tf.compat.v1.parse_single

Inserts a placeholder for a tensor that will be always fed.

tf.compat.v1.placeholder(
    dtype, shape=None, name=None
)

Used in the notebooks

Used in the guide	Used in the tutorials
Migrate your TensorFlow 1 code to TensorFlow 2	Fitting Dirichlet Process Mixture Model Using Preconditioned Stochastic Gradient Langevin Dynamics Generating Images with BigBiGAN Classify Flowers with Transfer Learning Generating Images with Little Data Using S3GAN Introduction to Fairness Indicators

For example:

x = tf.compat.v1.placeholder(tf.float32, shape=(1024, 1024))
y = tf.matmul(x, x)

with tf.compat.v1.Session() as sess:
  print(sess.run(y))  # ERROR: will fail because x was not fed.

  rand_array = np.random.rand(1024, 1024)
  print(sess.run(y, feed_dict={x: rand_array}))  # Will succeed.

Args
`dtype`	The type of elements in the tensor to be fed.
`shape`	The shape of the tensor to be fed (optional). If the shape is not specified, you can feed a tensor of any shape.
`name`	A name for the operation (optional).

Returns
A `Tensor` that may be used as a handle for feeding a value, but not evaluated directly.

Raises
`RuntimeError`	if eager execution is enabled

Eager Compatibility

Placeholders are not compatible with eager execution.

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.compat / v1 / v1.parse_single_example

Used in the notebooks

For example:

Args

Returns

Raises

Eager Compatibility