danielvarga / keras-deep-dream Goto Github PK

Thanks for your great implementation. Do you have any thoughts on why DD in Keras produces such different results from the Caffe version?

Specifically, in the Caffe version, whole objects (dogs, birds, buildings) appear in images after a few epochs. They also retain their native colours (brown dogs, gold metal, etc). In contrast, the Keras version seems to produce random, rainbow coloured swirls.

I suspect this is related to the underlying Keras implementation, so perhaps @fchollet would have some insight too.

I can't seem to get this to work. I am running a VM Ubuntu with the latest keras and tensorflow CPU.

When I run it:
deeplearning@deep-learning-virtual-machine:~/Desktop$ python3 deep_dream.py vgg16 jesse.png dream

I get the following:
Using TensorFlow backend.
(1080, 1620, 4)
(900, 1350, 4)
(750, 1125, 4)
Starting octave 0 with dimensions 1125 x 750
Traceback (most recent call last):
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/common_shapes.py", line 671, in _call_cpp_shape_fn_impl
input_tensors_as_shapes, status)
File "/usr/lib/python3.5/contextlib.py", line 66, in exit
next(self.gen)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Negative dimension size caused by subtracting 2 from 1 for 'block2_pool/MaxPool' (op: 'MaxPool') with input shapes: [?,1,375,128].

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "deep_dream.py", line 198, in
model = vgg16.VGG16(include_top=False, input_tensor=dream)
File "/home/deeplearning/Desktop/deep_learning_models/vgg16.py", line 119, in VGG16
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py", line 554, in call
output = self.call(inputs, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/keras/layers/pooling.py", line 154, in call
data_format=self.data_format)
File "/usr/local/lib/python3.5/dist-packages/keras/layers/pooling.py", line 217, in _pooling_function
pool_mode='max')
File "/usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py", line 3012, in pool2d
x = tf.nn.max_pool(x, pool_size, strides, padding=padding)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 1793, in max_pool
name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 1598, in _max_pool
data_format=data_format, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 763, in apply_op
op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2329, in create_op
set_shapes_for_outputs(ret)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1717, in set_shapes_for_outputs
shapes = shape_func(op)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1667, in call_with_requiring
return call_cpp_shape_fn(op, require_shape_fn=True)
File "/usr/local/lib/python3.5/dist-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/python3.5/dist-packages/tensorflow/python/framework/common_shapes.py", line 676, in _call_cpp_shape_fn_impl
raise ValueError(err.message)
ValueError: Negative dimension size caused by subtracting 2 from 1 for 'block2_pool/MaxPool' (op: 'MaxPool') with input shapes: [?,1,375,128].

Am I doing something wrong?
This looks like great work, I might just be a noob.
Thanks