In Chapter 2, we developed a logistic regression model for binary classification with one neuron and applied it to two digits of the MNIST dataset. The actual Python code
for the computational graph construction was just ten lines of code (excluding the part that performs the training of the model; review Chapter 2, if you don’t remember what we did there).
tf.reset_default_graph()
X = tf.placeholder(tf.float32, [n_dim, None])
Y = tf.placeholder(tf.float32, [1, None])
learning_rate = tf.placeholder(tf.float32, shape=())
W = tf.Variable(tf.zeros([1, n_dim])) ...
