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バッチ正規化と自己正規化ニューラルネットワークの違いを知りたいと思います。つまり、SELU（Scaled Exponential Linear Unit）はバッチの正規化をどのように置き換えるのですか？SELUによるバッチ正規化と自己正規化ニューラルネットワークの違い
さらに、私はSELUアクティベーションの値を調べた後、範囲内にあった：[-1, 1]。これはバッチの正規化では当てはまりませんが。代わりに、BN層の後の値（reluのアクティブ化の前）は、の値をとっており、[-1, 1]の値ではありません。次のように
バッチノルム層が定義されている...
batch_norm_layer = tf.Print(batch_norm_layer,
data=[tf.reduce_max(batch_norm_layer), tf.reduce_min(batch_norm_layer)],
message = name_scope + ' min and max')
そしてSELUのアクティベーションのための同様のコード：ここで
は、私がSELU活性化した後、バッチノルム層を形成した後の値を印刷する方法です。
def batch_norm(x, n_out, phase_train, in_conv_layer = True):
with tf.variable_scope('bn'):
beta = tf.Variable(tf.constant(0.0, shape=n_out),
name='beta', trainable=True)
gamma = tf.Variable(tf.constant(1.0, shape=n_out),
name='gamma', trainable=True)
if in_conv_layer:
batch_mean, batch_var = tf.nn.moments(x, [0, 1, 2], name='moments')
else:
batch_mean, batch_var = tf.nn.moments(x, [0, 1], name='moments')
ema = tf.train.ExponentialMovingAverage(decay=0.9999)
def mean_var_with_update():
ema_apply_op = ema.apply([batch_mean, batch_var])
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean), tf.identity(batch_var)
mean, var = tf.cond(phase_train,
mean_var_with_update,
lambda: (ema.average(batch_mean), ema.average(batch_var)))
normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, 1e-3)
return normed
したがって、バッチノルムは高い値を出力するため、損失は劇的に増加し、したがって、私はナンを得る。
さらに、私はバッチノルムで学習率を下げようとしましたが、それはうまく役に立たなかった。だからこの問題を解決するには???ここで
は、次のコードです：任意のヘルプははるかに高く評価されて
import tensorflow as tf
import numpy as np
import os
import cv2
batch_size = 32
num_epoch = 102
latent_dim = 100
def weight_variable(kernal_shape):
weights = tf.get_variable(name='weights', shape=kernal_shape, dtype=tf.float32, trainable=True,
initializer=tf.truncated_normal_initializer(stddev=0.02))
return weights
def bias_variable(shape):
initial = tf.constant(0.0, shape=shape)
return tf.Variable(initial)
def batch_norm(x, n_out, phase_train, convolutional = True):
with tf.variable_scope('bn'):
exp_moving_avg = tf.train.ExponentialMovingAverage(decay=0.9999)
beta = tf.Variable(tf.constant(0.0, shape=n_out),
name='beta', trainable=True)
gamma = tf.Variable(tf.constant(1.0, shape=n_out),
name='gamma', trainable=True)
if convolutional:
batch_mean, batch_var = tf.nn.moments(x, [0, 1, 2], name='moments')
else:
batch_mean, batch_var = tf.nn.moments(x, [0], name='moments')
update_moving_averages = exp_moving_avg.apply([batch_mean, batch_var])
m = tf.cond(phase_train, lambda: exp_moving_avg.average(batch_mean), lambda: batch_mean)
v = tf.cond(phase_train, lambda: exp_moving_avg.average(batch_var), lambda: batch_var)
normed = tf.nn.batch_normalization(x, m, v, beta, gamma, 1e-3)
normed = tf.Print(normed, data=[tf.shape(normed)], message='size of normed?')
return normed, update_moving_averages # Note that we should run the update_moving_averages with sess.run...
def conv_layer(x, w_shape, b_shape, padding='SAME'):
W = weight_variable(w_shape)
tf.summary.histogram("weights", W)
b = bias_variable(b_shape)
tf.summary.histogram("biases", b)
# Note that I used a stride of 2 on purpose in order not to use max pool layer.
conv = tf.nn.conv2d(x, W, strides=[1, 2, 2, 1], padding=padding) + b
conv_batch_norm, update_moving_averages = batch_norm(conv, b_shape, phase_train=tf.cast(True, tf.bool))
name_scope = tf.get_variable_scope().name
conv_batch_norm = tf.Print(conv_batch_norm,
data=[tf.reduce_max(conv_batch_norm), tf.reduce_min(conv_batch_norm)],
message = name_scope + ' min and max')
activations = tf.nn.relu(conv_batch_norm)
tf.summary.histogram("activations", activations)
return activations, update_moving_averages
def deconv_layer(x, w_shape, b_shape, padding="SAME", activation='selu'):
W = weight_variable(w_shape)
tf.summary.histogram("weights", W)
b = bias_variable(b_shape)
tf.summary.histogram('biases', b)
x_shape = tf.shape(x)
out_shape = tf.stack([x_shape[0], x_shape[1] * 2, x_shape[2] * 2, w_shape[2]])
if activation == 'selu':
conv_trans = tf.nn.conv2d_transpose(x, W, out_shape, [1, 2, 2, 1], padding=padding) + b
conv_trans_batch_norm, update_moving_averages = \
batch_norm(conv_trans, b_shape, phase_train=tf.cast(True, tf.bool))
transposed_activations = tf.nn.relu(conv_trans_batch_norm)
else:
conv_trans = tf.nn.conv2d_transpose(x, W, out_shape, [1, 2, 2, 1], padding=padding) + b
conv_trans_batch_norm, update_moving_averages = \
batch_norm(conv_trans, b_shape, phase_train=tf.cast(True, tf.bool))
transposed_activations = tf.nn.sigmoid(conv_trans_batch_norm)
tf.summary.histogram("transpose_activation", transposed_activations)
return transposed_activations, update_moving_averages
tfrecords_filename_seq = ["C:/Users/user/PycharmProjects/AffectiveComputing/P16_db.tfrecords"]
filename_queue = tf.train.string_input_producer(tfrecords_filename_seq, num_epochs=num_epoch, shuffle=False, name='queue')
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'height': tf.FixedLenFeature([], tf.int64),
'width': tf.FixedLenFeature([], tf.int64),
'image_raw': tf.FixedLenFeature([], tf.string),
'annotation_raw': tf.FixedLenFeature([], tf.string)
})
# This is how we create one example, that is, extract one example from the database.
image = tf.decode_raw(features['image_raw'], tf.uint8)
# The height and the weights are used to
height = tf.cast(features['height'], tf.int32)
width = tf.cast(features['width'], tf.int32)
# The image is reshaped since when stored as a binary format, it is flattened. Therefore, we need the
# height and the weight to restore the original image back.
image = tf.reshape(image, [height, width, 3])
annotation = tf.cast(features['annotation_raw'], tf.string)
min_after_dequeue = 100
num_threads = 1
capacity = min_after_dequeue + num_threads * batch_size
label_batch, images_batch = tf.train.batch([annotation, image],
shapes=[[], [112, 112, 3]],
batch_size=batch_size,
capacity=capacity,
num_threads=num_threads)
label_batch_splitted = tf.string_split(label_batch, delimiter=',')
label_batch_values = tf.reshape(label_batch_splitted.values, [batch_size, -1])
label_batch_numbers = tf.string_to_number(label_batch_values, out_type=tf.float32)
confidences = tf.slice(label_batch_numbers, begin=[0, 2], size=[-1, 1])
images_batch = tf.cast([images_batch], tf.float32)[0] # Note that casting the image will increases its rank.
with tf.name_scope('image_normal'):
images_batch = tf.map_fn(lambda img: tf.image.per_image_standardization(img), images_batch)
#images_batch = tf.Print(images_batch, data=[tf.reduce_max(images_batch), tf.reduce_min(images_batch)],
# message='min and max in images_batch')
with tf.variable_scope('conv1'):
conv1, uma_conv1 = conv_layer(images_batch, [4, 4, 3, 32], [32]) # image size: [56, 56]
with tf.variable_scope('conv2'):
conv2, uma_conv2 = conv_layer(conv1, [4, 4, 32, 64], [64]) # image size: [28, 28]
with tf.variable_scope('conv3'):
conv3, uma_conv3 = conv_layer(conv2, [4, 4, 64, 128], [128]) # image size: [14, 14]
with tf.variable_scope('conv4'):
conv4, uma_conv4 = conv_layer(conv3, [4, 4, 128, 256], [256]) # image size: [7, 7]
conv4_reshaped = tf.reshape(conv4, [-1, 7 * 7 * 256], name='conv4_reshaped')
w_c_mu = tf.Variable(tf.truncated_normal([7 * 7 * 256, latent_dim], stddev=0.1), name='weight_fc_mu')
b_c_mu = tf.Variable(tf.constant(0.1, shape=[latent_dim]), name='biases_fc_mu')
w_c_sig = tf.Variable(tf.truncated_normal([7 * 7 * 256, latent_dim], stddev=0.1), name='weight_fc_sig')
b_c_sig = tf.Variable(tf.constant(0.1, shape=[latent_dim]), name='biases_fc_sig')
epsilon = tf.random_normal([1, latent_dim])
tf.summary.histogram('weights_c_mu', w_c_mu)
tf.summary.histogram('biases_c_mu', b_c_mu)
tf.summary.histogram('weights_c_sig', w_c_sig)
tf.summary.histogram('biases_c_sig', b_c_sig)
with tf.variable_scope('mu'):
mu = tf.nn.bias_add(tf.matmul(conv4_reshaped, w_c_mu), b_c_mu)
tf.summary.histogram('mu', mu)
with tf.variable_scope('stddev'):
stddev = tf.nn.bias_add(tf.matmul(conv4_reshaped, w_c_sig), b_c_sig)
tf.summary.histogram('stddev', stddev)
with tf.variable_scope('z'):
latent_var = mu + tf.multiply(tf.sqrt(tf.exp(stddev)), epsilon)
tf.summary.histogram('features_sig', stddev)
w_dc = tf.Variable(tf.truncated_normal([latent_dim, 7 * 7 * 256], stddev=0.1), name='weights_dc')
b_dc = tf.Variable(tf.constant(0.0, shape=[7 * 7 * 256]), name='biases_dc')
tf.summary.histogram('weights_dc', w_dc)
tf.summary.histogram('biases_dc', b_dc)
with tf.variable_scope('deconv4'):
deconv4 = tf.nn.bias_add(tf.matmul(latent_var, w_dc), b_dc)
deconv4_batch_norm, uma_deconv4 = \
batch_norm(deconv4, [7 * 7 * 256], phase_train=tf.cast(True, tf.bool), convolutional=False)
deconv4 = tf.nn.relu(deconv4_batch_norm)
deconv4_reshaped = tf.reshape(deconv4, [-1, 7, 7, 256], name='deconv4_reshaped')
with tf.variable_scope('deconv3'):
deconv3, uma_deconv3 = deconv_layer(deconv4_reshaped, [3, 3, 128, 256], [128], activation='selu')
with tf.variable_scope('deconv2'):
deconv2, uma_deconv2 = deconv_layer(deconv3, [3, 3, 64, 128], [64], activation='selu')
with tf.variable_scope('deconv1'):
deconv1, uma_deconv1 = deconv_layer(deconv2, [3, 3, 32, 64], [32], activation='selu')
with tf.variable_scope('deconv_image'):
deconv_image_batch, uma_deconv = deconv_layer(deconv1, [3, 3, 3, 32], [3], activation='sigmoid')
# loss function.
with tf.name_scope('loss_likelihood'):
# temp1 shape: [32, 112, 112, 3]
temp1 = images_batch * tf.log(deconv_image_batch + 1e-9) + (1 - images_batch) * tf.log(1 - deconv_image_batch + 1e-9)
#temp1 = temp1 * confidences. This will give an error. Therefore, we should expand the dimension of confidence tensor
confidences_ = tf.expand_dims(tf.expand_dims(confidences, axis=1), axis=1) # shape: [32, 1, 1, 1].
temp1 = temp1 * confidences_
log_likelihood = -tf.reduce_sum(temp1, reduction_indices=[1, 2, 3])
log_likelihood_total = tf.reduce_sum(log_likelihood)
#l2_loss = tf.reduce_mean(tf.abs(tf.subtract(images_batch, deconv_image_batch)))
with tf.name_scope('loss_KL'):
# temp2 shape: [32, 200]
temp2 = 1 + tf.log(tf.square(stddev + 1e-9)) - tf.square(mu) - tf.square(stddev)
temp3 = temp2 * confidences # confidences shape is [32, 1]
KL_term = - 0.5 * tf.reduce_sum(temp3, reduction_indices=1)
KL_term_total = tf.reduce_sum(KL_term)
with tf.name_scope('total_loss'):
variational_lower_bound = tf.reduce_mean(log_likelihood + KL_term)
tf.summary.scalar('loss', variational_lower_bound)
with tf.name_scope('optimizer'):
optimizer = tf.train.AdamOptimizer(0.00001).minimize(variational_lower_bound)
init_op = tf.group(tf.local_variables_initializer(),
tf.global_variables_initializer())
saver = tf.train.Saver()
model_path = 'C:/Users/user/PycharmProjects/VariationalAutoEncoder/' \
'VariationalAutoEncoderFaces/tensorboard_logs/Graph_model/ckpt'
# Here is the session...
with tf.Session() as sess:
train_writer = tf.summary.FileWriter('C:/Users/user/PycharmProjects/VariationalAutoEncoder/'
'VariationalAutoEncoderFaces/tensorboard_logs/Event_files', sess.graph)
merged = tf.summary.merge_all()
# Note that init_op should start before the Coordinator and the thread otherwise, this will throw an error.
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
step = 0
to_run_list = [uma_conv1, uma_conv2, uma_conv3, uma_conv4, uma_deconv1, uma_deconv2, uma_deconv3,
uma_deconv4, uma_deconv, optimizer, variational_lower_bound, merged,
deconv_image_batch, image]
# Note that the last name "Graph_model" is the name of the saved checkpoints file => the ckpt is saved
# under tensorboard_logs.
ckpt = tf.train.get_checkpoint_state(
os.path.dirname(model_path))
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print('checkpoints are saved!!!')
else:
print('No stored checkpoints')
epoch = 0
while not coord.should_stop():
_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, loss, summary, reconstructed_image, original_image = \
sess.run(to_run_list)
print('total loss:', loss)
original_image = cv2.cvtColor(np.array(original_image), cv2.COLOR_RGB2BGR)
reconstructed_image = cv2.cvtColor(np.array(reconstructed_image[0]), cv2.COLOR_RGB2BGR)
cv2.imshow('original_image', original_image)
cv2.imshow('reconstructed_image', reconstructed_image)
cv2.waitKey(1)
if step % 234 == 0:
epoch += 1
print('epoch:', epoch)
if epoch == num_epoch - 2:
coord.request_stop()
if step % 100 == 0:
train_writer.add_summary(summary, step)
#print('total loss:', loss)
#print('log_likelihood_', log_likelihood_)
#print('KL_term', KL_term_)
step += 1
save_path = saver.save(sess, model_path)
coord.request_stop()
coord.join(threads)
train_writer.close()
!!
これはかなり広い質問です。あなたが探している答えの範囲を私が手助けすることができますか？ SELUは、他の非線形活性化関数のいずれかの代わりに使用することができ、他の変更を必要としません。バッチノルムはそれよりも複雑です –
私は、SELUが内部共変量シフト問題を解決するのですか？はいの場合は、batch_normレイヤー+ reluをSELUアクティベーション関数に置き換えることができますか？私は、2つの構成の間で出力の活性化がどのように異なるかについての直感を持ちたいと思う。ありがとう@ Ryan Stout –
SELUは、レイヤー上の（指定されたドメイン内の）アクティベーションの平均と分散を維持することができます。しかし、活性層がまだ正常に分布していることを保証するものではありません。 SELUを適用する前に、フィーチャを正規化する必要があります。 –