Converting Tensorflow Graph to use Estimator, get 'TypeError: data type not understood' at loss function...
I am trying to convert Tensorflow's official basic word2vec implementation to use tf.Estimator.
The issue is that the loss function( sampled_softmax_loss
or nce_loss
) gives an error when using Tensorflow Estimators. It works perfectly fine in the original implementation.
Here's is Tensorflow's official basic word2vec implementation:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
Here is the Google Colab notebook where I implemented this code, which is working.
https://colab.research.google.com/drive/1nTX77dRBHmXx6PEF5pmYpkIVxj_TqT5I
Here is the Google Colab notebook where I altered the code so that it uses Tensorflow Estimator, which is Not working.
https://colab.research.google.com/drive/1IVDqGwMx6BK5-Bgrw190jqHU6tt3ZR3e
For convenience, here is exact code from the Estimator version above where I define model_fn
batch_size = 128
embedding_size = 128 # Dimension of the embedding vector.
skip_window = 1 # How many words to consider left and right.
num_skips = 2 # How many times to reuse an input to generate a label.
num_sampled = 64 # Number of negative examples to sample.
def my_model( features, labels, mode, params):
with tf.name_scope('inputs'):
train_inputs = features
train_labels = labels
with tf.name_scope('embeddings'):
embeddings = tf.Variable(
tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
embed = tf.nn.embedding_lookup(embeddings, train_inputs)
with tf.name_scope('weights'):
nce_weights = tf.Variable(
tf.truncated_normal(
[vocabulary_size, embedding_size],
stddev=1.0 / math.sqrt(embedding_size)))
with tf.name_scope('biases'):
nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
with tf.name_scope('loss'):
loss = tf.reduce_mean(
tf.nn.nce_loss(
weights=nce_weights,
biases=nce_biases,
labels=train_labels,
inputs=embed,
num_sampled=num_sampled,
num_classes=vocabulary_size))
tf.summary.scalar('loss', loss)
if mode == "train":
with tf.name_scope('optimizer'):
optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=optimizer)
And here is where I call the estimator and training
word2vecEstimator = tf.estimator.Estimator(
model_fn=my_model,
params={
'batch_size': 16,
'embedding_size': 10,
'num_inputs': 3,
'num_sampled': 128,
'batch_size': 16
})
word2vecEstimator.train(
input_fn=generate_batch,
steps=10)
And this the error message I get when I call the Estimator training:
INFO:tensorflow:Calling model_fn.
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-22-955f44867ee5> in <module>()
1 word2vecEstimator.train(
2 input_fn=generate_batch,
----> 3 steps=10)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
352
353 saving_listeners = _check_listeners_type(saving_listeners)
--> 354 loss = self._train_model(input_fn, hooks, saving_listeners)
355 logging.info('Loss for final step: %s.', loss)
356 return self
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in _train_model(self, input_fn, hooks, saving_listeners)
1205 return self._train_model_distributed(input_fn, hooks, saving_listeners)
1206 else:
-> 1207 return self._train_model_default(input_fn, hooks, saving_listeners)
1208
1209 def _train_model_default(self, input_fn, hooks, saving_listeners):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in _train_model_default(self, input_fn, hooks, saving_listeners)
1235 worker_hooks.extend(input_hooks)
1236 estimator_spec = self._call_model_fn(
-> 1237 features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
1238 global_step_tensor = training_util.get_global_step(g)
1239 return self._train_with_estimator_spec(estimator_spec, worker_hooks,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in _call_model_fn(self, features, labels, mode, config)
1193
1194 logging.info('Calling model_fn.')
-> 1195 model_fn_results = self._model_fn(features=features, **kwargs)
1196 logging.info('Done calling model_fn.')
1197
<ipython-input-20-9d389437162a> in my_model(features, labels, mode, params)
33 inputs=embed,
34 num_sampled=num_sampled,
---> 35 num_classes=vocabulary_size))
36
37 # Add the loss value as a scalar to summary.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py in nce_loss(weights, biases, labels, inputs, num_sampled, num_classes, num_true, sampled_values, remove_accidental_hits, partition_strategy, name)
1246 remove_accidental_hits=remove_accidental_hits,
1247 partition_strategy=partition_strategy,
-> 1248 name=name)
1249 sampled_losses = sigmoid_cross_entropy_with_logits(
1250 labels=labels, logits=logits, name="sampled_losses")
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py in _compute_sampled_logits(weights, biases, labels, inputs, num_sampled, num_classes, num_true, sampled_values, subtract_log_q, remove_accidental_hits, partition_strategy, name, seed)
1029 with ops.name_scope(name, "compute_sampled_logits",
1030 weights + [biases, inputs, labels]):
-> 1031 if labels.dtype != dtypes.int64:
1032 labels = math_ops.cast(labels, dtypes.int64)
1033 labels_flat = array_ops.reshape(labels, [-1])
TypeError: data type not understood
Edit: Upon request, here's what a typical output for input_fn looks like
print(generate_batch(batch_size=8, num_skips=2, skip_window=1))
(array([3081, 3081, 12, 12, 6, 6, 195, 195], dtype=int32), array([[5234],
[ 12],
[ 6],
[3081],
[ 12],
[ 195],
[ 6],
[ 2]], dtype=int32))
python tensorflow tensorflow-estimator
add a comment |
I am trying to convert Tensorflow's official basic word2vec implementation to use tf.Estimator.
The issue is that the loss function( sampled_softmax_loss
or nce_loss
) gives an error when using Tensorflow Estimators. It works perfectly fine in the original implementation.
Here's is Tensorflow's official basic word2vec implementation:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
Here is the Google Colab notebook where I implemented this code, which is working.
https://colab.research.google.com/drive/1nTX77dRBHmXx6PEF5pmYpkIVxj_TqT5I
Here is the Google Colab notebook where I altered the code so that it uses Tensorflow Estimator, which is Not working.
https://colab.research.google.com/drive/1IVDqGwMx6BK5-Bgrw190jqHU6tt3ZR3e
For convenience, here is exact code from the Estimator version above where I define model_fn
batch_size = 128
embedding_size = 128 # Dimension of the embedding vector.
skip_window = 1 # How many words to consider left and right.
num_skips = 2 # How many times to reuse an input to generate a label.
num_sampled = 64 # Number of negative examples to sample.
def my_model( features, labels, mode, params):
with tf.name_scope('inputs'):
train_inputs = features
train_labels = labels
with tf.name_scope('embeddings'):
embeddings = tf.Variable(
tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
embed = tf.nn.embedding_lookup(embeddings, train_inputs)
with tf.name_scope('weights'):
nce_weights = tf.Variable(
tf.truncated_normal(
[vocabulary_size, embedding_size],
stddev=1.0 / math.sqrt(embedding_size)))
with tf.name_scope('biases'):
nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
with tf.name_scope('loss'):
loss = tf.reduce_mean(
tf.nn.nce_loss(
weights=nce_weights,
biases=nce_biases,
labels=train_labels,
inputs=embed,
num_sampled=num_sampled,
num_classes=vocabulary_size))
tf.summary.scalar('loss', loss)
if mode == "train":
with tf.name_scope('optimizer'):
optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=optimizer)
And here is where I call the estimator and training
word2vecEstimator = tf.estimator.Estimator(
model_fn=my_model,
params={
'batch_size': 16,
'embedding_size': 10,
'num_inputs': 3,
'num_sampled': 128,
'batch_size': 16
})
word2vecEstimator.train(
input_fn=generate_batch,
steps=10)
And this the error message I get when I call the Estimator training:
INFO:tensorflow:Calling model_fn.
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-22-955f44867ee5> in <module>()
1 word2vecEstimator.train(
2 input_fn=generate_batch,
----> 3 steps=10)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
352
353 saving_listeners = _check_listeners_type(saving_listeners)
--> 354 loss = self._train_model(input_fn, hooks, saving_listeners)
355 logging.info('Loss for final step: %s.', loss)
356 return self
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in _train_model(self, input_fn, hooks, saving_listeners)
1205 return self._train_model_distributed(input_fn, hooks, saving_listeners)
1206 else:
-> 1207 return self._train_model_default(input_fn, hooks, saving_listeners)
1208
1209 def _train_model_default(self, input_fn, hooks, saving_listeners):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in _train_model_default(self, input_fn, hooks, saving_listeners)
1235 worker_hooks.extend(input_hooks)
1236 estimator_spec = self._call_model_fn(
-> 1237 features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
1238 global_step_tensor = training_util.get_global_step(g)
1239 return self._train_with_estimator_spec(estimator_spec, worker_hooks,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in _call_model_fn(self, features, labels, mode, config)
1193
1194 logging.info('Calling model_fn.')
-> 1195 model_fn_results = self._model_fn(features=features, **kwargs)
1196 logging.info('Done calling model_fn.')
1197
<ipython-input-20-9d389437162a> in my_model(features, labels, mode, params)
33 inputs=embed,
34 num_sampled=num_sampled,
---> 35 num_classes=vocabulary_size))
36
37 # Add the loss value as a scalar to summary.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py in nce_loss(weights, biases, labels, inputs, num_sampled, num_classes, num_true, sampled_values, remove_accidental_hits, partition_strategy, name)
1246 remove_accidental_hits=remove_accidental_hits,
1247 partition_strategy=partition_strategy,
-> 1248 name=name)
1249 sampled_losses = sigmoid_cross_entropy_with_logits(
1250 labels=labels, logits=logits, name="sampled_losses")
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py in _compute_sampled_logits(weights, biases, labels, inputs, num_sampled, num_classes, num_true, sampled_values, subtract_log_q, remove_accidental_hits, partition_strategy, name, seed)
1029 with ops.name_scope(name, "compute_sampled_logits",
1030 weights + [biases, inputs, labels]):
-> 1031 if labels.dtype != dtypes.int64:
1032 labels = math_ops.cast(labels, dtypes.int64)
1033 labels_flat = array_ops.reshape(labels, [-1])
TypeError: data type not understood
Edit: Upon request, here's what a typical output for input_fn looks like
print(generate_batch(batch_size=8, num_skips=2, skip_window=1))
(array([3081, 3081, 12, 12, 6, 6, 195, 195], dtype=int32), array([[5234],
[ 12],
[ 6],
[3081],
[ 12],
[ 195],
[ 6],
[ 2]], dtype=int32))
python tensorflow tensorflow-estimator
What Python, TensorFlow and NumPy versions are you using? If they are not up to date (TensorFlow 1.12, NumPy 1.15), have you tried upgrading?
– jdehesa
Nov 27 '18 at 14:27
For Tensorflow , version '1.12.0' ; For Numpy , version '1.14.6'
– SantoshGupta7
Nov 29 '18 at 20:42
add a comment |
I am trying to convert Tensorflow's official basic word2vec implementation to use tf.Estimator.
The issue is that the loss function( sampled_softmax_loss
or nce_loss
) gives an error when using Tensorflow Estimators. It works perfectly fine in the original implementation.
Here's is Tensorflow's official basic word2vec implementation:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
Here is the Google Colab notebook where I implemented this code, which is working.
https://colab.research.google.com/drive/1nTX77dRBHmXx6PEF5pmYpkIVxj_TqT5I
Here is the Google Colab notebook where I altered the code so that it uses Tensorflow Estimator, which is Not working.
https://colab.research.google.com/drive/1IVDqGwMx6BK5-Bgrw190jqHU6tt3ZR3e
For convenience, here is exact code from the Estimator version above where I define model_fn
batch_size = 128
embedding_size = 128 # Dimension of the embedding vector.
skip_window = 1 # How many words to consider left and right.
num_skips = 2 # How many times to reuse an input to generate a label.
num_sampled = 64 # Number of negative examples to sample.
def my_model( features, labels, mode, params):
with tf.name_scope('inputs'):
train_inputs = features
train_labels = labels
with tf.name_scope('embeddings'):
embeddings = tf.Variable(
tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
embed = tf.nn.embedding_lookup(embeddings, train_inputs)
with tf.name_scope('weights'):
nce_weights = tf.Variable(
tf.truncated_normal(
[vocabulary_size, embedding_size],
stddev=1.0 / math.sqrt(embedding_size)))
with tf.name_scope('biases'):
nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
with tf.name_scope('loss'):
loss = tf.reduce_mean(
tf.nn.nce_loss(
weights=nce_weights,
biases=nce_biases,
labels=train_labels,
inputs=embed,
num_sampled=num_sampled,
num_classes=vocabulary_size))
tf.summary.scalar('loss', loss)
if mode == "train":
with tf.name_scope('optimizer'):
optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=optimizer)
And here is where I call the estimator and training
word2vecEstimator = tf.estimator.Estimator(
model_fn=my_model,
params={
'batch_size': 16,
'embedding_size': 10,
'num_inputs': 3,
'num_sampled': 128,
'batch_size': 16
})
word2vecEstimator.train(
input_fn=generate_batch,
steps=10)
And this the error message I get when I call the Estimator training:
INFO:tensorflow:Calling model_fn.
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-22-955f44867ee5> in <module>()
1 word2vecEstimator.train(
2 input_fn=generate_batch,
----> 3 steps=10)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
352
353 saving_listeners = _check_listeners_type(saving_listeners)
--> 354 loss = self._train_model(input_fn, hooks, saving_listeners)
355 logging.info('Loss for final step: %s.', loss)
356 return self
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in _train_model(self, input_fn, hooks, saving_listeners)
1205 return self._train_model_distributed(input_fn, hooks, saving_listeners)
1206 else:
-> 1207 return self._train_model_default(input_fn, hooks, saving_listeners)
1208
1209 def _train_model_default(self, input_fn, hooks, saving_listeners):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in _train_model_default(self, input_fn, hooks, saving_listeners)
1235 worker_hooks.extend(input_hooks)
1236 estimator_spec = self._call_model_fn(
-> 1237 features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
1238 global_step_tensor = training_util.get_global_step(g)
1239 return self._train_with_estimator_spec(estimator_spec, worker_hooks,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in _call_model_fn(self, features, labels, mode, config)
1193
1194 logging.info('Calling model_fn.')
-> 1195 model_fn_results = self._model_fn(features=features, **kwargs)
1196 logging.info('Done calling model_fn.')
1197
<ipython-input-20-9d389437162a> in my_model(features, labels, mode, params)
33 inputs=embed,
34 num_sampled=num_sampled,
---> 35 num_classes=vocabulary_size))
36
37 # Add the loss value as a scalar to summary.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py in nce_loss(weights, biases, labels, inputs, num_sampled, num_classes, num_true, sampled_values, remove_accidental_hits, partition_strategy, name)
1246 remove_accidental_hits=remove_accidental_hits,
1247 partition_strategy=partition_strategy,
-> 1248 name=name)
1249 sampled_losses = sigmoid_cross_entropy_with_logits(
1250 labels=labels, logits=logits, name="sampled_losses")
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py in _compute_sampled_logits(weights, biases, labels, inputs, num_sampled, num_classes, num_true, sampled_values, subtract_log_q, remove_accidental_hits, partition_strategy, name, seed)
1029 with ops.name_scope(name, "compute_sampled_logits",
1030 weights + [biases, inputs, labels]):
-> 1031 if labels.dtype != dtypes.int64:
1032 labels = math_ops.cast(labels, dtypes.int64)
1033 labels_flat = array_ops.reshape(labels, [-1])
TypeError: data type not understood
Edit: Upon request, here's what a typical output for input_fn looks like
print(generate_batch(batch_size=8, num_skips=2, skip_window=1))
(array([3081, 3081, 12, 12, 6, 6, 195, 195], dtype=int32), array([[5234],
[ 12],
[ 6],
[3081],
[ 12],
[ 195],
[ 6],
[ 2]], dtype=int32))
python tensorflow tensorflow-estimator
I am trying to convert Tensorflow's official basic word2vec implementation to use tf.Estimator.
The issue is that the loss function( sampled_softmax_loss
or nce_loss
) gives an error when using Tensorflow Estimators. It works perfectly fine in the original implementation.
Here's is Tensorflow's official basic word2vec implementation:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
Here is the Google Colab notebook where I implemented this code, which is working.
https://colab.research.google.com/drive/1nTX77dRBHmXx6PEF5pmYpkIVxj_TqT5I
Here is the Google Colab notebook where I altered the code so that it uses Tensorflow Estimator, which is Not working.
https://colab.research.google.com/drive/1IVDqGwMx6BK5-Bgrw190jqHU6tt3ZR3e
For convenience, here is exact code from the Estimator version above where I define model_fn
batch_size = 128
embedding_size = 128 # Dimension of the embedding vector.
skip_window = 1 # How many words to consider left and right.
num_skips = 2 # How many times to reuse an input to generate a label.
num_sampled = 64 # Number of negative examples to sample.
def my_model( features, labels, mode, params):
with tf.name_scope('inputs'):
train_inputs = features
train_labels = labels
with tf.name_scope('embeddings'):
embeddings = tf.Variable(
tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
embed = tf.nn.embedding_lookup(embeddings, train_inputs)
with tf.name_scope('weights'):
nce_weights = tf.Variable(
tf.truncated_normal(
[vocabulary_size, embedding_size],
stddev=1.0 / math.sqrt(embedding_size)))
with tf.name_scope('biases'):
nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
with tf.name_scope('loss'):
loss = tf.reduce_mean(
tf.nn.nce_loss(
weights=nce_weights,
biases=nce_biases,
labels=train_labels,
inputs=embed,
num_sampled=num_sampled,
num_classes=vocabulary_size))
tf.summary.scalar('loss', loss)
if mode == "train":
with tf.name_scope('optimizer'):
optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=optimizer)
And here is where I call the estimator and training
word2vecEstimator = tf.estimator.Estimator(
model_fn=my_model,
params={
'batch_size': 16,
'embedding_size': 10,
'num_inputs': 3,
'num_sampled': 128,
'batch_size': 16
})
word2vecEstimator.train(
input_fn=generate_batch,
steps=10)
And this the error message I get when I call the Estimator training:
INFO:tensorflow:Calling model_fn.
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-22-955f44867ee5> in <module>()
1 word2vecEstimator.train(
2 input_fn=generate_batch,
----> 3 steps=10)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
352
353 saving_listeners = _check_listeners_type(saving_listeners)
--> 354 loss = self._train_model(input_fn, hooks, saving_listeners)
355 logging.info('Loss for final step: %s.', loss)
356 return self
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in _train_model(self, input_fn, hooks, saving_listeners)
1205 return self._train_model_distributed(input_fn, hooks, saving_listeners)
1206 else:
-> 1207 return self._train_model_default(input_fn, hooks, saving_listeners)
1208
1209 def _train_model_default(self, input_fn, hooks, saving_listeners):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in _train_model_default(self, input_fn, hooks, saving_listeners)
1235 worker_hooks.extend(input_hooks)
1236 estimator_spec = self._call_model_fn(
-> 1237 features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
1238 global_step_tensor = training_util.get_global_step(g)
1239 return self._train_with_estimator_spec(estimator_spec, worker_hooks,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in _call_model_fn(self, features, labels, mode, config)
1193
1194 logging.info('Calling model_fn.')
-> 1195 model_fn_results = self._model_fn(features=features, **kwargs)
1196 logging.info('Done calling model_fn.')
1197
<ipython-input-20-9d389437162a> in my_model(features, labels, mode, params)
33 inputs=embed,
34 num_sampled=num_sampled,
---> 35 num_classes=vocabulary_size))
36
37 # Add the loss value as a scalar to summary.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py in nce_loss(weights, biases, labels, inputs, num_sampled, num_classes, num_true, sampled_values, remove_accidental_hits, partition_strategy, name)
1246 remove_accidental_hits=remove_accidental_hits,
1247 partition_strategy=partition_strategy,
-> 1248 name=name)
1249 sampled_losses = sigmoid_cross_entropy_with_logits(
1250 labels=labels, logits=logits, name="sampled_losses")
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py in _compute_sampled_logits(weights, biases, labels, inputs, num_sampled, num_classes, num_true, sampled_values, subtract_log_q, remove_accidental_hits, partition_strategy, name, seed)
1029 with ops.name_scope(name, "compute_sampled_logits",
1030 weights + [biases, inputs, labels]):
-> 1031 if labels.dtype != dtypes.int64:
1032 labels = math_ops.cast(labels, dtypes.int64)
1033 labels_flat = array_ops.reshape(labels, [-1])
TypeError: data type not understood
Edit: Upon request, here's what a typical output for input_fn looks like
print(generate_batch(batch_size=8, num_skips=2, skip_window=1))
(array([3081, 3081, 12, 12, 6, 6, 195, 195], dtype=int32), array([[5234],
[ 12],
[ 6],
[3081],
[ 12],
[ 195],
[ 6],
[ 2]], dtype=int32))
python tensorflow tensorflow-estimator
python tensorflow tensorflow-estimator
edited Dec 6 '18 at 6:45
SantoshGupta7
asked Nov 21 '18 at 5:17
SantoshGupta7SantoshGupta7
6811515
6811515
What Python, TensorFlow and NumPy versions are you using? If they are not up to date (TensorFlow 1.12, NumPy 1.15), have you tried upgrading?
– jdehesa
Nov 27 '18 at 14:27
For Tensorflow , version '1.12.0' ; For Numpy , version '1.14.6'
– SantoshGupta7
Nov 29 '18 at 20:42
add a comment |
What Python, TensorFlow and NumPy versions are you using? If they are not up to date (TensorFlow 1.12, NumPy 1.15), have you tried upgrading?
– jdehesa
Nov 27 '18 at 14:27
For Tensorflow , version '1.12.0' ; For Numpy , version '1.14.6'
– SantoshGupta7
Nov 29 '18 at 20:42
What Python, TensorFlow and NumPy versions are you using? If they are not up to date (TensorFlow 1.12, NumPy 1.15), have you tried upgrading?
– jdehesa
Nov 27 '18 at 14:27
What Python, TensorFlow and NumPy versions are you using? If they are not up to date (TensorFlow 1.12, NumPy 1.15), have you tried upgrading?
– jdehesa
Nov 27 '18 at 14:27
For Tensorflow , version '1.12.0' ; For Numpy , version '1.14.6'
– SantoshGupta7
Nov 29 '18 at 20:42
For Tensorflow , version '1.12.0' ; For Numpy , version '1.14.6'
– SantoshGupta7
Nov 29 '18 at 20:42
add a comment |
3 Answers
3
active
oldest
votes
You use generate_batch
like a variable here:
word2vecEstimator.train(
input_fn=generate_batch,
steps=10)
Call the function with generate_batch()
.
But I think you must pass some values to the function.
I set it up so that no values are passed to the function. I usedgenerate_batch()
but now I'm getting aTypeError: unsupported callable
error. The Official documentation says to treat it like a function, so it should be called likegenerate_batch
. tensorflow.org/guide/estimators . This is elaborated in this post stackoverflow.com/questions/47120637/…
– SantoshGupta7
Nov 29 '18 at 20:53
Can you show us the output of thegenerate_batch()
call?
– tifi90
Nov 29 '18 at 21:18
yeah, just updated the original post, the output ofgenerate_batch()
call is at the bottom.
– SantoshGupta7
Nov 29 '18 at 22:27
The size of the features array and the labels array is different. Length 16 and length 15. Shouldn't they have the same size?
– tifi90
Nov 30 '18 at 9:58
They'e both 16, I think it's a little confusing since the labels start on the same line as the features end. ` [1892528, 1352240, 1552349]], dtype=int32), array([[1635226],`, 1635226 is part of the 2nd array.
– SantoshGupta7
Dec 1 '18 at 3:19
add a comment |
It might be that tensors and ops must be in the input_fn
, not in the 'model_fn'
I found this issue #4026 which solved my problem ... Maybe it is just me being stupid, but it would be great if you mention that the tensors and ops all have to be inside the input_fn somewhere in the documentation.
You have to call read_batch_examples from somewhere inside input_fn so that the tensors it creates are in the graph that Estimator creates in fit().
https://github.com/tensorflow/tensorflow/issues/8042
Oh I feel like an idiot! I've been creating the op outside of the graph scope. It works now, can't believe I didn't think to try that. Thanks a lot! This is a non-issue and has been resolved
https://github.com/tensorflow/tensorflow/issues/4026
However, there still is not enough info on what's causing the issue. This is just a lead.
add a comment |
Found the answer
Error clearly says you have invalid type for labels.
You trying to pass numpy array instead of Tensor. Sometimes Tensorflow
performs implicit conversion from ndarray to Tensor under the hood
(what's why your code works outside of Estimator), but in this case it
don't.
.
No, official impl. feeds data from a placeholder. Placeholder is
always a Tensor, so it don't depends on implicit things.
But if you directly call loss function with a numpy array as input
(Notice: call during graph construction phase, so argument content
gets embedded into graph), it MAY work (however, I did not check it).
This code:
nce_loss(labels=[1,2,3]) will be called only ONCE during graph
construction. Labels will be statically embedded into graph as a
constant and potentially can be of any Tensor-compatible type (list,
ndarray, etc)
This code: ```Python def model(label_input):
nce_loss(labels=label_input)
estimator(model_fun=model).train() ``` can't embed labels variable
statically, because it content is not defined during graph
construction. So if you feed anything except the Tensor, it will throw
an error.
From
https://www.reddit.com/r/MachineLearning/comments/a39pef/r_tensorflow_estimators_managing_simplicity_vs/
So I used labels=tf.dtypes.cast( train_labels, tf.int64)
and it worked
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
You use generate_batch
like a variable here:
word2vecEstimator.train(
input_fn=generate_batch,
steps=10)
Call the function with generate_batch()
.
But I think you must pass some values to the function.
I set it up so that no values are passed to the function. I usedgenerate_batch()
but now I'm getting aTypeError: unsupported callable
error. The Official documentation says to treat it like a function, so it should be called likegenerate_batch
. tensorflow.org/guide/estimators . This is elaborated in this post stackoverflow.com/questions/47120637/…
– SantoshGupta7
Nov 29 '18 at 20:53
Can you show us the output of thegenerate_batch()
call?
– tifi90
Nov 29 '18 at 21:18
yeah, just updated the original post, the output ofgenerate_batch()
call is at the bottom.
– SantoshGupta7
Nov 29 '18 at 22:27
The size of the features array and the labels array is different. Length 16 and length 15. Shouldn't they have the same size?
– tifi90
Nov 30 '18 at 9:58
They'e both 16, I think it's a little confusing since the labels start on the same line as the features end. ` [1892528, 1352240, 1552349]], dtype=int32), array([[1635226],`, 1635226 is part of the 2nd array.
– SantoshGupta7
Dec 1 '18 at 3:19
add a comment |
You use generate_batch
like a variable here:
word2vecEstimator.train(
input_fn=generate_batch,
steps=10)
Call the function with generate_batch()
.
But I think you must pass some values to the function.
I set it up so that no values are passed to the function. I usedgenerate_batch()
but now I'm getting aTypeError: unsupported callable
error. The Official documentation says to treat it like a function, so it should be called likegenerate_batch
. tensorflow.org/guide/estimators . This is elaborated in this post stackoverflow.com/questions/47120637/…
– SantoshGupta7
Nov 29 '18 at 20:53
Can you show us the output of thegenerate_batch()
call?
– tifi90
Nov 29 '18 at 21:18
yeah, just updated the original post, the output ofgenerate_batch()
call is at the bottom.
– SantoshGupta7
Nov 29 '18 at 22:27
The size of the features array and the labels array is different. Length 16 and length 15. Shouldn't they have the same size?
– tifi90
Nov 30 '18 at 9:58
They'e both 16, I think it's a little confusing since the labels start on the same line as the features end. ` [1892528, 1352240, 1552349]], dtype=int32), array([[1635226],`, 1635226 is part of the 2nd array.
– SantoshGupta7
Dec 1 '18 at 3:19
add a comment |
You use generate_batch
like a variable here:
word2vecEstimator.train(
input_fn=generate_batch,
steps=10)
Call the function with generate_batch()
.
But I think you must pass some values to the function.
You use generate_batch
like a variable here:
word2vecEstimator.train(
input_fn=generate_batch,
steps=10)
Call the function with generate_batch()
.
But I think you must pass some values to the function.
edited Nov 29 '18 at 11:01
answered Nov 29 '18 at 10:31
tifi90tifi90
938
938
I set it up so that no values are passed to the function. I usedgenerate_batch()
but now I'm getting aTypeError: unsupported callable
error. The Official documentation says to treat it like a function, so it should be called likegenerate_batch
. tensorflow.org/guide/estimators . This is elaborated in this post stackoverflow.com/questions/47120637/…
– SantoshGupta7
Nov 29 '18 at 20:53
Can you show us the output of thegenerate_batch()
call?
– tifi90
Nov 29 '18 at 21:18
yeah, just updated the original post, the output ofgenerate_batch()
call is at the bottom.
– SantoshGupta7
Nov 29 '18 at 22:27
The size of the features array and the labels array is different. Length 16 and length 15. Shouldn't they have the same size?
– tifi90
Nov 30 '18 at 9:58
They'e both 16, I think it's a little confusing since the labels start on the same line as the features end. ` [1892528, 1352240, 1552349]], dtype=int32), array([[1635226],`, 1635226 is part of the 2nd array.
– SantoshGupta7
Dec 1 '18 at 3:19
add a comment |
I set it up so that no values are passed to the function. I usedgenerate_batch()
but now I'm getting aTypeError: unsupported callable
error. The Official documentation says to treat it like a function, so it should be called likegenerate_batch
. tensorflow.org/guide/estimators . This is elaborated in this post stackoverflow.com/questions/47120637/…
– SantoshGupta7
Nov 29 '18 at 20:53
Can you show us the output of thegenerate_batch()
call?
– tifi90
Nov 29 '18 at 21:18
yeah, just updated the original post, the output ofgenerate_batch()
call is at the bottom.
– SantoshGupta7
Nov 29 '18 at 22:27
The size of the features array and the labels array is different. Length 16 and length 15. Shouldn't they have the same size?
– tifi90
Nov 30 '18 at 9:58
They'e both 16, I think it's a little confusing since the labels start on the same line as the features end. ` [1892528, 1352240, 1552349]], dtype=int32), array([[1635226],`, 1635226 is part of the 2nd array.
– SantoshGupta7
Dec 1 '18 at 3:19
I set it up so that no values are passed to the function. I used
generate_batch()
but now I'm getting a TypeError: unsupported callable
error. The Official documentation says to treat it like a function, so it should be called like generate_batch
. tensorflow.org/guide/estimators . This is elaborated in this post stackoverflow.com/questions/47120637/…– SantoshGupta7
Nov 29 '18 at 20:53
I set it up so that no values are passed to the function. I used
generate_batch()
but now I'm getting a TypeError: unsupported callable
error. The Official documentation says to treat it like a function, so it should be called like generate_batch
. tensorflow.org/guide/estimators . This is elaborated in this post stackoverflow.com/questions/47120637/…– SantoshGupta7
Nov 29 '18 at 20:53
Can you show us the output of the
generate_batch()
call?– tifi90
Nov 29 '18 at 21:18
Can you show us the output of the
generate_batch()
call?– tifi90
Nov 29 '18 at 21:18
yeah, just updated the original post, the output of
generate_batch()
call is at the bottom.– SantoshGupta7
Nov 29 '18 at 22:27
yeah, just updated the original post, the output of
generate_batch()
call is at the bottom.– SantoshGupta7
Nov 29 '18 at 22:27
The size of the features array and the labels array is different. Length 16 and length 15. Shouldn't they have the same size?
– tifi90
Nov 30 '18 at 9:58
The size of the features array and the labels array is different. Length 16 and length 15. Shouldn't they have the same size?
– tifi90
Nov 30 '18 at 9:58
They'e both 16, I think it's a little confusing since the labels start on the same line as the features end. ` [1892528, 1352240, 1552349]], dtype=int32), array([[1635226],`, 1635226 is part of the 2nd array.
– SantoshGupta7
Dec 1 '18 at 3:19
They'e both 16, I think it's a little confusing since the labels start on the same line as the features end. ` [1892528, 1352240, 1552349]], dtype=int32), array([[1635226],`, 1635226 is part of the 2nd array.
– SantoshGupta7
Dec 1 '18 at 3:19
add a comment |
It might be that tensors and ops must be in the input_fn
, not in the 'model_fn'
I found this issue #4026 which solved my problem ... Maybe it is just me being stupid, but it would be great if you mention that the tensors and ops all have to be inside the input_fn somewhere in the documentation.
You have to call read_batch_examples from somewhere inside input_fn so that the tensors it creates are in the graph that Estimator creates in fit().
https://github.com/tensorflow/tensorflow/issues/8042
Oh I feel like an idiot! I've been creating the op outside of the graph scope. It works now, can't believe I didn't think to try that. Thanks a lot! This is a non-issue and has been resolved
https://github.com/tensorflow/tensorflow/issues/4026
However, there still is not enough info on what's causing the issue. This is just a lead.
add a comment |
It might be that tensors and ops must be in the input_fn
, not in the 'model_fn'
I found this issue #4026 which solved my problem ... Maybe it is just me being stupid, but it would be great if you mention that the tensors and ops all have to be inside the input_fn somewhere in the documentation.
You have to call read_batch_examples from somewhere inside input_fn so that the tensors it creates are in the graph that Estimator creates in fit().
https://github.com/tensorflow/tensorflow/issues/8042
Oh I feel like an idiot! I've been creating the op outside of the graph scope. It works now, can't believe I didn't think to try that. Thanks a lot! This is a non-issue and has been resolved
https://github.com/tensorflow/tensorflow/issues/4026
However, there still is not enough info on what's causing the issue. This is just a lead.
add a comment |
It might be that tensors and ops must be in the input_fn
, not in the 'model_fn'
I found this issue #4026 which solved my problem ... Maybe it is just me being stupid, but it would be great if you mention that the tensors and ops all have to be inside the input_fn somewhere in the documentation.
You have to call read_batch_examples from somewhere inside input_fn so that the tensors it creates are in the graph that Estimator creates in fit().
https://github.com/tensorflow/tensorflow/issues/8042
Oh I feel like an idiot! I've been creating the op outside of the graph scope. It works now, can't believe I didn't think to try that. Thanks a lot! This is a non-issue and has been resolved
https://github.com/tensorflow/tensorflow/issues/4026
However, there still is not enough info on what's causing the issue. This is just a lead.
It might be that tensors and ops must be in the input_fn
, not in the 'model_fn'
I found this issue #4026 which solved my problem ... Maybe it is just me being stupid, but it would be great if you mention that the tensors and ops all have to be inside the input_fn somewhere in the documentation.
You have to call read_batch_examples from somewhere inside input_fn so that the tensors it creates are in the graph that Estimator creates in fit().
https://github.com/tensorflow/tensorflow/issues/8042
Oh I feel like an idiot! I've been creating the op outside of the graph scope. It works now, can't believe I didn't think to try that. Thanks a lot! This is a non-issue and has been resolved
https://github.com/tensorflow/tensorflow/issues/4026
However, there still is not enough info on what's causing the issue. This is just a lead.
answered Dec 3 '18 at 18:36
SantoshGupta7SantoshGupta7
6811515
6811515
add a comment |
add a comment |
Found the answer
Error clearly says you have invalid type for labels.
You trying to pass numpy array instead of Tensor. Sometimes Tensorflow
performs implicit conversion from ndarray to Tensor under the hood
(what's why your code works outside of Estimator), but in this case it
don't.
.
No, official impl. feeds data from a placeholder. Placeholder is
always a Tensor, so it don't depends on implicit things.
But if you directly call loss function with a numpy array as input
(Notice: call during graph construction phase, so argument content
gets embedded into graph), it MAY work (however, I did not check it).
This code:
nce_loss(labels=[1,2,3]) will be called only ONCE during graph
construction. Labels will be statically embedded into graph as a
constant and potentially can be of any Tensor-compatible type (list,
ndarray, etc)
This code: ```Python def model(label_input):
nce_loss(labels=label_input)
estimator(model_fun=model).train() ``` can't embed labels variable
statically, because it content is not defined during graph
construction. So if you feed anything except the Tensor, it will throw
an error.
From
https://www.reddit.com/r/MachineLearning/comments/a39pef/r_tensorflow_estimators_managing_simplicity_vs/
So I used labels=tf.dtypes.cast( train_labels, tf.int64)
and it worked
add a comment |
Found the answer
Error clearly says you have invalid type for labels.
You trying to pass numpy array instead of Tensor. Sometimes Tensorflow
performs implicit conversion from ndarray to Tensor under the hood
(what's why your code works outside of Estimator), but in this case it
don't.
.
No, official impl. feeds data from a placeholder. Placeholder is
always a Tensor, so it don't depends on implicit things.
But if you directly call loss function with a numpy array as input
(Notice: call during graph construction phase, so argument content
gets embedded into graph), it MAY work (however, I did not check it).
This code:
nce_loss(labels=[1,2,3]) will be called only ONCE during graph
construction. Labels will be statically embedded into graph as a
constant and potentially can be of any Tensor-compatible type (list,
ndarray, etc)
This code: ```Python def model(label_input):
nce_loss(labels=label_input)
estimator(model_fun=model).train() ``` can't embed labels variable
statically, because it content is not defined during graph
construction. So if you feed anything except the Tensor, it will throw
an error.
From
https://www.reddit.com/r/MachineLearning/comments/a39pef/r_tensorflow_estimators_managing_simplicity_vs/
So I used labels=tf.dtypes.cast( train_labels, tf.int64)
and it worked
add a comment |
Found the answer
Error clearly says you have invalid type for labels.
You trying to pass numpy array instead of Tensor. Sometimes Tensorflow
performs implicit conversion from ndarray to Tensor under the hood
(what's why your code works outside of Estimator), but in this case it
don't.
.
No, official impl. feeds data from a placeholder. Placeholder is
always a Tensor, so it don't depends on implicit things.
But if you directly call loss function with a numpy array as input
(Notice: call during graph construction phase, so argument content
gets embedded into graph), it MAY work (however, I did not check it).
This code:
nce_loss(labels=[1,2,3]) will be called only ONCE during graph
construction. Labels will be statically embedded into graph as a
constant and potentially can be of any Tensor-compatible type (list,
ndarray, etc)
This code: ```Python def model(label_input):
nce_loss(labels=label_input)
estimator(model_fun=model).train() ``` can't embed labels variable
statically, because it content is not defined during graph
construction. So if you feed anything except the Tensor, it will throw
an error.
From
https://www.reddit.com/r/MachineLearning/comments/a39pef/r_tensorflow_estimators_managing_simplicity_vs/
So I used labels=tf.dtypes.cast( train_labels, tf.int64)
and it worked
Found the answer
Error clearly says you have invalid type for labels.
You trying to pass numpy array instead of Tensor. Sometimes Tensorflow
performs implicit conversion from ndarray to Tensor under the hood
(what's why your code works outside of Estimator), but in this case it
don't.
.
No, official impl. feeds data from a placeholder. Placeholder is
always a Tensor, so it don't depends on implicit things.
But if you directly call loss function with a numpy array as input
(Notice: call during graph construction phase, so argument content
gets embedded into graph), it MAY work (however, I did not check it).
This code:
nce_loss(labels=[1,2,3]) will be called only ONCE during graph
construction. Labels will be statically embedded into graph as a
constant and potentially can be of any Tensor-compatible type (list,
ndarray, etc)
This code: ```Python def model(label_input):
nce_loss(labels=label_input)
estimator(model_fun=model).train() ``` can't embed labels variable
statically, because it content is not defined during graph
construction. So if you feed anything except the Tensor, it will throw
an error.
From
https://www.reddit.com/r/MachineLearning/comments/a39pef/r_tensorflow_estimators_managing_simplicity_vs/
So I used labels=tf.dtypes.cast( train_labels, tf.int64)
and it worked
answered Dec 6 '18 at 20:09
SantoshGupta7SantoshGupta7
6811515
6811515
add a comment |
add a comment |
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What Python, TensorFlow and NumPy versions are you using? If they are not up to date (TensorFlow 1.12, NumPy 1.15), have you tried upgrading?
– jdehesa
Nov 27 '18 at 14:27
For Tensorflow , version '1.12.0' ; For Numpy , version '1.14.6'
– SantoshGupta7
Nov 29 '18 at 20:42