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python saving tensorflow variables

I am confused about how the Tensorflow variables are saved, so here is what I try to understand:
import tensorflow as tf

with tf.Session() as sess:
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(5, activation=tf.nn.relu, input_shape=(784,)))
#model.add(tf.keras.layers.Dense(1, activation=tf.nn.sigmoid))

model.compile(optimizer=tf.train.AdamOptimizer(2e-4), loss=’mse’)

x = tf.keras.Input(shape=(784,))
y = tf.keras.layers.Dense(3)(x)

model.fit(x, y, epochs=10)

cnt = 0
save_path = saver.save(sess, “output.model”)
with open(save_path, ‘r’) as f:
for line in f:
if line.startswith(‘Epoch’):
cnt += 1
print(“epoch:”, cnt, “loss:”, sess.run(model.loss))

The model is saved and I can read it in the terminal, so the model is saved.
But is it possible to read the model saved inside the script?

Yes, it is possible.
From here,

Restoring a model from a directory is usually no longer needed. However, you can use the model_dir argument for tf.keras.Model to write the model to a directory of your choice.

import tensor
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