lab04-op_shuffle_batch.py

import tensorflow as tf
import threading

# filename_queue = tf.train.string_input_producer(
#     ['data-01-test-score.csv', 'data-02-test-score.csv'], shuffle=False, name='filename_queue')
filename_queue = tf.train.string_input_producer(
    ['data-01-test-score.csv'], shuffle=False, name='filename_queue')

reader = tf.TextLineReader(skip_header_lines=1)  # skip header line
key, value = reader.read(filename_queue)
print(key, value)

# Default values, in case of empty columns. Also specifies the type of the decoded result.
record_defaults = [[0.], [0.], [0.], [0.]]
xy = tf.decode_csv(value, record_defaults=record_defaults)

## collect batches of csv in => shuffle_batch
# min_after_dequeue defines how big a buffer we will randomly sample
#   from -- bigger means better shuffling but slower start up and more memory used.
# capacity must be larger than min_after_dequeue and the amount larger
#   determines the maximum we will prefetch.  Recommendation:
#   min_after_dequeue + (num_threads + a small safety margin) * batch_size
batch_size = 10
min_after_dequeue = 10
capacity = min_after_dequeue + 1 * batch_size
train_x_batch, train_y_batch = tf.train.shuffle_batch(
    [xy[0:-1], xy[-1:]], batch_size=batch_size, capacity=capacity, min_after_dequeue=min_after_dequeue)

# placeholders for a tensor that will be always fed
X = tf.placeholder(tf.float32, shape=[None, 3])
Y = tf.placeholder(tf.float32, shape=[None, 1])

W = tf.Variable(tf.random_normal([3, 1]), name='weight')
b = tf.Variable(tf.random_normal([1]), name='bias')

# Hypothesis
hypothesis = tf.matmul(X, W) + b

# cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y))

# Minimize
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-5)
train = optimizer.minimize(cost)

# Launch the graph in a session.
sess = tf.Session()
# Initializes global variables in the graph
sess.run(tf.global_variables_initializer())

# Start populating the filename queue
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)

for step in range(2001):
    x_batch, y_batch = sess.run([train_x_batch, train_y_batch])
    cost_val, hy_val, _ = sess.run([cost, hypothesis, train],
                                   feed_dict={X: x_batch, Y: y_batch})
    if step % 10 == 0:
        print(step, "Cost: ", cost_val, "\nCurrent Thread: ", threading.get_ident(), "\nPrediction:\n", hy_val)

coord.request_stop()
coord.join(threads)

# Ask my score
print("Your score will be ", sess.run(hypothesis, feed_dict={X: [[100, 70, 101]]}))
print("Other score will be ", sess.run(hypothesis, feed_dict={X: [[60, 70, 110],
                                                                  [90, 100, 80]]}))