An encoder (autoencoder without the decoder part) that consists of two convolutional layers and one fully connected layer is presented as follows. The parent autoencoder was trained on the MNIST dataset. Therefore, the network takes as input an image of size 28x28x1 and at latent space, encodes it to a 10-dimensional vector, one dimension for each class:

# Only half of the autoencoder changed for classification
class CAE_CNN_Encoder(object):
    ......
    def build_graph(self, img_size=28):
        self.__x = tf.placeholder(tf.float32, shape=[None, img_size * img_size], name='IMAGE_IN')
        self.__x_image = tf.reshape(self.__x, [-1, img_size, img_size, 1])
        self.__y_ = tf.placeholder("float", shape=[None, 10], name='Y')

        with tf.name_scope('ENCODER'):
            ##### ENCODER
            # CONV1: Input 28x28x1 after CONV 5x5 P:2 S:2 H_out: 1 + (28+4-5)/2 = 14, 
            # W_out= 1 + (28+4-5)/2 = 14
            self.__conv1_act = tf.layers.conv2d(inputs=self.__x_image, strides=(2, 2), name='conv1',
                              filters=16, kernel_size=[5, 5], padding="same", activation=tf.nn.relu)

            # CONV2: Input 14x14x16 after CONV 5x5 P:0 S:2 H_out: 1 + (14+4-5)/2 = 7,
            # W_out= 1 + (14+4-5)/2 = 7
            self.__conv2_act = tf.layers.conv2d(inputs=self.__conv1_act, strides=(2, 2),      
                name='conv2', filters=32, kernel_size=[5, 5], padding="same", activation=tf.nn.relu)

        with tf.name_scope('LATENT'):
            # Reshape: Input 7x7x32 after [7x7x32]
            self.__enc_out = tf.layers.flatten(self.__conv2_act, name='flatten_conv2')
            self.__dense = tf.layers.dense(inputs=self.__enc_out, units=200, activation=tf.nn.relu,                                                                                                                                                                                               name='fc1')
            self.__logits = tf.layers.dense(inputs=self.__dense, units=10, name='logits')

    def __init__(self, img_size=28):
        if CAE_CNN_Encoder.__instance is None:
            self.build_graph(img_size)

    @property
    def output(self):
        return self.__logits

    @property
    def labels(self):
        return self.__y_

    @property
    def input(self):
        return self.__x

    @property
    def image_in(self):
        return self.__x_image