ValueError: None values not supported.

[sirwh]

Hi, I ran the code in jupyter notebook like the following
!python3 train.py dummy.csv --num_class 5 --epochs 200 --batch_size 64

I got the following error:

Traceback (most recent call last):
  File "train.py", line 219, in <module>
    class_weight=params['class_weight']
  File "/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py", line 1732, in fit_generator
    initial_epoch=initial_epoch)
  File "/usr/local/lib/python3.6/dist-packages/keras/engine/training_generator.py", line 42, in fit_generator
    model._make_train_function()
  File "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py", line 316, in _make_train_function
    loss=self.total_loss)
  File "/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/keras/optimizers.py", line 622, in get_updates
    p_t = p - lr_t * m_t / (u_t + self.epsilon)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py", line 888, in binary_op_wrapper
    y, dtype_hint=x.dtype.base_dtype, name="y")
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 1145, in convert_to_tensor_v2
    as_ref=False)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 1224, in internal_convert_to_tensor
    ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py", line 305, in _constant_tensor_conversion_function
    return constant(v, dtype=dtype, name=name)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py", line 246, in constant
    allow_broadcast=True)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py", line 284, in _constant_impl
    allow_broadcast=allow_broadcast))
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_util.py", line 454, in make_tensor_proto
    raise ValueError("None values not supported.")
ValueError: None values not supported.

---

I also tried to run the code step by step. After I got trainX and trainY, instead of using a generator, I just ran
model.fit(x=trainX, y=trainY, epochs=1, verbose=1)
but the error is the same:

ValueError                                Traceback (most recent call last)
<ipython-input-8-57446bf71ca4> in <module>
----> 1 model.fit(x=trainX, y=trainY, epochs=1, verbose=1)

/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, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
   1211         else:
   1212             fit_inputs = x + y + sample_weights
-> 1213         self._make_train_function()
   1214         fit_function = self.train_function
   1215 

/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in _make_train_function(self)
    314                     training_updates = self.optimizer.get_updates(
    315                         params=self._collected_trainable_weights,
--> 316                         loss=self.total_loss)
    317                 updates = self.updates + training_updates
    318 

/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/optimizers.py in get_updates(self, loss, params)
    620             m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
    621             u_t = K.maximum(self.beta_2 * u, K.abs(g))
--> 622             p_t = p - lr_t * m_t / (u_t + self.epsilon)
    623 
    624             self.updates.append(K.update(m, m_t))

/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py in binary_op_wrapper(x, y)
    886         try:
    887           y = ops.convert_to_tensor_v2(
--> 888               y, dtype_hint=x.dtype.base_dtype, name="y")
    889         except TypeError:
    890           # If the RHS is not a tensor, it might be a tensor aware object

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in convert_to_tensor_v2(value, dtype, dtype_hint, name)
   1143       name=name,
   1144       preferred_dtype=dtype_hint,
-> 1145       as_ref=False)
   1146 
   1147 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx, accept_symbolic_tensors, accept_composite_tensors)
   1222 
   1223     if ret is None:
-> 1224       ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
   1225 
   1226     if ret is NotImplemented:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
    303                                          as_ref=False):
    304   _ = as_ref
--> 305   return constant(v, dtype=dtype, name=name)
    306 
    307 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name)
    244   """
    245   return _constant_impl(value, dtype, shape, name, verify_shape=False,
--> 246                         allow_broadcast=True)
    247 
    248 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
    282       tensor_util.make_tensor_proto(
    283           value, dtype=dtype, shape=shape, verify_shape=verify_shape,
--> 284           allow_broadcast=allow_broadcast))
    285   dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
    286   const_tensor = g.create_op(

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape, allow_broadcast)
    452   else:
    453     if values is None:
--> 454       raise ValueError("None values not supported.")
    455     # if dtype is provided, forces numpy array to be the type
    456     # provided if possible.

ValueError: None values not supported.

---

Could you please help.