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conure_ret_t4.py
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conure_ret_t4.py
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import tensorflow as tf
import data_loader_rand as data_loader
import generator_prune as generator_recsys
import time
import numpy as np
import argparse
import config
def random_neq(l, r, s):
t = np.random.randint(l, r)
while t == s:
t = np.random.randint(l, r)
return t
def random_neq(l, r, s):
t = np.random.randint(l, r)
while t == s:
t = np.random.randint(l, r)
return t
def random_negs(l,r,no,s):
# set_s=set(s)
negs = []
for i in range(no):
t = np.random.randint(l, r)
# while (t in set_s):
while (t== s):
t = np.random.randint(l, r)
negs.append(t)
return negs
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--top_k', type=int, default=1,
help='Sample from top k predictions')
parser.add_argument('--beta1', type=float, default=0.9,
help='hyperpara-Adam')
parser.add_argument('--datapath', type=str, default='Data/Session/original_desen_gender.csv',
help='data path')
parser.add_argument('--datapath_index', type=str, default='Data/Session/index.csv',
help='data path')
parser.add_argument('--eval_iter', type=int, default=1000,
help='Sample generator output evry x steps')
parser.add_argument('--save_para_every', type=int, default=1000,
help='save model parameters every')
parser.add_argument('--tt_percentage', type=float, default=0.2,
help='0.2 means 80% training 20% testing')
parser.add_argument('--is_generatesubsession', type=bool, default=False,
help='whether generating a subsessions, e.g., 12345-->01234,00123,00012 It may be useful for very some very long sequences')
parser.add_argument('--has_positionalembedding', type=bool, default=False,
help='whether contains positional embedding before performing cnnn')
parser.add_argument('--max_position', type=int, default=1000,
help='maximum number of for positional embedding, it has to be larger than the sequence lens')
parser.add_argument('--padtoken', type=str, default='0',
help='is the padding token in the beggining of the sequence')
parser.add_argument('--negtive_samples', type=int, default='1',
help='the number of negative examples for each positive one')
args = parser.parse_args()
dl = data_loader.Data_Loader({'model_type': 'generator', 'dir_name': args.datapath,'dir_name_index': args.datapath_index})
items = dl.item_dict
items_len = len(items)
print "len(source)", len(items)
# targets_len=len(targets)+items_len
targets = dl.target_dict
targets_len=len(targets)
print "len(targets)", targets_len
print "len(allitems)", dl.embed_len
bigemb = dl.embed_len
top_k=args.top_k
all_samples = dl.example
negtive_samples = args.negtive_samples
# Randomly shuffle data
np.random.seed(10)
shuffle_indices = np.random.permutation(np.arange(len(all_samples)))
all_samples = all_samples[shuffle_indices]
# Split train/test set
dev_sample_index = -1 * int(args.tt_percentage * float(len(all_samples)))
train_set, valid_set = all_samples[:dev_sample_index], all_samples[dev_sample_index:]
model_para = {
'item_size': len(items),
'bigemb':bigemb,
'dilated_channels': 256,
'target_item_size': targets_len,
'dilations': [1,4,1,4,1,4,1,4,],
'kernel_size': 3,
'learning_rate':0.0001,
'batch_size':512,
'iterations':50,
'has_positionalembedding': args.has_positionalembedding,
'max_position': args.max_position,
'is_negsample':True,
'taskID':config.taskID_4th
}
sess = tf.Session()
taskID=model_para['taskID']
itemrec = generator_recsys.NextItNet_Decoder(model_para)
itemrec.train_graph(ispre=False)
for index in range(taskID - config.taskID_1st):
t_name = config.taskID_1st + index
if index == 0:
softmax_w = tf.get_variable("softmax_w_{}".format(t_name),
[model_para['item_size'], model_para['dilated_channels']],
tf.float32,
tf.random_normal_initializer(0.0, 0.01))
softmax_b = tf.get_variable("softmax_b_{}".format(t_name), [model_para['item_size']], tf.float32,
tf.constant_initializer(0.1))
else:
softmax_size = config.task_conf['task_itemsize'][index]
softmax_w = tf.get_variable("softmax_w_{}".format(t_name),
[softmax_size, model_para['dilated_channels']],
tf.float32,
tf.random_normal_initializer(0.0, 0.01))
init = tf.global_variables_initializer()
trainable_vars = tf.trainable_variables()
allable_vars=tf.all_variables()
variables_to_restore =trainable_vars
bias=[v for v in allable_vars if v.name.find("bias") != -1]
mask_var_all=[v for v in allable_vars if v.name.find("mask_val") != -1]
ln_var_all = [v for v in trainable_vars if v.name.find("layer_norm") != -1]
ln_var = [v for v in ln_var_all if v.name.find(str(taskID) + "_layer_norm") != -1] # current
weight = [v for v in trainable_vars if v.name.find("weight") != -1]
softmax_name_curtask = "softmax_w_{}".format(taskID)
softmax_var = [v for v in trainable_vars if v.name.find(softmax_name_curtask) != -1]
variables_to_restore.extend(bias)
variables_to_restore.extend(mask_var_all)
sess.run(init)
saver = tf.train.Saver(variables_to_restore)
saver.restore(sess, "Data/Models/generation_model_t4/model_nextitnet_transfer_pretrain.ckpt")
saver_ft = tf.train.Saver()
source_item_embedding = itemrec.dilate_input
source_item_embedding = tf.reduce_mean(source_item_embedding[:, -1:, :], 1) # use the last token
embedding_size = tf.shape(source_item_embedding)[-1]
with tf.variable_scope("target-item"):
allitem_embeddings_target = itemrec.allitem_embeddings_out # only difference
is_training = tf.placeholder(tf.bool, shape=())
# training
itemseq_input_target_pos = tf.placeholder('int32',
[None, None], name='itemseq_input_pos')
itemseq_input_target_neg = tf.placeholder('int32',
[None, None], name='itemseq_input_neg')
target_item_embedding_pos = tf.nn.embedding_lookup(allitem_embeddings_target,
itemseq_input_target_pos,
name="target_item_embedding_pos")
target_item_embedding_neg = tf.nn.embedding_lookup(allitem_embeddings_target,
itemseq_input_target_neg,
name="target_item_embedding_neg")
pos_score = source_item_embedding * tf.reshape(target_item_embedding_pos, [-1, embedding_size])
neg_score = source_item_embedding * tf.reshape(target_item_embedding_neg, [-1, embedding_size])
pos_logits = tf.reduce_sum(pos_score, -1)
neg_logits = tf.reduce_sum(neg_score, -1)
# testing
itemseq_input_target_label = tf.placeholder('int32',
[None, None], name='itemseq_input_target_label')
target_label_item_embedding = tf.nn.embedding_lookup(allitem_embeddings_target,
itemseq_input_target_label,
name="target_label_item_embedding")
source_item_embedding_test = tf.expand_dims(source_item_embedding, 1) # (batch, 1, embeddingsize)
target_item_embedding = tf.transpose(target_label_item_embedding, [0, 2, 1]) # transpose
score_test = tf.matmul(source_item_embedding_test, target_item_embedding)
top_k_test = tf.nn.top_k(score_test[:, :], k=top_k, name='top-k')
tf.add_to_collection("top_k", top_k_test[1])
target_loss = tf.reduce_mean(
- tf.log(tf.sigmoid(pos_logits) + 1e-24) -
tf.log(1 - tf.sigmoid(neg_logits) + 1e-24)
)
reg_losses = tf.reduce_mean(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
# reg_losses = 0.001 * tf.reduce_mean([tf.nn.l2_loss(v) for v in tf.trainable_variables()])
target_loss += reg_losses
loss = target_loss
optimizer = tf.train.AdamOptimizer(model_para['learning_rate'], beta1=args.beta1, name='Adam2').minimize(loss,
var_list=[
softmax_var,weight])
unitialized_vars = []
for var in tf.global_variables():
try:
sess.run(var)
except tf.errors.FailedPreconditionError:
unitialized_vars.append(var)
initialize_op = tf.variables_initializer(unitialized_vars)
sess.run(initialize_op)
numIters = 1
for iter in range(model_para['iterations']):
batch_no = 0
batch_size = model_para['batch_size']
while (batch_no + 1) * batch_size < train_set.shape[0]:
start = time.time()
item_batch = train_set[batch_no * batch_size: (batch_no + 1) * batch_size, :]
pos_batch = item_batch[:, -1] # [3 6] used for negative sampling
source_batch = item_batch[:, :-2] #
pos_target = item_batch[:, -1:] # [[3][6]]
# neg_target = np.array([[random_neq(items_len, targets_len, s)] for s in pos_batch])
neg_target = np.array([[random_neq(1, targets_len, s)] for s in pos_batch])
# neg_target = np.array([[random_neq(0, targets_len, s)] for s in pos_batch]) #20% worse
_, loss_out = sess.run(
[optimizer, loss],
feed_dict={
itemrec.itemseq_input: item_batch,
itemseq_input_target_pos: pos_target,
itemseq_input_target_neg: neg_target
})
end = time.time()
if numIters % args.eval_iter == 0:
print "-------------------------------------------------------train1"
print "LOSS: {}\tITER: {}\tBATCH_NO: {}\t STEP:{}\t total_batches:{}".format(
loss_out, iter, batch_no, numIters, train_set.shape[0] / batch_size)
# print "TIME FOR BATCH", end - start
print "TIME FOR ITER (mins)", (end - start) * (train_set.shape[0] / batch_size) / 60.0
batch_no += 1
if numIters % args.eval_iter == 0:
batch_no_test = 0
batch_size_test = batch_size * 1
# batch_size_test = 1
hits = [] # 1
while (batch_no_test + 1) * batch_size_test < valid_set.shape[0]:
if (numIters / (args.eval_iter) < 10):
if (batch_no_test > 500):
break
else:
if (batch_no_test > 500):
break
item_batch = valid_set[batch_no_test * batch_size_test: (batch_no_test + 1) * batch_size_test, :]
pos_batch = item_batch[:, -1] # [3 6] used for negative sampling
# source_batch = item_batch[:, :-1] #
source_batch = item_batch[:, :-2]
pos_target = item_batch[:, -1:] # [[3][6]]
# randomly choose 999 negative items
# neg_target = np.array([random_negs(items_len, targets_len, negtive_samples, s) for s in pos_batch])
neg_target = np.array([random_negs(1, targets_len, negtive_samples, s) for s in pos_batch])
target = np.array(np.concatenate([neg_target, pos_target], 1))
[top_k_batch] = sess.run(
[top_k_test],
feed_dict={
itemrec.itemseq_input: item_batch,
itemseq_input_target_label: target
})
# note that in top_k_batch[1], such as [1 9 4 5 0], we just need to check whether 0 is here, that's fine
top_k = np.squeeze(
top_k_batch[1]) # remove one dimension since e.g., [[[1,2,4]],[[34,2,4]]]-->[[1,2,4],[34,2,4]]
for i in range(top_k.shape[0]):
top_k_per_batch = top_k[i]
if negtive_samples == top_k_per_batch:
hits.append(1.0)
else:
hits.append(0.0)
batch_no_test += 1
print "-------------------------------------------------------Accuracy"
if len(hits) != 0:
print "Accuracy hit_n:", sum(hits) / float(len(hits)) # 5
if numIters % args.save_para_every == 0:
save_path = saver_ft.save(sess,
"Data/Models/generation_model_finetune_t4/model_nextitnet_transfer_pretrain.ckpt".format(iter, numIters))
print "Save models done!"
numIters += 1
if __name__ == '__main__':
main()