AttributeError: 'generator' object has no attribute 'ndim' - keras

Question

I'm working on Triplets networks on Keras to find the image similarity. However, I'm getting an error when feeding triplets to the model. Request you to kindly help on this.
Basically I'm trying to feel the model with 3 inputs(anchor, positive, negative)
I'm working on Python3 and fitting the model with fit_model using Keras. This is my function to train the model:
def train(trainDB, testDB, n_iter, batch_size, evaluate_every, test_size,
loss_every):
print("Starting training process!")
print("-------------------------------------")
best = -1
t_start = time.time()
inputs=trainDB.getTripletTrainData(batch_size)
targets=np.ones([batch_size])
for i in range(0, n_iter):
loss=tripletNet.fit(inputs, targets)
#print("Loss: {0}".format(loss))
if i % evaluate_every == 0:
print("Time for {0} iterations: {1}".format(i, time.time()-t_start))
val_acc = self.test_oneshot(testDB, test_size)
if val_acc > best:
print("Current best: {0}, previous best: {1}".format(val_acc, best))
print("Saving weights to: {0} \n".format(weights_path))
self.tripletNet.save_weights(weights_path)
best=val_acc
if i % loss_every == 0:
print("iteration {}, training loss: {:.2f},".format(i,loss))
Error message:
Starting training process!
-------------------------------------
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-163-b5442e61de2d> in <module>()
----> 1 train(trainDatabase, testDatabase, n_iter, batch_size, evaluate_every, test_size, loss_every)
5 frames
<ipython-input-161-f417f0ebcfc7> in train(trainDB, testDB, n_iter, batch_size, evaluate_every, test_size, loss_every)
10
11 for i in range(0, n_iter):
---> 12 loss=tripletNet.fit(inputs, targets)
13
14 #print("Loss: {0}".format(loss))
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
950 sample_weight=sample_weight,
951 class_weight=class_weight,
--> 952 batch_size=batch_size)
953 # Prepare validation data.
954 do_validation = False
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
749 feed_input_shapes,
750 check_batch_axis=False, # Don't enforce the batch size.
--> 751 exception_prefix='input')
752
753 if y is not None:
/usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
90 data = data.values if data.__class__.__name__ == 'DataFrame' else data
91 data = [data]
---> 92 data = [standardize_single_array(x) for x in data]
93
94 if len(data) != len(names):
/usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py in <listcomp>(.0)
90 data = data.values if data.__class__.__name__ == 'DataFrame' else data
91 data = [data]
---> 92 data = [standardize_single_array(x) for x in data]
93
94 if len(data) != len(names):
/usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py in standardize_single_array(x)
25 'Got tensor with shape: %s' % str(shape))
26 return x
---> 27 elif x.ndim == 1:
28 x = np.expand_dims(x, 1)
29 return x
**AttributeError: 'generator' object has no attribute 'ndim'**
If i use fit_generator.. Im getting the below error..
Starting training process!
-------------------------------------
Epoch 1/1
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-194-b5442e61de2d> in <module>()
----> 1 train(trainDatabase, testDatabase, n_iter, batch_size, evaluate_every, test_size, loss_every)
3 frames
<ipython-input-193-8ad964a9916f> in train(trainDB, testDB, n_iter, batch_size, evaluate_every, test_size, loss_every)
10
11 for i in range(0, n_iter):
---> 12 loss=tripletNet.fit_generator(inputs, targets)
13
14 #print("Loss: {0}".format(loss))
/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name + '` call to the ' +
90 'Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1416 use_multiprocessing=use_multiprocessing,
1417 shuffle=shuffle,
-> 1418 initial_epoch=initial_epoch)
1419
1420 #interfaces.legacy_generator_methods_support
/usr/local/lib/python3.6/dist-packages/keras/engine/training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
178 steps_done = 0
179 batch_index = 0
--> 180 while steps_done < steps_per_epoch:
181 generator_output = next(output_generator)
182
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()