Tensorflow InvalidArgumentError (indices) while training with Keras

Question

I'm trying to train a LSTM network on some data, unfortunately I keep running into following error:
InvalidArgumentError: indices[] = is not in [0, 4704)

Train on 180596 samples, validate on 45149 samples
Epoch 1/1
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-52-a434c3619685> in <module>()
     14                     epochs=1,
     15                     batch_size=128,
---> 16                     validation_split=0.2)

c:\program files\python3x64\lib\site-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)
   1040                                         initial_epoch=initial_epoch,
   1041                                         steps_per_epoch=steps_per_epoch,
-> 1042                                         validation_steps=validation_steps)
   1043 
   1044     def evaluate(self, x=None, y=None,

c:\program files\python3x64\lib\site-packages\keras\engine\training_arrays.py in fit_loop(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)
    197                     ins_batch[i] = ins_batch[i].toarray()
    198 
--> 199                 outs = f(ins_batch)
    200                 if not isinstance(outs, list):
    201                     outs = [outs]

c:\program files\python3x64\lib\site-packages\keras\backend\tensorflow_backend.py in __call__(self, inputs)
   2659                 return self._legacy_call(inputs)
   2660 
-> 2661             return self._call(inputs)
   2662         else:
   2663             if py_any(is_tensor(x) for x in inputs):

c:\program files\python3x64\lib\site-packages\keras\backend\tensorflow_backend.py in _call(self, inputs)
   2629                                 symbol_vals,
   2630                                 session)
-> 2631         fetched = self._callable_fn(*array_vals)
   2632         return fetched[:len(self.outputs)]
   2633 

c:\program files\python3x64\lib\site-packages\tensorflow\python\client\session.py in __call__(self, *args)
   1452         else:
   1453           return tf_session.TF_DeprecatedSessionRunCallable(
-> 1454               self._session._session, self._handle, args, status, None)
   1455 
   1456     def __del__(self):

c:\program files\python3x64\lib\site-packages\tensorflow\python\framework\errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
    517             None, None,
    518             compat.as_text(c_api.TF_Message(self.status.status)),
--> 519             c_api.TF_GetCode(self.status.status))
    520     # Delete the underlying status object from memory otherwise it stays alive
    521     # as there is a reference to status from this from the traceback due to

InvalidArgumentError: indices[62,0] = 15757 is not in [0, 4704)
     [[Node: embedding_15/embedding_lookup = GatherV2[Taxis=DT_INT32, Tindices=DT_INT32, Tparams=DT_FLOAT, _class=["loc:@training_14/RMSprop/Assign_1"], _device="/job:localhost/replica:0/task:0/device:CPU:0"](embedding_15/embeddings/read, embedding_15/Cast, training_14/RMSprop/gradients/embedding_15/embedding_lookup_grad/concat/axis)]]

I've tried to find a solution to this, but to no avail.
The data I am using are One Hot Encoded and normalized netflows.
At least in terms of NaN and Infinity-values I should at least be good to go. Here's a glance at the input data:

In [14]:
print(flows_nd.shape)
print(type(flows_nd))
print(type(flows_nd[0]))

(225745, 4704)
<class 'numpy.ndarray'>
<class 'numpy.ndarray'>

This is the network itself:

from keras.models import Sequential
from keras.layers import Dense, Embedding
from keras.layers import LSTM

model = Sequential()
model.add(Embedding(flows_nd.shape[-1], 32))
model.add(LSTM(32))
model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['acc'])
history = model.fit(flows_nd, enc_labels,
                    epochs=1,
                    batch_size=128,
                    validation_split=0.2)

From what I've read there could be something wrong with the vocab size in tensorflow, but I am not certain that I can change that out of my Keras layer.

I've assembled a Jupyter Notebook with my current code, if anyone wants a closer look.

Any help is greatly appreciated. Thanks in advance!

Solution:
As Mitiku pointed out, I was passing the number of features into the embedding layer, not the vocabulary size (Keras Embedding Layer Documentation). This is how I updated my code to a working example:

#find the maximum vocabulary size
voc_size = (flows_scaled.max()+1).astype('int64')
print(voc_size)

# build the model
from keras.models import Sequential
from keras.layers import Dense, Embedding
from keras.layers import LSTM

model = Sequential()
model.add(Embedding(voc_size, 32)) 
model.add(LSTM(32))
model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['acc'])

history = model.fit(flows_scaled, enc_labels,
                    epochs=1,
                    batch_size=128,
                    validation_split=0.2)

Answer 1

Embedding layer expects the first argument to be size of vocabulary, i.e it is the maximum integer index + 1

But you are passing the number of features as vocabulary size. What you can do is, find the maximum number in flows_nd and pass the maximum number plus one to the Embedding layer.

voc_size = flows_nd.max()+1

model = Sequential()
model.add(Embedding(voc_size, 32))