is this architecture an autoencoder - tensorflow

Question

I want to create an autoencodre i build this architecture it works but i want to know if it is an autoencoder architecture
Encoder
layer = layers.Conv2D(16, (3, 3), activation="relu", padding="same",data_format = 'channels_first')(input)
layer = layers.MaxPooling2D((2, 2), padding="same",data_format = 'channels_first')(layer)
layer = layers.Conv2D(32, (3, 3), activation="relu", padding="same",data_format = 'channels_first')(layer)
layer = layers.MaxPooling2D((2, 2), padding="same",data_format = 'channels_first')(layer)
## Decoder
layer = layers.Conv2DTranspose(16, (3, 3), strides=2, activation="relu", padding="same",data_format = 'channels_first')(layer)
layer = layers.UpSampling2D((2,2))(layer)
layer = layers.Conv2DTranspose(32, (3, 3), strides=2, activation="relu", padding="same",data_format = 'channels_first')(layer)
layer = layers.UpSampling2D((2,2))(layer)
#layer = layers.UpSampling2D((2,2))(layer)
layer = layers.Flatten()(layer)
dense = layers.Dense(784, activation="sigmoid")
output = dense(layer)

Answer 1

There are some problems in your code:
You need an input layer to your model if you are using functional:
input = layers.Input(shape=(3, 192, 192))
In an autoencoder, the output of your model needs to have the same dimensions as the input. However, in your model your output is a dense vector (1D), while your input is obviously at least 2D (or 3D if you have channels, like in images).
You have specified the argument data_format = 'channels_first' which means that your input tensor has the channel dimension in the position 0. For example, if your input is an rgb image, it has shape (color_channel, width, height), instead of the more common (width, heigth, color_channel). That is ok, but 1) Make sure your images have channels first and 2) You need to pass the same argument on your upsampling layers.
With a couple of changes, the model looks like this:
## Encoder
input = layers.Input(shape=(3, 192, 192))
layer = layers.Conv2D(16, (3, 3), activation="relu", padding="same",data_format = 'channels_first')(input)
layer = layers.MaxPooling2D((2, 2), padding="same",data_format = 'channels_first')(layer)
layer = layers.Conv2D(32, (3, 3), activation="relu", padding="same",data_format = 'channels_first')(layer)
layer = layers.MaxPooling2D((2, 2), padding="same",data_format = 'channels_first')(layer)
## Decoder
layer = layers.Conv2DTranspose(16, (3, 3), strides=1, activation="relu", padding="same",data_format = 'channels_first')(layer)
layer = layers.UpSampling2D((2,2), data_format='channels_first')(layer)
layer = layers.Conv2DTranspose(32, (3, 3), strides=1, activation="relu", padding="same",data_format = 'channels_first')(layer)
layer = layers.UpSampling2D((2,2), data_format='channels_first')(layer)
output = layers.Conv2DTranspose(3, (3, 3), strides=1, activation="relu", padding="same",data_format = 'channels_first')(layer)
model = tf.keras.Model(inputs=input, outputs=output)
model.summary()
Model: "model_8"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_10 (InputLayer) [(None, 3, 192, 192)] 0
_________________________________________________________________
conv2d_19 (Conv2D) (None, 16, 192, 192) 448
_________________________________________________________________
max_pooling2d_18 (MaxPooling (None, 16, 96, 96) 0
_________________________________________________________________
conv2d_20 (Conv2D) (None, 32, 96, 96) 4640
_________________________________________________________________
max_pooling2d_19 (MaxPooling (None, 32, 48, 48) 0
_________________________________________________________________
conv2d_transpose_19 (Conv2DT (None, 16, 48, 48) 4624
_________________________________________________________________
up_sampling2d_17 (UpSampling (None, 16, 96, 96) 0
_________________________________________________________________
conv2d_transpose_20 (Conv2DT (None, 32, 96, 96) 4640
_________________________________________________________________
up_sampling2d_18 (UpSampling (None, 32, 192, 192) 0
_________________________________________________________________
conv2d_transpose_21 (Conv2DT (None, 3, 192, 192) 867
=================================================================
Total params: 15,219
Trainable params: 15,219
Non-trainable params: 0