Tensorflow 1.3 tutorial learning Tensorflow 2.0 - tensorflow2.0

Question

I am new to tensorflow and am trying to use the new version 2.0. I understand what is happening in the 1.3 tutorial but a lot of things have been depreciated. If someone could please make the code below work with the 2.0 version it would help me to understand how to convert what is being done in the tutorials to the new version on tensorflow.
Everything commented out doesn't work due to depredations.
I do not want to use any tensorflow versions below 2.0
n_features = 10
n_dense_neurons = 3
#x = tf.placeholder(tf.float32,(None, n_features))
W = tf.Variable(tf.random.normal([n_features,n_dense_neurons]))
b = tf.Variable(tf.ones([n_dense_neurons]))
#xW = tf.matmul(x,W)
#z = tf.add(xW,b)
#a = tf.sigmoid(z)
#init = tf.global_variables_initializer()
#with tf.Session() as sess:
#sess.run(init)
#layer_out = sess.run(a,feed_dict={x:np.random.random([1,n_features])})
#print(layer_out)

Answer 1

It's pretty straightforward: forget about the tf.Session object and about tf.placeholder, just execute the code line by line, it works! (this is the eager execution, that is the default in TensorFlow 2.0).
Thus, you don't have to declare a placeholder, but you have to just use an input value (I added a tf.cast operation because np.random.random returns a double but TensorFlow MatMul operation works with tf.float32 value).
import numpy as np
import tensorflow as tf
n_features = 10
n_dense_neurons = 3
x = tf.cast(np.random.random([1,n_features]), tf.float32)
W = tf.Variable(tf.random.normal([n_features,n_dense_neurons]))
b = tf.Variable(tf.ones([n_dense_neurons]))
xW = tf.matmul(x,W)
z = tf.add(xW,b)
a = tf.sigmoid(z)
print(a)