tf.keras.layers.Cropping3D

A layer that produces a dense Tensor based on given feature_columns.

Inherits From: DenseFeatures, Layer, Module

tf.keras.layers.DenseFeatures(
    feature_columns, trainable=True, name=None, **kwargs
)

Used in the notebooks

Used in the tutorials
Boosted trees using Estimators Build a linear model with Estimators Classify structured data with feature columns End to end example for BigQuery TensorFlow reader

Generally a single example in training data is described with FeatureColumns. At the first layer of the model, this column oriented data should be converted to a single Tensor.

This layer can be called multiple times with different features.

This is the V2 version of this layer that uses name_scopes to create variables instead of variable_scopes. But this approach currently lacks support for partitioned variables. In that case, use the V1 version instead.

Example:

price = tf.feature_column.numeric_column('price')
keywords_embedded = tf.feature_column.embedding_column(
    tf.feature_column.categorical_column_with_hash_bucket("keywords", 10K),
    dimensions=16)
columns = [price, keywords_embedded, ...]
feature_layer = tf.keras.layers.DenseFeatures(columns)

features = tf.io.parse_example(
    ..., features=tf.feature_column.make_parse_example_spec(columns))
dense_tensor = feature_layer(features)
for units in [128, 64, 32]:
  dense_tensor = tf.keras.layers.Dense(units, activation='relu')(dense_tensor)
prediction = tf.keras.layers.Dense(1)(dense_tensor)

Args
`feature_columns`	An iterable containing the FeatureColumns to use as inputs to your model. All items should be instances of classes derived from `DenseColumn` such as `numeric_column`, `embedding_column`, `bucketized_column`, `indicator_column`. If you have categorical features, you can wrap them with an `embedding_column` or `indicator_column`.
`trainable`	Boolean, whether the layer's variables will be updated via gradient descent during training.
`name`	Name to give to the DenseFeatures.
`**kwargs`	Keyword arguments to construct a layer.

Raises
`ValueError`	if an item in `feature_columns` is not a `DenseColumn`.

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.keras / layers / layers.Cropping3D

Used in the notebooks

Example:

Args

Raises