ImageDataGenerator fed to 3D CNN in keras

Hey everyone, I am new in keras and python I am trying to use 3D CNN with keras, I did the following code shown below and there is an error relates to the dimension of the input shape:

The error says expecting 5d input but found 4d!! I know that 3D CNN input shape should be 5d but how can I reshape the train_data to be 5d? please.

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, Conv3D, MaxPooling3D
from keras.layers.advanced_activations import LeakyReLU
from keras.optimizers import SGD, RMSprop
from keras.utils import np_utils, generic_utils
from keras.losses import categorical_crossentropy
from keras.optimizers import Adam
import os
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import cv2
from sklearn.cross_validation import train_test_split
from sklearn import cross_validation
from sklearn import preprocessing

datagen = ImageDataGenerator()
train_data=datagen.flow_from_directory('C:\\Users\\AA\\Data\\Training', target_size=(80, 100), color_mode='grayscale', classes=None, class_mode='categorical', batch_size=32, interpolation='nearest')
test_data=datagen.flow_from_directory('C:\\Users\\AA\\Data\\Testing', target_size=(80, 100), color_mode='grayscale', classes=None, class_mode='categorical', batch_size=32, interpolation='nearest')



    ins = (100, 100, 16, 1)
    model = Sequential()
    model.add(Conv3D(32, kernel_size=(3, 3, 3), input_shape=ins, padding=0))
    model.add(Activation('relu'))
    model.add(Conv3D(32, kernel_size=(3, 3, 3), padding=0))
    model.add(Activation('softmax'))
    model.add(MaxPooling3D(pool_size=(3, 3, 3), padding=0))
    model.add(Dropout(0.25))

    model.add(Conv3D(64, kernel_size=(3, 3, 3), padding=0))
    model.add(Activation('relu'))
    model.add(Conv3D(64, kernel_size=(3, 3, 3), padding=0))
    model.add(Activation('softmax'))
    model.add(MaxPooling3D(pool_size=(3, 3, 3), padding=0))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(512, activation='sigmoid'))
    model.add(Dropout(0.5))
    model.add(Dense(8, activation='softmax'))


 model.compile(loss=categorical_crossentropy, optimizer=Adam(), metrics=['accuracy'])    


   model.fit_generator(train_data,
        steps_per_epoch=2000,
        epochs=50,
        validation_data=test_data,
        validation_steps=800)

[emadeldeen24]

You can use my tweaked version of ImageDataGenerator class that fits this kind of problems.

It’s the same as the main Keras ImageDataGenerator but I added an option to take more than one image/frame on each iteration. This is by changing the parameter frames_per_step to specify the number of frames/images you want to include in each iteration.

So here’s how to use it:

from tweaked_ImageGenerator_v2 import ImageDataGenerator
datagen = ImageDataGenerator()
train_data=datagen.flow_from_directory('path/to/data', target_size=(x, y), batch_size=32, frames_per_step=4)

[dhuy228]

@MarcelBeining hey mate, I just customised the Keras’ ImageDataGenerator for medical image analysis applications, i.e. 3D Dicom slices (10,32,32,32,1). It might help with your problem.

https://github.com/dhuy228/augmented-volumetric-image-generator/blob/master/generator.py