Credits: Forked from TensorFlow-Examples by Aymeric Damien
Refer to the setup instructions
# Import MINST data
import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
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
import tensorflow as tf
# Parameters
learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1
# 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 model
# Set model weights
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
# Construct model
activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax
# Minimize error using cross entropy
# Cross entropy
cost = -tf.reduce_sum(y*tf.log(activation))
# Gradient Descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
# Initializing the variables
init = tf.initialize_all_variables()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# Training cycle
for epoch in range(training_epochs):
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 average loss
avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch
# Display logs per epoch step
if epoch % display_step == 0:
print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)
print "Optimization Finished!"
# Test model
correct_prediction = tf.equal(tf.argmax(activation, 1), tf.argmax(y, 1))
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})
Epoch: 0001 cost= 29.860479714 Epoch: 0002 cost= 22.080549484 Epoch: 0003 cost= 21.237104595 Epoch: 0004 cost= 20.460196280 Epoch: 0005 cost= 20.185128237 Epoch: 0006 cost= 19.940297202 Epoch: 0007 cost= 19.645111119 Epoch: 0008 cost= 19.507218031 Epoch: 0009 cost= 19.389794492 Epoch: 0010 cost= 19.177005816 Epoch: 0011 cost= 19.082493615 Epoch: 0012 cost= 19.072873598 Epoch: 0013 cost= 18.938005402 Epoch: 0014 cost= 18.891806430 Epoch: 0015 cost= 18.839480221 Epoch: 0016 cost= 18.769349510 Epoch: 0017 cost= 18.590865587 Epoch: 0018 cost= 18.623413677 Epoch: 0019 cost= 18.546149085 Epoch: 0020 cost= 18.432274895 Epoch: 0021 cost= 18.358189004 Epoch: 0022 cost= 18.380014628 Epoch: 0023 cost= 18.499993471 Epoch: 0024 cost= 18.386477311 Epoch: 0025 cost= 18.258080609 Optimization Finished! Accuracy: 0.9048