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@@ -1,3 +1,10 @@
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+'''
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+A linear regression learning algorithm example using TensorFlow library.
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+
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+Author: Aymeric Damien
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+Project: https://github.com/aymericdamien/TensorFlow-Examples/
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+'''
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+
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import tensorflow as tf
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import numpy
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import matplotlib.pyplot as plt
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@@ -11,29 +18,38 @@ display_step = 50
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# Training Data
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train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1])
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train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3])
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+n_samples = train_X.shape[0]
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+
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+# tf Graph Input
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+X = tf.placeholder("float")
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+Y = tf.placeholder("float")
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# Create Model
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+
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+# Set model weights
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W = tf.Variable(rng.randn(), name="weight")
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b = tf.Variable(rng.randn(), name="bias")
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-X = tf.placeholder("float")
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-Y = tf.placeholder("float")
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-n_samples = train_X.shape[0]
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-
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-activation = tf.add(tf.mul(X, W), b) #linear
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-cost = tf.reduce_sum(tf.pow(activation-Y, 2))/(2*n_samples) #L2
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+# Construct a linear model
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+activation = tf.add(tf.mul(X, W), b)
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-optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
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+# Minimize the squared errors
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+cost = tf.reduce_sum(tf.pow(activation-Y, 2))/(2*n_samples) #L2 loss
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+optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent
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+# Initializing the variables
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init = tf.initialize_all_variables()
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+# Launch the graph
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with tf.Session() as sess:
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sess.run(init)
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+ # Fit all training data
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for epoch in range(training_epochs):
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for (x, y) in zip(train_X, train_Y):
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sess.run(optimizer, feed_dict={X: x, Y: y})
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+ #Display logs per epoch step
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if epoch % display_step == 0:
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print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(sess.run(cost, feed_dict={X: train_X, Y:train_Y})), \
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"W=", sess.run(W), "b=", sess.run(b)
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@@ -41,6 +57,7 @@ with tf.Session() as sess:
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print "Optimization Finished!"
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print "cost=", sess.run(cost, feed_dict={X: train_X, Y: train_Y}), "W=", sess.run(W), "b=", sess.run(b)
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+ #Graphic display
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plt.plot(train_X, train_Y, 'ro', label='Original data')
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plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
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plt.legend()
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