Shapes Incompatible in Keras with CNN - keras

Question

I am implementing a network that takes a 2d image and outputs a 3D binary voxels for it.
I am using an autoencoder with LSTM module.
The current shape of images and voxels are as follows:
print(x_train.shape)
print(y_train.shape)
>>> (792, 127, 127, 3)
>>> (792, 32, 32, 32)
792 RGB images 127 x 127
792 corresponding voxels with 3D Binary Tensor (32 x 32 x 32)
Running the following encoder model:
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, LeakyReLU, MaxPooling2D, Dense, Flatten, Conv3D, MaxPool3D, GRU, Reshape, UpSampling3D
from tensorflow import keras
enc_filter = [96, 128, 256, 256, 256, 256]
fc_filters = [1024]
model = Sequential()
epochs = 5
batch_size = 24
input_shape=(127,127,3)
model.add(Conv2D(enc_filter[0], kernel_size=(7, 7), strides=(1,1),activation='relu',input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(LeakyReLU(alpha=0.1))
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.SGD(lr=0.01),
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs)
yields the following:
ValueError: Shapes (24, 32, 32, 32) and (24, 1024) are incompatible
Can someone address why the shapes are incompatible? I tried removing layers and test others but all yields compatibility issues.

Answer 1

Your model has a dense layer with 1024 output, but you are passing 32,32,32 shaped array.
You need to reshape your model output so that it has proper shape.
This is a dummy model, you need to change the parameters to find the suitable architecture.
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, LeakyReLU, MaxPooling2D, Dense, Flatten, Conv3D, MaxPool3D, GRU, Reshape, UpSampling3D
from tensorflow import keras
import numpy as np
# dummy data
x_train = np.random.randn(792, 127, 127, 3)
y_train = np.random.randn(792, 32, 32, 32)
enc_filter = [96, 128, 256, 2]
fc_filters = [1024]
model = Sequential()
epochs = 5
batch_size = 24
input_shape=(127,127,3)
model.add(Conv2D(enc_filter[0], kernel_size=(7, 7), strides=(1,1),activation='relu',input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(LeakyReLU(alpha=0.1))
model.add(Conv2D(enc_filter[1], kernel_size=(7, 7), strides=(1,1),activation='relu',input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(LeakyReLU(alpha=0.1))
model.add(Conv2D(enc_filter[2], kernel_size=(7, 7), strides=(1,1),activation='relu',input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(LeakyReLU(alpha=0.1))
model.add(Conv2D(enc_filter[3], kernel_size=(7, 7), strides=(1,1),activation='relu',input_shape=input_shape)) # bottolneck
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(LeakyReLU(alpha=0.1))
model.add(Flatten())
model.add(Dense(32*32*32, activation='relu'))
model.add(Reshape((32,32,32)))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.SGD(lr=0.01),
metrics=['accuracy'])
model.summary()
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs)
Model: "sequential_10"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_24 (Conv2D) (None, 121, 121, 96) 14208
_________________________________________________________________
max_pooling2d_24 (MaxPooling (None, 60, 60, 96) 0
_________________________________________________________________
leaky_re_lu_24 (LeakyReLU) (None, 60, 60, 96) 0
_________________________________________________________________
conv2d_25 (Conv2D) (None, 54, 54, 128) 602240
_________________________________________________________________
max_pooling2d_25 (MaxPooling (None, 27, 27, 128) 0
_________________________________________________________________
leaky_re_lu_25 (LeakyReLU) (None, 27, 27, 128) 0
_________________________________________________________________
conv2d_26 (Conv2D) (None, 21, 21, 256) 1605888
_________________________________________________________________
max_pooling2d_26 (MaxPooling (None, 10, 10, 256) 0
_________________________________________________________________
leaky_re_lu_26 (LeakyReLU) (None, 10, 10, 256) 0
_________________________________________________________________
conv2d_27 (Conv2D) (None, 4, 4, 2) 25090
_________________________________________________________________
max_pooling2d_27 (MaxPooling (None, 2, 2, 2) 0
_________________________________________________________________
leaky_re_lu_27 (LeakyReLU) (None, 2, 2, 2) 0
_________________________________________________________________
flatten_10 (Flatten) (None, 8) 0
_________________________________________________________________
dense_1 (Dense) (None, 32768) 294912
_________________________________________________________________
reshape_10 (Reshape) (None, 32, 32, 32) 0
=================================================================
Total params: 2,542,338
Trainable params: 2,542,338
Non-trainable params: 0
In the summary, you can see I add a dense layer with 32x32x32 neurons and then reshape it.