Hii @hugh2017
Can you post the line number in which you are getting the error?
And are you trying this on CIFAR10 or on your own dataset?

from all-conv-keras.

hi ibrantabhi19 :

Thank you for your reply.
I think I really use CIFAR10, see below the picture.

The following is the whole message：
Using TensorFlow backend.
X_train shape: (50000, 32, 32, 3)
50000 train samples
10000 test samples
(32, 32, 3)
allconv.py:78: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(96, (3, 3), padding="same", input_shape=(3, 32, 32...)
model.add(Convolution2D(96, 3, 3, border_mode = 'same', input_shape=(3, 32, 32)))
allconv.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'))
allconv.py:82: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(96, (3, 3), padding="same", strides=(2, 2))
model.add(Convolution2D(96, 3, 3, border_mode='same', subsample = (2,2)))
allconv.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'))
allconv.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'))
allconv.py:89: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(192, (3, 3), padding="same", strides=(2, 2))
model.add(Convolution2D(192, 3, 3,border_mode='same', subsample = (2,2)))
allconv.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'))
allconv.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'))
allconv.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:\Toolkits\Anaconda3\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 671, in _call_cpp_shape_fn_impl
input_tensors_as_shapes, status)
File "C:\Toolkits\Anaconda3\lib\contextlib.py", line 66, in exit
next(self.gen)
File "C:\Toolkits\Anaconda3\lib\site-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: Dimension 0 in both shapes must be equal, but 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].

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "allconv.py", line 102, in
model = make_parallel(model, 4)
File "allconv.py", line 37, in make_parallel
slice_n = Lambda(get_slice, output_shape=input_shape, arguments={'idx':i,'parts':gpu_count})(x)
File "C:\Toolkits\Anaconda3\lib\site-packages\keras\engine\topology.py", line 578, in call
output = self.call(inputs, **kwargs)
File "C:\Toolkits\Anaconda3\lib\site-packages\keras\layers\core.py", line 659, in call
return self.function(inputs, **arguments)
File "allconv.py", line 19, in get_slice
size = tf.concat(0, [ shape[:1] // parts, shape[1:] ])
File "C:\Toolkits\Anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py", line 1029, in concat
dtype=dtypes.int32).get_shape(
File "C:\Toolkits\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 639, in convert_to_tensor
as_ref=False)
File "C:\Toolkits\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 704, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "C:\Toolkits\Anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py", line 905, in _autopacking_conversion_function
return _autopacking_helper(v, inferred_dtype, name or "packed")
File "C:\Toolkits\Anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py", line 868, in _autopacking_helper
return gen_array_ops._pack(elems_as_tensors, name=scope)
File "C:\Toolkits\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 1889, in _pack
result = _op_def_lib.apply_op("Pack", values=values, axis=axis, name=name)
File "C:\Toolkits\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 768, in apply_op
op_def=op_def)
File "C:\Toolkits\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 2338, in create_op
set_shapes_for_outputs(ret)
File "C:\Toolkits\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1719, in set_shapes_for_outputs
shapes = shape_func(op)
File "C:\Toolkits\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1669, in call_with_requiring
return call_cpp_shape_fn(op, require_shape_fn=True)
File "C:\Toolkits\Anaconda3\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 610, in call_cpp_shape_fn
debug_python_shape_fn, require_shape_fn)
File "C:\Toolkits\Anaconda3\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 676, in _call_cpp_shape_fn_impl
raise ValueError(err.message)
ValueError: Dimension 0 in both shapes must be equal, but 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].

Hope to successfully handle the problem.
Thank you.

from all-conv-keras.

I assume that you are running this code on your personal computer so you are having only a single GPU to play with, so a temporary hack would be remove the make_parallel function (as this function is to distribute the data onto the different GPU available and make training easier). Alternatively, you can try switching back to a previous version of keras as there are few changes in the latest 2.0 which I have not accounted for. (Maybe a PR from anyone would be highly appreciated).
Also check that the image_dim_ordering is set to "tf" as in "image_dim_ordering": "tf". You can check this in keras.json file.
Let me know if this solves your problem

from all-conv-keras.

hi ibrantabhi19 :

I have set keras.json to "image_dim_ordering": "tf".
And I will also line this #model = make_parallel (model, 0) annotation, then execute the code and get the following error message：

Using TensorFlow backend.
X_train shape: (50000, 32, 32, 3)
50000 train samples
10000 test samples
(32, 32, 3)
allconv.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)))
allconv.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'))
allconv.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)))
allconv.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'))
allconv.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'))
allconv.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)))
allconv.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'))
allconv.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'))
allconv.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'))

Layer (type) Output Shape Param #

conv2d_1 (Conv2D) (None, 3, 32, 96) 27744

activation_1 (Activation) (None, 3, 32, 96) 0

conv2d_2 (Conv2D) (None, 3, 32, 96) 83040

activation_2 (Activation) (None, 3, 32, 96) 0

conv2d_3 (Conv2D) (None, 2, 16, 96) 83040

dropout_1 (Dropout) (None, 2, 16, 96) 0

conv2d_4 (Conv2D) (None, 2, 16, 192) 166080

activation_3 (Activation) (None, 2, 16, 192) 0

conv2d_5 (Conv2D) (None, 2, 16, 192) 331968

activation_4 (Activation) (None, 2, 16, 192) 0

conv2d_6 (Conv2D) (None, 1, 8, 192) 331968

dropout_2 (Dropout) (None, 1, 8, 192) 0

conv2d_7 (Conv2D) (None, 1, 8, 192) 331968

activation_5 (Activation) (None, 1, 8, 192) 0

conv2d_8 (Conv2D) (None, 1, 8, 192) 37056

activation_6 (Activation) (None, 1, 8, 192) 0

conv2d_9 (Conv2D) (None, 1, 8, 10) 1930

global_average_pooling2d_1 ( (None, 10) 0

activation_7 (Activation) (None, 10) 0

Total params: 1,394,794
Trainable params: 1,394,794
Non-trainable params: 0

None
allconv.py:135: UserWarning: Update your fit_generator call to the Keras 2 API: fit_generator(<keras.pre..., epochs=350, steps_per_epoch=1562, validation_data=(array([[[..., callbacks=[<keras.ca..., verbose=0)
nb_epoch=nb_epoch, validation_data=(X_test, Y_test), callbacks=callbacks_list, verbose=0)
Traceback (most recent call last):
File "allconv.py", line 135, in
nb_epoch=nb_epoch, validation_data=(X_test, Y_test), callbacks=callbacks_list, verbose=0)
File "C:\Toolkits\Anaconda3\lib\site-packages\keras\legacy\interfaces.py", line 88, in wrapper
return func(*args, **kwargs)
File "C:\Toolkits\Anaconda3\lib\site-packages\keras\models.py", line 1110, in fit_generator
initial_epoch=initial_epoch)
File "C:\Toolkits\Anaconda3\lib\site-packages\keras\legacy\interfaces.py", line 88, in wrapper
return func(*args, **kwargs)
File "C:\Toolkits\Anaconda3\lib\site-packages\keras\engine\training.py", line 1838, in fit_generator
val_x, val_y, val_sample_weight)
File "C:\Toolkits\Anaconda3\lib\site-packages\keras\engine\training.py", line 1305, in _standardize_user_data
exception_prefix='input')
File "C:\Toolkits\Anaconda3\lib\site-packages\keras\engine\training.py", line 139, in _standardize_input_data
str(array.shape))
ValueError: Error when checking input: expected conv2d_1_input to have shape (None, 3, 32, 32) but got array with shape (10000, 32, 32, 3)

Then I downgrade keras to 1.1.0 and execute python.code again, and I get the following error message:
...
2017-05-20 00:07:14.418492: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:908] DMA: 0
2017-05-20 00:07:14.422350: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:918] 0: Y
2017-05-20 00:07:14.423313: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1060 3GB, pci bus id: 0000:09:00.0)
Traceback (most recent call last):
File "allconv2t.py", line 83, in
model.add(Dropout(0.5))
File "C:\Toolkits\Anaconda3\lib\site-packages\keras-1.1.0-py3.5.egg\keras\models.py", line 308, in add
output_tensor = layer(self.outputs[0])
File "C:\Toolkits\Anaconda3\lib\site-packages\keras-1.1.0-py3.5.egg\keras\engine\topology.py", line 514, in call
self.add_inbound_node(inbound_layers, node_indices, tensor_indices)
File "C:\Toolkits\Anaconda3\lib\site-packages\keras-1.1.0-py3.5.egg\keras\engine\topology.py", line 572, in add_inbound_node
Node.create_node(self, inbound_layers, node_indices, tensor_indices)
File "C:\Toolkits\Anaconda3\lib\site-packages\keras-1.1.0-py3.5.egg\keras\engine\topology.py", line 149, in create_node
output_tensors = to_list(outbound_layer.call(input_tensors[0], mask=input_masks[0]))
File "C:\Toolkits\Anaconda3\lib\site-packages\keras-1.1.0-py3.5.egg\keras\layers\core.py", line 90, in call
x = K.in_train_phase(K.dropout(x, self.p, noise_shape), x)
File "C:\Toolkits\Anaconda3\lib\site-packages\keras-1.1.0-py3.5.egg\keras\backend\tensorflow_backend.py", line 1304, in in_train_phase
x = tf.python.control_flow_ops.cond(tf.cast(_LEARNING_PHASE, 'bool'),
AttributeError: module 'tensorflow' has no attribute 'python'
Exception ignored in: <bound method BaseSession.del of <tensorflow.python.client.session.Session object at 0x0000029D2858C470>>
Traceback (most recent call last):
File "C:\Toolkits\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 587, in del
AttributeError: 'NoneType' object has no attribute 'TF_NewStatus'

**
What should I do next?

from all-conv-keras.

hi vibrantabhi19 :

The latest progress, I downgrade the keras version to 1.2.1. And modify this line
model.add(Convolution2D(96, 3, 3, border_mode = 'same', input_shape=(3, 32, 32)))
to
model.add(Convolution2D(96, 3, 3, border_mode = 'same', input_shape=(32, 32, 3)))

Then, i can use the code to perform the training. But in the end there are still small problems,
Error message :
Traceback (most recent call last):
File "allconv.py", line 137, in
im = cv2.resize(cv2.imread('image.jpg'), (224, 224)).astype(np.float32)
NameError: name 'cv2' is not defined

from all-conv-keras.

The above modification works because of the image_dim_ordering. Kindly refer to the keras docs for details.
The NameError is because you have uninstalled dependencies. Make sure that you have openCV installed and the OpenCV library has been changed to cv2 from cv, so you have to import cv2 instead of import cv.

I just noticed that the import cv2 is missing from the code. Would really appreciate a PR with the changes made.

from all-conv-keras.

Yes, I found the CV2 problem.

At the same time, I also found that the need to import numpy as np, otherwise print (np.argmax (out)). This line will be a problem.

However, after I have finished modifying the 2 lines, execute the code again.
There are new problems that appear as follows:
Traceback (most recent call last):
File "allconve1.py", line 13x, in
out = model.predict(im)
File "C:\Toolkits\Anaconda3\lib\site-packages\keras-1.2.1-py3.5.egg\keras\models.py", line 724, in predict
return self.model.predict(x, batch_size=batch_size, verbose=verbose)
File "C:\Toolkits\Anaconda3\lib\site-packages\keras-1.2.1-py3.5.egg\keras\engine\training.py", line 1250, in predict
check_batch_axis=False)
File "C:\Toolkits\Anaconda3\lib\site-packages\keras-1.2.1-py3.5.egg\keras\engine\training.py", line 112, in standardize_input_data
str(array.shape))
ValueError: Error when checking : expected convolution2d_input_1 to have 4 dimensions, but got array with shape (224, 224, 3)

from all-conv-keras.

You are training the model on CIFAR-10 and the images in CIFAR are of 3232 so you need to pass in the image with the exact same resolution.
So replace the im = cv2.resize(cv2.imread('image.jpg'), (224, 224)).astype(np.float32) to im = cv2.resize(cv2.imread('image.jpg'), (image_dimensions)).astype(np.float32) where image_dimensions are the dimension of the image that you trained upon.
For your particular problem, replace it with 3232 and you will be good to go.

from all-conv-keras.

I think the problem is in this line.

out = model.predict(im)

The error message is still as follows:

str(array.shape))
ValueError: Error when checking : expected convolution2d_input_1 to have 4 dimensions, but got array with shape (32, 32, 3)

If I annotate that line, no error will occur.

from all-conv-keras.