Attributeerror: module tensorflow has no attribute set_random_seed

by

The “attributeerror: module tensorflow has no attribute set_random_seed“ is an error message that occurs mainly because of incompatible versions.

But you don’t have to worry because it has simple solutions. However, if you’re new to this error, we understand that it’s hard for you to fix it right away.

That is why this article is indeed intended for you. Do you want to know why? Okay, there you have it. In this article, we’ll discuss different solutions for “module tensorflow has no attribute set_random_seed.”

But before we dive into the solutions, we need to understand first what this error is all about and why it occurs.

What is “tensorflow” module?

The “TensorFlow” module is a popular open-source machine learning library developed by Google. This module allows developers and researchers to build and train machine learning models for a wide range of applications that include:

  • Natural language processing
  • Computer vision
  • Reinforcement learning

In addition to that, TensorFlow provides a powerful and flexible platform for building and training neural networks as well as other types of machine learning models.

What is set_random_seed attribute?

The “set_random_seed” is an attribute that was previously available in the TensorFlow library prior to version 2.1.

It is used to set the random seed for TensorFlow operations, making sure that the results of the operations are reproducible across multiple runs of the code.

What is “attributeerror: module tensorflow has no attribute set_random_seed”?

This “attributeerror: module ‘tensorflow’ has no attribute ‘set_random_seed‘” error message happens if you are using a tensorflow module. And you are trying to use the function “set_random_seed“ to set a random seed for reproducibility.

However, “set_random_seed” has been deprecated, and it is not available in the TensorFlow version you are currently using.

It’s likely because you are using a version of TensorFlow that does not support the “set_random_seed” attribute.

The reason is that…

The “set_random_seed” is a function in Python that was available in earlier versions of TensorFlow; however, it has been deprecated or removed from recent versions in favor of the “tf.random.set_seed function.”

The “tf.random.set_seed” function can be used to set the random seed for both CPU and GPU operations.

How to fix this error?

In order to fix this module ‘tensorflow’ has no attribute ‘set_random_seed’ error, you can use “tf.random.set_seed” to set a random seed instead of “set_random_seed” if your TensorFlow version is 2.1 or later.

In addition to that, when you are using an earlier version of TensorFlow, you just have to upgrade it to a newer version, or certainly you can use a different method to set a random seed.

Solutions for “attributeerror: module tensorflow has no attribute set_random_seed” error

You already know why this error occurs, as we have explained it above. Let’s proceed to the solutions.

Here, you’ll get the different solutions: “attributeerror: module ‘tensorflow’ has no attribute ‘set_random_seed’“.

1. Use “tf.random.set_seed” instead of “set_random_seed”

Here’s an example of how you are going to use tf.random.set_seed to set a random seed in TensorFlow 2.1 and later.

import tensorflow as tf
import numpy as np

# Create a random tensor with shape (3, 3)
sample_tensor = tf.random.normal(shape=(3, 3))
print("Random tensor:\n", sample_tensor.numpy())

# Set a random seed for reproducibility
tf.random.set_seed(50)

# Create another random tensor with shape (3, 3)
sample_tensor_2 = tf.random.normal(shape=(3, 3))
print("Random tensor 2:\n", sample_tensor_2.numpy())

# Verify that the two tensors are the same
print("Tensors are equal:", np.array_equal(sample_tensor.numpy(), sample_tensor_2.numpy()))

Output:

Random tensor:
[[ 0.23413189 0.71020585 -0.7339084 ]
[-0.4590371 0.7471016 0.2760755 ]
[ 0.16898936 -1.2342649 -1.2399613 ]]
Random tensor 2:
[[ 0.30275086 0.1671059 1.1508838 ]
[-0.1895873 0.14606944 -0.4369221 ]
[-1.2623419 -0.2596544 0.39352348]]
Tensors are equal: False

Another example code:

import tensorflow as tf
import numpy as np

# Define a simple model
model = tf.keras.Sequential([
tf.keras.layers.Dense(10, activation='relu', input_shape=(5,)),
tf.keras.layers.Dense(1, activation='sigmoid')
])

# Generate some random training data
x_train = np.random.randn(100, 5)
y_train = np.random.randint(0, 2, size=(100, 1))

# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy')

# Train the model without setting the random seed
history = model.fit(x_train, y_train, epochs=10, batch_size=32)

# Set the random seed for reproducibility
tf.random.set_seed(50)

# Train the model again with the same data and parameters
history_reproducible = model.fit(x_train, y_train, epochs=10, batch_size=32)

# Compare the loss between the two training runs

print("Loss without setting the seed:", history.history['loss'])
print("Loss with seed set:", history_reproducible.history['loss'])

Output:

Epoch 1/10
4/4 [==============================] - 1s 5ms/step - loss: 0.8728
Epoch 2/10
4/4 [==============================] - 0s 2ms/step - loss: 0.8653
Epoch 3/10
4/4 [==============================] - 0s 2ms/step - loss: 0.8591
Epoch 4/10
4/4 [==============================] - 0s 1ms/step - loss: 0.8540
Epoch 5/10
4/4 [==============================] - 0s 2ms/step - loss: 0.8492
Epoch 6/10
4/4 [==============================] - 0s 1ms/step - loss: 0.8438
Epoch 7/10
4/4 [==============================] - 0s 1ms/step - loss: 0.8394
Epoch 8/10
4/4 [==============================] - 0s 2ms/step - loss: 0.8353
Epoch 9/10
4/4 [==============================] - 0s 2ms/step - loss: 0.8310
Epoch 10/10
4/4 [==============================] - 0s 2ms/step - loss: 0.8266
Epoch 1/10
4/4 [==============================] - 0s 3ms/step - loss: 0.8232
Epoch 2/10
4/4 [==============================] - 0s 1ms/step - loss: 0.8192
Epoch 3/10
4/4 [==============================] - 0s 1ms/step - loss: 0.8154
Epoch 4/10
4/4 [==============================] - 0s 1ms/step - loss: 0.8114
Epoch 5/10
4/4 [==============================] - 0s 1ms/step - loss: 0.8076
Epoch 6/10
4/4 [==============================] - 0s 1ms/step - loss: 0.8042
Epoch 7/10
4/4 [==============================] - 0s 999us/step - loss: 0.8009
Epoch 8/10
4/4 [==============================] - 0s 1ms/step - loss: 0.7976
Epoch 9/10
4/4 [==============================] - 0s 1ms/step - loss: 0.7945
Epoch 10/10
4/4 [==============================] - 0s 1ms/step - loss: 0.7912
Loss without setting the seed: [0.87276291847229, 0.8652744889259338, 0.8591136336326599, 0.8540161848068237, 0.8492275476455688, 0.8438339233398438, 0.8394479155540466, 0.8352565765380859, 0.8309919834136963, 0.8265816569328308]
Loss with seed set: [0.8231893181800842, 0.8191773891448975, 0.8153956532478333, 0.8114357590675354, 0.8076046705245972, 0.8042371869087219, 0.8008878231048584, 0.7975940108299255, 0.7945308089256287, 0.7912166118621826]

2. Use ‘tf.compat.v1.set_random_seed’

You can use ‘tf.compat.v1.set_random_seed’ if you don’t want to use tf.random.set_seed.

import tensorflow as tf
import numpy as np

# Set random seed using tf.compat.v1.set_random_seed
tf.compat.v1.set_random_seed(50)

# Set random seed using numpy.random.seed
np.random.seed(50)

# Generate some random data using TensorFlow and numpy
x = tf.random.normal([10], mean=0.0, stddev=1.0, dtype=tf.float32)
y = np.random.normal(loc=0.0, scale=1.0, size=10)

# Print the generated data
print("Generated data using TensorFlow:", x)
print("Generated data using numpy:", y)

This code sets the random seed using tf.compat.v1.set_random_seed from the TensorFlow 1.x API and numpy.random.seed.

Output:

Generated data using TensorFlow: tf.Tensor(
[ 0.30275086  0.1671059   1.1508838  -0.1895873   0.14606944 -0.4369221
 -1.2623419  -0.2596544   0.39352348  0.39061502], shape=(10,), dtype=float32)
Generated data using numpy: [-1.56035211 -0.0309776  -0.62092842 -1.46458049  1.41194612 -0.47673214
 -0.78046921  1.07026774 -1.2822926  -1.3274789 ]

3. Use tf.random.Generator

You can create a new generator object and set its seed using “tf.random.Generator.from_seed,” as you can see in our example code below:

import tensorflow as tf

# Create a new generator object with a seed
generator = tf.random.Generator.from_seed(50)

# Generate some random data using the generator
a = generator.normal([10])

# Print the generated data
print("Generated data:", a)

Output:

Generated data: tf.Tensor(
[ 0.45331386  1.1487608  -1.2659091  -0.47450137  2.006022    0.28288034
 -0.30288252 -1.443651    1.0034493   0.20857747], shape=(10,), dtype=float32)

Related Articles for Python Errors

Conclusion

This article has already provided different solutions that you can use to fix the “attributeerror: module tensorflow has no attribute set_random_seed” error message in Python while you are using TensorFlow.

This error “module tensorflow has no attribute set_random_seed” occurs mainly because you are using a version of TesorFlow where set_random_seed has been deprecated or doesn’t support the attribute.

We are hoping that this article provides you with a sufficient solution; if yes, we would love to hear some thoughts from you.

Thank you very much for reading to the end of this article. Just in case you have more questions or inquiries, feel free to comment, and you can also visit our website for additional information.

Leave a Comment