The document describes code for generating synthetic classification data and building a logistic regression model in TensorFlow to classify the data. It generates data points from two Gaussian distributions, plots the data, builds the TensorFlow graph with placeholders, weights, predictions, loss and training operation. It is missing code to actually train the model, extract the trained weights to make predictions, and plot the predictions over the original data.

1. Please help fill in the missing code below in order for it run correctly. Areas that are
needed/missing are in highlighted in bold .
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
#1) Generate the synthetic data using the following Python code snippet.
# Generate synthetic data
N = 100
# Zeros form a Gaussian centered at (-1, -1)
x_zeros = np.random.multivariate_normal(mean=np.array((-1, -1)), cov=.1*np.eye(2),
size=(N//2,))
y_zeros = np.zeros((N//2,))
# Ones form a Gaussian centered at (1, 1)
x_ones = np.random.multivariate_normal(mean=np.array((1, 1)), cov=.1*np.eye(2),
size=(N//2,))
y_ones = np.ones((N//2,))
x_np = np.vstack([x_zeros, x_ones])
y_np = np.concatenate([y_zeros, y_ones])
# Plot x_zeros and x_ones on the same graph
plt.scatter(x_zeros[:,0], x_zeros[:,1], label='class 0')
plt.scatter(x_ones[:,0], x_ones[:,1], label='class 1')
plt.legend()
plt.show()
#3) Generate a TensorFlow graph.

2. with tf.name_scope("placeholders"):
x = tf.constant(x_np, dtype=tf.float32)
y = tf.constant(y_np, dtype=tf.float32)
with tf.name_scope("weights"):
W = tf.Variable(tf.random.normal((2, 1)))
b = tf.Variable(tf.random.normal((1,)))
with tf.name_scope("prediction"):
y_logit = tf.squeeze(tf.matmul(x, W) + b)
# the sigmoid gives the class probability of 1
y_one_prob = tf.sigmoid(y_logit)
# Rounding P(y=1) will give the correct prediction.
y_pred = tf.round(y_one_prob)
with tf.name_scope("loss"):
# Compute the cross-entropy term for each datapoint
entropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=y_logit, labels=y)
# Sum all contributions
l = tf.reduce_sum(entropy)
with tf.name_scope("optim"):
train_op = tf.compat.v1.train.AdamOptimizer(.01).minimize(l)
with tf.name_scope("summaries"):
tf.compat.v1.summary.scalar("loss", l)
merged = tf.compat.v1.summary.merge_all()
train_writer = tf.compat.v1.summary.FileWriter('logistic-train',
tf.compat.v1.get_default_graph())

3. #4) Train the model, get the weights, and make predictions.
#5) Plot the predicted outputs on top of the data.
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
#1) Generate the synthetic data using the following Python code snippet.
# Generate synthetic data
N = 100
# Zeros form a Gaussian centered at (-1, -1)
x_zeros = np.random.multivariate_normal(mean=np.array((-1, -1)),
cov=.1*np.eye(2), size=(N//2,))
y_zeros = np.zeros((N//2,))
# Ones form a Gaussian centered at (1, 1)
x_ones = np.random.multivariate_normal(mean=np.array((1, 1)),
cov=.1*np.eye(2), size=(N//2,))
y_ones = np.ones((N//2,))
x_np = np.vstack([x_zeros, x_ones])
y_np = np.concatenate([y_zeros, y_ones])
# Plot x_zeros and x_ones on the same graph
plt.scatter(x_zeros[:,0], x_zeros[:,1], label='class 0')
plt.scatter(x_ones[:,0], x_ones[:,1], label='class 1')
plt.legend()

4. plt.show()
#3) Generate a TensorFlow graph.
with tf.name_scope("placeholders"):
x = tf.constant(x_np, dtype=tf.float32)
y = tf.constant(y_np, dtype=tf.float32)
with tf.name_scope("weights"):
W = tf.Variable(tf.random.normal((2, 1)))
b = tf.Variable(tf.random.normal((1,)))
with tf.name_scope("prediction"):
y_logit = tf.squeeze(tf.matmul(x, W) + b)
# the sigmoid gives the class probability of 1
y_one_prob = tf.sigmoid(y_logit)
# Rounding P(y=1) will give the correct prediction.
y_pred = tf.round(y_one_prob)
with tf.name_scope("loss"):
# Compute the cross-entropy term for each datapoint
entropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=y_logit, labels=y)
# Sum all contributions
l = tf.reduce_sum(entropy)
with tf.name_scope("optim"):
train_op = tf.compat.v1.train.AdamOptimizer(.01).minimize(l)
with tf.name_scope("summaries"):
tf.compat.v1.summary.scalar("loss", l)

5. merged = tf.compat.v1.summary.merge_all()
train_writer = tf.compat.v1.summary.FileWriter('logistic-train',
tf.compat.v1.get_default_graph())
#4) Train the model, get the weights, and make predictions.
#5) Plot the predicted outputs on top of the data.