tf.contrib.slim , AttributeError: module 'tensorflow' has no attribute 'contrib'

Expert [addtoany]

Sep '24

The tf.contrib module was part of TensorFlow's library, providing experimental and less stable functionalities, including the tf.contrib.slim module for high-level neural network construction. However, starting from TensorFlow 2.0, tf.contrib has been deprecated and removed. The functionalities provided by tf.contrib have been either integrated into the main TensorFlow API or moved to separate repositories and packages.

Problem Explanation

The error AttributeError: module 'tensorflow' has no attribute 'contrib' occurs because tf.contrib is no longer available in TensorFlow 2.x. If you have code that uses tf.contrib, you will need to update it to be compatible with TensorFlow 2.x.

Solution

To resolve the issue, you have two main approaches:

Update Code for TensorFlow 2.x: Transition your code to use TensorFlow 2.x compatible functions. For example, the functionalities of tf.contrib.slim can be replaced with the tf.keras API in TensorFlow 2.x.
Use TensorFlow 1.x: If you need to maintain compatibility with older codebases, you can use TensorFlow 1.x, which still supports tf.contrib.

Here’s how you can address this issue with both approaches:

Approach 1: Updating Code to TensorFlow 2.x

If you were using tf.contrib.slim for neural network construction, you can switch to tf.keras, which is now the recommended high-level API for building models.

Example Code Using tf.contrib.slim (TensorFlow 1.x):

Example


import tensorflow as tf
from tensorflow.contrib import slim

# Define a simple model using tf.contrib.slim
def my_model(inputs):
    net = slim.fully_connected(inputs, 128, scope='fc1')
    net = slim.fully_connected(net, 64, scope='fc2')
    return net

inputs = tf.placeholder(tf.float32, shape=[None, 784])
outputs = my_model(inputs)

Updated Code Using tf.keras (TensorFlow 2.x):

Example


import tensorflow as tf

# Define a simple model using tf.keras
def my_model():
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(128, activation='relu', input_shape=(784,)),
        tf.keras.layers.Dense(64, activation='relu'),
    ])
    return model

model = my_model()

Approach 2: Using TensorFlow 1.x

If you need to run code that relies on tf.contrib and are unable to update it immediately, you can use TensorFlow 1.x. Here’s how to install TensorFlow 1.x:

Example


pip install tensorflow==1.15

Example Code with TensorFlow 1.x:

Example


import tensorflow as tf
from tensorflow.contrib import slim

# Define a simple model using tf.contrib.slim
def my_model(inputs):
    net = slim.fully_connected(inputs, 128, scope='fc1')
    net = slim.fully_connected(net, 64, scope='fc2')
    return net

inputs = tf.placeholder(tf.float32, shape=[None, 784])
outputs = my_model(inputs)

Summary

TensorFlow 2.x: Use tf.keras as a replacement for tf.contrib.slim.
TensorFlow 1.x: You can use TensorFlow 1.x if you need to run legacy code that depends on tf.contrib.

For more information, refer to the TensorFlow migration guide for upgrading code from TensorFlow 1.x to TensorFlow 2.x.

tf.contrib.slim , AttributeError: module 'tensorflow' has no attribute 'contrib'