pot.py 文件源码

def encoder(self, opts, input_, is_training=False, reuse=False, keep_prob=1.):
        if opts['e_add_noise']:
            def add_noise(x):
                shape = tf.shape(x)
                return x + tf.truncated_normal(shape, 0.0, 0.01)
            def do_nothing(x):
                return x
            input_ = tf.cond(is_training, lambda: add_noise(input_), lambda: do_nothing(input_))
        num_units = opts['e_num_filters']
        num_layers = opts['e_num_layers']
        with tf.variable_scope("ENCODER", reuse=reuse):
            if not opts['convolutions']:
                hi = input_
                for i in range(num_layers):
                    hi = ops.linear(opts, hi, num_units, scope='h%d_lin' % i)
                    if opts['batch_norm']:
                        hi = ops.batch_norm(opts, hi, is_training, reuse, scope='bn%d' % i)
                    hi = tf.nn.relu(hi)
                if opts['e_is_random']:
                    latent_mean = ops.linear(
                        opts, hi, opts['latent_space_dim'], 'h%d_lin' % (i + 1))
                    log_latent_sigmas = ops.linear(
                        opts, hi, opts['latent_space_dim'], 'h%d_lin_sigma' % (i + 1))
                    return latent_mean, log_latent_sigmas
                else:
                    return ops.linear(opts, hi, opts['latent_space_dim'], 'h%d_lin' % (i + 1))
            elif opts['e_arch'] == 'dcgan':
                return self.dcgan_encoder(opts, input_, is_training, reuse, keep_prob)
            elif opts['e_arch'] == 'ali':
                return self.ali_encoder(opts, input_, is_training, reuse, keep_prob)
            elif opts['e_arch'] == 'began':
                return self.began_encoder(opts, input_, is_training, reuse, keep_prob)
            else:
                raise ValueError('%s Unknown' % opts['e_arch'])