Generating Images with CNN: Why is the Size Not Increasing in Each Layer?

[BRN]

Hello everybody,
I have this problem:
starting from a vector of 100 random values, I have to generate an image of size 128x128x3 using a model consisting of a fully completely layer and 5 layer deconv.
This is my model

def generator_model(noise_dim):
   
    n_layers = 5
    k_w, k_h = [8, 8]
    input_dim = (noise_dim,)
    i_w, i_h, i_d = [8, 8, 1024] # starting filters
    strides = (1, 1)
    weight_initializer = None

    model = tf.keras.Sequential()
   
    model.add(tf.keras.layers.Dense(i_w * i_h * i_d, input_shape = input_dim, kernel_initializer = weight_initializer))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.ReLU())
   
    model.add(tf.keras.layers.Reshape((i_w, i_h, i_d)))
    for i in range(n_layers - 1):
        print(k_w, k_h)
        model.add(tf.keras.layers.Conv2DTranspose(i_d, (k_w, k_h), strides, padding = 'same', use_bias = False))
        model.add(tf.keras.layers.BatchNormalization())
        model.add(tf.keras.layers.ReLU())
        i_d = int(i_d / 2)
        k_w = int(k_w * 2)
        k_h = int(k_h * 2)
       
    k_w = i_d
    k_h = i_d
    model.add(tf.keras.layers.Conv2DTranspose(3, (k_w, k_h), strides, padding = 'same', use_bias = False))

    return model

Why do I always get an 8x8x3 image without having an increase in size in each layer?

Thank's

 

[BRN]

Ok, I have the solution.

The size of the outputs of a CNN "conv" is given by the equation

$$o=\left ( \frac{i - k + 2p}{s} \right )+1$$

but, as in my case, for a transpose convolution "deconv" the size of the outputs is

$$o=s\left (i -1 \right )+ k - 2p$$

Then, with stride $s=2$, the correct code is this

def generator_model(noise_dim):
 
    n_layers = 4
    k_w, k_h = [16, 16] # starting kernel size
    input_dim = (noise_dim,)
    i_w, i_h, i_d = [8, 8, 1024] # starting filters
    strides = (2, 2)
    weight_initializer = None

    model = tf.keras.Sequential()
 
    model.add(tf.keras.layers.Dense(i_w * i_h * i_d, input_shape = input_dim, kernel_initializer = weight_initializer))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.ReLU())
 
    model.add(tf.keras.layers.Reshape((i_w, i_h, i_d)))
    i_d = int(i_d / 2)
    for i in range(n_layers - 1):
#        print(i_d, k_w, k_h)
        model.add(tf.keras.layers.Conv2DTranspose(i_d, (k_w, k_h), strides, padding = 'same', use_bias = False))
        model.add(tf.keras.layers.BatchNormalization())
        model.add(tf.keras.layers.ReLU())
        i_d = int(i_d / 2)
        k_w = int(k_w * 2)
        k_h = int(k_h * 2)

    model.add(tf.keras.layers.Conv2DTranspose(3, (k_w, k_h), strides, padding = 'same', use_bias = False))

    return model

And this solves my problem

 

[Mark44]

And this solves my problem

Great!
In the future, it would be helpful to readers to expand acronyms such as CNN, which might not be generally known. I'm assuming it has something to do with neural networks, but that's only a guess.

 

[BRN]

Hello and thanks!
You're right, CNN means Convolutional Neural Network.
Next time I will write the acronyms explicitly