In [13]:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

In [14]:
def get_weights(n_features, n_labels):
    Return TensorFlow weights
    :param n_features: Number of features
    :param n_labels: Number of labels
    :return: TensorFlow weights
    return tf.Variable(tf.truncated_normal((n_features, n_labels)))

def get_biases(n_labels):
    Return TensorFlow bias
    :param n_labels: Number of labels
    :return: TensorFlow bias
    return tf.Variable(tf.zeros(n_labels))

def linear(input, w, b):
    Return linear function in TensorFlow
    :param input: TensorFlow input
    :param w: TensorFlow weights
    :param b: TensorFlow biases
    :return: TensorFlow linear function
    # TODO: Linear Function (xW + b)
    return tf.add(tf.matmul(input, w), b)

In [15]:
def mnist_features_labels(n_labels):
    Gets the first <n> labels from the MNIST dataset
    :param n_labels: Number of labels to use
    :return: Tuple of feature list and label list
    mnist_features = []
    mnist_labels = []

    mnist = input_data.read_data_sets('./datasets/ud730/mnist', one_hot=True)

    # In order to make quizzes run faster, we're only looking at 10000 images
    for mnist_feature, mnist_label in zip(*mnist.train.next_batch(10000)):

        # Add features and labels if it's for the first <n>th labels
        if mnist_label[:n_labels].any():

    return mnist_features, mnist_labels

# Number of features (28*28 image is 784 features)
n_features = 784
# Number of labels
n_labels = 3

# Features and Labels
features = tf.placeholder(tf.float32)
labels = tf.placeholder(tf.float32)

# Weights and Biases
w = get_weights(n_features, n_labels)
b = get_biases(n_labels)

# Linear Function xW + b
logits = linear(features, w, b)

# Training data
train_features, train_labels = mnist_features_labels(n_labels)

with tf.Session() as session:
    # Softmax
    prediction = tf.nn.softmax(logits)

    # Cross entropy
    # This quantifies how far off the predictions were.
    # You'll learn more about this in future lessons.
    cross_entropy = -tf.reduce_sum(labels * tf.log(prediction), reduction_indices=1)

    # Training loss
    # You'll learn more about this in future lessons.
    loss = tf.reduce_mean(cross_entropy)

    # Rate at which the weights are changed
    # You'll learn more about this in future lessons.
    learning_rate = 0.08

    # Gradient Descent
    # This is the method used to train the model
    # You'll learn more about this in future lessons.
    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)

    # Run optimizer and get loss
    _, l =
        [optimizer, loss],
        feed_dict={features: train_features, labels: train_labels})

# Print loss
print('Loss: {}'.format(l))

Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting ./datasets/ud730/mnist/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting ./datasets/ud730/mnist/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting ./datasets/ud730/mnist/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting ./datasets/ud730/mnist/t10k-labels-idx1-ubyte.gz
Loss: 8.918489456176758

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