from __future__ import print_function
import tensorflow as tf
from tensorflow.contrib import rnn
import csv
import bisect
import glob
import re
import numpy as np
import random
import data
import config

cfg = config.Config()
graph_data = data.Graph_data(cfg)

X = tf.placeholder("float", [None, cfg.N_movies])
Y = tf.placeholder("float", [None, cfg.N_movies])

def Dense(x):
    hidden_layer_1 = tf.layers.dense(inputs=x, units=500, activation=tf.nn.relu)
    hidden_layer_2 = tf.layers.dense(inputs=hidden_layer_1, units=50, activation=tf.nn.relu)
    output_layer = tf.layers.dense(inputs = hidden_layer_2, units= cfg.N_movies, activation=tf.nn.softmax)
    return output_layer

logits = Dense(X)
cross_entropy = tf.reduce_sum(- Y * tf.log(logits), 1)
loss_op = tf.reduce_mean(cross_entropy)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=cfg.learning_rate)
train_op = optimizer.minimize(loss_op)

init = tf.global_variables_initializer()

with tf.Session() as sess:
  sess.run(init)

  for step in range(1, cfg.training_steps+1):
    batch_x, batch_y = graph_data.train_next_batch(cfg.batch_size)
    sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})
    if step % cfg.display_step == 0 or step == 1:
      loss = sess.run(loss_op, feed_dict={X: batch_x,
                                                                  Y: batch_y})
      print("loss = ",loss)