Tensor Transformations

Note: Functions taking Tensor arguments can also take anything accepted by tf.convert_to_tensor.

Casting

TensorFlow provides several operations that you can use to cast tensor data types in your graph.

• tf.string_to_number
• tf.to_double
• tf.to_float
• tf.to_bfloat16
• tf.to_int32
• tf.to_int64
• tf.cast
• tf.bitcast
• tf.saturate_cast

Shapes and Shaping

TensorFlow provides several operations that you can use to determine the shape of a tensor and change the shape of a tensor.

• tf.broadcast_dynamic_shape
• tf.broadcast_static_shape
• tf.shape
• tf.shape_n
• tf.size
• tf.rank
• tf.reshape
• tf.squeeze
• tf.expand_dims
• tf.meshgrid

Slicing and Joining

TensorFlow provides several operations to slice or extract parts of a tensor, or join multiple tensors together.

• tf.slice
• tf.strided_slice
• tf.split
• tf.tile
• tf.pad
• tf.concat
• tf.stack
• tf.parallel_stack
• tf.unstack
• tf.reverse_sequence
• tf.reverse
• tf.reverse_v2
• tf.transpose
• tf.extract_image_patches
• tf.space_to_batch_nd
• tf.space_to_batch
• tf.required_space_to_batch_paddings
• tf.batch_to_space_nd
• tf.batch_to_space
• tf.space_to_depth
• tf.depth_to_space
• tf.gather
• tf.gather_nd
• tf.unique_with_counts
• tf.scatter_nd
• tf.dynamic_partition
• tf.dynamic_stitch
• tf.boolean_mask
• tf.one_hot
• tf.sequence_mask
• tf.dequantize
• tf.quantize_v2
• tf.quantized_concat
• tf.setdiff1d

Fake quantization

Operations used to help train for better quantization accuracy.

• tf.fake_quant_with_min_max_args
• tf.fake_quant_with_min_max_args_gradient
• tf.fake_quant_with_min_max_vars
• tf.fake_quant_with_min_max_vars_gradient
• tf.fake_quant_with_min_max_vars_per_channel
• tf.fake_quant_with_min_max_vars_per_channel_gradient