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import tensorflow as tf
# Create a placeholder for input data
input_placeholder = tf.placeholder(tf.float32, shape=(None, 10))
# More code...
# Continue with the rest of the TensorFlow operations
# ...
# Remember to execute the TensorFlow graph inside a session
with tf.Session() as sess:
# Run the graph with some input data
output = sess.run(some_tensor, feed_dict={input_placeholder: input_data})
# More code...
# Continue with the rest of the post-processing or evaluation
# ...
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#replace import tensorflow as tf by following
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
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change the <tf.placeholder> as <tf.compat.v1.placeholder>
such as
x = tf.placeholder(shape = [None, image_pixels], dtype = tf.float32)
change as
x = tf.compat.v1.placeholder(shape = [None, image_pixels], dtype = tf.float32)
but there would be another problem about runtime error with eager execution add <tf.compat.v1.disable_eager_execution()> after import part
import tensorflow as tf
tf.compat.v1.disable_eager_execution()