Now we can try a more complex example implementing a logistic regression algorithm. The first step, as usual, is creating a dummy dataset:
from sklearn.datasets import make_classification>>> nb_samples = 500>>> X, Y = make_classification(n_samples=nb_samples, n_features=2, n_redundant=0, n_classes=2)
The dataset is shown in the following figure:
At this point, we can create the graph and all placeholders, variables, and operations:
import tensorflow as tf>>> graph = tf.Graph()>>> with graph.as_default():>>> Xt = tf.placeholder(tf.float32, shape=(None, 2), name='points')>>> Yt = tf.placeholder(tf.float32, shape=(None, ...
