linear_regression_one_feature_using_mini_batch_with_tensorboard.py

import numpy as np
import tensorflow as tf

# CUSTOMIZABLE: Collect/Prepare data
datapoint_size = 1000
batch_size = 1
steps = 10000
actual_W = 2
actual_b = 10
learn_rate = 0.001
log_file = "/tmp/feature_1_batch_1"

# Model linear regression y = Wx + b
x = tf.placeholder(tf.float32, [None, 1], name="x")
W = tf.Variable(tf.zeros([1,1]), name="W")
b = tf.Variable(tf.zeros([1]), name="b")
with tf.name_scope("Wx_b") as scope:
  product = tf.matmul(x,W)
  y = product + b

# Add summary ops to collect data
W_hist = tf.summary.histogram("weights", W)
b_hist = tf.summary.histogram("biases", b)
y_hist = tf.summary.histogram("y", y)

y_ = tf.placeholder(tf.float32, [None, 1], name="y_")

# Cost function sum((y_-y)**2)
with tf.name_scope("cost") as scope:
  cost = tf.reduce_mean(tf.square(y_-y))
  cost_sum = tf.summary.scalar("cost", cost)

# Training using Gradient Descent to minimize cost
with tf.name_scope("train") as scope:
  train_step = tf.train.GradientDescentOptimizer(learn_rate).minimize(cost)

all_xs = []
all_ys = []
for i in range(datapoint_size):
  # Create fake data for y = W.x + b where W = 2, b = actual_b
  all_xs.append(i%10)
  all_ys.append(actual_W*(i%10)+actual_b)

all_xs = np.transpose([all_xs])
all_ys = np.transpose([all_ys])

sess = tf.Session()

# Merge all the summaries and write them out to /tmp/mnist_logs
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter(log_file, sess.graph_def)

init = tf.initialize_all_variables()
sess.run(init)

for i in range(steps):
  if datapoint_size == batch_size:
    batch_start_idx = 0
  elif datapoint_size < batch_size:
    raise ValueError("datapoint_size: %d, must be greater than batch_size: %d" % (datapoint_size, batch_size))
  else:
    batch_start_idx = (i * batch_size) % (datapoint_size - batch_size)
  batch_end_idx = batch_start_idx + batch_size
  batch_xs = all_xs[batch_start_idx:batch_end_idx]
  batch_ys = all_ys[batch_start_idx:batch_end_idx]
  xs = np.array(batch_xs)
  ys = np.array(batch_ys)
  # Record summary data, and the accuracy every 10 steps
  if i % 10 == 0:
    all_feed = { x: all_xs, y_: all_ys }
    result = sess.run(merged, feed_dict=all_feed)
    writer.add_summary(result, i)
  else:
    feed = { x: xs, y_: ys }
    sess.run(train_step, feed_dict=feed)
    print("y: %s" % sess.run(y, feed_dict=feed))
    print("y_: %s" % ys)
    print("cost: %f" % sess.run(cost, feed_dict=feed))
  print("After %d iteration:" % i)
  print("W: %f" % sess.run(W))
  print("b: %f" % sess.run(b))

# NOTE: W should be close to actual_W, and b should be close to actual_b