TensorFlow 1.x Deep Learning Cookbook

How to do it...

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 unitsn = 20  # Number of Hidden unitsrecommender = 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 ...