I am trying to run a lstm model using tfLearn and I get this error:
File "...city_names.py", line 16, in <module>
g = tflearn.lstm(g, 256, activation='relu', return_seq=True)
File "...\tflearn\layers\recurrent.py", line 197, in lstm
inference = tf.unpack(inference)
AttributeError: module 'tensorflow' has no attribute 'unpack'
with the following line:
g = tflearn.input_data(shape=[None, maxlen, len(char_idx)])
These are the lines of code:
path = "US_cities.txt"
maxlen = 20
X, Y, char_idx = textfile_to_semi_redundant_sequences(path, seq_maxlen=maxlen, redun_step=3)
g = tflearn.input_data(shape=[None, maxlen, len(char_idx)])
g = tflearn.input_data(shape=[None, maxlen, len(char_idx)])
In tf 1.0, there's no unpack. You may want to use unstack instead.
To upgrade previous code, you can refer to https://www.tensorflow.org/install/migration.
But I don't know if there's a tool for updating an entire deep learning library such like tflearn = =
I had the same problem and installed the latest ('bleeding edge') version of TFLearn and I did not get the 'unpack' attribute error anymore with TensorFlow 1.0 .
I used the following command in terminal to install TFLearn 0.3:
pip install git+https://github.com/tflearn/tflearn.git
This is according to instructions on the TFLearn GitHub page.
Related
I am reading the online TensorFlow Probability (TFP) version of "Bayesian Methods for Hackers".
But when I excecute the first cell of Ch2_MorePyMC_TFP.ipynb
the following error occurs:
AttributeError: module 'tensorflow' has no attribute 'contrib'
I suppose this version of "Bayesian Methods for Hackers" jupyter notebook was written for TF1.
Do you have an easy fix or a updated version of this jupyter notebook working with TF2 ?
Some of the contrib functions are removed and some of them are merged into TensorFlow core. You need to find the equivalent version of them.
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions
tfb = tfp.bijectors
print(tf.__version__) # 2.5.0
print(tfp.__version__) # 0.12.1
For example first contrib functions are available in TensorFlow and can be re-written as:
parameter = tfd.Exponential(rate=1., name="poisson_param").sample()
rv_data_generator = tfd.Poisson(parameter, name="data_generator")
data_generator = rv_data_generator.sample()
data_generator_ = tf.nest.pack_sequence_as(
data_generator,
[t.numpy() if tf.is_tensor(t) else t
for t in tf.nest.flatten(data_generator)])
print("Value of sample from data generator random variable:", data_generator_)
For other TF Operations you can replace them like this:
with tf.compat.v1.variable_scope(tf.compat.v1.get_variable_scope(), reuse=tf.compat.v1.AUTO_REUSE):
step_size = tf.compat.v1.get_variable(
name='step_size',
initializer=tf.constant(0.5, dtype=tf.float32),
trainable=False,
use_resource=True
)
More info can be found in the documentation
Frightera, I have problems getting rid of the following error :
module 'tensorflow' has no attribute 'variable_scope'
at cell :
# Initialize the step_size. (It will be automatically adapted.)
with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
step_size = tf.get_variable(
name='step_size',
initializer=tf.constant(0.5, dtype=tf.float),
trainable=False,
use_resource=True
)
Do you have any clue how to replace this one?
This is the code
from https://keras.io/examples/vision/image_classification_from_scratch/
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
# generate a dataset
image_size = (180,180)
batch_size = 32
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
"PetImages",
validation_split = 0.2,
subset = "training",
seed = 1337,
image_size = image_size,
batch_size = batch_size,
)
Error is
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-21-bb7f2d14bf63> in <module>
3 batch_size = 32
4
----> 5 train_ds = tf.keras.preprocessing.image_dataset_from_directory(
6 "PetImages",
7 validation_split = 0.2,
AttributeError: module 'tensorflow.keras.preprocessing' has no attribute 'image_dataset_from_directory'
Any smallest detail which I am overlooking now?
It has been addressed under this issue.
The specific function (tf.keras.preprocessing.image_dataset_from_directory) is not available under TensorFlow v2.1.x or v2.2.0 yet. It is only available with the tf-nightly builds and is existent in the source code of the master branch.
Too bad they didn't indicate it anywhere on site. Better to use flow_from_directory for now. Or switch to tf-nightly and carry on.
v2.5.0
I got the same error using that code:
tf.keras.utils.image_dataset_from_directory(...)
changing it to:
tf.keras.preprocessing.image_dataset_from_directory(...)
fix my problem
I also had the same problem. When I upgraded the TensorFlow version to 2.3.0, it worked.
The code is like this:
import tensorflow as tf
from keras_contrib.layers import CRF
from tensorflow import keras
def create_model(max_seq_len, adapter_size=64):
"""Creates a classification model."""
# adapter_size = 64 # see - arXiv:1902.00751
# create the bert layer
with tf.io.gfile.GFile(bert_config_file, "r") as reader:
bc = StockBertConfig.from_json_string(reader.read())
bert_params = map_stock_config_to_params(bc)
bert_params.adapter_size = adapter_size
bert = BertModelLayer.from_params(bert_params, name="bert")
input_ids = keras.layers.Input(shape=(max_seq_len,), dtype='int32', name="input_ids")
# token_type_ids = keras.layers.Input(shape=(max_seq_len,), dtype='int32', name="token_type_ids")
# output = bert([input_ids, token_type_ids])
bert_output = bert(input_ids)
print("bert_output.shape: {}".format(bert_output.shape)) # (?, 100, 768)
crf = CRF(len(tag2idx))
logits = crf(bert_output)
model = keras.Model(inputs=input_ids, outputs=logits)
model.build(input_shape=(None, max_seq_len))
# load the pre-trained model weights
load_stock_weights(bert, bert_ckpt_file)
# freeze weights if adapter-BERT is used
if adapter_size is not None:
freeze_bert_layers(bert)
model.compile('adam', loss=crf.loss_function, metrics=[crf.accuracy])
model.summary()
return model
I am using tensorflow keras and also use keras_contrib package, to do NER. it seems the tensorflow keras package does not work well with keras_contrib package.
The Traceback information is listed below:
Traceback (most recent call last):
File "F:/_gitclone3/bert_examples/bert_ner_example_eval.py", line 120, in <module>
model = create_model(max_seq_len, adapter_size=adapter_size)
File "F:/_gitclone3/bert_examples/bert_ner_example_eval.py", line 101, in create_model
logits = crf(bert_output)
File "C:\Users\yuexiang\Anaconda3\lib\site-packages\keras\engine\base_layer.py", line 443, in __call__
previous_mask = _collect_previous_mask(inputs)
File "C:\Users\yuexiang\Anaconda3\lib\site-packages\keras\engine\base_layer.py", line 1311, in _collect_previous_mask
mask = node.output_masks[tensor_index]
AttributeError: 'Node' object has no attribute 'output_masks'
How do I use CRF with tensorflow keras?
I run into a similar problem and spent a lot of time trying to get things to work. Here's what worked for me using python 3.6.5:
Seqeval:
pip install seqeval==0.0.5
Keras:
pip install keras==2.2.4
Keras-contrib (2.0.8):
git clone https://www.github.com/keras-team/keras-contrib.git
cd keras-contrib
python setup.py install
TensorFlow:
pip install tensorflow==1.14.0
Do pip list to make sure you have actually installed those versions (eg pip seqeval may automatically update your keras)
Then in your code import like so:
from keras.models import *
from keras.layers import LSTM, Embedding, Dense, TimeDistributed, Dropout, Bidirectional, Input
from keras_contrib.layers import CRF
#etc.
Hope this helps, good luck!
You can try tensorflow add-ons.(If you are using tensorflow version 2).
You can try tf-crf-layer (if you are using tensorflow==1.15.0)
They have it mentioned on their README.
git clone https://www.github.com/keras-team/keras-contrib.git
cd keras-contrib
python convert_to_tf_keras.py
USE_TF_KERAS=1 python setup.py install
I have gone through the possible solutions, mentioning which worked for me:
Install tf2crf (https://pypi.org/project/tf2crf/): It provides a simple CRF layer for TensorFlow 2 keras.
Use TensorFlow SIG Addons: ( https://www.tensorflow.org/addons/api_docs/python/tfa/layers/CRF): It provides the functionality that is not available in core TensorFlow.
I am new to tensorflow and trying to set it up.
When I try to train a model using CuDNNGRU it seems to load correctly and then gives an error :
tensorflow.python.framework.errors_impl.NotFoundError: Op type not
registered 'CudnnRNN'
I do see a Cudnn_rnn directory in tensorflow/contrib for what that is worth.
I have python 3.6 and VS2013.
I have tried the following, but still getting an error:
Both Cuda 8/9
uninstalling/reinstalling tensorflow/Theano/Keras/TensorFlow
Honestly the setup seems so convoluted and touchy, I may have screwed something up.
Am I missing a to-do? Some way to manually fix? Thanks!
Sample code I am trying to replicate:
def get_model(embedding, sequence_length, dropout_rate, recurrent, dense_size):
input_layer = Input(shape=(sequence_length,))
embedding_layer = Embedding(embedding.shape[0], embedding.shape[1],
weights=[embedding], trainable=False)(input_layer)
x = Bidirectional(CuDNNGRU(recurrent, return_sequences=True))(embedding_layer)
x = Dropout(dropout_rate)(x)
x = Bidirectional(CuDNNGRU(recurrent, return_sequences=False))(x)
x = Dense(dense_size, activation="relu")(x)
I fixed this by doing :
pip install tensorflow --ignore-installed --upgrade
and then
from tensorflow.python.client import device_lib print(device_lib.list_local_devices())
I have been using Keras (version 1.1.1) LSTM with Theano as backend without any problem. Now I would like to switch to Tensorflow (version 0.8.0) and could not get a simple example to work. The problem can be boiled down to following code snippet copied from this Keras-Tensorflow interface tutorial.
from keras.layers import LSTM
import tensorflow as tf
my_graph = tf.Graph()
with my_graph.as_default():
x = tf.placeholder(tf.float32, shape=(None, 20, 64))
y = LSTM(32)(x)
And I got following error when last line is executed:
File "/home/xxx/local/lib/python2.7/site-packages/Keras-1.1.1-py2.7.egg/keras/engine/topology.py", line 529, in call
return self.call(x, mask)
File "/home/xxx/local/lib/python2.7/site-packages/Keras-1.1.1-py2.7.egg/keras/layers/recurrent.py", line 227, in call
input_length=input_shape1)
File "/home/xxx/local/lib/python2.7/site-packages/Keras-1.1.1-py2.7.egg/keras/backend/tensorflow_backend.py", line 1306, in rnn
axes = [1, 0] + list(range(2, len(outputs.get_shape())))
File "/usr/local/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/tensor_shape.py", line 462, in len
raise ValueError("Cannot take the length of Shape with unknown rank.")
ValueError: Cannot take the length of Shape with unknown rank.
Any suggestions?
You can't mix tensorflow as keras like that. Keras keeps track of the shape of its tensors separately from how tensorflow does.
Try using x = Input(shape=(20,64))