Keras: TypeError: __call__() 得到了一个意外的关键字参数“name”

Question

I am attempting to get DeepCell to work with keras. I'm running keras 2, but I added in the UID code from keras 1 to let DeepCell run. other than that I just downloaded the dependencies and software files, and I'm getting the following error.

Traceback:

  Traceback (most recent call last):
  File "/home/birtwistlelab/DeepCell/keras_version/running_template.py",line 65, in <module>
cytoplasm_predictions = run_models_on_directory(data_location,cyto_channel_names, cyto_location, model_fn = cyto_fn,list_of_weights = list_of_cyto_weights, image_size_x = image_size_x, image_size_y = image_size_y,win_x = win_cyto, win_y = win_cyto, std = False, split = False)
 File "/home/birtwistlelab/DeepCell/keras_version/cnn_functions.py", line 1491, in run_models_on_directory
model = model_fn(batch_input_shape = batch_input_shape, n_features = n_features, weights_path = list_of_weights[0])
File "/home/birtwistlelab/DeepCell/keras_version/model_zoo.py", line 528, in sparse_bn_feature_net_61x61
model.add(sparse_Convolution2D(64, 3, 3, d = d, init = init,  batch_input_shape = batch_input_shape, border_mode='valid', W_regularizer = l2(reg)))
File "/home/birtwistlelab/miniconda2/lib/python2.7/site-packages/keras/models.py", line 436, in add
layer(x)
File "/home/birtwistlelab/miniconda2/lib/python2.7/site-packages/keras/engine/topology.py", line 569, in __call__
self.build(input_shapes[0])
File "/home/birtwistlelab/DeepCell/keras_version/cnn_functions.py", line 1012, in build
self.W = self.init(self.W_shape, name='{}_W'.format(self.name))
TypeError: __call__() got an unexpected keyword argument 'name'

Code I am trying to run:

"""
Load data
"""
direc_name = "/home/birtwistlelab/DeepCell/validation_data/HeLa/"
data_location = os.path.join(direc_name, 'RawImages')
cyto_location = os.path.join(direc_name, 'Cytoplasm')
nuclear_location = os.path.join(direc_name, 'Nuclear')
mask_location = os.path.join(direc_name, 'Masks')

cyto_channel_names = ['phase','farred']
nuclear_channel_names = ['farred']

trained_network_cyto_directory = "/home/birtwistlelab/DeepCell/trained_networks/HeLa/"
trained_network_nuclear_directory = "/home/birtwistlelab/DeepCell/trained_networks/Nuclear/"

cyto_prefix = "2017-06-21_HeLa_all_61x61_bn_feature_net_61x61_"
nuclear_prefix = "2016-07-12_nuclei_all_61x61_bn_feature_net_61x61_"

win_cyto = 30
win_nuclear = 30

image_size_x, image_size_y = get_image_sizes(data_location, nuclear_channel_names)
image_size_x /= 2
image_size_y /= 2

"""
Define model
"""

list_of_cyto_weights = []
for j in xrange(5):
    cyto_weights = os.path.join(trained_network_cyto_directory,  cyto_prefix + str(j) + ".h5")
    list_of_cyto_weights += [cyto_weights]

list_of_nuclear_weights = []
for j in xrange(5):
    nuclear_weights = os.path.join(trained_network_nuclear_directory,  nuclear_prefix + str(j) + ".h5")
    list_of_nuclear_weights += [nuclear_weights]

 print list_of_nuclear_weights

 """
 Run model on directory
 """

 cytoplasm_predictions = run_models_on_directory(data_location, cyto_channel_names, cyto_location, model_fn = cyto_fn,list_of_weights = list_of_cyto_weights, image_size_x = image_size_x, image_size_y = image_size_y,win_x = win_cyto, win_y = win_cyto, std = False, split = False)

nuclear_predictions = run_models_on_directory(data_location, nuclear_channel_names, nuclear_location, model_fn = nuclear_fn,list_of_weights = list_of_nuclear_weights, image_size_x = image_size_x, image_size_y = image_size_y,win_x = win_nuclear, win_y = win_nuclear, std = False, split = False)



"""
Refine segmentation with active contours
"""

    nuclear_masks = segment_nuclei(nuclear_predictions, mask_location = mask_location, threshold = 0.75, area_threshold = 100, solidity_threshold = 0.75, eccentricity_threshold = 0.95)
cytoplasm_masks = segment_cytoplasm(cytoplasm_predictions, nuclear_masks = nuclear_masks, mask_location = mask_location, smoothing = 1, num_iters = 120)


"""
Compute validation metrics (optional)
"""
direc_val = os.path.join(direc_name, 'Validation')
imglist_val = nikon_getfiles(direc_val, 'validation_interior')
val_name = os.path.join(direc_val, imglist_val[0]) 
print val_name
val = get_image(val_name)
val = val[win_cyto:-win_cyto,win_cyto:-win_cyto]
cyto = cytoplasm_masks[0,win_cyto:-win_cyto,win_cyto:-win_cyto]
nuc = nuclear_masks[0,win_cyto:-win_cyto,win_cyto:-win_cyto]
print val.shape, cyto.shape, nuc.shape
dice_jaccard_indices(cyto, val, nuc)

I'm using the https://covertlab.github.io/DeepCell/starting/ guide. I've think it probably has something to due with the versions of the dependencies, as most of the other problems I was having were, but I'm not entirely sure. Any help would be greatly appreciated, is any other information is needed to help, let me know.

Answer 1

DeepCell (at least this version,) is not fully compatible with Keras 2 .

• [GitHub]: CovertLab/DeepCell - (master) DeepCell/keras_version/cnn_functions.py :

  • Line #971 (part of __init__ ):

     d = 1, init='glorot_uniform', activation='linear', weights=None,

  • Line #982 :

     self.init = initializations.get(init)

  • Line #1012 :

     self.W = self.init(self.W_shape, name='{}_W'.format(self.name))

• [GitHub]: keras-team/keras - (keras-1) keras/keras/initializations.py:56 :

   def glorot_uniform(shape, name=None, dim_ordering='th'):

• [GitHub]: keras-team/keras - (keras-2) keras/keras/initializers.py:325 :

   def glorot_uniform(seed=None):

So, in Keras 2 the initializers headers were (drastically) changed, and DeepCell code doesn't reflect that. In order to fix your error, either:

• Switch to Keras 1
• Modify DeepCell code to be Keras 2 compatible. But I seriously doubt that cnn_functions.py:1012 would be the only place to modify