nn_word_embedding

# Deep learning: 
from keras.models import Input, Model
from keras.layers import Dense

# Defining the size of the embedding
embed_size = 2

# Defining the neural network
inp = Input(shape=(X.shape[1],))
x = Dense(units=embed_size, activation='linear')(inp)
x = Dense(units=Y.shape[1], activation='softmax')(x)
model = Model(inputs=inp, outputs=x)
model.compile(loss = 'categorical_crossentropy', optimizer = 'adam')

# Optimizing the network weights
model.fit(
    x=X, 
    y=Y, 
    batch_size=256,
    epochs=1000
    )

# Obtaining the weights from the neural network. 
# These are the so called word embeddings

# The input layer 
weights = model.get_weights()[0]

# Creating a dictionary to store the embeddings in. The key is a unique word and 
# the value is the numeric vector
embedding_dict = {}
for word in words: 
    embedding_dict.update({
        word: weights[unique_word_dict.get(word)]
        })