I am implementing a Decoder (a type of Artificial Neural Network) using keras:
latent_dim = 25
latent_inputs = keras.Input(shape=(latent_dim,))
x = layers.Dense(units=100, activation="relu")(latent_inputs)
x = layers.Dense(units=1024, activation="relu")(x)
x = layers.Dense(units=4096, activation="relu")(x)
x = layers.Reshape((4, 4, 256))(x)
x = layers.Conv2DTranspose(filters=256, kernel_size=3, activation="relu", strides=2, padding="same")(x)
x = layers.Conv2DTranspose(filters=128, kernel_size=3, activation="relu", strides=1, padding="same")(x)
x = layers.Conv2DTranspose(filters=128, kernel_size=3, activation="relu", strides=2, padding="same")(x)
x = layers.Conv2DTranspose(filters=64, kernel_size=3, activation="relu", strides=1, padding="same")(x)
x = layers.Conv2DTranspose(filters=64, kernel_size=3, activation="relu", strides=2, padding="same")(x)
decoder_outputs = layers.Conv2DTranspose(filters=1, kernel_size=3, activation="sigmoid", padding="same")(x)
decoder = keras.Model(latent_inputs, decoder_outputs, name="decoder")
decoder.summary()
whose output is:
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 25)] 0
dense (Dense) (None, 100) 2600
dense_1 (Dense) (None, 1024) 103424
dense_2 (Dense) (None, 4096) 4198400
reshape (Reshape) (None, 4, 4, 256) 0
conv2d_transpose (Conv2DTr (None, 8, 8, 256) 590080
anspose)
conv2d_transpose_1 (Conv2D (None, 8, 8, 128) 295040
Transpose)
conv2d_transpose_2 (Conv2D (None, 16, 16, 128) 147584
Transpose)
conv2d_transpose_3 (Conv2D (None, 16, 16, 64) 73792
Transpose)
conv2d_transpose_4 (Conv2D (None, 32, 32, 64) 36928
Transpose)
conv2d_transpose_5 (Conv2D (None, 32, 32, 1) 577
Transpose)
I want to adjust my model so that decoder_outputs shape is (None, 40, 40, 1) instead of (None, 32, 32, 1). This is what I tried to do:
latent_dim = 25
latent_inputs = keras.Input(shape=(latent_dim,))
x = layers.Dense(units=100, activation="relu")(latent_inputs)
x = layers.Dense(units=1024, activation="relu")(x)
x = layers.Dense(units=1600, activation="relu")(x) # Adjusted units to match 40*40*1
x = layers.Reshape((40, 40, 1))(x) # Reshaped to (40, 40, 1)
x = layers.Conv2DTranspose(filters=128, kernel_size=3, activation="relu", strides=2, padding="same")(x)
x = layers.Conv2DTranspose(filters=64, kernel_size=3, activation="relu", strides=1, padding="same")(x)
x = layers.Conv2DTranspose(filters=64, kernel_size=3, activation="relu", strides=2, padding="same")(x)
decoder_outputs = layers.Conv2DTranspose(filters=1, kernel_size=3, activation="sigmoid", padding="same")(x)
decoder = keras.Model(latent_inputs, decoder_outputs, name="decoder")
decoder.summary()
but unfortunately decoder_outputs shape is (None, 160, 160, 1).
Can you help me, please?
EDIT
I tried the following solution:
latent_dim = 25
latent_inputs = keras.Input(shape=(latent_dim,))
x = layers.Dense(units=100, activation="relu")(latent_inputs)
x = layers.Dense(units=1024, activation="relu")(x)
x = layers.Dense(units=4096, activation="relu")(x)
x = layers.Reshape((4, 4, 256))(x)
x = layers.Conv2DTranspose(filters=256, kernel_size=3, activation="relu", strides=2, padding="same")(x)
x = layers.Conv2DTranspose(filters=128, kernel_size=3, activation="relu", strides=1, padding="same")(x)
x = layers.Conv2DTranspose(filters=128, kernel_size=3, activation="relu", strides=2, padding="valid")(x)
x = layers.Conv2DTranspose(filters=64, kernel_size=3, activation="relu", strides=1, padding="valid")(x)
x = layers.Conv2DTranspose(filters=64, kernel_size=3, activation="relu", strides=2, padding="valid")(x)
decoder_outputs = layers.Conv2DTranspose(filters=1, kernel_size=3, activation="sigmoid", padding="valid")(x)
decoder = keras.Model(latent_inputs, decoder_outputs, name="decoder")
decoder.summary()
that is using padding="same" for some layers, but this is the output I get:
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 25)] 0
dense (Dense) (None, 100) 2600
dense_1 (Dense) (None, 1024) 103424
dense_2 (Dense) (None, 4096) 4198400
reshape (Reshape) (None, 4, 4, 256) 0
conv2d_transpose (Conv2DTr (None, 8, 8, 256) 590080
anspose)
conv2d_transpose_1 (Conv2D (None, 8, 8, 128) 295040
Transpose)
conv2d_transpose_2 (Conv2D (None, 16, 16, 128) 147584
Transpose)
conv2d_transpose_3 (Conv2D (None, 18, 18, 64) 73792
Transpose)
conv2d_transpose_4 (Conv2D (None, 37, 37, 64) 36928
Transpose)
conv2d_transpose_5 (Conv2D (None, 39, 39, 1) 577
Transpose)
As you can see decoder_outputs shape is now (None, 39, 39, 1). I want it to be (None, 40, 40, 1). How may I fix?
so the reason I was asking for the model input was so that I could do the calculations to create your desired output. If you want to learn how to calculate the output for your CNN layers, here is a thread to help you out. Here is the code:
latent_dim = 25
latent_inputs = keras.Input(shape=(latent_dim,))
x = layers.Dense(units=100, activation="relu")(latent_inputs)
x = layers.Dense(units=1024, activation="relu")(x)
x = layers.Dense(units=4096, activation="relu")(x)
x = layers.Reshape((4, 4, 256))(x)
x = layers.Conv2DTranspose(filters=256, kernel_size=3, activation="relu", strides=2, padding="same")(x)
x = layers.Conv2DTranspose(filters=128, kernel_size=3, activation="relu", strides=1, padding="same")(x)
x = layers.Conv2DTranspose(filters=128, kernel_size=3, activation="relu", strides=5, padding="same")(x)
x = layers.Conv2DTranspose(filters=64, kernel_size=3, activation="relu", strides=1, padding="same")(x)
x = layers.Conv2DTranspose(filters=64, kernel_size=3, activation="relu", strides=1, padding="same")(x)
decoder_outputs = layers.Conv2DTranspose(filters=1, kernel_size=3, activation="sigmoid", padding="same")(x)
decoder = keras.Model(latent_inputs, decoder_outputs, name="decoder")
decoder.summary()
Here is the output:
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_46 (InputLayer) [(None, 25)] 0
dense_138 (Dense) (None, 100) 2600
dense_139 (Dense) (None, 1024) 103424
dense_140 (Dense) (None, 4096) 4198400
reshape_46 (Reshape) (None, 4, 4, 256) 0
conv2d_transpose_265 (Conv2 (None, 8, 8, 256) 590080
DTranspose)
conv2d_transpose_266 (Conv2 (None, 8, 8, 128) 295040
DTranspose)
conv2d_transpose_267 (Conv2 (None, 40, 40, 128) 147584
DTranspose)
conv2d_transpose_268 (Conv2 (None, 40, 40, 64) 73792
DTranspose)
conv2d_transpose_269 (Conv2 (None, 40, 40, 64) 36928
DTranspose)
conv2d_transpose_270 (Conv2 (None, 40, 40, 1) 577
DTranspose)
=================================================================
Total params: 5,448,425
Trainable params: 5,448,425
Non-trainable params: 0
_________________________________________________________________