Working version for Tensorflow 1.4 | Python 2

[zmatthias]

Hello, I made the code work for myself using Tensorflow v 1.4 and Python 2. Maybe it works for you too.

board.py (complete file):

# Import MNIST data
import input_data

mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

import tensorflow as tf

# Set parameters
learning_rate = 0.01
training_iteration = 30
batch_size = 100
display_step = 2

# TF graph input
x = tf.placeholder("float", [None, 784])  # mnist data image of shape 28*28=784
y = tf.placeholder("float", [None, 10])  # 0-9 digits recognition => 10 classes

# Create a model

# Set model weights
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

with tf.name_scope("Wx_b") as scope:
    # Construct a linear model
    model = tf.nn.softmax(tf.matmul(x, W) + b)  # Softmax

# Add summary ops to collect data
w_h = tf.summary.histogram('weights', W)
b_h = tf.summary.histogram('biases', b)

# More name scopes will clean up graph representation
with tf.name_scope("cost_function") as scope:
    # Minimize error using cross entropy
    # Cross entropy
    cost_function = -tf.reduce_sum(y * tf.log(model))
    # Create a summary to monitor the cost function
    tf.summary.scalar("cost_function", cost_function)

with tf.name_scope("train") as scope:
    # Gradient descent
    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function)

# Initializing the variables
init = tf.global_variables_initializer()
merged_summary = tf.summary.merge_all()

# Launch the graph
with tf.Session() as sess:
    sess.run(init)

    # Change this to a location on your computer
    summary_writer = tf.summary.FileWriter('/home/z/Dropbox/bachelorarbeit/tensorflow_demo', graph_def=sess.graph_def)

    # Training cycle
    for iteration in range(training_iteration):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples / batch_size)
        # Loop over all batches
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            # Fit training using batch data
            sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})
            # Compute the average loss
            avg_cost += sess.run(cost_function, feed_dict={x: batch_xs, y: batch_ys}) / total_batch
            # Write logs for each iteration
            summary_str = sess.run(merged_summary, feed_dict={x: batch_xs, y: batch_ys})
            summary_writer.add_summary(summary_str, iteration * total_batch + i)
        # Display logs per iteration step
        if iteration % display_step == 0:
            print "Iteration:", '%04d' % (iteration + 1), "cost=", "{:.9f}".format(avg_cost)

    print "Tuning completed!"

    # Test the model
    predictions = tf.equal(tf.argmax(model, 1), tf.argmax(y, 1))
    # Calculate accuracy
    accuracy = tf.reduce_mean(tf.cast(predictions, "float"))
    print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})

input_data.py (additionally, I needed to change this function under linux, may be optional)

def _read32(bytestream):
    dt = numpy.dtype(numpy.uint32).newbyteorder('>')
    return numpy.asscalar(numpy.frombuffer(bytestream.read(4), dtype=dt))

[colinechabran]

Thank you for sharing !

[bitsnaps]

I got similar errors using Conda + Python3 on Windows7, also I had to update some deprecated codes (using graph instead of graph_def for FileWriter), I got Accuracy: 0.9008 thanks for sharing.

[iCodeForBananas]

Python 3.6

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

import tensorflow as tf

# Set parameters
learning_rate = 0.01
training_iteration = 30
batch_size = 100
display_step = 2

# TF graph input
x = tf.placeholder("float", [None, 784]) # mnist data image of shape 28*28=784
y = tf.placeholder("float", [None, 10]) # 0-9 digits recognition => 10 classes

# Create a model

# Set model weights
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

with tf.name_scope("Wx_b") as scope:
    # Construct a linear model
    model = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax

# Add summary ops to collect data
w_h = tf.summary.histogram("weights", W)
b_h = tf.summary.histogram("biases", b)

# More name scopes will clean up graph representation
with tf.name_scope("cost_function") as scope:
    # Minimize error using cross entropy
    # Cross entropy
    cost_function = -tf.reduce_sum(y*tf.log(model))
    # Create a summary to monitor the cost function
    tf.summary.scalar("cost_function", cost_function)

with tf.name_scope("train") as scope:
    # Gradient descent
    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function)

# Initializing the variables
init = tf.initialize_all_variables()

# Merge all summaries into a single operator
merged_summary = tf.summary.merge_all()

# Launch the graph
with tf.Session() as sess:
    sess.run(init)

    # Change this to a location on your computer
    summary_writer = tf.summary.FileWriter('/Users/michaelcalkins/Downloads/', graph_def=sess.graph_def)

    # Training cycle
    for iteration in range(training_iteration):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples/batch_size)
        # Loop over all batches
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            # Fit training using batch data
            sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})
            # Compute the average loss
            avg_cost += sess.run(cost_function, feed_dict={x: batch_xs, y: batch_ys})/total_batch
            # Write logs for each iteration
            summary_str = sess.run(merged_summary, feed_dict={x: batch_xs, y: batch_ys})
            summary_writer.add_summary(summary_str, iteration*total_batch + i)
        # Display logs per iteration step
        if iteration % display_step == 0:
            print("Iteration:", '%04d' % (iteration + 1), "cost=", "{:.9f}".format(avg_cost))

    print("Tuning completed!")

    # Test the model
    predictions = tf.equal(tf.argmax(model, 1), tf.argmax(y, 1))
    # Calculate accuracy
    accuracy = tf.reduce_mean(tf.cast(predictions, "float"))
    print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))

[joozz]

Can anyone explain how to print the expected and predicted values?