model.py

from tensorflow.keras.layers import Embedding, Dense, Flatten
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from sklearn.preprocessing import OneHotEncoder

# this one is so we might grab the embeddings
model_emb = Sequential()
embedding = Embedding(num_letters, 2, input_length=1)
model_emb.add(embedding)

output_array = model_emb.predict(integers_in)

# this one is so we might learn the mapping
model_pred = Sequential()
model_pred.add(embedding)
model_pred.add(Flatten())
model_pred.add(Dense(num_letters, activation="softmax"))

adam = Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False)

model_pred.compile('adam', 'categorical_crossentropy', metrics=['accuracy'])


to_predict = OneHotEncoder(sparse=False).fit_transform(integers_out)
model_pred.fit(integers_in, to_predict, epochs=30, verbose=1)

output_array = model_emb.predict(integers_in)