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from __future__ import print_function | |
import tensorflow as tf | |
# Import MNIST data | |
from tensorflow.examples.tutorials.mnist import input_data | |
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) | |
# Parameters | |
learning_rate = 0.01 | |
training_epochs = 25 | |
batch_size = 100 | |
display_step = 1 | |
# tf Graph Input | |
x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784 | |
y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes | |
# Set model weights | |
W = tf.Variable(tf.zeros([784, 10])) | |
b = tf.Variable(tf.zeros([10])) | |
# Construct model | |
pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax | |
# Minimize error using cross entropy | |
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1)) | |
# Gradient Descent | |
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) | |
# Initializing the variables | |
init = tf.global_variables_initializer() | |
# 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) | |
# Run optimization op (backprop) and cost op (to get loss value) | |
_, c = sess.run([optimizer, cost], feed_dict={x: batch_xs, | |
y: batch_ys}) | |
# Compute average loss | |
avg_cost += c / total_batch | |
# Display logs per epoch step | |
if (epoch+1) % display_step == 0: | |
print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)) | |
print("Optimization Finished!") | |
# Test model | |
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) | |
# Calculate accuracy | |
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) | |
print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})) | |
wp_embed_register_handler( 'gist', '/https?:\/\/gist\.github\.com\/([a-z0-9]+)(\?file=.*)?/i', 'bhww_embed_handler_gist' ); | |
function bhww_embed_handler_gist( $matches, $attr, $url, $rawattr ) { | |
$embed = sprintf( | |
'<script src="https://gist.github.com/%1$s.js%2$s"></script>', | |
esc_attr($matches[1]), | |
esc_attr($matches[2]) | |
); | |
return apply_filters( 'embed_gist', $embed, $matches, $attr, $url, $rawattr ); | |
} |
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