Reputation: 53
x = tf.placeholder(dtype = tf.float32, shape = [None, 28, 28])
y = tf.placeholder(dtype = tf.int32, shape = [None])
images_flat = tf.contrib.layers.flatten(x)
logits = tf.contrib.layers.fully_connected(images_flat, 62, tf.nn.relu)
loss =
tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
labels = y, logits = logits))
train_op =
tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
correct_pred = tf.argmax(logits, 1)
accuracy = tf.reduce_mean(tf.cast(correct_pred,
tf.float32))
print("images_flat: ", images_flat)
print("logits: ", logits)
print("loss: ", loss)
print("predicted_labels: ", correct_pred)
AttributeError Traceback (most recent call last)
<ipython-input-17-183722ce66a3> in <module>
1 x = tf.placeholder(dtype = tf.float32, shape = [None, 28, 28])
2 y = tf.placeholder(dtype = tf.int32, shape = [None])
----> 3 images_flat = tf.contrib.layers.flatten(x)
4 logits = tf.contrib.layers.fully_connected(images_flat, 62, tf.nn.relu)
5 loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels = y, logits = logits))
AttributeError: module 'tensorflow_core.compat.v1' has no attribute 'contrib'
2.This is my code in Jupyter Notebook. I just started with python and get the error I mentioned in the headline. I would be very thankful if someone could help me wizh a code example to solve the problem.
Upvotes: 5
Views: 53923
Reputation: 11
As per my understanding, the contrib got deleted in TF2. So there is a quick solution to replace it with an estimator suggested here in this link
https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
in the case of using tf1
tf.contib.layers.batch_norm throws error "AttributeError: module 'tensorflow.compat.v1' has no attribute 'contrib'".
Solution: tf.estimator.layers.batch_norm
Upvotes: 1
Reputation: 397
contrib is a headache of Google Team. We have to deal with the issue of contrib case by case. I just take two examples as follows.
1.With regard to CNN, it has the following method
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
# -initializer = tf.contrib.layers.xavier_initializer(seed=1)
initializer = tf.truncated_normal_initializer(stddev=0.1)
2.With regard to RNN/LSTM, it has the following different method.
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
# -outputs, states = tf.contrib.rnn.static_rnn(lstm_cells, _X, dtype=tf.float32)
outputs, states = tf.compat.v1.nn.static_rnn(lstm_cells, _X, dtype=tf.float32)
Upvotes: 1
Reputation: 4617
I think you need to add the following line in your python file which you are going to execute it.
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
Upvotes: -1
Reputation: 14993
tf.contrib
was removed from TensorFlow once with TensorFlow 2.0 alpha version.
Most likely, you are already using TensorFlow 2.0.
You can find more details here: https://github.com/tensorflow/tensorflow/releases/tag/v2.0.0-alpha0
For using specific versions of tensorflow, use
pip install tensorflow==1.14
or
pip install tensorflow-gpu==1.14
Upvotes: 13