A relatively simple block of code running in Jupyter notebook (because I cannot for the life of me get it working in Google Colab) but I'm getting the below error when running the model. The linked post shows some more of the detail around the inputs I'm working with.
I think it may be a GPU related error. I've followed several similarly titled posts and CUDA/Visual Studio installation tutorials, to no avail.
import gensim
import os
from gensim.models import KeyedVectors
from gensim.test.utils import datapath, get_tmpfile
from gensim.scripts.glove2word2vec import glove2word2vec
glove_file = datapath('glove.6B.50d.txt')
word2vec_glove_file = get_tmpfile("glove.6B.50d.word2vec.txt")
glove2word2vec(glove_file, word2vec_glove_file)
model = KeyedVectors.load_word2vec_format(word2vec_glove_file, binary=False)
# get the vector for each word in the glove file
emb_dict = {}
glove = open(glove_file, encoding="utf8")
for line in glove:
values = line.split()
word = values[0]
vector = numpy.asarray(values[1:], dtype='float32')
emb_dict[word] = vector
glove.close()
# get the top 10k words in the tweets, and find their vector from the glove files
num_words = 10000
glove_dims = 50
emb_matrix = numpy.zeros((num_words, glove_dims))
for w, i in token.word_index.items():
if i < num_words:
vect = emb_dict.get(w)
if vect is not None:
emb_matrix[i] = vect
else:
break
from keras import models, layers
from keras.layers import Embedding, Flatten, Dense
glove_model = models.Sequential()
glove_model.add(Embedding(num_words, glove_dims, input_length=max_tweet_len))
glove_model.add(layers.LSTM(100))
#glove_model.add(Flatten())
glove_model.add(Dense(3, activation='softmax'))
glove_model.layers[0].set_weights([emb_matrix])
glove_model.layers[0].trainable = False
glove_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
history = glove_model.fit(train_seq_x, y_train, epochs=10, batch_size=64)
The error returned is:
Epoch 1/10
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-11-224ee1a00f0e> in <module>
13 glove_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
14
---> 15 history = glove_model.fit(train_seq_x, y_train, epochs=10, batch_size=64)
~\anaconda3\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, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
1237 steps_per_epoch=steps_per_epoch,
1238 validation_steps=validation_steps,
-> 1239 validation_freq=validation_freq)
1240
1241 def evaluate(self,
~\anaconda3\lib\site-packages\keras\engine\training_arrays.py in fit_loop(model, fit_function, fit_inputs, out_labels, batch_size, epochs, verbose, callbacks, val_function, val_inputs, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq)
194 ins_batch[i] = ins_batch[i].toarray()
195
--> 196 outs = fit_function(ins_batch)
197 outs = to_list(outs)
198 for l, o in zip(out_labels, outs):
~\anaconda3\lib\site-packages\tensorflow\python\keras\backend.py in __call__(self, inputs)
3790 value = math_ops.cast(value, tensor.dtype)
3791 converted_inputs.append(value)
-> 3792 outputs = self._graph_fn(*converted_inputs)
3793
3794 # EagerTensor.numpy() will often make a copy to ensure memory safety.
~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in __call__(self, *args, **kwargs)
1603 TypeError: For invalid positional/keyword argument combinations.
1604 """
-> 1605 return self._call_impl(args, kwargs)
1606
1607 def _call_impl(self, args, kwargs, cancellation_manager=None):
~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _call_impl(self, args, kwargs, cancellation_manager)
1643 raise TypeError("Keyword arguments {} unknown. Expected {}.".format(
1644 list(kwargs.keys()), list(self._arg_keywords)))
-> 1645 return self._call_flat(args, self.captured_inputs, cancellation_manager)
1646
1647 def _filtered_call(self, args, kwargs):
~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1744 # No tape is watching; skip to running the function.
1745 return self._build_call_outputs(self._inference_function.call(
-> 1746 ctx, args, cancellation_manager=cancellation_manager))
1747 forward_backward = self._select_forward_and_backward_functions(
1748 args,
~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in call(self, ctx, args, cancellation_manager)
596 inputs=args,
597 attrs=attrs,
--> 598 ctx=ctx)
599 else:
600 outputs = execute.execute_with_cancellation(
~\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
58 ctx.ensure_initialized()
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
62 if name is not None:
InvalidArgumentError: indices[40,19] = 11263 is not in [0, 10000)
[[node embedding_1/embedding_lookup (defined at C:\Users\Jorda\anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:3009) ]] [Op:__inference_keras_scratch_graph_1707]
Function call stack:
keras_scratch_graph
As requested in the comments, the method of tokenisation is as follows:
from keras.preprocessing import text, sequence
# create a tokenizer
token = text.Tokenizer()
token.fit_on_texts(tweets)
word_index = token.word_index
max_tweet_len = 30
# convert text to sequence of tokens and pad them to ensure equal length vectors
train_seq_x = sequence.pad_sequences(token.texts_to_sequences(x_train), maxlen=max_tweet_len)
test_seq_x = sequence.pad_sequences(token.texts_to_sequences(x_test), maxlen=max_tweet_len)
print(train_seq_x)
print(test_seq_x)
