1. We will use the RBM class created in the previous recipe. Let's define our RBM network; the number of visible units will be the number of movies, in our case, 3,883 (movies_df is the data frame containing data from the movies.dat file):

m = len(movies_df)  # Number of visible units
n = 20  # Number of Hidden units
recommender = rbm.RBM(m,n)

2. We created a list, trX, of about 1,000 users with their normalized movie ratings using Pandas merge and groupby commands. The list is of size 1000 x 3883. We use this to train our RBM:

Xtrain = np.array(trX) 
init = tf.global_variables_initializer()
with tf.Session() as sess:
 sess.run(init)
 recommender.set_session(sess)
 err = recommender.fit(Xtrain, epochs=10)

3. The cross-logit error per epoch decreases:

4. The network is now trained; we use it get the recommendation for a random user with index 150 (it could be any existing user):

user_index = 150
x = np.array([Xtrain[user_index, :]])
init = tf.global_variables_initializer()
with tf.Session() as sess:
 sess.run(init)
 recommender.set_session(sess)
 out = recommender.reconstruct(x.astype(np.float32))

5. The result is merged with the existing data frame and we can see the recommendation score for this user: