library(tensorflow)
# --- construct ---
shape() # tf.TensorShape() # scalar
shape(NULL) # tf.TensorShape([None]) # 1-D array of unknown length
shape(NA) # tf.TensorShape([None]) # 1-D array of unknown length, NA is a synonym for NULL
shape(dims = NULL) # TensorShape(None) # Unknown rank, unknown size
shape(3, 4) # TensorShape([3, 4]) # 2-D array (matrix) with 3 rows, 4 columns
shape(NA, 4) # TensorShape([None, 4]) # 2-D array (matrix) with unknown rows, 4 columns
shape(dims = c(NA, 4)) # TensorShape([None, 4]) # same as above; bypass ... and pass dims directly
# --- inspect ---
length(shape(dims = NULL)) # NA_integer_
length(shape(1,2,3,NA)) # 4L
# ---convert ---
x <- shape(dims = list(3L, 5L))
as.list(x) # list(3L, 5L)
as.integer(x) # c(3L, 5L)
as.numeric(x) # c(3, 5)
as.double(x) # c(3, 5) # alias for as.numeric
as_tensor(x) # tf.Tensor([3 5], shape=(2,), dtype=int32)
# convert partially undefined shapes
x <- shape(NA, 3)
as.list(x) # list(NULL, 3L)
as.integer(x) # c(NA, 3L)
as_tensor(x) # tf.Tensor([-1 3], shape=(2,), dtype=int32) # unspecified dims default is -1
# as_tensor() converts undefined dimensions to -1, which is useful for
# tf functions that only accept tensors for shapes, e.g,
tf$reshape(tf$zeros(shape(8)),
as_tensor(shape(NA, 4)))
# tf.Tensor([[0. 0. 0. 0.]
# [0. 0. 0. 0.]], shape=(2, 4), dtype=float32)
# converting fully unknown shapes raises an error
try(as.list(shape(dims = NULL))) # ValueError: as_list() is not defined on an unknown TensorShape.
# test for rank first if this a concern:
as.list_or_null <- function(x) if(is.na(length(x))) NULL else as.list(x)
as.list_or_null(shape(dims = NULL))
# --- compare ---
# Fully known shapes return TRUE if and only if each element is equal
shape(3, 4) == shape(3, 4) # TRUE
shape(3, 4) == shape(4, 4) # FALSE
# two unknown dimensions are treated as equal
shape(NA, 4) == shape(NA, 4) # TRUE
shape(NA, 4) == shape(3, 4) # FALSE
# Two unknown shapes, return TRUE
shape(dims = NULL) == shape(dims = NULL) # TRUE
# Comparing an unknown shape to a partially or fully defined shape returns FALSE
shape(dims = NULL) == shape(NULL) # FALSE
shape(dims = NULL) == shape(4) # FALSE
values of length greater than one supplied to `...` are automatically flattened
shape(1, c(2, 3), 4) # shape(1, 2, 3, 4)
shape(1, shape(2, 3), 4) # shape(1, 2, 3, 4)
shape(1, as_tensor(2, 3), 4) # shape(1, 2, 3, 4)
# --- extract or replace ---
# regular R-list semantics for `[`, `[[`, `[<-`, `[[<-`
x <- shape(1, 2, 3)
x[1] # TensorShape([1])
x[[1]] # 1L
x[2:3] # TensorShape([2, 3])
x[-1] # TensorShape([2, 3])
x[1] <- 11 ; x # TensorShape([11, 2, 3])
x[1] <- shape(11) ; x # TensorShape([11, 2, 3])
x[1] <- list(11) ; x # TensorShape([11, 2, 3])
x[[1]] <- 22 ; x # TensorShape([22, 2, 3])
x[1:2] <- c(NA, 99) ; x # TensorShape([None, 99, 3])
x[1:2] <- shape(33, 44) ; x # TensorShape([33, 44, 3])
# --- concatenate ---
c(shape(1), shape(2, 3), shape(4, NA)) # TensorShape([1, 2, 3, 4, None])
# --- merge ---
merge(shape(NA, 2),
shape(1 , 2)) # TensorShape([1, 2])
try(merge(shape(2, 2),
shape(1, 2))) # ValueError: Shapes (2, 2) and (1, 2) are not compatible
rm(x) # cleanup shape
Create a tf.TensorShape object
Description
Create a tf.TensorShape object
Usage
shape(..., dims = list(...)) Arguments
| Arguments | Description |
|---|---|
| … | Tensor dimensions as integers or NULL for an unknown dimensions. NA and -1 are synonyms for NULL. |
| dims | Tensor dimensions as a vector. |