I was able to pass assignment test for djmodel function implementation, but in the following lines it throws an error during the execution of the following cell
history = model.fit([X, a0, c0], list(Y), epochs=100, verbose = 0)
Traceback
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-39-f10a44fe26fa> in <module>
----> 1 history = model.fit([X, a0, c0], list(Y), epochs=100)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
106 def _method_wrapper(self, *args, **kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self, *args, **kwargs)
109
110 # Running inside `run_distribute_coordinator` already.
/opt/conda/lib/python3.7/site-packages/tensorflow/python/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, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1061 use_multiprocessing=use_multiprocessing,
1062 model=self,
-> 1063 steps_per_execution=self._steps_per_execution)
1064
1065 # Container that configures and calls `tf.keras.Callback`s.
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution)
1115 use_multiprocessing=use_multiprocessing,
1116 distribution_strategy=ds_context.get_strategy(),
-> 1117 model=model)
1118
1119 strategy = ds_context.get_strategy()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weights, sample_weight_modes, batch_size, epochs, steps, shuffle, **kwargs)
273 inputs = pack_x_y_sample_weight(x, y, sample_weights)
274
--> 275 num_samples = set(int(i.shape[0]) for i in nest.flatten(inputs))
276 if len(num_samples) > 1:
277 msg = "Data cardinality is ambiguous:\n"
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py in <genexpr>(.0)
273 inputs = pack_x_y_sample_weight(x, y, sample_weights)
274
--> 275 num_samples = set(int(i.shape[0]) for i in nest.flatten(inputs))
276 if len(num_samples) > 1:
277 msg = "Data cardinality is ambiguous:\n"
TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'
I can’t find out the issue, please help.