Does not work out of the box

[JohnTailor]

Thanks for the code. Without trying to look for reasons, it seems to fail for newer tensorflow/keras versions - though that could also be specific to my setup.

Anyway, I get:
tensorflow.python.framework.errors_impl.InvalidArgumentError: Dimension 0 in both shapes must be equal, but are 1 and 3. Shapes are [1] and [3].

Full trace:

Using TensorFlow backend.
C:\Users\Deeplearning.keras\datasets\cifar-10-batches-py
X_train shape: (50000, 32, 32, 3)
50000 train samples
10000 test samples
(32, 32, 3)
C:/Users/Deeplearning/Desktop/DeepRepo2/greendatamining/DeepLearn/DeepCodeOwnNetwork/simplenet.py:78: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(96, (3, 3), input_shape=(3, 32, 32..., padding="same")
model.add(Convolution2D(96, 3, 3, border_mode='same', input_shape=(3, 32, 32)))
C:/Users/Deeplearning/Desktop/DeepRepo2/greendatamining/DeepLearn/DeepCodeOwnNetwork/simplenet.py:80: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(96, (3, 3), padding="same")
model.add(Convolution2D(96, 3, 3, border_mode='same'))
C:/Users/Deeplearning/Desktop/DeepRepo2/greendatamining/DeepLearn/DeepCodeOwnNetwork/simplenet.py:82: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(96, (3, 3), strides=(2, 2), padding="same")
model.add(Convolution2D(96, 3, 3, border_mode='same', subsample=(2, 2)))
C:/Users/Deeplearning/Desktop/DeepRepo2/greendatamining/DeepLearn/DeepCodeOwnNetwork/simplenet.py:85: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(192, (3, 3), padding="same")
model.add(Convolution2D(192, 3, 3, border_mode='same'))
C:/Users/Deeplearning/Desktop/DeepRepo2/greendatamining/DeepLearn/DeepCodeOwnNetwork/simplenet.py:87: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(192, (3, 3), padding="same")
model.add(Convolution2D(192, 3, 3, border_mode='same'))
C:/Users/Deeplearning/Desktop/DeepRepo2/greendatamining/DeepLearn/DeepCodeOwnNetwork/simplenet.py:89: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(192, (3, 3), strides=(2, 2), padding="same")
model.add(Convolution2D(192, 3, 3, border_mode='same', subsample=(2, 2)))
C:/Users/Deeplearning/Desktop/DeepRepo2/greendatamining/DeepLearn/DeepCodeOwnNetwork/simplenet.py:92: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(192, (3, 3), padding="same")
model.add(Convolution2D(192, 3, 3, border_mode='same'))
C:/Users/Deeplearning/Desktop/DeepRepo2/greendatamining/DeepLearn/DeepCodeOwnNetwork/simplenet.py:94: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(192, (1, 1), padding="valid")
model.add(Convolution2D(192, 1, 1, border_mode='valid'))
C:/Users/Deeplearning/Desktop/DeepRepo2/greendatamining/DeepLearn/DeepCodeOwnNetwork/simplenet.py:96: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(10, (1, 1), padding="valid")
model.add(Convolution2D(10, 1, 1, border_mode='valid'))
Traceback (most recent call last):
File "C:\Users\Deeplearning\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 1576, in _create_c_op
c_op = c_api.TF_FinishOperation(op_desc)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Dimension 0 in both shapes must be equal, but are 1 and 3. Shapes are [1] and [3].
From merging shape 0 with other shapes. for 'tower_0/lambda_1/concat/concat_dim' (op: 'Pack') with input shapes: [1], [3].

[RyanZHe]

Try change K.set_image_dim_ordering('tf') to K.set_image_dim_ordering('th')

Source: https://datascience.stackexchange.com/questions/14467/what-does-theano-dimension-ordering-mean