If I try and install TensorFlow on my machine, it'll install numpy 1.19.5
If I try and install Pandas, it'll install numpy 1.22
If I stick with numpy 1.19.5 and try to import pandas, I get a complaint from pandas:
ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject
If I go with numpy 1.22, I get
NotImplementedError: Cannot convert a symbolic Tensor (lstm_2/strided_slice:0) to a numpy array
Which I've heard means it's because tensorflow can't run on 1.22
So what am I supposed to do to have pandas and tensorflow working at the same time?
You can use Pandas version==1.3.5 which supports all later versions of Tensorflow 2.6 to 2.9 and Numpy version >= 1.17.3
Related
sequence_input = Input(shape=(max_len,), dtype="int32")
embedded_sequences = Embedding(vocab_size, 128, input_length=max_len,
mask_zero=True)(sequence_input)
lstm = Bidirectional(LSTM(64, dropout=0.5, return_sequences=True))(embedded_sequences)
The third line of code gives the following error:
Cannot convert a symbolic Tensor (bidirectional/forward_lstm/strided_slice:0) to a numpy array.
This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported
When I was looking for a solution to the same error as me, I saw a lot of answers on stackoverflow telling me to lower the numpy version to less than 1.20.
But since I use featuretools, I need to set the numpy version to 1.2 or higher.
So, my question is, is there currently no way to fix this error without downgrading the numpy version?
(my tensorflow version is 2.3.0, numpy version is 1.23)
I solved this error by changing the cuda version and installing the latest tensorflow. (2.3 -> 2.10)
I want to run code that uses tensorflow == 1.15 but has tensorflow 2.7 installed on my system. According to Tensorflow at this address https://www.tensorflow.org/guide/migrate/migrate_tf2, I use the following lines so that I can run the code without changing in tensorflow 2.7:
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
But I do not know what to do instead of the following lines in my code!
Because, according to Tensorflow "You can still run unmodified TF1.x code (except for contrib) against TF2 binary installations"
l2_reg = tf.contrib.layers.l2_regularizer(scale=self.beta)
xavier = tf.contrib.layers.xavier_initializer()
please help
Tf.contrib is deprecated in Tensorflow 2.x
Replace
tf.contrib.layers.l2_regularizer
with
tf.compat.v1.keras.regularizers.l2
Replace
tf.contrib.layers.xavier_initializer
With
tf.compat.v1.keras.initializers.glorot_normal
During running LSTM model , I'm getting this error when my code is calling the following function:
model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))
error is:
" a NumPy call, which is not supported".format(self.name))
NotImplementedError: Cannot convert a symbolic Tensor
(lstm/strided_slice:0) to a numpy array. This error may indicate that
you're trying to pass a Tensor to a NumPy call, which is not
supported"
Im using-
tensorflow 2.4.0
Numpy 1.20.0
pandas=1.3.4
python 3.7.3
platform: Pi 4 + 32bit OS
Try to use the lower version of Numpy i.e 1.19.5 or 1.19 using
pip install numpy == 1.19.5
Or
pip install numpy == 1.19
This problem usually occurs when you try to evaluate something using a Symbolic Tensor ( For Example Tensor array etc) with Non-Symbolic Types like NumPy, It is quite difficult to avoid because we might use symbolic tensors like tf.zeros() or tf.ones() as the parameters of your model, which use NumPy internally. Try for a Lower version of NumPy or the Latest Version of Tensorflow version i.e. Tensorflow Core v2.6.0
I solved this problem by changing my OS from 32 bit OS to 64 bit OS. I tried with 32 bit OS+ numpy 1.19.5 + tf 2.4.0; I was receiving this error constantly. later I changed the OS to 64 bit with numpy 1.19.5 and tf 2.4.0 and its working now.
The code below used to work last year, but updates in keras/tensorflow/numpy broke it. It now outputs the exception below. Does anyone know how to make it work again?
I'm using:
Tensorflow 2.4.1
Keras 2.4.3
Numpy 1.20.1
Python 3.9.1
import numpy as np
from keras.layers import LSTM, Embedding, Input, Bidirectional
dim = 30
max_seq_length = 40
vecs = np.random.rand(45,dim)
input_layer = Input(shape=(max_seq_length,))
embedding_layer = Embedding(len(vecs), dim, weights=[vecs], input_length=max_seq_length, trainable=False, name="layerA")(input_layer)
lstm_nobi = LSTM(max_seq_length, return_sequences=True, activation="linear", name="layerB")
lstm = Bidirectional(lstm_nobi, name="layerC")(embedding_layer)
Complete output of the script above: https://pastebin.com/DsQNWVwz
Shortened output:
2021-02-10 17:51:13.037468: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-02-10 17:51:13.037899: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-02-10 17:51:13.038418: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.
Traceback (most recent call last):
File "/run/media/volker/DATA/configruns/load/./test.py", line 13, in <module>
lstm = Bidirectional(lstm_nobi, name="layerC")(embedding_layer)
... omitted, see pastebin ...
File "/usr/lib/python3.9/site-packages/tensorflow/python/framework/ops.py", line 852, in __array__
raise NotImplementedError(
NotImplementedError: Cannot convert a symbolic Tensor (layerC/forward_layerB/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported
Installing numpy 1.19.5 works for me even with Python 3.9
pip install -U numpy==1.19.5
My environment is Windows and since I do not have Visual C++ Compiler installed, I ride on third party whl file installation
pip install -U https://mirrors.aliyun.com/pypi/packages/bc/40/d6f7ba9ce5406b578e538325828ea43849a3dfd8db63d1147a257d19c8d1/numpy-1.19.5-cp39-cp39-win_amd64.whl#sha256=0eef32ca3132a48e43f6a0f5a82cb508f22ce5a3d6f67a8329c81c8e226d3f6e
Solution: Use Python 3.8, because Python 3.9 is not supported by Tensorflow.
I wanted to try out the embeddings provided in tensorflow-hub, the 'universal-sentence-encoder' to be specific. I tried the examples provided (https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/semantic_similarity_with_tf_hub_universal_encoder.ipynb)
and it worked fine. So I tried to do the same with 'multilingual' model but every time the multilingual model is loaded, the colab kernel fails and restarts. What is the problem and how can I get around this?
import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import re
import seaborn as sns
import tf_sentencepiece
import sentencepiece
# Import the Universal Sentence Encoder's TF Hub module
embed = hub.Module("https://tfhub.dev/google/universal-sentence-encoder-multilingual/1") // This is where the kernel dies.
print("imported model")
# Compute a representation for each message, showing various lengths supported.
word = "코끼리"
sentence = "나는 한국어로 쓰여진 문장이야."
paragraph = (
"동해물과 백두산이 마르고 닳도록. "
"하느님이 보우하사 우리나라 만세~")
messages = [word, sentence, paragraph]
# Reduce logging output.
tf.logging.set_verbosity(tf.logging.ERROR)
with tf.Session() as session:
session.run([tf.global_variables_initializer(), tf.tables_initializer()])
message_embeddings = session.run(embed(messages))
for i, message_embedding in enumerate(np.array(message_embeddings).tolist()):
print("Message: {}".format(messages[i]))
print("Embedding size: {}".format(len(message_embedding)))
message_embedding_snippet = ", ".join(
(str(x) for x in message_embedding[:3]))
print("Embedding: [{}, ...]\n".format(message_embedding_snippet))
I had similar issues with the multilingual sentence encoder. I resolved it by specifying tensorflow version to 1.14.0 and tf-sentencepiece to 0.1.83, so before running your code in colab try:
!pip3 install tensorflow==1.14.0
!pip3 install tensorflow-hub
!pip3 install sentencepiece
!pip3 install tf-sentencepiece==0.1.83
I was able to replicate your problem in colab and this solution loaded the model correctly:
It seems to be a compatibility problem between sentencepiece and tensorflow, check for updates on this issue here.
Let us know how it goes. Best of luck and I hope this helps.
EDIT: If tensorflow version 1.14.0 does not work, change it to 1.13.1. This problem should be resolved once compatibility between tensorflow and sentencepiece is figured it out.