def train(network_architecture,
minibatches,
type,
learning_rate=0.001,
batch_size=200,
training_epochs=100,
display_step=5):
tf.reset_default_graph()
print("type = ", type)
vae = ''
if type == 'prodlda':
vae = prodlda.VAE(network_architecture,
learning_rate=learning_rate,
batch_size=batch_size)
elif type == 'nvlda':
vae = nvlda.VAE(network_architecture,
learning_rate=learning_rate,
batch_size=batch_size)
elif type == 'prodlda2':
vae = prodlda2.VAE(network_architecture,
learning_rate=learning_rate,
batch_size=batch_size)
emb = 0
# Training cycle
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = int(n_samples_tr / batch_size)
# Loop over all batches
for i in range(total_batch):
batch_xs = minibatches.next()
# Fit training using batch data
cost, emb = vae.partial_fit(batch_xs)
# Compute average loss
avg_cost += cost / n_samples_tr * batch_size
if np.isnan(avg_cost):
print epoch, i, np.sum(batch_xs,
1).astype(np.int), batch_xs.shape
print 'Encountered NaN, stopping training. Please check the learning_rate settings and the momentum.'
# return vae,emb
sys.exit()
# Display logs per epoch step
if epoch % display_step == 0:
print "Epoch:", '%04d' % (epoch+1), \
"cost=", "{:.9f}".format(avg_cost)
return vae, emb
def train(config):
tf.reset_default_graph()
vocab, docs_tr = load_data('train')
vocab, docs_va_h1, docs_va_h2 = load_data('valid')
#if type=='prodlda':
#vae = prodlda.VAE(network_architecture, learning_rate=learning_rate, batch_size=batch_size)
network_architecture = {
'n_hidden_recog_1': config['layer1'],
'n_hidden_recog_2': config['layer2'],
'n_hidden_gener_1': docs_tr.shape[1],
'n_input': docs_tr.shape[1],
'n_z': int(t)
}
vae = prodlda.VAE(network_architecture,
learning_rate=config['learning_rate'],
batch_size=config['batch_size'],
keep_prob=config['keep_prob'])
saver = tf.train.Saver()
vae.sess.graph.finalize()
#elif type=='nvlda':
# vae = nvlda.VAE(network_architecture, learning_rate=learning_rate, batch_size=batch_size)
#summaries = get_summaries(vae.sess)
#writer = tf.summary.FileWriter(ckpt + '/logs/', vae.sess.graph)
for epoch in range(config['epochs']):
avg_cost = 0.
#total_batch = int(n_samples_tr / batch_size)
indices = np.random.permutation(docs_tr.shape[0])
for base in range(0, docs_tr.shape[0], config['batch_size']):
batch_xs = docs_tr[
indices[base:min(base +
config['batch_size'], docs_tr.shape[0])]]
cost = vae.partial_fit(batch_xs)
# Compute average loss
avg_cost += cost / docs_tr.shape[0] * config['batch_size']
if np.isnan(avg_cost):
#print(epoch,i,np.sum(batch_xs,1).astype(np.int),batch_xs.shape)
#print('Encountered NaN, stopping training. Please check the learning_rate settings and the momentum.')
return
#sys.exit()
#parameters = {v.name: v for v in tf.trainable_variables() if v.name == 'beta/kernel:0' or v.name == 'beta/bias:0'}
#emb = parameters['beta/kernel:0'].eval(vae.sess) + parameters['beta/bias:0'].eval(vae.sess)
emb = [
v for v in tf.trainable_variables() if v.name == 'beta/kernel:0'
][0].eval(vae.sess)
perplexity = evaluate(vae, emb, docs_va_h1, docs_va_h2, 'valid',
config)
#perplexity = evaluate(vae, emb, 'valid', summaries, writer, saver, vae.sess, epoch)
# Display logs per epoch step
#if epoch % display_step == 0:
# print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
saver.save(vae.sess, os.path.join(save_path, 'model.ckpt'))
tune.track.log(validation_perplexity=perplexity)
return
vocab, docs_tr = load_data('train')
vocab, docs_te_h1, docs_te_h2 = load_data('test')
#config = tf.ConfigProto()
#config.gpu_options.allow_growth=True
#sess = tf.Session(config=config)
network_architecture = {
'n_hidden_recog_1': config['layer1'],
'n_hidden_recog_2': config['layer2'],
'n_hidden_gener_1': docs_tr.shape[1],
'n_input': docs_tr.shape[1],
'n_z': int(t)
}
vae = prodlda.VAE(network_architecture,
learning_rate=config['learning_rate'],
batch_size=config['batch_size'],
keep_prob=config['keep_prob'])
#if m=='prodlda':
# vae = prodlda.VAE(network_architecture, learning_rate=learning_rate, batch_size=batch_size)
#else:
# vae = nvlda.VAE(network_architecture, learning_rate=learning_rate, batch_size=batch_size)
saver = tf.train.Saver()
saver.restore(vae.sess, load_from)
print("Model restored.")
#emb = sess.run(vae.network_weights['weights_gener']['h2'])
emb = [
v for v in tf.trainable_variables() if v.name == 'beta/kernel:0'
][0].eval(vae.sess)
perplexity = evaluate(vae, emb, docs_te_h1, docs_te_h2, 'test', config,