Tensor object has no ndim attribute

I am trying to get a trained model to output its predictions for a segmentation problem using the following code.

import h5py
import tifffile as tiff

from cnn_functions import nikon_getfiles, get_image, run_models_on_directory, get_image_sizes, segment_nuclei, segment_cytoplasm, dice_jaccard_indices
from model_zoo import sparse_bn_feature_net_31x31 as cyto_fn

import os
import numpy as np

direc_name = "C:/Users/Zein/Documents/Neural_Networks/CNN/"
data_location = os.path.join(direc_name, 'RawImages')

cyto_location = os.path.join(direc_name, 'Cytoplasm')
mask_location = os.path.join(direc_name, 'Masks')

cyto_channel_names = ['phase']

trained_network_cyto_directory = "C:/Users/Zein/Documents/Neural_Networks/CNN/trained_networks/"

cyto_prefix = "2017-03-06_Kcells_all_31x31_bn_feature_net_31x31_

win_cyto = 15
image_size_x, image_size_y = get_image_sizes(data_location, cyto_channel_names)[0:2]

list_of_cyto_weights = []
for j in range(2):
    cyto_weights = os.path.join(trained_network_cyto_directory,  cyto_prefix + str(j) + ".h5")
    list_of_cyto_weights += [cyto_weights]

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, split = False)

cytoplasm_masks = segment_cytoplasm(cytoplasm_predictions, nuclear_masks = nuclear_masks, mask_location = mask_location, smoothing = 1, num_iters = 120)

      

        
        
        
      

    

However, I am getting the following error.

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-6-3ce4003728e5> in <module>()
      1 cytoplasm_predictions = run_models_on_directory(data_location, cyto_channel_names, cyto_location, model_fn = cyto_fn, 
      2         list_of_weights = list_of_cyto_weights, image_size_x = image_size_x, image_size_y = image_size_y,
----> 3     win_x = win_cyto, win_y = win_cyto, split = False)

C:\Users\Zein\Documents\Neural_Networks\CNN\cnn_functions.py in run_models_on_directory(data_location, channel_names, output_location, model_fn, list_of_weights, n_features, image_size_x, image_size_y, win_x, win_y, std, split, process, save)
   1480 
   1481         batch_input_shape = (1,len(channel_names),image_size_x+win_x, image_size_y+win_y)
-> 1482         model = model_fn(batch_input_shape = batch_input_shape, n_features = n_features, weights_path = list_of_weights[0])
   1483         n_features = model.layers[-1].output_shape[1]
   1484 

C:\Users\Zein\Documents\Neural_Networks\CNN\model_zoo.py in sparse_bn_feature_net_31x31(batch_input_shape, n_features, reg, init, weights_path)
    353         model.add(BatchNormalization(axis=1))
    354         model.add(Activation('relu'))
--> 355         model.add(sparse_MaxPooling2D(pool_size=(2, 2), strides=(d, d)))
    356         d *= 2
    357         model.add(Conv2DTranspose(64,3, strides=d, kernel_initializer=init, padding='valid', kernel_regularizer=l2(reg)))

C:\Users\Zein\Anaconda3\envs\TF352\lib\site-packages\keras\models.py in add(self, layer)
    464                           output_shapes=[self.outputs[0]._keras_shape])
    465         else:
--> 466             output_tensor = layer(self.outputs[0])
    467             if isinstance(output_tensor, list):
    468                 raise TypeError('All layers in a Sequential model '

C:\Users\Zein\Anaconda3\envs\TF352\lib\site-packages\keras\engine\topology.py in __call__(self, inputs, **kwargs)
    583 
    584             # Actually call the layer, collecting output(s), mask(s), and shape(s).
--> 585             output = self.call(inputs, **kwargs)
    586             output_mask = self.compute_mask(inputs, previous_mask)
    587 

C:\Users\Zein\Documents\Neural_Networks\CNN\cnn_functions.py in call(self, x, mask)
   1128                                                                                 strides=self.strides,
   1129                                                                                 border_mode=self.border_mode,
-> 1130                                         dim_ordering=self.dim_ordering)
   1131                 return output
   1132 

C:\Users\Zein\Documents\Neural_Networks\CNN\cnn_functions.py in _pooling_function(self, inputs, pool_size, strides, border_mode, dim_ordering)
   1121     def _pooling_function(self, inputs, pool_size, strides,
   1122                           border_mode, dim_ordering):
-> 1123                 output = sparse_pool(inputs, pool_size = pool_size, stride = strides[0])
   1124                 return output
   1125 

C:\Users\Zein\Documents\Neural_Networks\CNN\cnn_functions.py in sparse_pool(input_image, stride, pool_size, mode)
    252         for offset_x in range(stride):
    253                 for offset_y in range(stride):
--> 254                         pooled_array +=[pool_2d(input_image[:, :, offset_x::stride, offset_y::stride], pool_size, stride = (1,1), mode = mode, pad = (0,0), ignore_border = True)]
    255                         counter += 1
    256 

C:\Users\Zein\Anaconda3\envs\TF352\lib\site-packages\theano\tensor\signal\pool.py in pool_2d(input, ws, ignore_border, stride, pad, mode, ds, st, padding)
    127             pad = padding
    128 
--> 129     if input.ndim < 2:
    130         raise NotImplementedError('pool_2d requires a dimension >= 2')
    131     if ignore_border is None:

AttributeError: 'Tensor' object has no attribute 'ndim'

      

        
        
        
      

    

I'm using Keras with a Tensorflow backend, however the pool2_d function belongs to Theano. Is this a problem or can Keras use both TF and Theano functions in the same script? Or perhaps the challenge is simply devalued?