Notebook 01A: Can't fit SLR with tensorflow using iterative optimizer steps

[abhi8893]

intercept = tf.Variable(polymodel.params[0], dtype=tf.float32)
slope = tf.Variable(polymodel.params[1], dtype=tf.float32)

def predict(intercept, slope, X):
    m = PolynomialModel([intercept, slope], 0)
    return m(X)

def mse(labels, preds):
    return tf.keras.losses.mse(tf.squeeze(labels), tf.squeeze(preds))

def loss_func(intercept, slope, X, y):
    predictions = predict(intercept, slope, X)
    return mse(y, predictions)

with tf.GradientTape() as tape:
    tape.watch(intercept)
    tape.watch(slope)

opt = tf.keras.optimizers.Adam(lr=0.05)
for i in range(20):
    opt.minimize(lambda : loss_func(intercept, slope, X, y), 
                 var_list=[intercept, slope])

Traceback:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-20-3ef69543ef29> in <module>
     19 opt = tf.keras.optimizers.Adam(learning_rate=0.01)
     20 for i in range(20):
---> 21     opt.minimize(lambda : loss_func(intercept, slope, X, y), var_list=[intercept, slope])

~\anaconda3\envs\ds-py37\lib\site-packages\tensorflow\python\keras\optimizer_v2\optimizer_v2.py in minimize(self, loss, var_list, grad_loss, name, tape)
    496     grads_and_vars = self._compute_gradients(
    497         loss, var_list=var_list, grad_loss=grad_loss, tape=tape)
--> 498     return self.apply_gradients(grads_and_vars, name=name)
    499 
    500   def _compute_gradients(self, loss, var_list, grad_loss=None, tape=None):

~\anaconda3\envs\ds-py37\lib\site-packages\tensorflow\python\keras\optimizer_v2\optimizer_v2.py in apply_gradients(self, grads_and_vars, name, experimental_aggregate_gradients)
    596       RuntimeError: If called in a cross-replica context.
    597     """
--> 598     grads_and_vars = optimizer_utils.filter_empty_gradients(grads_and_vars)
    599     var_list = [v for (_, v) in grads_and_vars]
    600 

~\anaconda3\envs\ds-py37\lib\site-packages\tensorflow\python\keras\optimizer_v2\utils.py in filter_empty_gradients(grads_and_vars)
     77   if not filtered:
     78     raise ValueError("No gradients provided for any variable: %s." %
---> 79                      ([v.name for _, v in grads_and_vars],))
     80   if vars_with_empty_grads:
     81     logging.warning(

ValueError: No gradients provided for any variable: ['Variable:0', 'Variable:0'].

[abhi8893]

Relates #19