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))

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'**