4-4-1-9. Quiz: Mini-batch

Mini-batching

In this section, you’ll go over what mini-batching is and how to apply it in TensorFlow.

Mini-batching is a technique for training on subsets of the dataset instead of all the data at one time. This provides the ability to train a model, even if a computer lacks the memory to store the entire dataset.

Mini-batching is computationally inefficient, since you can’t calculate the loss simultaneously across all samples. However, this is a small price to pay in order to be able to run the model at all.

It’s also quite useful combined with SGD. The idea is to randomly shuffle the data at the start of each epoch, then create the mini-batches. For each mini-batch, you train the network weights with gradient descent. Since these batches are random, you’re performing SGD with each batch.

Let’s look at the MNIST dataset with weights and a bias to see if your machine can handle it.

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

n_input = 784  # MNIST data input (img shape: 28*28)
n_classes = 10  # MNIST total classes (0-9 digits)

# Import MNIST data
mnist = input_data.read_data_sets('/datasets/ud730/mnist', one_hot=True)

# The features are already scaled and the data is shuffled
train_features = mnist.train.images
test_features = mnist.test.images

train_labels = mnist.train.labels.astype(np.float32)
test_labels = mnist.test.labels.astype(np.float32)

# Weights & bias
weights = tf.Variable(tf.random_normal([n_input, n_classes]))
bias = tf.Variable(tf.random_normal([n_classes]))

Question 1

Calculate the memory size of train_featurestrain_labelsweights, and bias in bytes. Ignore memory for overhead, just calculate the memory required for the stored data.

You may have to look up how much memory a float32 requires, using this link.

train_features Shape: (55000, 784) Type: float32

train_labels Shape: (55000, 10) Type: float32

weights Shape: (784, 10) Type: float32

bias Shape: (10,) Type: float32

The total memory space required for the inputs, weights and bias is around 174 megabytes, which isn’t that much memory. You could train this whole dataset on most CPUs and GPUs.

But larger datasets that you’ll use in the future measured in gigabytes or more. It’s possible to purchase more memory, but it’s expensive. A Titan X GPU with 12 GB of memory costs over $1,000.

Instead, in order to run large models on your machine, you’ll learn how to use mini-batching.

Let’s look at how you implement mini-batching in TensorFlow.

TensorFlow Mini-batching

In order to use mini-batching, you must first divide your data into batches.

Unfortunately, it’s sometimes impossible to divide the data into batches of exactly equal size. For example, imagine you’d like to create batches of 128 samples each from a dataset of 1000 samples. Since 128 does not evenly divide into 1000, you’d wind up with 7 batches of 128 samples, and 1 batch of 104 samples. (7*128 + 1*104 = 1000)

In that case, the size of the batches would vary, so you need to take advantage of TensorFlow’s tf.placeholder() function to receive the varying batch sizes.

Continuing the example, if each sample had n_input = 784 features and n_classes = 10 possible labels, the dimensions for features would be [None, n_input] and labels would be [None, n_classes].

# Features and Labels
features = tf.placeholder(tf.float32, [None, n_input])
labels = tf.placeholder(tf.float32, [None, n_classes])

What does None do here?

The None dimension is a placeholder for the batch size. At runtime, TensorFlow will accept any batch size greater than 0.

Going back to our earlier example, this setup allows you to feed features and labels into the model as either the batches of 128 samples or the single batch of 104 samples.

Question 2

Use the parameters below, how many batches are there, and what is the last batch size?

features is (50000, 400)

labels is (50000, 10)

batch_size is 128

Now that you know the basics, let’s learn how to implement mini-batching.

Question 3

Implement the batches function to batch features and labels. The function should return each batch with a maximum size of batch_size. To help you with the quiz, look at the following example output of a working batches function.

# 4 Samples of features
example_features = [
    ['F11','F12','F13','F14'],
    ['F21','F22','F23','F24'],
    ['F31','F32','F33','F34'],
    ['F41','F42','F43','F44']]
# 4 Samples of labels
example_labels = [
    ['L11','L12'],
    ['L21','L22'],
    ['L31','L32'],
    ['L41','L42']]

example_batches = batches(3, example_features, example_labels)

The example_batches variable would be the following:

[
    # 2 batches:
    #   First is a batch of size 3.
    #   Second is a batch of size 1
    [
        # First Batch is size 3
        [
            # 3 samples of features.
            # There are 4 features per sample.
            ['F11', 'F12', 'F13', 'F14'],
            ['F21', 'F22', 'F23', 'F24'],
            ['F31', 'F32', 'F33', 'F34']
        ], [
            # 3 samples of labels.
            # There are 2 labels per sample.
            ['L11', 'L12'],
            ['L21', 'L22'],
            ['L31', 'L32']
        ]
    ], [
        # Second Batch is size 1.
        # Since batch size is 3, there is only one sample left from the 4 samples.
        [
            # 1 sample of features.
            ['F41', 'F42', 'F43', 'F44']
        ], [
            # 1 sample of labels.
            ['L41', 'L42']
        ]
    ]
]

Implement the batches function in the “quiz.py” file below.

sandbox.py

from quiz import batches
from pprint import pprint

# 4 Samples of features
example_features = [
    ['F11','F12','F13','F14'],
    ['F21','F22','F23','F24'],
    ['F31','F32','F33','F34'],
    ['F41','F42','F43','F44']]
# 4 Samples of labels
example_labels = [
    ['L11','L12'],
    ['L21','L22'],
    ['L31','L32'],
    ['L41','L42']]

# PPrint prints data structures like 2d arrays, so they are easier to read
pprint(batches(3, example_features, example_labels))

quiz.py

import math
def batches(batch_size, features, labels):
    """
    Create batches of features and labels
    :param batch_size: The batch size
    :param features: List of features
    :param labels: List of labels
    :return: Batches of (Features, Labels)
    """
    assert len(features) == len(labels)
    # TODO: Implement batching
    pass

quiz_solution.py

import math
def batches(batch_size, features, labels):
    """
    Create batches of features and labels
    :param batch_size: The batch size
    :param features: List of features
    :param labels: List of labels
    :return: Batches of (Features, Labels)
    """
    assert len(features) == len(labels)
    # TODO: Implement batching
    output_batches = []
    
    sample_size = len(features)
    for start_i in range(0, sample_size, batch_size):
        end_i = start_i + batch_size
        batch = [features[start_i:end_i], labels[start_i:end_i]]
        output_batches.append(batch)
        
    return output_batches

Let’s use mini-batching to feed batches of MNIST features and labels into a linear model.

Set the batch size and run the optimizer over all the batches with the batches function. The recommended batch size is 128. If you have memory restrictions, feel free to make it smaller.

quiz.py

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import numpy as np
from helper import batches

learning_rate = 0.001
n_input = 784  # MNIST data input (img shape: 28*28)
n_classes = 10  # MNIST total classes (0-9 digits)

# Import MNIST data
mnist = input_data.read_data_sets('/datasets/ud730/mnist', one_hot=True)

# The features are already scaled and the data is shuffled
train_features = mnist.train.images
test_features = mnist.test.images

train_labels = mnist.train.labels.astype(np.float32)
test_labels = mnist.test.labels.astype(np.float32)

# Features and Labels
features = tf.placeholder(tf.float32, [None, n_input])
labels = tf.placeholder(tf.float32, [None, n_classes])

# Weights & bias
weights = tf.Variable(tf.random_normal([n_input, n_classes]))
bias = tf.Variable(tf.random_normal([n_classes]))

# Logits - xW + b
logits = tf.add(tf.matmul(features, weights), bias)

# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)

# Calculate accuracy
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))


# TODO: Set batch size
batch_size = None
assert batch_size is not None, 'You must set the batch size'

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    
    # TODO: Train optimizer on all batches
    # for batch_features, batch_labels in ______
    sess.run(optimizer, feed_dict={features: batch_features, labels: batch_labels})

    # Calculate accuracy for test dataset
    test_accuracy = sess.run(
        accuracy,
        feed_dict={features: test_features, labels: test_labels})

print('Test Accuracy: {}'.format(test_accuracy))

helper.py

import math
def batches(batch_size, features, labels):
    """
    Create batches of features and labels
    :param batch_size: The batch size
    :param features: List of features
    :param labels: List of labels
    :return: Batches of (Features, Labels)
    """
    assert len(features) == len(labels)
    outout_batches = []
    
    sample_size = len(features)
    for start_i in range(0, sample_size, batch_size):
        end_i = start_i + batch_size
        batch = [features[start_i:end_i], labels[start_i:end_i]]
        outout_batches.append(batch)
        
    return outout_batches

quiz_solution.py

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import numpy as np
from helper import batches

learning_rate = 0.001
n_input = 784  # MNIST data input (img shape: 28*28)
n_classes = 10  # MNIST total classes (0-9 digits)

# Import MNIST data
mnist = input_data.read_data_sets('/datasets/ud730/mnist', one_hot=True)

# The features are already scaled and the data is shuffled
train_features = mnist.train.images
test_features = mnist.test.images

train_labels = mnist.train.labels.astype(np.float32)
test_labels = mnist.test.labels.astype(np.float32)

# Features and Labels
features = tf.placeholder(tf.float32, [None, n_input])
labels = tf.placeholder(tf.float32, [None, n_classes])

# Weights & bias
weights = tf.Variable(tf.random_normal([n_input, n_classes]))
bias = tf.Variable(tf.random_normal([n_classes]))

# Logits - xW + b
logits = tf.add(tf.matmul(features, weights), bias)

# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)

# Calculate accuracy
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))


# TODO: Set batch size
batch_size = 128
assert batch_size is not None, 'You must set the batch size'

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    
    # TODO: Train optimizer on all batches
    for batch_features, batch_labels in batches(batch_size, train_features, train_labels):
        sess.run(optimizer, feed_dict={features: batch_features, labels: batch_labels})

    # Calculate accuracy for test dataset
    test_accuracy = sess.run(
        accuracy,
        feed_dict={features: test_features, labels: test_labels})

print('Test Accuracy: {}'.format(test_accuracy))

The accuracy is low, but you probably know that you could train on the dataset more than once. You can train a model using the dataset multiple times. You’ll go over this subject in the next section where we talk about “epochs”.

%d 블로거가 이것을 좋아합니다: