plot_x = list()
plot_y = list()
plot_y_log = list()
plot_y_soft = list()
with tf.Session() as session:
session.run(tf.global_variables_initializer())
epoch_index = 0
while epoch_index < epochs:
samples, labels = batcher.get_batch(batch_size)
model.train_model(session, samples, labels)
log_model.train_model(session, samples, labels)
soft_model.train_model(session, samples, labels)
if batcher.epoch_finished():
batcher.reset_epoch()
test_samples, test_labels = batcher.get_test_batch()
fp = 0
fn = 0
pred = np.argmax(model.predict(session, test_samples), axis=1)
for index, i in enumerate(np.argmax(test_labels, axis=1)):
if pred[index] == 1 and i != pred[index]:
fp += 1
if pred[index] == 0 and i != pred[index]:
fn += 1
acc = model.get_accuracy(session, test_samples, test_labels)
acc_log = log_model.get_accuracy(session, test_samples,
test_labels)
acc_soft = soft_model.get_accuracy(session, test_samples,
test_labels)
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from data_batcher import DataBatcher
from vae_model import VAEModel
from time import time
import numpy as np
import tensorflow as tf
print("Finding training data...")
batcher = DataBatcher("generated_data")
print("Building model...")
model = VAEModel(50, [40, 35, 30])
batch_size = 5000
training_steps = 200000
print("Starting training...")
with tf.Session() as session:
session.run(tf.global_variables_initializer())
for i in range(training_steps):
batch, epoch_complete = batcher.get_batch(batch_size)
model.train_model(session, inputs=batch)
if epoch_complete:
test_batch = batcher.get_test_batch()
loss = model.get_loss(session, inputs=test_batch)
print("Epoch complete - loss: {}".format(loss))
if i % 500 == 0:
test_batch = batcher.get_test_batch()
loss = model.get_loss(session, inputs=test_batch)
print("Step {} - loss: {}".format(i, loss))