Error when fitting 3D convolutional neural network - python-3.x

Question

I am trying to train a convolutional neural network on google colab for a medical classification problem. The data set is 89 256x256x256 images for training and 11 for testing. When I try to make my model train it gives me the following error:
import keras
from keras import optimizers
import keras.models
from keras.models import Sequential
import keras.layers
from keras.layers.convolutional import Conv3D
from keras.layers.convolutional import MaxPooling3D
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers import Dense
from keras import metrics
model = Sequential()
model.add(Conv3D(64, kernel_size=(3,3,3),
activation='relu',
input_shape=(10,1,256,256,256)))
model.add(Conv3D(64, (2,2,2), activation='relu'))
model.add(MaxPooling3D(pool_size=(2,2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
opt=keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
model.compile(opt, loss='categorical_crossentropy', metrics=['mae','acc'])
model.fit(x=train_data, y=train_labels,epochs=100, batch_size=10, verbose=2 ,callbacks=None, validation_split=0.0, validation_data=(validation_data,validation_labels), shuffle=True)
This is the error i get:
ValueError: Input 0 is incompatible with layer conv3d_56: expected ndim=5, found ndim=6

Answer 1

Assuming you are using channels first data_format, your input_shape argugment to the first Conv3D layer should be (CHANNELS, HEIGHT, WIDTH, DEPTH). But your input shape tuple has length of 5, and that is not what Conv3D layer expecting. Assuming the batch_size(of 10) is specified by mistake, making the following changes should fix the problem
model.add(Conv3D(64, kernel_size=(3,3,3),
activation='relu',
input_shape=(1,256,256,256)))
Edit
If you are using channels_last data-format your input_shape should be (HEIGHT, WIDTH, DEPTH, CHANNELS). And assuming your images have 1 channels, the above line should be,
model.add(Conv3D(64, kernel_size=(3,3,3),
activation='relu',
input_shape=(256,256,256, 1)))