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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.


  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.
image_size Optional. If specified it should be a tuple of image height and width to use with the module. Note that it depends on the module on whether the default size can be overridden and what the permissible values are.

_DenseColumn that converts from pixel data.

ValueError if module_spec is not suitable for use in this feature column.