CODEWITHSUNDEEP
python-3.xkerasconv-neural-network
Hossein Shahbodaghkhan
Hossein Shahbodaghkhan

Reputation: 35

3D image classification using 3D CNN

I design a CNN network in order to work with "cifar10" dataset in keras. here is my code:

input_layer = Input(shape=(32,32,3))
x = Conv3D(32,(5,5,3),activation='relu',padding='same')(input_layer)
x = Conv3D(32,(5,5,3),activation='relu',padding='same')(x)
x = MaxPool3D(pool_size=2, padding='same')(x)
x = Conv3D(32,(5,5,3),activation='relu',padding='same')(x)
x = Conv3D(32,(5,5,3),activation='relu',padding='same')(x)
x = MaxPool3D(pool_size=2, padding='same')(x)
x = Flatten()(x)
x = Dense(128,kernel_initializer='random_normal', bias_initializer='zeros')(x)
x = Dense(128,kernel_initializer='random_normal', bias_initializer='zeros')(x)
output_layer = Dense(10,activation='softmax',kernel_initializer='random_normal', bias_initializer='zeros')(x)
Cifar10_CNN = Model(input_layer, output_layer)

When I build the model I get this error:

Input 0 is incompatible with layer conv3d_5: expected ndim=5, found ndim=4

How can I solve this?

Upvotes: 0

Views: 688

Answers (1)

thushv89
thushv89

Reputation: 11333

You probably should read up about differences between Conv2D, Conv3D. Though it can be confusing (given images are in fact 3 dimensional), they are still considered 2D (you don't consider the channel dimension when thinking about convolution in Keras. Convolution anyway happens on the channels dimension). So You don't need Conv3D for images, you need Conv2D. 

from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPool2D, Flatten
from tensorflow.keras.models import Model

input_layer = Input(shape=(32,32,3))
x = Conv2D(32,(5,5),activation='relu',padding='same')(input_layer)
x = Conv2D(32,(5,5),activation='relu',padding='same')(x)
x = MaxPool2D(pool_size=2, padding='same')(x)
x = Conv2D(32,(5,5),activation='relu',padding='same')(x)
x = Conv2D(32,(5,5),activation='relu',padding='same')(x)
x = MaxPool2D(pool_size=2, padding='same')(x)
x = Flatten()(x)
x = Dense(128,kernel_initializer='random_normal', bias_initializer='zeros')(x)
x = Dense(128,kernel_initializer='random_normal', bias_initializer='zeros')(x)
output_layer = Dense(10,activation='softmax',kernel_initializer='random_normal', bias_initializer='zeros')(x)
Cifar10_CNN = Model(input_layer, output_layer)
print(Cifar10_CNN.summary())

Upvotes: 1

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