diff --git a/examples/2_BasicModels/factorization_machines.py b/examples/2_BasicModels/factorization_machines.py
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+""" Factorisation Machines for Multiclass Classification.
+A simple factorization machine algorithm implementation with TensorFlow. 
+This example is using the MNIST database
+of handwritten digits (http://yann.lecun.com/exdb/mnist/).
+The implementation includes regularisation of the parameters for the weights 
+as well as the interaction factors. It also includes eta (learning rate) parameter  
+
+Links:
+    [MNIST Dataset](http://yann.lecun.com/exdb/mnist/).
+Author: Aymeric Damien
+Project: https://github.com/aymericdamien/TensorFlow-Examples/
+"""
+
+from __future__ import print_function
+
+# Import MNIST data
+from tensorflow.examples.tutorials.mnist import input_data
+mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
+from sklearn import metrics
+import tensorflow as tf
+
+# Parameters
+learning_rate = 0.01
+num_steps = 8000
+batch_size = 256
+display_step = 100
+
+# FM Parameters
+number_latent = 5
+num_input = 784 # MNIST data input (img shape: 28*28)
+num_classes = 10 # MNIST total classes (0-9 digits)
+
+# Regularization terms for W and V matrices
+lambda_w = tf.constant(0.0015, name='lambda_w')
+lambda_v = tf.constant(0.0015, name='lambda_v')
+
+# tf Graph input
+X = tf.placeholder("float", [None, num_input])
+Y = tf.placeholder("float", [None, num_classes])
+
+# Store layers weight & bias
+weights = {
+    'W': tf.Variable(tf.random_normal([num_input, num_classes])),
+    'b': tf.Variable(tf.random_normal([num_classes]))
+}
+interactions = {
+    'V': tf.Variable(tf.random_normal([num_input, number_latent, num_classes])),
+}
+
+# Create model
+def fm(x):
+    # Linear terms 
+    linear_terms = tf.add(tf.matmul(x, weights['W']), weights['b'])
+    # Simplification follows: Rendle, 2010 and Rendle, 2012.
+    
+    # Squered sum of product (using tensordot as we are having tensors 
+    # instead of matrices for multiclass classification)
+    s1 = tf.pow(
+        tf.reduce_sum(
+            tf.tensordot(
+                x, 
+                interactions['V'], [[1], [0]]
+            ), 1
+        ), 2)
+    
+    # Sum of squared product 
+    s2 = tf.reduce_sum(
+        tf.tensordot(
+            tf.pow(x, 2), 
+            tf.pow(interactions['V'], 2), 
+            [[1], [0]]
+        ), 1)
+    outputs = linear_terms + 1/2*(s1-s2)
+    return outputs
+
+# Construct model
+logits = fm(X)
+prediction = tf.nn.softmax(logits)
+
+# Define loss, regularisation, and optimizer
+error = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
+    logits=logits, labels=Y))
+
+
+regularisation = tf.add(tf.reduce_sum(lambda_v * tf.math.abs(interactions['V'])),
+                        tf.reduce_sum(lambda_w * tf.pow(weights['W'],2)))
+loss_op = error + regularisation
+optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
+train_op = optimizer.minimize(loss_op)
+
+# Evaluate model
+correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
+accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
+cm = tf.math.confusion_matrix(tf.argmax(Y, 1), tf.argmax(prediction, 1))
+
+# Initialize the variables (i.e. assign their default value)
+init = tf.global_variables_initializer()
+
+# Start training
+with tf.Session() as sess:
+
+    # Run the initializer
+    sess.run(init)
+
+    for step in range(1, num_steps+1):
+        if step%1000 == 0:
+            learning_rate = learning_rate/2
+        batch_x, batch_y = mnist.train.next_batch(batch_size)
+        # Run optimization op (backprop)
+        sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})
+        if step % display_step == 0 or step == 1:
+            # Calculate batch loss and accuracy
+            loss, acc = sess.run([error, accuracy], feed_dict={X: batch_x, Y: batch_y})
+            
+            print("Step " + str(step) + ", Minibatch Loss= " + \
+                  "{:.4f}".format(loss) + ", Training Accuracy= " + \
+                  "{:.3f}".format(acc))
+
+    print("Optimization Finished!")
+
+    # Calculate accuracy for MNIST test images
+    print("Testing Accuracy:", \
+        sess.run(accuracy, feed_dict={X: mnist.test.images,
+                                      Y: mnist.test.labels}))
+    print("Testing Confusion Matrix: \n", \
+        sess.run(cm, feed_dict={X: mnist.test.images,
+                                      Y: mnist.test.labels}))