Logistic Regression with Eager API

A logistic regression implemented using TensorFlow's Eager API.

• Author: Aymeric Damien
• Project: https://github.com/aymericdamien/TensorFlow-Examples/

MNIST Dataset Overview

This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).

More info: http://yann.lecun.com/exdb/mnist/

from __future__ import absolute_import, division, print_function

import tensorflow as tf
import tensorflow.contrib.eager as tfe

# Set Eager API
tfe.enable_eager_execution()

# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=False)

Extracting /tmp/data/train-images-idx3-ubyte.gz
Extracting /tmp/data/train-labels-idx1-ubyte.gz
Extracting /tmp/data/t10k-images-idx3-ubyte.gz
Extracting /tmp/data/t10k-labels-idx1-ubyte.gz

# Parameters
learning_rate = 0.1
batch_size = 128
num_steps = 1000
display_step = 100

# Iterator for the dataset
dataset = tf.data.Dataset.from_tensor_slices(
    (mnist.train.images, mnist.train.labels)).batch(batch_size)
dataset_iter = tfe.Iterator(dataset)

# Variables
W = tfe.Variable(tf.zeros([784, 10]), name='weights')
b = tfe.Variable(tf.zeros([10]), name='bias')

# Logistic regression (Wx + b)
def logistic_regression(inputs):
    return tf.matmul(inputs, W) + b

# Cross-Entropy loss function
def loss_fn(inference_fn, inputs, labels):
    # Using sparse_softmax cross entropy
    return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
        logits=inference_fn(inputs), labels=labels))

# Calculate accuracy
def accuracy_fn(inference_fn, inputs, labels):
    prediction = tf.nn.softmax(inference_fn(inputs))
    correct_pred = tf.equal(tf.argmax(prediction, 1), labels)
    return tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# SGD Optimizer
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)

# Compute gradients
grad = tfe.implicit_gradients(loss_fn)

# Training
average_loss = 0.
average_acc = 0.
for step in range(num_steps):

    # Iterate through the dataset
    try:
        d = dataset_iter.next()
    except StopIteration:
        # Refill queue
        dataset_iter = tfe.Iterator(dataset)
        d = dataset_iter.next()

    # Images
    x_batch = d[0]
    # Labels
    y_batch = tf.cast(d[1], dtype=tf.int64)

    # Compute the batch loss
    batch_loss = loss_fn(logistic_regression, x_batch, y_batch)
    average_loss += batch_loss
    # Compute the batch accuracy
    batch_accuracy = accuracy_fn(logistic_regression, x_batch, y_batch)
    average_acc += batch_accuracy

    if step == 0:
        # Display the initial cost, before optimizing
        print("Initial loss= {:.9f}".format(average_loss))

    # Update the variables following gradients info
    optimizer.apply_gradients(grad(logistic_regression, x_batch, y_batch))

    # Display info
    if (step + 1) % display_step == 0 or step == 0:
        if step > 0:
            average_loss /= display_step
            average_acc /= display_step
        print("Step:", '%04d' % (step + 1), " loss=",
              "{:.9f}".format(average_loss), " accuracy=",
              "{:.4f}".format(average_acc))
        average_loss = 0.
        average_acc = 0.

Initial loss= 2.302584887
Step: 0001  loss= 2.302584887  accuracy= 0.1172
Step: 0100  loss= 0.952338457  accuracy= 0.7955
Step: 0200  loss= 0.535867393  accuracy= 0.8712
Step: 0300  loss= 0.485415280  accuracy= 0.8757
Step: 0400  loss= 0.433947206  accuracy= 0.8843
Step: 0500  loss= 0.381990731  accuracy= 0.8971
Step: 0600  loss= 0.394154936  accuracy= 0.8947
Step: 0700  loss= 0.391497582  accuracy= 0.8905
Step: 0800  loss= 0.386373103  accuracy= 0.8945
Step: 0900  loss= 0.332039326  accuracy= 0.9096
Step: 1000  loss= 0.358993769  accuracy= 0.9002

# Evaluate model on the test image set
testX = mnist.test.images
testY = mnist.test.labels

test_acc = accuracy_fn(logistic_regression, testX, testY)
print("Testset Accuracy: {:.4f}".format(test_acc))

Testset Accuracy: 0.9083