moar

model.py

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+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)