When using the keras model to do predict, I got the error below
AttributeError: 'Tensor' object has no attribute 'ndim'
The reason is that the weights is numpy array, not tensor.
So how to convert numpy array to keras tensor?
2 Answers
In Tensorflow it can be done the following way:
import tensorflow.keras.backend as K
import numpy as np
a = np.array([1,2,3])
b = K.constant(a)
print(b)
# <tf.Tensor 'Const_1:0' shape=(3,) dtype=float32>
print(K.eval(b))
# array([1., 2., 3.], dtype=float32)
In raw keras it should be done replacing import tensorflow.keras.backend as K with from keras import backend as K.
2 Comments
KareemJ
A variable isn't the same as a tensor according to Keras's documentation. <code> >>> keras_var = K.variable(np_var)` <br> >>> K.is_keras_tensor(keras_var) # A variable is not a Tensor.<br> False<br> >>> keras_placeholder = K.placeholder(shape=(2, 4, 5))<br> >>> K.is_keras_tensor(keras_placeholder) # A placeholder is a Tensor.<br> True<br> </code>
Matias Haeussler
You are right, I have just updated my answer to use K.constant() instead of K.variable(), it returns a tensor instead of a variable as print() says
To convert numpy array to tensor,
import tensor as tf
#Considering y variable holds numpy array
y_tensor = tf.convert_to_tensor(y, dtype=tf.int64)
#You can use any of the available datatypes that suits best - https://www.tensorflow.org/api_docs/python/tf/dtypes/DType
1 Comment
Daniel González Cortés
also you can use
import tensorflow as tf
.variable