import pandas as pd
import tensorflow as tf
import numpy as np

#%%
model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(250, input_shape=(1,), activation="relu"),
    tf.keras.layers.Dense(100, activation=tf.keras.activations.relu),
])

#%%
model.compile(
    loss=tf.keras.losses.mse,
    optimizer=tf.keras.optimizers.Adam(),
    metrics=["mae"]
)
7c41eda48b19be188ccf5ffc7dd292d6b0136ed8
<mask token>

<mask token>
model.compile(loss=tf.keras.losses.mse, optimizer=tf.keras.optimizers.Adam(
    ), metrics=['mae'])

<mask token>
model = tf.keras.models.Sequential([tf.keras.layers.Dense(250, input_shape=
    (1,), activation='relu'), tf.keras.layers.Dense(100, activation=tf.
    keras.activations.relu)])
model.compile(loss=tf.keras.losses.mse, optimizer=tf.keras.optimizers.Adam(
    ), metrics=['mae'])

import pandas as pd
import tensorflow as tf
import numpy as np
model = tf.keras.models.Sequential([tf.keras.layers.Dense(250, input_shape=
    (1,), activation='relu'), tf.keras.layers.Dense(100, activation=tf.
    keras.activations.relu)])
model.compile(loss=tf.keras.losses.mse, optimizer=tf.keras.optimizers.Adam(
    ), metrics=['mae'])

import pandas as pd
import tensorflow as tf
import numpy as np

#%%
model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(250, input_shape=(1,), activation="relu"),
    tf.keras.layers.Dense(100, activation=tf.keras.activations.relu),
])

#%%
model.compile(
    loss=tf.keras.losses.mse,
    optimizer=tf.keras.optimizers.Adam(),
    metrics=["mae"]
)