我正在训练CGAN从损坏的图像中重建图像。我已经为可变批量大小编写了所有代码，因此我也可以在可变批量大小上进行训练（我没有收到错误或任何提示）。当我使用批量大小1时，在2分钟后，重建的图像不再有任何奇怪的伪像。但是，这是我的问题：对于任何其他批次大小，即使尝试不同的学习率或训练多个小时，我也会得到非常奇怪的棋盘工件。
This是经过一段时间训练后批量大小为2的重建图像。 （这些怪异的工件不在损坏的数据中。）
This是批次大小为2时生成器损失的对抗成分。
This是批次大小2时的发电机损耗。
This是批次大小为2时的鉴别器损失。
为了进行比较，以批次大小1：
橙色是火车，蓝色是验证
当批处理大小大于1时，似乎我的代码会做一些完全不同的事情。我确定批次已正确加载。我要疯了吗？
我的模特：
self.original = tf.placeholder(tf.float32, shape=(None,conf.fig_size, conf.fig_size, conf.fig_channel), name="original")
self.corrupted = tf.placeholder(tf.float32, shape=(None,conf.fig_size, conf.fig_size, conf.fig_channel), name="corrupted")
self.reconstructed = self.generator(self.corrupted)
pos = self.discriminator(self.original, self.corrupted, False)
neg = self.discriminator(self.original, self.corrupted, True)
pos_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=pos, labels=tf.ones_like(pos)))
neg_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=neg, labels=tf.zeros_like(neg)))
self.d_loss = pos_loss + neg_loss
adv_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=neg, labels=tf.ones_like(neg)))
self.g_loss = adv_loss + conf.l1_lambda * tf.reduce_mean(tf.abs(self.original - self.reconstructed))
t_vars = tf.trainable_variables()
self.d_vars = [var for var in t_vars if 'disc' in var.name]
self.g_vars = [var for var in t_vars if 'gen' in var.name]
self.merged = tf.summary.merge_all()
def generator(self, corrupted):
conf = self.config
with tf.variable_scope("gen"):
feature = conf.conv_channel_base
e1 = conv2d(corrupted, feature, name="e1")
e2 = batch_norm(conv2d(lrelu(e1), feature*2, name="e2"), "e2", conf.batch_norm_decay)
e3 = batch_norm(conv2d(lrelu(e2), feature*4, name="e3"), "e3", conf.batch_norm_decay)
e4 = batch_norm(conv2d(lrelu(e3), feature*8, name="e4"), "e4", conf.batch_norm_decay)
e5 = batch_norm(conv2d(lrelu(e4), feature*8, name="e5"), "e5", conf.batch_norm_decay)
e6 = batch_norm(conv2d(lrelu(e5), feature*8, name="e6"), "e6", conf.batch_norm_decay)
e7 = batch_norm(conv2d(lrelu(e6), feature*8, name="e7"), "e7", conf.batch_norm_decay)
e8 = batch_norm(conv2d(lrelu(e7), feature*8, name="e8"), "e8", conf.batch_norm_decay)
size = conf.fig_size
num = [0] * 9
for i in range(1,9):
num[9-i]=size
size =(size+1)/2
d1 = deconv2d(tf.nn.relu(e8), [num[1],num[1],feature*8], name="d1")
d1 = tf.concat([tf.nn.dropout(batch_norm(d1, "d1", conf.batch_norm_decay), 0.5), e7], 3)
d2 = deconv2d(tf.nn.relu(d1), [num[2],num[2],feature*8], name="d2")
d2 = tf.concat([tf.nn.dropout(batch_norm(d2, "d2", conf.batch_norm_decay), 0.5), e6], 3)
d3 = deconv2d(tf.nn.relu(d2), [num[3],num[3],feature*8], name="d3")
d3 = tf.concat([tf.nn.dropout(batch_norm(d3, "d3", conf.batch_norm_decay), 0.5), e5], 3)
d4 = deconv2d(tf.nn.relu(d3), [num[4],num[4],feature*8], name="d4")
d4 = tf.concat([batch_norm(d4, "d4", conf.batch_norm_decay), e4], 3)
d5 = deconv2d(tf.nn.relu(d4), [num[5],num[5],feature*4], name="d5")
d5 = tf.concat([batch_norm(d5, "d5", conf.batch_norm_decay), e3], 3)
d6 = deconv2d(tf.nn.relu(d5), [num[6],num[6],feature*2], name="d6")
d6 = tf.concat([batch_norm(d6, "d6", conf.batch_norm_decay), e2], 3)
d7 = deconv2d(tf.nn.relu(d6), [num[7],num[7],feature], name="d7")
d7 = tf.concat([batch_norm(d7, "d7", conf.batch_norm_decay), e1], 3)
d8 = deconv2d(tf.nn.relu(d7), [num[8],num[8],conf.fig_channel], name="d8")
return tf.nn.tanh(d8)
def discriminator(self, original, corrupted, reuse):
conf = self.config
dim = len(original.get_shape())
with tf.variable_scope("disc", reuse=reuse):
image_pair = tf.concat([original, corrupted], dim - 1)
feature = conf.conv_channel_base
h0 = lrelu(conv2d(image_pair, feature, name="h0"))
h1 = lrelu(batch_norm(conv2d(h0, feature*2, name="h1"), "h1", conf.batch_norm_decay))
h2 = lrelu(batch_norm(conv2d(h1, feature*4, name="h2"), "h2", conf.batch_norm_decay))
h3 = lrelu(batch_norm(conv2d(h2, feature*8, name="h3"), "h3", conf.batch_norm_decay))
h4 = linear(tf.reshape(h3, [-1,h3.shape[1]*h3.shape[2]*h3.shape[3]]), 1, "linear")
return h4
def batch_norm(x, scope, decay):
return tf.contrib.layers.batch_norm(x, decay=decay, updates_collections=None, epsilon=1e-5, scale=True, scope=scope)
def conv2d(input, output_dim, k_h=4, k_w=4, d_h=2, d_w=2, stddev=0.02, name="conv2d"):
with tf.variable_scope(name):
weight = tf.get_variable('weight', [k_h, k_w, input.get_shape()[-1], output_dim],
initializer=tf.truncated_normal_initializer(stddev=stddev))
bias = tf.get_variable('bias', [output_dim], initializer=tf.constant_initializer(0.0))
conv = tf.nn.bias_add(tf.nn.conv2d(input, weight, strides=[1, d_h, d_w, 1], padding='SAME'), bias)
return conv
def deconv2d(input, output_shape, k_h=4, k_w=4, d_h=2, d_w=2, stddev=0.02, name="deconv2d"):
with tf.variable_scope(name):
dyn_batch_size = tf.shape(input)[0]
weight = tf.get_variable('weight', [k_h, k_w, output_shape[-1], input.get_shape()[-1]],initializer=tf.random_normal_initializer(stddev=stddev))
bias = tf.get_variable('bias', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
output_shape = tf.stack([dyn_batch_size,output_shape[0],output_shape[1],output_shape[2]])
deconv = tf.nn.bias_add(tf.nn.conv2d_transpose(input, weight, output_shape=output_shape, strides=[1, d_h, d_w, 1]), bias)
return deconv
def lrelu(x, leak=0.2):
return tf.maximum(x, leak * x)
def linear(input, output_size, name="Linear", stddev=0.02, bias_start=0.0):
shape = input.get_shape().as_list()
with tf.variable_scope(name):
weight = tf.get_variable("weight", [shape[1], output_size], tf.float32,
tf.random_normal_initializer(stddev=stddev))
bias = tf.get_variable("bias", [output_size],
initializer=tf.constant_initializer(bias_start))
return tf.matmul(input, weight) + bias
我的训练：
d_opt = tf.train.AdamOptimizer(learning_rate=conf.learning_rate).minimize(model.d_loss, var_list=model.d_vars)
g_opt = tf.train.AdamOptimizer(learning_rate=conf.learning_rate).minimize(model.g_loss, var_list=model.g_vars)
with tf.Session(config=configProto) as sess:
for epoch in xrange(0, conf.max_epoch):
batch_index = 0
for original, corrupted in data.iterate_batches_train():
feed_dict = {model.original:preprocess(original), model.corrupted:preprocess(corrupted)}
sess.run([d_opt], feed_dict = feed_dict)
sess.run([d_opt], feed_dict = feed_dict)
sess.run([g_opt], feed_dict = feed_dict)
第一批大小的默认配置：
self.fig_size = 424
self.fig_channel = 1
self.conv_channel_base = 64
self.l1_lambda = 100
self.batch_norm_decay = 0.9
self.batch_size = 1
self.max_epoch = 20
self.learning_rate = 0.0002
我很感谢您可能有任何见识...
答案 0 :(得分：1)
我认为这是因为您使用了批量标准化。
批处理大小= 1时，BN并不是真正有意义的操作。
小批量> 1时，您正在使用的统计数据并不能真正反映出您的人口，因此情况可能会变得很奇怪。
您可以尝试以批量大小= 2并且没有BN进行训练吗？