3D Convolutional Neural Network input shape


I’m having a problem feeding a 3D CNN using Keras and Python to classify 3D shapes. I have a folder with some models in JSON format. I read those models into a Numpy Array. The models are 25*25*25 and represent the occupancy grid of the voxelized model (each position represents if the voxel in position (i,j,k) has points in it or no), so I only have 1 channel of input, like grayscale images in 2D images. The code that I have is the following:

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution3D, MaxPooling3D
from keras.optimizers import SGD
from keras.utils import np_utils
from keras import backend as K

# Number of Classes and Epochs of Training
nb_classes = 3 # cube, cone or sphere
nb_epoch = 100
batch_size = 2

# Input Image Dimensions
img_rows, img_cols, img_depth = 25, 25, 25

# Number of Convolutional Filters to use
nb_filters = 32

# Convolution Kernel Size
kernel_size = [5,5,5]

X_train, Y_train = [], []

# Read from File
import os
import json

for filename in os.listdir(os.path.join(os.getcwd(), 'models')):
    with open(os.path.join(os.getcwd(), 'models', filename)) as f:
        file = f.readlines()
        json_file = '\n'.join(file)
        content = json.loads(json_file)
        occupancy = content['model']['occupancy']
        form = []
        for value in occupancy:
        final_model = [ [ [ 0 for i in range(img_rows) ]
                              for j in range(img_cols) ]
                              for k in range(img_depth) ]
        a = 0
        for i in range(img_rows):
            for j in range(img_cols):
                for k in range(img_depth):
                    final_model[i][j][k] = form[a]
                    a = a + 1

X_train = np.array(X_train)
Y_train = np.array(Y_train)

# (1 channel, 25 rows, 25 cols, 25 of depth)
input_shape = (1, img_rows, img_cols, img_depth)

# Init
model = Sequential()

# 3D Convolution layer
model.add(Convolution3D(nb_filters, kernel_size[0], kernel_size[1], kernel_size[2],

# Fully Connected layer

# Softmax Layer

# Compile

# Fit network
model.fit(X_train, Y_train, nb_epoch=nb_epoch,

After this, I get the following error

Using TensorFlow backend. Traceback (most recent call last): File
line 670, in _call_cpp_shape_fn_impl
status) File “/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/contextlib.py”,
line 89, in exit
next(self.gen) File “/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py”,
line 469, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status)) tensorflow.python.framework.errors_impl.InvalidArgumentError: Negative
dimension size caused by subtracting 5 from 1 for ‘Conv3D’ (op:
‘Conv3D’) with input shapes: [?,1,25,25,25], [5,5,5,25,32].

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File “CNN_3D.py”, line 76, in

activation=’relu’)) File “/usr/local/lib/python3.6/site-packages/keras/models.py”, line 299, in
layer.create_input_layer(batch_input_shape, input_dtype) File “/usr/local/lib/python3.6/site-packages/keras/engine/topology.py”,
line 401, in create_input_layer
self(x) File “/usr/local/lib/python3.6/site-packages/keras/engine/topology.py”,
line 572, in call
self.add_inbound_node(inbound_layers, node_indices, tensor_indices) File
line 635, in add_inbound_node
Node.create_node(self, inbound_layers, node_indices, tensor_indices) File
line 166, in create_node
output_tensors = to_list(outbound_layer.call(input_tensors[0], mask=input_masks[0])) File
line 1234, in call
filter_shape=self.W_shape) File “/usr/local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py”,
line 2831, in conv3d
x = tf.nn.conv3d(x, kernel, strides, padding) File “/usr/local/lib/python3.6/site-packages/tensorflow/python/ops/gen_nn_ops.py”,
line 522, in conv3d
strides=strides, padding=padding, name=name) File “/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py”,
line 763, in apply_op
op_def=op_def) File “/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py”,
line 2397, in create_op
set_shapes_for_outputs(ret) File “/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py”,
line 1757, in set_shapes_for_outputs
shapes = shape_func(op) File “/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py”,
line 1707, in call_with_requiring
return call_cpp_shape_fn(op, require_shape_fn=True) File “/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/common_shapes.py”,
line 610, in call_cpp_shape_fn
debug_python_shape_fn, require_shape_fn) File “/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/common_shapes.py”,
line 675, in _call_cpp_shape_fn_impl
raise ValueError(err.message) ValueError: Negative dimension size caused by subtracting 5 from 1 for ‘Conv3D’ (op: ‘Conv3D’) with input
shapes: [?,1,25,25,25], [5,5,5,25,32].

What am I doing wrong to get this error?


I think that the problem is that you are setting the input shape in Theano ordering but you are using Keras with Tensorflow backend and Tensorflow img ordering. In addition the y_train array has to be converted to categorical labels.

Updated code:

from keras.utils import np_utils
from keras import backend as K

if K.image_dim_ordering() == 'th':
    X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols, img_depth)
    input_shape = (1, img_rows, img_cols, img_depth)
    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, img_depth, 1)
    input_shape = (img_rows, img_cols, img_depth, 1)

Y_train = np_utils.to_categorical(Y_train, nb_classes)

Adding this lines should fix it.

Answered By – David de la Iglesia

This Answer collected from stackoverflow, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0

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