lstm.py

#Sequential model has a 50 cell LSTM layer before Dense layers
model = tf.keras.models.Sequential()


model.add(tf.keras.layers.LSTM(50))
model.add(tf.keras.layers.Dense(20,activation='relu'))
model.add(tf.keras.layers.Dense(5,activation='relu'))

model.add(tf.keras.layers.Dense(1,activation='sigmoid'))

#Compile model with binary_crossentropy loss, Adam optimizer, and accuracy metrics
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate = 1e-4),\
              loss="binary_crossentropy", metrics=['accuracy'])

#Train model on 10 epochs
model.fit(indiv, y_train,validation_data=[test,y_test],epochs=10)