Tensorflow pre-trained embedding matrix part of graph
I am currently trying to use my own pre-trained word embeddings in my tensorflow model_fn, but after a single epoch, the model building fails due to the following error;
ValueError: Fetch argument <tf.Variable 'embedding/embeddings:0' shape=(86565, 300) dtype=float32_ref> cannot be interpreted a
s a Tensor. (Tensor Tensor("embedding/embeddings:0", shape=(86565, 300), dtype=float32_ref) is not an element of this graph.)
How do I modify my code in order to maintain the embeddings in the graph over several epochs? Should I be initializing them in a different way? Or outside of the model function?
def word_embeddings_matrix():
..... load my embeddings .....
return embedding_matrix
embedding_matrix = word_embeddings_matrix()
def model_fn(features, labels, mode, params):
vocab_table = lookup.index_table_from_file(vocabulary_file='dataset/vocab.csv', num_oov_buckets=1, default_value=-1)
text = features[commons.FEATURE_COL]
words = tf.string_split(text)
dense_words = tf.sparse_tensor_to_dense(words, default_value=commons.PAD_WORD)
word_ids = vocab_table.lookup(dense_words)
padding = tf.constant([[0, 0], [0, commons.MAX_DOCUMENT_LENGTH]])
# Pad all the word_ids entries to the maximum document length
word_ids_padded = tf.pad(word_ids, padding)
word_id_vector = tf.slice(word_ids_padded, [0, 0], [-1, commons.MAX_DOCUMENT_LENGTH])
embedding_matrix = word_embeddings_matrix()
if mode == tf.estimator.ModeKeys.TRAIN:
tf.keras.backend.set_learning_phase(True)
else:
tf.keras.backend.set_learning_phase(False)
# embedded_sequences = tf.keras.layers.Embedding(params.N_WORDS,
# 10, input_length=commons.MAX_DOCUMENT_LENGTH)(word_id_vector)
embedded_sequences = tf.keras.layers.Embedding(params.N_WORDS,
300,
weights=[embedding_matrix],
input_length=commons.MAX_DOCUMENT_LENGTH,
trainable=True)(word_id_vector)
conv = tf.keras.layers.Conv1D(filters=64, kernel_size=2, activation='relu')(embedded_sequences)
pool = tf.keras.layers.GlobalAveragePooling1D()(conv)
drop = tf.keras.layers.Dropout(0.5)(pool)
logits = tf.keras.layers.Dense(commons.TARGET_SIZE, activation=None)(drop)
predictions = tf.nn.softmax(logits)
prediction_indices = tf.argmax(predictions, axis=1)
python tensorflow
add a comment |
I am currently trying to use my own pre-trained word embeddings in my tensorflow model_fn, but after a single epoch, the model building fails due to the following error;
ValueError: Fetch argument <tf.Variable 'embedding/embeddings:0' shape=(86565, 300) dtype=float32_ref> cannot be interpreted a
s a Tensor. (Tensor Tensor("embedding/embeddings:0", shape=(86565, 300), dtype=float32_ref) is not an element of this graph.)
How do I modify my code in order to maintain the embeddings in the graph over several epochs? Should I be initializing them in a different way? Or outside of the model function?
def word_embeddings_matrix():
..... load my embeddings .....
return embedding_matrix
embedding_matrix = word_embeddings_matrix()
def model_fn(features, labels, mode, params):
vocab_table = lookup.index_table_from_file(vocabulary_file='dataset/vocab.csv', num_oov_buckets=1, default_value=-1)
text = features[commons.FEATURE_COL]
words = tf.string_split(text)
dense_words = tf.sparse_tensor_to_dense(words, default_value=commons.PAD_WORD)
word_ids = vocab_table.lookup(dense_words)
padding = tf.constant([[0, 0], [0, commons.MAX_DOCUMENT_LENGTH]])
# Pad all the word_ids entries to the maximum document length
word_ids_padded = tf.pad(word_ids, padding)
word_id_vector = tf.slice(word_ids_padded, [0, 0], [-1, commons.MAX_DOCUMENT_LENGTH])
embedding_matrix = word_embeddings_matrix()
if mode == tf.estimator.ModeKeys.TRAIN:
tf.keras.backend.set_learning_phase(True)
else:
tf.keras.backend.set_learning_phase(False)
# embedded_sequences = tf.keras.layers.Embedding(params.N_WORDS,
# 10, input_length=commons.MAX_DOCUMENT_LENGTH)(word_id_vector)
embedded_sequences = tf.keras.layers.Embedding(params.N_WORDS,
300,
weights=[embedding_matrix],
input_length=commons.MAX_DOCUMENT_LENGTH,
trainable=True)(word_id_vector)
conv = tf.keras.layers.Conv1D(filters=64, kernel_size=2, activation='relu')(embedded_sequences)
pool = tf.keras.layers.GlobalAveragePooling1D()(conv)
drop = tf.keras.layers.Dropout(0.5)(pool)
logits = tf.keras.layers.Dense(commons.TARGET_SIZE, activation=None)(drop)
predictions = tf.nn.softmax(logits)
prediction_indices = tf.argmax(predictions, axis=1)
python tensorflow
add a comment |
I am currently trying to use my own pre-trained word embeddings in my tensorflow model_fn, but after a single epoch, the model building fails due to the following error;
ValueError: Fetch argument <tf.Variable 'embedding/embeddings:0' shape=(86565, 300) dtype=float32_ref> cannot be interpreted a
s a Tensor. (Tensor Tensor("embedding/embeddings:0", shape=(86565, 300), dtype=float32_ref) is not an element of this graph.)
How do I modify my code in order to maintain the embeddings in the graph over several epochs? Should I be initializing them in a different way? Or outside of the model function?
def word_embeddings_matrix():
..... load my embeddings .....
return embedding_matrix
embedding_matrix = word_embeddings_matrix()
def model_fn(features, labels, mode, params):
vocab_table = lookup.index_table_from_file(vocabulary_file='dataset/vocab.csv', num_oov_buckets=1, default_value=-1)
text = features[commons.FEATURE_COL]
words = tf.string_split(text)
dense_words = tf.sparse_tensor_to_dense(words, default_value=commons.PAD_WORD)
word_ids = vocab_table.lookup(dense_words)
padding = tf.constant([[0, 0], [0, commons.MAX_DOCUMENT_LENGTH]])
# Pad all the word_ids entries to the maximum document length
word_ids_padded = tf.pad(word_ids, padding)
word_id_vector = tf.slice(word_ids_padded, [0, 0], [-1, commons.MAX_DOCUMENT_LENGTH])
embedding_matrix = word_embeddings_matrix()
if mode == tf.estimator.ModeKeys.TRAIN:
tf.keras.backend.set_learning_phase(True)
else:
tf.keras.backend.set_learning_phase(False)
# embedded_sequences = tf.keras.layers.Embedding(params.N_WORDS,
# 10, input_length=commons.MAX_DOCUMENT_LENGTH)(word_id_vector)
embedded_sequences = tf.keras.layers.Embedding(params.N_WORDS,
300,
weights=[embedding_matrix],
input_length=commons.MAX_DOCUMENT_LENGTH,
trainable=True)(word_id_vector)
conv = tf.keras.layers.Conv1D(filters=64, kernel_size=2, activation='relu')(embedded_sequences)
pool = tf.keras.layers.GlobalAveragePooling1D()(conv)
drop = tf.keras.layers.Dropout(0.5)(pool)
logits = tf.keras.layers.Dense(commons.TARGET_SIZE, activation=None)(drop)
predictions = tf.nn.softmax(logits)
prediction_indices = tf.argmax(predictions, axis=1)
python tensorflow
I am currently trying to use my own pre-trained word embeddings in my tensorflow model_fn, but after a single epoch, the model building fails due to the following error;
ValueError: Fetch argument <tf.Variable 'embedding/embeddings:0' shape=(86565, 300) dtype=float32_ref> cannot be interpreted a
s a Tensor. (Tensor Tensor("embedding/embeddings:0", shape=(86565, 300), dtype=float32_ref) is not an element of this graph.)
How do I modify my code in order to maintain the embeddings in the graph over several epochs? Should I be initializing them in a different way? Or outside of the model function?
def word_embeddings_matrix():
..... load my embeddings .....
return embedding_matrix
embedding_matrix = word_embeddings_matrix()
def model_fn(features, labels, mode, params):
vocab_table = lookup.index_table_from_file(vocabulary_file='dataset/vocab.csv', num_oov_buckets=1, default_value=-1)
text = features[commons.FEATURE_COL]
words = tf.string_split(text)
dense_words = tf.sparse_tensor_to_dense(words, default_value=commons.PAD_WORD)
word_ids = vocab_table.lookup(dense_words)
padding = tf.constant([[0, 0], [0, commons.MAX_DOCUMENT_LENGTH]])
# Pad all the word_ids entries to the maximum document length
word_ids_padded = tf.pad(word_ids, padding)
word_id_vector = tf.slice(word_ids_padded, [0, 0], [-1, commons.MAX_DOCUMENT_LENGTH])
embedding_matrix = word_embeddings_matrix()
if mode == tf.estimator.ModeKeys.TRAIN:
tf.keras.backend.set_learning_phase(True)
else:
tf.keras.backend.set_learning_phase(False)
# embedded_sequences = tf.keras.layers.Embedding(params.N_WORDS,
# 10, input_length=commons.MAX_DOCUMENT_LENGTH)(word_id_vector)
embedded_sequences = tf.keras.layers.Embedding(params.N_WORDS,
300,
weights=[embedding_matrix],
input_length=commons.MAX_DOCUMENT_LENGTH,
trainable=True)(word_id_vector)
conv = tf.keras.layers.Conv1D(filters=64, kernel_size=2, activation='relu')(embedded_sequences)
pool = tf.keras.layers.GlobalAveragePooling1D()(conv)
drop = tf.keras.layers.Dropout(0.5)(pool)
logits = tf.keras.layers.Dense(commons.TARGET_SIZE, activation=None)(drop)
predictions = tf.nn.softmax(logits)
prediction_indices = tf.argmax(predictions, axis=1)
python tensorflow
python tensorflow
asked Nov 23 '18 at 16:00
chattrat423chattrat423
2631316
2631316
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