我试图让DeepCell与keras合作。我正在运行keras 2，但我在keras 1的UID代码中添加了让DeepCell运行。除了我刚刚下载的依赖项和软件文件，我收到以下错误。
回溯：
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'
我正试图运行的代码：
"""
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)
我正在使用https://covertlab.github.io/DeepCell/starting/指南。我认为它可能与依赖项的版本有关，因为我遇到的大多数其他问题都是，但我不完全确定。任何帮助将不胜感激，是否需要任何其他信息来帮助，让我知道。
答案 0 :(得分：1)
DeepCell （至少此版本）与 keras 2 不完全兼容。
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d = 1, init='glorot_uniform', activation='linear', weights=None, self.init = initializations.get(init) self.W = self.init(self.W_shape, name='{}_W'.format(self.name)) def glorot_uniform(shape, name=None, dim_ordering='th'): 因此，在 keras 2 中，初始化程序标题已更改（大幅度），而 DeepCell 代码并未反映出来。为了解决您的错误，请执行以下操作之一：