I got a model like this for autoencoder from a tutorial.
input_img = layers.Input(shape=(28,28,1))
x = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
x = layers.MaxPooling2D((2, 2), padding='same')(x)
x = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = layers.MaxPooling2D((2, 2), padding='same')(x)
x = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(x)
encoded = layers.MaxPooling2D((2, 2), padding='same')(x)
x = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(encoded)
x = layers.UpSampling2D((2, 2))(x)
x = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = layers.UpSampling2D((2, 2))(x)
x = layers.Conv2D(32, (3, 3), activation='relu')(x)
x = layers.UpSampling2D((2, 2))(x)
decoded = layers.Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
autoencoder = keras.Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
This returns output shape (28, 28, 1) But when I use (512,512,1) as the shape output shape is (508, 508, 1). Can someone provide way to adjust model that gives output as (512,512,1)
Try to add padding='same' on this line
x = layers.Conv2D(32, (3, 3), activation='relu')(x)
Full code:
input_img = layers.Input(shape=(512,512,1))
x = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
x = layers.MaxPooling2D((2, 2), padding='same')(x)
x = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = layers.MaxPooling2D((2, 2), padding='same')(x)
x = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(x)
encoded = layers.MaxPooling2D((2, 2), padding='same')(x)
x = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(encoded)
x = layers.UpSampling2D((2, 2))(x)
x = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = layers.UpSampling2D((2, 2))(x)
x = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = layers.UpSampling2D((2, 2))(x)
decoded = layers.Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
autoencoder = keras.Model(input_img, decoded)
autoencoder.summary()
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
Summary of model:
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 512, 512, 1)] 0
_________________________________________________________________
conv2d (Conv2D) (None, 512, 512, 32) 320
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 256, 256, 32) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 256, 256, 16) 4624
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 128, 128, 16) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 128, 128, 16) 2320
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 64, 64, 16) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 64, 64, 16) 2320
_________________________________________________________________
up_sampling2d (UpSampling2D) (None, 128, 128, 16) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 128, 128, 16) 2320
_________________________________________________________________
up_sampling2d_1 (UpSampling2 (None, 256, 256, 16) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 256, 256, 32) 4640
_________________________________________________________________
up_sampling2d_2 (UpSampling2 (None, 512, 512, 32) 0
_________________________________________________________________
conv2d_6 (Conv2D) (None, 512, 512, 1) 289
=================================================================
Total params: 16,833
Trainable params: 16,833
Non-trainable params: 0