Following code gives me an import error.
from keras.layers.embeddings import DropoutEmbedding
What did DropoutEmbedding change its name to? Or what substitute should I use?
There is no "DropoutEmbedding" layer in Keras. I think you are looking for "Dropout" and "Embedding" layers. Import them like this:
from keras.layers import Dropout,Embedding
Related
I have import tensorflow as tf,When I want use tensorflow.kera. Why i should from tensorflow import keras, instead from tf import keras.
I am trying to.implement image classification for.extracting features from images I am using DeepImageFeaturizer and using Inceptionv3 model
But the from sparkdl import DeepImageFeaturizer is returning error
Import error: cannot import name ResNet50 from Keras.applications in pyspark colab.
I think the error is related to the versions. I don't recommend it but you can try this :
Open this file :
/home/user/.local/lib/python3.8/site-packages/sparkdl/transformers/keras_applications.py
instead of
from keras.applications import resnet50
to
from tensorflow.keras.applications import resnet50
According to this task:
Principal Component Analysis (PCA) in Python
I included this line
import from matplotlib.mlab import PCA
but I get the error message:
cannot import name 'PCA' from 'matplotlib.mlab'
I'm using Python3.7 and I have no idea how I can use the PCA function from matlab. Is the new version of matplotlib depricated or is PCA included to another library?
I really don't know if it is too late to reply now. But I will just place it here anyways.
import numpy as np
from sklearn.decomposition import PCA
I import a text file from the desktop to do with gensim model on jupyter notebook. However, it return that:
"AttributeError Traceback (most recent call last)
in
----> 1 model = word2vec.load(r'C:\Users\qlm\Desktop\globalwarming.txt')
AttributeError: module 'gensim.models.word2vec' has no attribute
'load'"
How can I fix this problem
import numpy as np
import pandas as pd
import gensim
from matplotlib import pyplot as plt
from gensim.models import word2vec
from collections import defaultdict
from sklearn.cluster import KMeans
model = word2vec.Text8Corpus(r'C:\Users\qlm\Desktop\globalwarming.txt')
model = word2vec.load(r'C:\Users\qlm\Desktop\globalwarming.txt')
There is a module named word2vec and inside it a class named Word2Vec, since the Word2Vec class is imported in __init__.py of gensim.models you can import it as you tried before:
from gensim.models import Word2Vec
Then you'll have access to the load method.
You can also use the full namespace too.
So:
# Will work as long as models.__init__ keep it available
from gensim.models import Word2Vec
But:
# Will always work as long as the namespace is not altered
from gensim.models.word2vec import Word2Vec
I personally prefer the second choice.
I am getting this error when trying our rasa_nlu with spacy
AttributeError: 'sklearn_crfsuite' object has no attribute 'CRF'
rasa_nlu was importing this way
import sklearn_crfsuite
So I tried importing like below before calling rasa_nlu
from sklearn_crfsuite import CRF
But getting a different
error - cannot import name 'CRF'
Looking for some suggestions.
If you want to do sklearn_crfsuite.CRF, then do import sklearn_crfsuite to import it. If you're importing with from sklearn_crfsuite import CRF, then just use CRF by itself.