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The Basics
Get Started
Setting up your API key
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Learn the basics
Transpiling Functions from PyTorch to TensorFlow
Transpiling Models from PyTorch to TensorFlow
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Examples and Demos
Training PyTorch ResNet in your TensorFlow Projects
Background
Motivation
ML Explosion
Why Transpile?
Related Work
Comparing Ivy with ONNX
Graph Tracers
Frameworks
Contributors
Design
Building Blocks
Ivy as a Transpiler
Contributing
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Exception Handling
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Building the Docs Pipeline
Fix Failing Tests:
Glossary
FAQ
API Reference
One liners
ivy.trace_graph()
ivy.transpile()
Functions
Activations
gelu
hardswish
leaky_relu
log_softmax
mish
relu
sigmoid
softmax
softplus
softsign
Constants
Control flow ops
cmp_is
cmp_isnot
for_loop
if_else
try_except
while_loop
Creation
arange
array
asarray
complex
copy_array
empty
empty_like
eye
from_dlpack
frombuffer
full
full_like
linspace
logspace
meshgrid
native_array
one_hot
ones
ones_like
to_dlpack
tril
triu
triu_indices
zeros
zeros_like
Data type
as_ivy_dtype
as_native_dtype
astype
broadcast_arrays
broadcast_to
can_cast
check_float
closest_valid_dtype
default_complex_dtype
default_dtype
default_float_dtype
default_int_dtype
default_uint_dtype
dtype
dtype_bits
finfo
function_supported_dtypes
function_unsupported_dtypes
iinfo
infer_default_dtype
invalid_dtype
is_bool_dtype
is_complex_dtype
is_float_dtype
is_hashable_dtype
is_int_dtype
is_native_dtype
is_uint_dtype
promote_types
promote_types_of_inputs
result_type
set_default_complex_dtype
set_default_dtype
set_default_float_dtype
set_default_int_dtype
set_default_uint_dtype
type_promote_arrays
unset_default_complex_dtype
unset_default_dtype
unset_default_float_dtype
unset_default_int_dtype
unset_default_uint_dtype
valid_dtype
Device
as_ivy_dev
as_native_dev
clear_cached_mem_on_dev
default_device
dev
dev_util
function_supported_devices
function_unsupported_devices
get_all_ivy_arrays_on_dev
gpu_is_available
handle_soft_device_variable
num_cpu_cores
num_gpus
num_ivy_arrays_on_dev
percent_used_mem_on_dev
print_all_ivy_arrays_on_dev
set_default_device
set_soft_device_mode
set_split_factor
split_factor
split_func_call
to_device
total_mem_on_dev
tpu_is_available
unset_default_device
unset_soft_device_mode
used_mem_on_dev
Elementwise
abs
acos
acosh
add
angle
asin
asinh
atan
atan2
atanh
bitwise_and
bitwise_invert
bitwise_left_shift
bitwise_or
bitwise_right_shift
bitwise_xor
ceil
cos
cosh
deg2rad
divide
equal
erf
exp
exp2
expm1
floor
floor_divide
fmin
fmod
gcd
greater
greater_equal
imag
isfinite
isinf
isnan
isreal
lcm
less
less_equal
log
log10
log1p
log2
logaddexp
logaddexp2
logical_and
logical_not
logical_or
logical_xor
maximum
minimum
multiply
nan_to_num
negative
not_equal
positive
pow
rad2deg
real
reciprocal
remainder
round
sign
sin
sinh
sqrt
square
subtract
tan
tanh
trapz
trunc
trunc_divide
Experimental
Activations
celu
elu
hardshrink
hardsilu
hardtanh
logit
logsigmoid
prelu
relu6
scaled_tanh
selu
silu
softshrink
stanh
tanhshrink
threshold
thresholded_relu
Constants
Creation
blackman_window
eye_like
hamming_window
hann_window
indices
kaiser_bessel_derived_window
kaiser_window
mel_weight_matrix
ndenumerate
ndindex
polyval
random_cp
random_parafac2
random_tr
random_tt
random_tucker
tril_indices
trilu
unsorted_segment_mean
unsorted_segment_min
unsorted_segment_sum
vorbis_window
Data type
Device
Elementwise
allclose
amax
amin
binarizer
conj
copysign
count_nonzero
diff
digamma
erfc
erfinv
fix
float_power
fmax
frexp
gradient
hypot
isclose
ldexp
lerp
lgamma
modf
nansum
nextafter
signbit
sinc
sparsify_tensor
xlogy
zeta
General
reduce
Gradients
bind_custom_gradient_function
jvp
vjp
Layers
adaptive_avg_pool1d
adaptive_avg_pool2d
adaptive_max_pool2d
adaptive_max_pool3d
area_interpolate
avg_pool1d
avg_pool2d
avg_pool3d
dct
dft
dropout1d
dropout2d
dropout3d
embedding
fft
fft2
generate_einsum_equation
get_interpolate_kernel
idct
ifft
ifftn
interp
interpolate
max_pool1d
max_pool2d
max_pool3d
max_unpool1d
nearest_interpolate
pool
reduce_window
rfft
rfftn
rnn
sliding_window
stft
Linear algebra
adjoint
batched_outer
cond
diagflat
dot
eig
eigh_tridiagonal
eigvals
general_inner_product
higher_order_moment
initialize_tucker
khatri_rao
kron
kronecker
lu_factor
lu_solve
make_svd_non_negative
matrix_exp
mode_dot
multi_dot
multi_mode_dot
partial_tucker
solve_triangular
svd_flip
tensor_train
truncated_svd
tt_matrix_to_tensor
tucker
Losses
hinge_embedding_loss
huber_loss
kl_div
l1_loss
log_poisson_loss
poisson_nll_loss
smooth_l1_loss
soft_margin_loss
Manipulation
as_strided
associative_scan
atleast_1d
atleast_2d
atleast_3d
broadcast_shapes
check_scalar
choose
column_stack
concat_from_sequence
dsplit
dstack
expand
fill_diagonal
flatten
fliplr
flipud
fold
heaviside
hsplit
hstack
i0
matricize
moveaxis
pad
pad_sequence
partial_fold
partial_tensor_to_vec
partial_unfold
partial_vec_to_tensor
put_along_axis
rot90
soft_thresholding
take
take_along_axis
top_k
trim_zeros
unflatten
unfold
unique_consecutive
vsplit
vstack
Meta
Nest
Norms
batch_norm
group_norm
instance_norm
l1_normalize
l2_normalize
local_response_norm
lp_normalize
Random
bernoulli
beta
dirichlet
gamma
poisson
Searching
unravel_index
Set
Sorting
invert_permutation
lexsort
Sparse array
is_ivy_sparse_array
is_native_sparse_array
native_sparse_array
native_sparse_array_to_indices_values_and_shape
Statistical
bincount
corrcoef
cov
cummax
cummin
histogram
igamma
median
nanmean
nanmedian
nanmin
nanprod
quantile
Utility
optional_get_element
General
all_equal
arg_info
arg_names
array_equal
assert_supports_inplace
cache_fn
clip_matrix_norm
clip_vector_norm
container_types
current_backend_str
default
einops_rearrange
einops_reduce
einops_repeat
exists
fourier_encode
function_supported_devices_and_dtypes
function_unsupported_devices_and_dtypes
gather
gather_nd
get_all_arrays_in_memory
get_item
get_num_dims
get_referrers_recursive
has_nans
inplace_arrays_supported
inplace_decrement
inplace_increment
inplace_update
inplace_variables_supported
is_array
is_ivy_array
is_ivy_container
is_ivy_nested_array
is_native_array
isin
isscalar
itemsize
match_kwargs
multiprocessing
num_arrays_in_memory
print_all_arrays_in_memory
scatter_flat
scatter_nd
set_array_mode
set_exception_trace_mode
set_inplace_mode
set_item
set_min_base
set_min_denominator
set_nestable_mode
set_precise_mode
set_queue_timeout
set_shape_array_mode
set_show_func_wrapper_trace_mode
set_tmp_dir
shape
size
stable_divide
stable_pow
strides
supports_inplace_updates
to_ivy_shape
to_list
to_native_shape
to_numpy
to_scalar
try_else_none
unset_array_mode
unset_exception_trace_mode
unset_inplace_mode
unset_min_base
unset_min_denominator
unset_nestable_mode
unset_precise_mode
unset_queue_timeout
unset_shape_array_mode
unset_show_func_wrapper_trace_mode
unset_tmp_dir
value_is_nan
vmap
Gradients
adam_step
adam_update
execute_with_gradients
grad
gradient_descent_update
jac
lamb_update
lars_update
optimizer_update
requires_gradient
stop_gradient
value_and_grad
Layers
conv
conv1d
conv1d_transpose
conv2d
conv2d_transpose
conv3d
conv3d_transpose
conv_general_dilated
conv_general_transpose
depthwise_conv2d
dropout
linear
lstm
lstm_update
multi_head_attention
nms
roi_align
scaled_dot_product_attention
Linear algebra
cholesky
cross
det
diag
diagonal
eig
eigh
eigvalsh
inner
inv
matmul
matrix_norm
matrix_power
matrix_rank
matrix_transpose
outer
pinv
qr
slogdet
solve
svd
svdvals
tensordot
tensorsolve
trace
vander
vecdot
vector_norm
vector_to_skew_symmetric_matrix
Losses
binary_cross_entropy
cross_entropy
sparse_cross_entropy
ssim_loss
wasserstein_loss_discriminator
wasserstein_loss_generator
Manipulation
clip
concat
constant_pad
expand_dims
flip
permute_dims
repeat
reshape
roll
split
squeeze
stack
swapaxes
tile
unstack
zero_pad
Meta
fomaml_step
maml_step
reptile_step
Nest
all_nested_indices
copy_nest
duplicate_array_index_chains
index_nest
insert_into_nest_at_index
insert_into_nest_at_indices
map
map_nest_at_index
map_nest_at_indices
multi_index_nest
nested_any
nested_argwhere
nested_map
nested_multi_map
prune_empty
prune_nest_at_index
prune_nest_at_indices
set_nest_at_index
set_nest_at_indices
Norms
layer_norm
Random
multinomial
randint
random_normal
random_uniform
seed
shuffle
Searching
argmax
argmin
argwhere
nonzero
where
Set
unique_all
unique_counts
unique_inverse
unique_values
Sorting
argsort
msort
searchsorted
sort
Statistical
cumprod
cumsum
einsum
max
mean
min
prod
std
sum
var
Utility
all
any
load
save
Data classes
Array
Activations
Conversions
Creation
Data type
Device
Elementwise
Experimental
General
Gradients
Image
Layers
Linear algebra
Losses
Manipulation
Norms
Random
Searching
Set
Sorting
Statistical
Utility
Wrapping
Container
Activations
Base
Conversions
Creation
Data type
Device
Elementwise
Experimental
General
Gradients
Image
Layers
Linear algebra
Losses
Manipulation
Norms
Random
Searching
Set
Sorting
Statistical
Utility
Wrapping
Factorized tensor
Base
Cp tensor
Parafac2 tensor
Tr tensor
Tt tensor
Tucker tensor
Nested array
Base
Elementwise
Framework classes
Activations
Converters
Helpers
Initializers
Layers
Losses
Module
Norms
Optimizers
Sequential
Utilities
Utils
Assertions
Backend
Ast helpers
Handler
Sub backend handler
Binaries
Decorator utils
Dynamic import
Einsum parser
Einsum path helpers
Exceptions
Inspection
Logging
Profiler
Testing
Assertions
Available frameworks
Function testing
Globals
Hypothesis helpers
Array helpers
Dtype helpers
General helpers
Number helpers
Multiprocessing
Pipeline helper
Structs
Test parameter flags
Testing helpers
Functions
Utility
load
load
#
ivy.
load
(
filepath
,
format
=
None
,
type
=
'module'
)
[source]
#
On this page
load()