ValueError: tape is required when a Tensor loss is passed - tensorflow

Question

As said in https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Optimizer?hl=en#minimize, the first parameter of minmize should satisfy the requirement,
Tensor or callable. If a callable, loss should take no arguments and return the value to minimize. If a Tensor, the tape argument must be passed.
The first piece of code takes tensor as the input of minimize(), and it requires the gradient tape, but I don't know how.
The second piece of code takes callable function as the input of minimize(), which is easy
import numpy as np
import tensorflow as tf
from tensorflow import keras
x_train = [1, 2, 3]
y_train = [1, 2, 3]
W = tf.Variable(tf.random.normal([1]), name='weight')
b = tf.Variable(tf.random.normal([1]), name='bias')
hypothesis = W * x_train + b
#tf.function
def cost():
y_model = W * x_train + b
error = tf.reduce_mean(tf.square(y_train - y_model))
return error
optimizer = tf.optimizers.SGD(learning_rate=0.01)
cost_value = cost()
train = tf.keras.optimizers.Adam().minimize(cost_value, var_list=[W, b])
tf.print(W)
tf.print(b)
How to add the gradient tape, I know the following code certainly works.
import numpy as np
import tensorflow as tf
from tensorflow import keras
x_train = [1, 2, 3]
y_train = [1, 2, 3]
W = tf.Variable(tf.random.normal([1]), name='weight')
b = tf.Variable(tf.random.normal([1]), name='bias')
hypothesis = W * x_train + b
#tf.function
def cost():
y_model = W * x_train + b
error = tf.reduce_mean(tf.square(y_train - y_model))
return error
optimizer = tf.optimizers.SGD(learning_rate=0.01)
cost_value = cost()
train = tf.keras.optimizers.Adam().minimize(cost, var_list=[W, b])
tf.print(W)
tf.print(b)
Please help me revise the first piece of code and let it run, thanks!

Answer 1

This occurs because .minimize() expects a function. While cost_value&cost(), is a tf.Tensor object, cost is a tf.function. You should directly pass your loss function into the minimize as tf.keras.optimizers.Adam().minimize(cost, var_list=[W, b]).
Changed part for Gradient:
train = tf.keras.optimizers.Adam().minimize(cost(), var_list=[W, b],tape=tf.GradientTape())

Answer 2

This is a late answer (Hakan basically got it for you), but I write this in hopes that it will help people in the future that are stuck and googling this exact question (like I was). This is also an alternate implementation using the tf.GradientTape() directly.
import numpy as np
import tensorflow as tf
from tensorflow import keras
x_train = [1, 2, 3]
y_train = [1, 2, 3]
W = tf.Variable(tf.random.normal([1]), trainable = True, name='weight')
b = tf.Variable(tf.random.normal([1]), trainable = True, name='bias')
#tf.function
def cost(W, b):
y_model = W * x_train + b
error = tf.reduce_mean(tf.square(y_train - y_model))
return error
optimizer = tf.optimizers.SGD(learning_rate=0.01)
trainable_vars = [W,b]
epochs = 100 #(or however many iterations you want it to run)
for _ in range(epochs):
with tf.GradientTape() as tp:
#your loss/cost function must always be contained within the gradient tape instantiation
cost_fn = cost(W, b)
gradients = tp.gradient(cost_fn, trainable_vars)
optimizer.apply_gradients(zip(gradients, trainable_vars))
tf.print(W)
tf.print(b)
This should give you the value of your weights and biases after the number of epochs you ran.
You must compute the loss function everytime a new gradient tape is invoked. Then you get the gradient of your loss function, and then call optimizer.apply_gradient to do your minimization according to what tensorflow documentation says here: https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Optimizer#apply_gradients.