#Start with the NN

model = tf.keras.Sequential([
    tf.keras.layers.Embedding(np.amax(ml_input)+1, 300, input_length = x_train.shape[1]),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(500, activation=tf.keras.activations.softmax),
    tf.keras.layers.Dense(1, activation = tf.keras.activations.linear)
])

model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.01),
              loss=tf.keras.losses.mean_absolute_error,
              metrics=[R_squared])
model.summary()



#Train the Model
callback = [tf.keras.callbacks.EarlyStopping(monitor='loss', min_delta=5.0, patience=15),
           tf.keras.callbacks.ReduceLROnPlateau(monitor='loss', factor=0.1, patience=5, min_delta=5.00, min_lr=0)
           ]
history = model.fit(x_train, y_train, epochs=50,  batch_size=64, verbose =2, callbacks = callback)