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# Graph Visualization with TensorFlow.
# This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)
# Author: Aymeric Damien
# Project: https://github.com/aymericdamien/TensorFlow-Examples/
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import tensorflow as tf
import numpy
# Import MINST data
import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
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# Use Logistic Regression from our previous example
# Parameters
learning_rate = 0.01
training_epochs = 10
batch_size = 100
display_step = 1
# tf Graph Input
x = tf.placeholder("float", [None, 784], name='x') # mnist data image of shape 28*28=784
y = tf.placeholder("float", [None, 10], name='y') # 0-9 digits recognition => 10 classes
# Create model
# Set model weights
W = tf.Variable(tf.zeros([784, 10]), name="weights")
b = tf.Variable(tf.zeros([10]), name="bias")
# Construct model
activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax
# Minimize error using cross entropy
cost = -tf.reduce_sum(y*tf.log(activation)) # Cross entropy
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Gradient Descent
# Initializing the variables
init = tf.initialize_all_variables()
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# Launch the graph
with tf.Session() as sess:
sess.run(init)
# Set logs writer into folder /tmp/tensorflow_logs
summary_writer = tf.train.SummaryWriter('/tmp/tensorflow_logs', graph_def=sess.graph_def)
# 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})
tensorboard --logdir=/tmp/tensorflow_logs
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# Graph Visualization
# Tensorflow makes it easy for you to visualize all computation graph,
# you can click on any part of the graph for more in-depth details
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# Weights details
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# Gradient descent details
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