如何在 Tensorflow 2.0 中打印 tensorflow.python.framework.ops.Tensor 的值？

Question

I have a few tensors in my code and and need to get the values of those tensors. This is one them. How to print the values of tensor OA?

Input:OA
Output: <tf.Tensor 'Sum_1:0' shape=(1, 600) dtype=float32>

Input:type(OA)
Output: tensorflow.python.framework.ops.Tensor

I have tried all the available functions like tf.print(), eval(), tensor.numpy(). None of them worked for me in Tensorflow 2.0. It seems they work only for 'EagerTensor' and not for 'ops.Tensor'.

1) OA.eval(session=sess) Error: ValueError: Cannot use the given session to evaluate tensor: the tensor's graph is different from the session's graph.

2) tf.print(OA) Output:

3) print (OA.numpy()) Output: AttributeError: 'Tensor' object has no attribute 'numpy'

Is there any way to convert ops.Tensor to EagerTensor to try the above functions? Or is there any other option to print the values of ops.Tensor. Please advise.

-- Adding the minimal code to reproduce the example ops.Tensor in TF2.0.

!pip install tensorflow==2.0.0
tf.__version__

import tensorflow as tf
from keras.layers import Dense, Conv1D, MaxPooling1D, Flatten, Dropout, Input, Embedding, Bidirectional, LSTM
from tensorflow.keras import regularizers

EMBEDDING_DIM = 300
max_length = 120
batch_size = 512
vocab_size = 1000
units = 300

from keras.layers import Dense, Conv1D, MaxPooling1D, Flatten, Dropout, Input, Embedding, Bidirectional, LSTM
from tensorflow.keras import regularizers

input_text = tf.keras.Input(shape= (max_length), batch_size=batch_size)

embedding_layer = tf.keras.layers.Embedding(vocab_size, EMBEDDING_DIM, input_length =max_length, name="Embedding_Layer_1")
embedding_sequence = embedding_layer(input_text)

HQ = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(units,recurrent_dropout=0.5,kernel_regularizer=regularizers.l2(0.001),return_sequences=True,name='Bidirectional_1'))(embedding_sequence)
HQ = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(units,recurrent_dropout=0.5,kernel_regularizer=regularizers.l2(0.001),name='Bidirectional_2'))(HQ)

print (HQ)

Output: Tensor("bidirectional_3/concat:0", shape=(512, 600), dtype=float32)

type(HQ)

Output: tensorflow.python.framework.ops.Tensor

How to check the actual values of this tensor?

Answer 1

Your graph is not complete at the point you are printing HQ. You need to complete the model creation. Presumably something like

output = tf.keras.layers.xyz()(HQ)
model = tf.keras.models.Model(input_text, output)

The trick to print an intermediate layer is to just make it an output. You can make it an additional output of your existing model temporarily, or just make a new model.

inspection_model = tf.keras.models.Model(input_text, [output, HQ])

now run inference on your inspection_model to get the value of the intermediate activation HQ.

print(inspection_model(xyz))

Answer 2

Directly you cannot print the values of tensors the way you are trying to do. Tensorflow 2.x by default runs in eager mode and also you have given no input to your incomplete model.

The way to do it is by using the custom training loop. Suppose the layers mentioned in your code stack up to create a model my_model .

from tensorflow.keras import Model


my_model = Model(input_text, outputs=HQ)

with tf.GradientTape() as t:
    HQ_predictions = my_model(input_data)
print(HQ_predictions)

Answer 3

使用.numpy()属性，如：

your_tensor.numpy()

Answer 4

my tensorflow version is 2.5.0,here is an example of showing values:

import tensorflow as tf

print(tf.__version__) # 2.5.0

num=1
print(type(tf.constant(num,dtype="int64"))) # <class 'tensorflow.python.framework.ops.EagerTensor'>
print(tf.constant(num,dtype="int64").numpy()) # 1