pot.py 文件源码

def generator(self, opts, noise, is_training=False, reuse=False, keep_prob=1.):
        """ Decoder actually.

        """

        output_shape = self._data.data_shape
        num_units = opts['g_num_filters']

        with tf.variable_scope("GENERATOR", reuse=reuse):
            # if not opts['convolutions']:
            if opts['g_arch'] == 'mlp':
                layer_x = noise
                for i in range(opts['g_num_layers']):
                    layer_x = ops.linear(opts, layer_x, num_units, 'h%d_lin' % i)
                    layer_x = tf.nn.relu(layer_x)
                    if opts['batch_norm']:
                        layer_x = ops.batch_norm(
                            opts, layer_x, is_training, reuse, scope='bn%d' % i)
                out = ops.linear(opts, layer_x, np.prod(output_shape), 'h%d_lin' % (i + 1))
                out = tf.reshape(out, [-1] + list(output_shape))
                if opts['input_normalize_sym']:
                    return tf.nn.tanh(out)
                else:
                    return tf.nn.sigmoid(out)
            elif opts['g_arch'] in ['dcgan', 'dcgan_mod']:
                return self.dcgan_like_arch(opts, noise, is_training, reuse, keep_prob)
            elif opts['g_arch'] == 'conv_up_res':
                return self.conv_up_res(opts, noise, is_training, reuse, keep_prob)
            elif opts['g_arch'] == 'ali':
                return self.ali_deconv(opts, noise, is_training, reuse, keep_prob)
            elif opts['g_arch'] == 'began':
                return self.began_dec(opts, noise, is_training, reuse, keep_prob)
            else:
                raise ValueError('%s unknown' % opts['g_arch'])