def VAE_encode_dataset(model_path, dataset, factor_dim, dim_z,
M1_hidden_layers_px, M1_hidden_layers_qz):
batch_size = 100
VAE = Conv2dVariationalAutoencoder(dim_x=factor_dim,
dim_z=dim_z,
batch_size=batch_size,
hidden_layers_px=M1_hidden_layers_px,
hidden_layers_qz=M1_hidden_layers_qz)
enc_x_mean = None
enc_x_var = None
with VAE.session:
VAE.saver.restore(VAE.session, model_path)
for x_batch in utils.feed_numpy(batch_size, dataset):
if enc_x_mean is None:
enc_x_mean, enc_x_var = VAE.encode(x_batch)
else:
tmp_mean, tmp_var = VAE.encode(x_batch)
enc_x_mean = np.vstack([enc_x_mean, tmp_mean])
enc_x_var = np.vstack([enc_x_var, tmp_var])
print("Shape of enc_x_mean is ", np.shape(enc_x_mean))
print("Shape of enc_x_var is ", np.shape(enc_x_var))
return enc_x_mean, enc_x_var
def train(
self,
x,
x_valid,
epochs, #num_batches,
save_path=None,
print_every=1,
learning_rate=3e-4,
beta1=0.9,
beta2=0.999,
seed=31415,
stop_iter=100,
load_path=None,
draw_img=1):
self.num_examples = x.shape[0]
# self.num_batches = num_batches
assert self.num_examples % self.batch_size == 0, '#Examples % #Batches != 0'
# self.batch_size = self.num_examples // self.num_batches
# x_size = x.shape[0]
# x_valid_size = x.shape[0]
# x = np.reshape(x, [x_size, self.dim_x, 1, 1])
# x_valid = np.reshape(x_valid, [x_valid_size, self.dim_x, 1, 1])
''' Session and Summary '''
if save_path is None:
self.save_path = 'checkpoints/model_CONV2D-VAE_{}.cpkt'.format(
time.strftime("%m-%d-%H%M%S", time.localtime()))
else:
self.save_path = save_path
np.random.seed(seed)
tf.set_random_seed(seed)
with self.G.as_default():
self.optimiser = tf.train.AdamOptimizer(
learning_rate=learning_rate, beta1=beta1, beta2=beta2)
self.train_op = self.optimiser.minimize(self.cost)
init = tf.global_variables_initializer(
) # tf.initialize_all_variables()
self._test_vars = None
with self.session as sess:
sess.run(init)
# 实际上并没有执行
if load_path == 'default':
self.saver.restore(sess, self.save_path)
elif load_path is not None:
self.saver.restore(sess, load_path)
training_cost = 0.
best_eval_log_lik = -np.inf
stop_counter = 0
for epoch in range(epochs):
''' Shuffle Data '''
np.random.shuffle(x)
''' Training '''
for x_batch in utils.feed_numpy(self.batch_size, x):
training_result = sess.run([self.train_op, self.cost],
feed_dict={self.x: x_batch})
training_cost = training_result[1]
''' Evaluation '''
stop_counter += 1
# 训练，更新不断跟新参数
if epoch % print_every == 0:
test_vars = tf.get_collection(
bookkeeper.GraphKeys.TEST_VARIABLES)
if test_vars:
if test_vars != self._test_vars:
self._test_vars = list(test_vars)
self._test_var_init_op = tf.initialize_variables(
test_vars)
self._test_var_init_op.run()
# eval_log_lik, x_recon_eval = \
# sess.run([self.eval_log_lik, self.x_recon_eval],
# feed_dict={self.x: x_valid})
eval_log_lik = 0
x_recon_eval = 0
valid_times = x_valid.shape[0] / self.batch_size
for x_valid_batch in utils.feed_numpy(
self.batch_size, x_valid):
log_lik, recon_eval = sess.run(
[self.eval_log_lik, self.x_recon_eval],
feed_dict={self.x: x_valid_batch})
eval_log_lik += log_lik
x_recon_eval += recon_eval
eval_log_lik /= valid_times
x_recon_eval /= valid_times
if eval_log_lik > best_eval_log_lik:
best_eval_log_lik = eval_log_lik
self.saver.save(sess, self.save_path)
stop_counter = 0
utils.print_metrics(
epoch + 1, ['Training', 'cost', training_cost],
['Validation', 'log-likelihood', eval_log_lik])
## 画图
# if draw_img > 0 and epoch % draw_img == 0:
#
# import matplotlib
# matplotlib.use('Agg')
# import pylab
# import seaborn as sns
#
# five_random = np.random.random_integers(x_valid.shape[0], size = 5)
# x_sample = x_valid[five_random]
# x_recon_sample = x_recon_eval[five_random]
#
# sns.set_style('white')
# f, axes = pylab.subplots(5, 2, figsize=(8,12))
# for i,row in enumerate(axes):
#
# row[0].imshow(x_sample[i].reshape(28, 28), vmin=0, vmax=1)
# im = row[1].imshow(x_recon_sample[i].reshape(28, 28), vmin=0, vmax=1,
# cmap=sns.light_palette((1.0, 0.4980, 0.0549), input="rgb", as_cmap=True))
#
# pylab.setp([a.get_xticklabels() for a in row], visible=False)
# pylab.setp([a.get_yticklabels() for a in row], visible=False)
#
# f.subplots_adjust(left=0.0, right=0.9, bottom=0.0, top=1.0)
# cbar_ax = f.add_axes([0.9, 0.1, 0.04, 0.8])
# f.colorbar(im, cax=cbar_ax, use_gridspec=True)
#
# pylab.tight_layout()
# pylab.savefig('img/recon-'+str(epoch)+'.png', format='png')
# pylab.clf()
# pylab.close('all')
if stop_counter >= stop_iter:
print('Stopping VAE training')
print(
'No change in validation log-likelihood for {} iterations'
.format(stop_iter))
print('Best validation log-likelihood: {}'.format(
best_eval_log_lik))
print('Model saved in {}'.format(self.save_path))
break