run.py

import tensorflow as tf
import pandas as pd
import numpy as np
# from matplotlib import pyplot
# from mpl_toolkits.mplot3d import Axes3D


df = pd.read_csv('data/training.csv')
data = np.array(df)

data[:,0] -= 1
data = np.asfarray(data)	

index = pd.read_csv('data/train_index.csv')
userId = data[:,2]
movId = data[:,0]
data[:,2] = index['user_index']
data[:,0] = index['mov_index']



NUM_ROW = data.shape[0]
np.random.shuffle(data)
split = int(0.8*NUM_ROW)
train_data = data[:split]
test_data = data[split:-13]

NUM_TR_ROW = train_data.shape[0]
NUM_TS_ROW = test_data.shape[0]
print("Data preprocessing completed.")

print("Start here..")


num = 15

# X_ = (train_data[0:num,0]-np.mean(train_data[:num,0]))/np.std(train_data[num:,0])
# Y_ = (train_data[:num,1]-np.mean(train_data[:num,1]))/np.std(train_data[:num,1])
# # Z_ = (train_data[:num,2]-np.mean(train_data[:num,2]))/np.std(train_data[:num,2])
# ax.scatter(X_, Y_, train_data[:num,2], color='b')



sess=tf.Session()    
#First let's load meta graph and restore weights
saver = tf.train.import_meta_graph('user/user-model.meta')
saver.restore(sess,tf.train.latest_checkpoint('./user'))


# Access saved Variables directly
# print(sess.run('cost:0'))
# This will print 2, which is the value of bias that we saved


# Now, let's access and create placeholders variables and
# create feed-dict to feed new data

graph = tf.get_default_graph()
user_batch = graph.get_tensor_by_name("user_batch:0")
movie_batch = graph.get_tensor_by_name("movie_batch:0")
rating_batch = graph.get_tensor_by_name("rating_batch:0")
genres = pd.read_csv('data/genres.csv')
W_MOVIE = np.array(genres).astype(np.float32)

feed_dict ={user_batch:train_data[0:num,2],movie_batch:train_data[0:num,0],rating_batch:train_data[0:num,1]}

#Now, access the op that you want to run. 
op_to_restore = graph.get_tensor_by_name("output:0")

predict = sess.run(op_to_restore,feed_dict)
# ax.scatter(X_, Y_, predict, color='r')
# ax.set_xlabel('Users')
# ax.set_ylabel('Movies')
# ax.set_zlabel('Ratings')
# pyplot.show()

print "Act ::", train_data[0:num,1]
print "Pred::", np.around(predict*2)/2

#This will print 60 which is calculated