from src.models.AbstractModel import AbstractModel
from keras.models import Sequential
from keras.layers import Dense, Lambda, Embedding
import keras.backend as K
from keras import optimizers


class Word2VecModel(AbstractModel):

    def __init__(self, save_directory, vocabulary_size, output_dim,
        input_length):
        super().__init__(save_directory)
        self.vocabulary_size = vocabulary_size
        self.output_dim = output_dim
        self.input_length = input_length
        self.build_model()

    def build_model(self):
        model = Sequential()
        model.add(Embedding(input_dim=self.vocabulary_size, output_dim=self
            .output_dim, input_length=self.input_length))
        model.add(Lambda(lambda x: K.mean(x, axis=1), output_shape=(self.
            output_dim,)))
        model.add(Dense(self.vocabulary_size, activation='softmax'))
        adam = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=
            None, decay=0, amsgrad=False)
        model.compile(loss='categorical_crossentropy', optimizer=adam,
            metrics=['categorical_accuracy'])
        self.model = model
        return self.model

8616696a48e1b523ca49dfeeeb774fb767b88104
<mask token>


class Word2VecModel(AbstractModel):
    <mask token>
    <mask token>

<mask token>


class Word2VecModel(AbstractModel):
    <mask token>

    def build_model(self):
        model = Sequential()
        model.add(Embedding(input_dim=self.vocabulary_size, output_dim=self
            .output_dim, input_length=self.input_length))
        model.add(Lambda(lambda x: K.mean(x, axis=1), output_shape=(self.
            output_dim,)))
        model.add(Dense(self.vocabulary_size, activation='softmax'))
        adam = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=
            None, decay=0, amsgrad=False)
        model.compile(loss='categorical_crossentropy', optimizer=adam,
            metrics=['categorical_accuracy'])
        self.model = model
        return self.model

<mask token>


class Word2VecModel(AbstractModel):

    def __init__(self, save_directory, vocabulary_size, output_dim,
        input_length):
        super().__init__(save_directory)
        self.vocabulary_size = vocabulary_size
        self.output_dim = output_dim
        self.input_length = input_length
        self.build_model()

    def build_model(self):
        model = Sequential()
        model.add(Embedding(input_dim=self.vocabulary_size, output_dim=self
            .output_dim, input_length=self.input_length))
        model.add(Lambda(lambda x: K.mean(x, axis=1), output_shape=(self.
            output_dim,)))
        model.add(Dense(self.vocabulary_size, activation='softmax'))
        adam = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=
            None, decay=0, amsgrad=False)
        model.compile(loss='categorical_crossentropy', optimizer=adam,
            metrics=['categorical_accuracy'])
        self.model = model
        return self.model

from src.models.AbstractModel import AbstractModel
from keras.models import Sequential
from keras.layers import Dense, Lambda, Embedding
import keras.backend as K
from keras import optimizers


class Word2VecModel(AbstractModel):

    def __init__(self, save_directory, vocabulary_size, output_dim,
        input_length):
        super().__init__(save_directory)
        self.vocabulary_size = vocabulary_size
        self.output_dim = output_dim
        self.input_length = input_length
        self.build_model()

    def build_model(self):
        model = Sequential()
        model.add(Embedding(input_dim=self.vocabulary_size, output_dim=self
            .output_dim, input_length=self.input_length))
        model.add(Lambda(lambda x: K.mean(x, axis=1), output_shape=(self.
            output_dim,)))
        model.add(Dense(self.vocabulary_size, activation='softmax'))
        adam = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=
            None, decay=0, amsgrad=False)
        model.compile(loss='categorical_crossentropy', optimizer=adam,
            metrics=['categorical_accuracy'])
        self.model = model
        return self.model
