hub.image_embedding_column( key, module_spec )
Uses a Module to get a dense 1-D representation from the pixels of images.
This feature column can be used on images, represented as float32 tensors of RGB pixel data in the range [0,1]. This can be read from a numeric_column() if the tf.Example input data happens to have decoded images, all with the same shape [height, width, 3]. More commonly, the input_fn will have code to explicitly decode images, resize them (possibly after performing data augmentation such as random crops etc.), and provide a batch of shape [batch_size, height, width, 3].
The result of this feature column is the result of passing its input
through the module m
instantiated from module_spec
, as per result = m({"images": input})
. The result
must have dtype float32 and shape [batch_size, num_features]
with a known value of num_features.
Example:
image_column = hub.image_embedding_column("embeddings", "/tmp/image-module") feature_columns = [image_column, ...] estimator = tf.estimator.LinearClassifier(feature_columns, ...) height, width = hub.get_expected_image_size(image_column.module_spec) input_fn = ... # Provides "embeddings" with shape [None, height, width, 3]. estimator.train(input_fn, ...)
key
: A string or _FeatureColumn
identifying the input image data.module_spec
: A string handle or a ModuleSpec
identifying the module._DenseColumn
that converts from pixel data.
ValueError
: if module_spec is not suitable for use in this feature column.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/hub/image_embedding_column