start_i,
end_i,
batch_size,
permutate=False)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
"""init params"""
sess.run(tf.global_variables_initializer())
train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
saver = tf.train.Saver(var_list=train_vars, max_to_keep=100)
if not is_seq2seq:
saver.restore(sess,
"seq2seq/07-17_05-49-50/breakpoints/at_step_18000.ckpt")
cvae.set_sess(sess)
total_step = (num_epoch * len(train_data[0]) / batch_size)
global_step = 1
start_epoch = 1
for epoch in range(start_epoch, num_epoch + 1):
train_batches = batch_generator(train_data, start_i, end_i, batch_size)
recon_l = []
kl_l = []
bow_l = []
for batch in train_batches:
""" TRAIN """
if is_seq2seq:
kl_weight = 1.
p = Printer(log_f, index2word)
"""build graphs and init params"""
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
graph0 = tf.Graph()
with graph0.as_default():
kl_ceiling = 0.48
seq2seq = CVAE(vocab_size, embed_size, num_unit, latent_dim, emoji_dim, batch_size,
kl_ceiling, 1, decoder_layer,
start_i, end_i, beam_width, maximum_iterations, max_gradient_norm, lr, dropout, num_gpu,
cell_type,
is_seq2seq=False)
sess0 = tf.Session(graph=graph0, config=config)
sess0.run(tf.global_variables_initializer())
seq2seq.set_sess(sess0)
train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
saver0 = tf.train.Saver(var_list=train_vars, max_to_keep=25)
saver0.restore(sess0, "cvae/07-17_15-51-04/breakpoints/at_step_36500.ckpt")
graph0.finalize()
graph1 = tf.Graph()
with graph1.as_default():
classifier = EmojiClassifier(batch_size, vocab_size, emoji_num, embed_size, num_unit, num_gpu,
lr=0.001, dropout=0.2, cell_type=tf.nn.rnn_cell.GRUCell)
saver1 = tf.train.Saver()
sess1 = tf.Session(graph=graph1, config=config)
classifier.set_sess(sess1)
classifier.set_emoji_index(emoji_b2s)
saver1.restore(sess1, "classifier/07-16_14-33-58/breakpoints/best_test_loss.ckpt")
graph1.finalize()
