layer_resizing {keras3}

A preprocessing layer which resizes images.

Description

This layer resizes an image input to a target height and width. The input should be a 4D (batched) or 3D (unbatched) tensor in "channels_last" format. Input pixel values can be of any range (e.g. ⁠[0., 1.)⁠ or ⁠[0, 255]⁠).

Usage

layer_resizing(
  object,
  height,
  width,
  interpolation = "bilinear",
  crop_to_aspect_ratio = FALSE,
  data_format = NULL,
  ...
)

Arguments

object

Object to compose the layer with. A tensor, array, or sequential model.

height

Integer, the height of the output shape.

width

Integer, the width of the output shape.

interpolation

String, the interpolation method. Supports "bilinear", "nearest", "bicubic", "lanczos3", "lanczos5". Defaults to "bilinear".

crop_to_aspect_ratio

If TRUE, resize the images without aspect ratio distortion. When the original aspect ratio differs from the target aspect ratio, the output image will be cropped so as to return the largest possible window in the image (of size ⁠(height, width)⁠) that matches the target aspect ratio. By default (crop_to_aspect_ratio=FALSE), aspect ratio may not be preserved.

data_format

string, either "channels_last" or "channels_first". The ordering of the dimensions in the inputs. "channels_last" corresponds to inputs with shape ⁠(batch, height, width, channels)⁠ while "channels_first" corresponds to inputs with shape ⁠(batch, channels, height, width)⁠. It defaults to the image_data_format value found in your Keras config file at ⁠~/.keras/keras.json⁠. If you never set it, then it will be "channels_last".

...

Base layer keyword arguments, such as name and dtype.

Value

The return value depends on the value provided for the first argument. If object is:

• a keras_model_sequential(), then the layer is added to the sequential model (which is modified in place). To enable piping, the sequential model is also returned, invisibly.

• a keras_input(), then the output tensor from calling layer(input) is returned.

• NULL or missing, then a Layer instance is returned.

Input Shape

3D (unbatched) or 4D (batched) tensor with shape: ⁠(..., height, width, channels)⁠, in "channels_last" format, or ⁠(..., channels, height, width)⁠, in "channels_first" format.

Output Shape

3D (unbatched) or 4D (batched) tensor with shape: ⁠(..., target_height, target_width, channels)⁠, or ⁠(..., channels, target_height, target_width)⁠, in "channels_first" format.

Note: This layer is safe to use inside a tf.data pipeline (independently of which backend you're using).

See Also

• https://keras.io/api/layers/preprocessing_layers/image_preprocessing/resizing#resizing-class

Other image preprocessing layers:
layer_center_crop()
layer_rescaling()

Other preprocessing layers:
layer_category_encoding()
layer_center_crop()
layer_discretization()
layer_feature_space()
layer_hashed_crossing()
layer_hashing()
layer_integer_lookup()
layer_mel_spectrogram()
layer_normalization()
layer_random_brightness()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_rotation()
layer_random_translation()
layer_random_zoom()
layer_rescaling()
layer_string_lookup()
layer_text_vectorization()

Other layers:
Layer()
layer_activation()
layer_activation_elu()
layer_activation_leaky_relu()
layer_activation_parametric_relu()
layer_activation_relu()
layer_activation_softmax()
layer_activity_regularization()
layer_add()
layer_additive_attention()
layer_alpha_dropout()
layer_attention()
layer_average()
layer_average_pooling_1d()
layer_average_pooling_2d()
layer_average_pooling_3d()
layer_batch_normalization()
layer_bidirectional()
layer_category_encoding()
layer_center_crop()
layer_concatenate()
layer_conv_1d()
layer_conv_1d_transpose()
layer_conv_2d()
layer_conv_2d_transpose()
layer_conv_3d()
layer_conv_3d_transpose()
layer_conv_lstm_1d()
layer_conv_lstm_2d()
layer_conv_lstm_3d()
layer_cropping_1d()
layer_cropping_2d()
layer_cropping_3d()
layer_dense()
layer_depthwise_conv_1d()
layer_depthwise_conv_2d()
layer_discretization()
layer_dot()
layer_dropout()
layer_einsum_dense()
layer_embedding()
layer_feature_space()
layer_flatten()
layer_flax_module_wrapper()
layer_gaussian_dropout()
layer_gaussian_noise()
layer_global_average_pooling_1d()
layer_global_average_pooling_2d()
layer_global_average_pooling_3d()
layer_global_max_pooling_1d()
layer_global_max_pooling_2d()
layer_global_max_pooling_3d()
layer_group_normalization()
layer_group_query_attention()
layer_gru()
layer_hashed_crossing()
layer_hashing()
layer_identity()
layer_integer_lookup()
layer_jax_model_wrapper()
layer_lambda()
layer_layer_normalization()
layer_lstm()
layer_masking()
layer_max_pooling_1d()
layer_max_pooling_2d()
layer_max_pooling_3d()
layer_maximum()
layer_mel_spectrogram()
layer_minimum()
layer_multi_head_attention()
layer_multiply()
layer_normalization()
layer_permute()
layer_random_brightness()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_rotation()
layer_random_translation()
layer_random_zoom()
layer_repeat_vector()
layer_rescaling()
layer_reshape()
layer_rnn()
layer_separable_conv_1d()
layer_separable_conv_2d()
layer_simple_rnn()
layer_spatial_dropout_1d()
layer_spatial_dropout_2d()
layer_spatial_dropout_3d()
layer_spectral_normalization()
layer_string_lookup()
layer_subtract()
layer_text_vectorization()
layer_tfsm()
layer_time_distributed()
layer_torch_module_wrapper()
layer_unit_normalization()
layer_upsampling_1d()
layer_upsampling_2d()
layer_upsampling_3d()
layer_zero_padding_1d()
layer_zero_padding_2d()
layer_zero_padding_3d()
rnn_cell_gru()
rnn_cell_lstm()
rnn_cell_simple()
rnn_cells_stack()