,
                    activation=activation)(allele_embedding_layer)

            if peptide_allele_merge_method == 'concatenate':
                current_layer = keras.layers.concatenate([
                    current_layer, allele_embedding_layer
                ], name="allele_peptide_merged")
            elif peptide_allele_merge_method == 'multiply':
                current_layer = keras.layers.multiply([
                    current_layer, allele_embedding_layer
                ], name="allele_peptide_merged")
            else:
                raise ValueError(
                    "Unsupported peptide_allele_encoding_merge_method: %s"
                    % peptide_allele_merge_method)

            if peptide_allele_merge_activation:
                current_layer = keras.layers.Activation(
                    peptide_allele_merge_activation,
                    name="alelle_peptide_merged_%s" %
                         peptide_allele_merge_activation)(current_layer)
            
        for (i, layer_size) in enumerate(layer_sizes):
            current_layer = Dense(
                layer_size,
                activation=activation,
                kernel_regularizer=kernel_regularizer,
                name="dense_%d" % i)(current_layer)

            if batch_normalization:
                current_layer = BatchNormalization(name="batch_norm_%d" % i)\
                    (current_layer)

            if dropout_probability > 0:
                current_layer = Dropout(
                    dropout_probability, name="dropout_%d" % i)(current_layer)

        output = Dense(
            1,
            kernel_initializer=init,
            activation=output_activation,
            name="output")(current_layer)
        model = keras.models.Model(
            inputs=inputs,
            outputs=[output],
            name="predictor")
        return model