自编码器代码python_变分自动编码器的实现

下面是如何计算mu和{}以及{}的计算方法：

我不确定您代码的linear部分。因此，我建议：

请注意，在编码器端的完全连接层之前，我有一个conv4形状层：[7, 7, 256]。在# These are the weights and biases of the mu and sigma on the encoder side

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])

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.v