Don’t Repeat Yourself
Mix.install([
{:axon_onnx, git: "https://github.com/mortont/axon_onnx.git"},
{:axon, "~> 0.5"},
{:nx, "~> 0.5"},
{:exla, "~> 0.5"},
{:stb_image, "~> 0.6"},
{:kino, "~> 0.8"}
])
Cats and Dogs Again
Application.put_env(
:exla,
:clients,
cuda: [
platforms: :cuda,
lazy_transformers: :never
]
)
Nx.global_default_backend(EXLA.Backend)
Nx.Defn.default_options(compiler: EXLA)
[]
defmodule CatsAndDogs do
def pipeline(paths, batch_size, target_height, target_width) do
paths
|> Enum.shuffle()
|> Task.async_stream(&parse_image/1)
|> Stream.filter(fn
{:ok, {%StbImage{}, _}} -> true
_ -> false
end)
|> Stream.map(&to_tensors(&1, target_height, target_width))
|> Stream.chunk_every(batch_size, batch_size, :discard)
|> Stream.map(fn chunks ->
{img_chunk, label_chunk} = Enum.unzip(chunks)
{Nx.stack(img_chunk), Nx.stack(label_chunk)}
end)
end
def pipeline_with_augmentations(
paths,
batch_size,
target_height,
target_width
) do
paths
|> Enum.shuffle()
|> Task.async_stream(&parse_image/1)
|> Stream.filter(fn
{:ok, {%StbImage{}, _}} -> true
_ -> false
end)
|> Stream.map(&to_tensors(&1, target_height, target_width))
|> Stream.map(&random_flip(&1, :height))
|> Stream.map(&random_flip(&1, :width))
|> Stream.chunk_every(batch_size, batch_size, :discard)
|> Stream.map(fn chunks ->
{img_chunk, label_chunk} = Enum.unzip(chunks)
{Nx.stack(img_chunk), Nx.stack(label_chunk)}
end)
end
defp parse_image(path) do
label = if String.contains?(path, "cat"), do: 0, else: 1
case StbImage.read_file(path) do
{:ok, img} -> {img, label}
_error -> :error
end
end
defp to_tensors({:ok, {img, label}}, target_height, target_width) do
img_tensor =
img
|> StbImage.resize(target_height, target_width)
|> StbImage.to_nx()
|> Nx.divide(255)
|> Nx.transpose(axes: [:channels, :height, :width])
label_tensor = Nx.tensor([label])
{img_tensor, label_tensor}
end
defp random_flip({image, label}, axis) do
if :rand.uniform() < 0.5 do
{Nx.reverse(image, axes: [axis]), label}
else
{image, label}
end
end
end
{:module, CatsAndDogs, <<70, 79, 82, 49, 0, 0, 20, ...>>, {:random_flip, 2}}
{test_paths, train_paths} =
Path.wildcard("train/*.jpg")
|> Enum.shuffle()
|> Enum.split(1000)
{test_paths, val_paths} = Enum.split(test_paths, 750)
batch_size = 32
target_height = 160
target_width = 160
train_pipeline =
CatsAndDogs.pipeline_with_augmentations(
train_paths,
batch_size,
target_height,
target_width
)
val_pipeline =
CatsAndDogs.pipeline(
val_paths,
batch_size,
target_height,
target_width
)
test_pipeline =
CatsAndDogs.pipeline(
test_paths,
batch_size,
target_height,
target_width
)
Enum.take(train_pipeline, 1)
18:00:37.908 [debug] No platform configuration specified, falling back to host platform
Available platforms are: [:host, :interpreter]
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f32[32][channels: 3][height: 160][width: 160]
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{cnn_base, cnn_base_params} = AxonOnnx.import("mobilenetv2-7.onnx", batch_size: batch_size)
...
>
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>
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[0.09069628268480301, 0.011556080542504787, -0.044405367225408554],
[0.1974998563528061, -0.05144780874252319, -0.15406860411167145],
[0.03294772282242775, -0.08055609464645386, -0.10401368886232376]
]
],
[
[
[0.004530614707618952, -0.016135336831212044, 0.010837683454155922],
[-0.013911036774516106, -0.17941752076148987, 0.0742872878909111],
[0.03701423481106758, 0.17489252984523773, 0.049539677798748016]
]
],
[
[
[-0.03442509472370148, -0.04338786378502846, -8.070958429016173e-4],
[-0.06931383162736893, ...],
...
]
],
...
]
>
},
"mobilenetv20_features_linearbottleneck1_conv0_fwd" => %{
"kernel" => #Nx.Tensor<
f32[96][16][1][1]
EXLA.Backend
[
[
[
[-0.26627230644226074]
],
[
[0.1223592683672905]
],
[
[-0.17675793170928955]
],
[
[-0.14175795018672943]
],
[
[-0.04847275838255882]
],
[
[0.02572016417980194]
],
[
[0.18247880041599274]
],
[
[-0.22272470593452454]
],
[
[-0.0375950001180172]
],
[
[-0.12036115676164627]
],
[
[-0.42806223034858704]
],
[
[0.2049194574356079]
],
[
[0.025416148826479912]
],
[
[-0.20868347585201263]
],
[
[-0.00869692862033844]
],
[
[0.10014521330595016]
]
],
[
[
[0.1901395469903946]
],
[
[-0.15854698419570923]
],
[
[-0.05858785659074783]
],
[
[-0.016011832281947136]
],
[
[-0.07135247439146042]
],
...
],
...
]
>
},
"mobilenetv20_features_linearbottleneck2_batchnorm2_fwd" => %{
"beta" => #Nx.Tensor<
f32[24]
EXLA.Backend
[-6.408389890566468e-5, -1.0128262510988861e-4, -4.3479518353706226e-5, -1.589354214956984e-4, -7.557999197160825e-5, -2.3196584152174182e-5, -2.216532493548584e-6, 9.525781933916733e-6, -8.600373985245824e-5, -2.3008385323919356e-4, -2.3174902889877558e-4, -2.012878976529464e-4, -9.838923870120198e-5, -1.22729703434743e-4, -9.646145917940885e-5, -2.797471643134486e-5, -1.039204653352499e-4, -1.2249314750079066e-4, -8.16335596027784e-5, -1.541685633128509e-4, ...]
>,
"gamma" => #Nx.Tensor<
f32[24]
EXLA.Backend
[0.5065965056419373, 0.44786974787712097, 0.5534567832946777, 0.3781253695487976, 0.44674113392829895, 0.28593751788139343, 0.5029821395874023, 0.19650447368621826, 0.3918337821960449, 0.33050450682640076, 0.31709131598472595, 0.1402808576822281, 0.4198921024799347, 0.02611447498202324, 0.5337468981742859, 0.49816271662712097, 0.32386210560798645, 0.40961867570877075, 0.3363959491252899, ...]
>,
"mean" => #Nx.Tensor<
f32[24]
EXLA.Backend
[-0.49676281213760376, -0.5412184000015259, 0.2777172029018402, 0.30402812361717224, -0.038367077708244324, 0.19269412755966187, -0.20861922204494476, 0.3330950438976288, -0.35258007049560547, -0.0413212850689888, -0.44278526306152344, 0.43556883931159973, -0.007557356264442205, -0.049789782613515854, -0.1810888946056366, -0.1666330248117447, 0.15477608144283295, 0.1351827085018158, ...]
>,
"var" => #Nx.Tensor<
f32[24]
EXLA.Backend
[0.0421639047563076, 0.03801903501152992, 0.04582618921995163, 0.028266852721571922, 0.025787167251110077, 0.026369646191596985, 0.03499341756105423, 0.02089923992753029, 0.02381058968603611, 0.04512317106127739, 0.018083490431308746, 0.02251904457807541, 0.023619014769792557, 0.005619029048830271, 0.04942920058965683, 0.02654757723212242, 0.02269740216434002, ...]
>
},
"mobilenetv20_features_linearbottleneck8_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[384][1][3][3]
EXLA.Backend
[
[
[
[-0.0020327651873230934, -0.03829871118068695, -0.013563335873186588],
[-0.07023981958627701, -0.0530773401260376, -0.05999664217233658],
[-0.02172546088695526, -0.05160605534911156, -0.02340804785490036]
]
],
[
[
[-0.06414368003606796, -0.09891236573457718, 0.09147416055202484],
[-0.035863131284713745, -0.026917265728116035, 0.07736452668905258],
[0.08244364708662033, 0.10789858549833298, -0.16784991323947906]
]
],
[
[
[0.13550637662410736, ...],
...
]
],
...
]
>
},
"mobilenetv20_features_linearbottleneck11_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[576][1][3][3]
EXLA.Backend
[
[
[
[0.0561985969543457, -0.08534234762191772, -0.0728735700249672],
[0.20427849888801575, -0.03384411707520485, -0.18976956605911255],
[0.10962294042110443, 0.006988390814512968, -0.03254517540335655]
]
],
[
[
[-0.007281317375600338, -0.10424316674470901, -0.0011419359361752868],
[-0.0659799799323082, -0.2147035449743271, -0.10787361860275269],
[0.004407626111060381, 0.44921964406967163, 0.06901120394468307]
]
],
...
]
>
},
"mobilenetv20_features_linearbottleneck14_conv0_fwd" => %{
"kernel" => #Nx.Tensor<
f32[960][160][1][1]
EXLA.Backend
[
[
[
[0.037727002054452896]
],
[
[-0.07920914888381958]
],
[
[-0.0031863711774349213]
],
[
[-0.017933860421180725]
],
[
[-0.012083934620022774]
],
[
[-0.03305855393409729]
],
[
[0.010104630142450333]
],
[
[-0.01614421419799328]
],
[
[0.0415346585214138]
],
[
[-0.05910014733672142]
],
[
[3.380556881893426e-4]
],
[
[0.02735193446278572]
],
[
[-0.0788668617606163]
],
[
[-0.028101010248064995]
],
[
[-0.01531447283923626]
],
[
[-0.04052167013287544]
],
[
[0.0598626434803009]
],
...
],
...
]
>
},
"mobilenetv20_features_linearbottleneck15_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[960][1][3][3]
EXLA.Backend
[
[
[
[0.008491688407957554, 0.01882096938788891, -0.001548346015624702],
[0.009763970039784908, 0.2359771430492401, 0.03868749365210533],
[-0.05791253224015236, 0.028106149286031723, -0.07315897196531296]
]
],
[
[
[-0.01212818082422018, -0.16292721033096313, -0.033156707882881165],
[0.005929793696850538, 0.2561517059803009, 0.014003193937242031],
[-0.04701530933380127, ...]
]
],
...
]
>
},
"mobilenetv20_features_linearbottleneck7_conv2_fwd" => %{
"kernel" => #Nx.Tensor<
f32[64][384][1][1]
EXLA.Backend
[
[
[
[-0.03408721089363098]
],
[
[-0.017362290993332863]
],
[
[0.01995295286178589]
],
[
[-0.051705457270145416]
],
[
[0.0689198449254036]
],
[
[0.08685684949159622]
],
[
[-0.09933704882860184]
],
[
[0.07178854197263718]
],
[
[0.044526729732751846]
],
[
[-0.018590005114674568]
],
[
[0.013196618296205997]
],
[
[-0.0795169249176979]
],
[
[-0.10800802707672119]
],
[
[-0.042785294353961945]
],
[
[0.028526680544018745]
],
...
],
...
]
>
},
"mobilenetv20_features_linearbottleneck4_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[192][1][3][3]
EXLA.Backend
[
[
[
[-0.031621839851140976, 0.18290314078330994, 0.040609210729599],
[-0.19228996336460114, 0.031516727060079575, 0.09460115432739258],
[-0.026457147672772408, 0.05563095211982727, 0.0026122310664504766]
]
],
[
[
[-0.00655049504712224, -0.004564541857689619, -0.01380461547523737],
[-0.002132135210558772, -0.17951297760009766, ...],
...
]
],
...
]
>
},
"mobilenetv20_features_linearbottleneck16_conv0_fwd" => %{
"kernel" => #Nx.Tensor<
f32[960][160][1][1]
EXLA.Backend
[
[
[
[0.04804954305291176]
],
[
[-0.0361880324780941]
],
[
[0.09869731217622757]
],
[
[0.03218498453497887]
],
[
[-0.05613848939538002]
],
[
[0.03235843405127525]
],
[
[0.035072293132543564]
],
[
[0.04535597562789917]
],
[
[0.031406670808792114]
],
[
[0.003971233498305082]
],
[
[0.016072509810328484]
],
[
[-0.041956160217523575]
],
[
[-0.018940964713692665]
],
...
],
...
]
>
},
"mobilenetv20_features_linearbottleneck10_batchnorm2_fwd" => %{
"beta" => #Nx.Tensor<
f32[96]
EXLA.Backend
[2.1335902999908285e-7, 3.535939754328865e-7, 5.073925990473072e-7, 5.08670098042785e-7, 3.3390904263796983e-7, 4.946827516505437e-7, 4.795706445293035e-7, 3.743390948329761e-7, 5.272184466775798e-7, 5.93354513966915e-7, 2.1419212714590685e-7, 6.500247309304541e-7, ...]
>,
"gamma" => #Nx.Tensor<
f32[96]
EXLA.Backend
[0.2979993522167206, 0.3140060007572174, 0.2445983588695526, 0.2865447998046875, 0.35691216588020325, 0.3592449426651001, 0.27936986088752747, 0.25922414660453796, 0.2633695900440216, 0.2901868522167206, 0.2989289462566376, ...]
>,
"mean" => #Nx.Tensor<
f32[96]
EXLA.Backend
[-0.02239207923412323, -0.5143733024597168, 0.05194510519504547, -0.3102657198905945, -0.03491494804620743, 0.21602749824523926, 0.09913935512304306, -0.2323945015668869, -0.47182056307792664, 0.019973447546362877, ...]
>,
"var" => #Nx.Tensor<
f32[96]
EXLA.Backend
[0.10519497841596603, 0.09767355769872665, 0.06126777082681656, 0.09396423399448395, 0.17368470132350922, 0.14595730602741241, 0.0813060998916626, 0.07312573492527008, 0.059541765600442886, ...]
>
},
"mobilenetv20_features_linearbottleneck14_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[960][1][3][3]
EXLA.Backend
[
[
[
[0.03072543255984783, 0.024941517040133476, -0.044987138360738754],
[0.2105334848165512, -0.09797866642475128, -0.12514935433864594],
[0.049303051084280014, -0.004124515224248171, -0.0421803742647171]
]
],
[
[
[0.027953263372182846, -0.0323273129761219, ...],
...
]
],
...
]
>
},
"mobilenetv20_features_linearbottleneck12_conv0_fwd" => %{
"kernel" => #Nx.Tensor<
f32[576][96][1][1]
EXLA.Backend
[
[
[
[-0.020311787724494934]
],
[
[0.00342985475435853]
],
[
[-0.04346230626106262]
],
[
[-0.016626032069325447]
],
[
[-0.012114123441278934]
],
[
[-0.012785198166966438]
],
[
[0.056096695363521576]
],
[
[0.09021388739347458]
],
[
[0.16139942407608032]
],
[
[0.06402886658906937]
],
...
],
...
]
>
},
"mobilenetv20_features_linearbottleneck0_batchnorm1_fwd" => %{
"beta" => #Nx.Tensor<
f32[32]
EXLA.Backend
[1.037217140197754, 1.2865849733352661, 0.7645959258079529, 0.6374938488006592, 1.069508671760559, 0.6837575435638428, 0.9550023674964905, 0.9839419722557068, 1.1442939043045044, ...]
>,
"gamma" => #Nx.Tensor<
f32[32]
EXLA.Backend
[0.2217620462179184, 0.11614178121089935, 0.1586529165506363, 0.15199987590312958, 0.23966756463050842, 0.17114117741584778, 0.18509696424007416, 0.18513236939907074, ...]
>,
"mean" => #Nx.Tensor<
f32[32]
EXLA.Backend
[0.6259090900421143, -0.08340238034725189, 1.0731103420257568, 2.4088242053985596, -5.609257698059082, 3.4169440269470215, 7.146847724914551, ...]
>,
"var" => #Nx.Tensor<
f32[32]
EXLA.Backend
[0.22989171743392944, 0.007035647518932819, 0.011941581964492798, 0.3442769944667816, 2.0481817722320557, 0.31869152188301086, ...]
>
},
"mobilenetv20_features_linearbottleneck6_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[192][1][3][3]
EXLA.Backend
[
[
[
[0.014363789930939674, 0.13237811625003815, 0.09334706515073776],
[0.12919868528842926, -0.35225632786750793, -0.1466313749551773],
[0.06540388613939285, -0.08005677908658981, ...]
]
],
...
]
>
},
"mobilenetv20_features_linearbottleneck16_batchnorm2_fwd" => %{
"beta" => #Nx.Tensor<
f32[320]
EXLA.Backend
[-5.189255602999765e-7, -2.7314237627251714e-7, -2.1782213366350334e-7, 1.9330661871208576e-7, 2.6874428016299134e-8, -2.4903325623881756e-8, -2.696318688322208e-7, ...]
>,
"gamma" => #Nx.Tensor<
f32[320]
EXLA.Backend
[0.25196632742881775, 0.2486555427312851, 0.24830971658229828, 0.24681176245212555, 0.24941202998161316, 0.2485744059085846, ...]
>,
"mean" => #Nx.Tensor<
f32[320]
EXLA.Backend
[-0.13978023827075958, -0.05438295006752014, -0.14319643378257751, 0.1896054595708847, -0.13925714790821075, ...]
>,
"var" => #Nx.Tensor<
f32[320]
EXLA.Backend
[0.008434775285422802, 0.007849316112697124, 0.0076168072409927845, 0.007443031296133995, ...]
>
},
"mobilenetv20_features_linearbottleneck10_conv2_fwd" => %{
"kernel" => #Nx.Tensor<
f32[96][384][1][1]
EXLA.Backend
[
[
[
[0.01824713684618473]
],
[
[0.08710300177335739]
],
[
[-0.10191874951124191]
],
[
[-0.07710960507392883]
],
[
[-0.042343202978372574]
],
[
[-0.014866536483168602]
],
...
],
...
]
>
},
"mobilenetv20_features_linearbottleneck8_conv0_fwd" => %{
"kernel" => #Nx.Tensor<
f32[384][64][1][1]
EXLA.Backend
[
[
[
[0.015875784680247307]
],
[
[0.00680553587153554]
],
[
[0.09295158088207245]
],
[
[-0.05411294475197792]
],
[
[0.026204092428088188]
],
...
],
...
]
>
},
"mobilenetv20_features_conv0_fwd" => %{
"kernel" => #Nx.Tensor<
f32[32][3][3][3]
EXLA.Backend
[
[
[
[-0.11137320101261139, -0.22142910957336426, 0.10717468708753586],
[4.23799006966874e-4, ...],
...
],
...
],
...
]
>
},
"mobilenetv20_features_linearbottleneck11_batchnorm2_fwd" => %{
"beta" => #Nx.Tensor<
f32[96]
EXLA.Backend
[3.9526614159512974e-7, 5.761955890193349e-7, 7.01267651948001e-7, ...]
>,
"gamma" => #Nx.Tensor<
f32[96]
EXLA.Backend
[0.21182486414909363, 0.20167435705661774, ...]
>,
"mean" => #Nx.Tensor<
f32[96]
EXLA.Backend
[-0.039972804486751556, ...]
>,
"var" => #Nx.Tensor<
f32[96]
EXLA.Backend
[...]
>
},
"mobilenetv20_features_linearbottleneck6_conv2_fwd" => %{
"kernel" => #Nx.Tensor<
f32[64][192][1][1]
EXLA.Backend
[
[
[
[0.07365027070045471]
],
[
[-0.03652043268084526]
],
...
],
...
]
>
},
"mobilenetv20_features_linearbottleneck3_conv2_fwd" => %{
"kernel" => #Nx.Tensor<
f32[32][144][1][1]
EXLA.Backend
[
[
[
[0.02765008620917797]
],
...
],
...
]
>
},
"mobilenetv20_features_linearbottleneck4_conv0_fwd" => %{
"kernel" => #Nx.Tensor<
f32[192][32][1][1]
EXLA.Backend
[
...
]
>
},
"mobilenetv20_features_linearbottleneck10_conv0_fwd" => %{...},
...
}}
input_template = Nx.template({1, 3, target_height, target_width}, :f32)
Axon.Display.as_graph(cnn_base, input_template)
graph TD;
33["mobilenetv20_features_linearbottleneck3_conv0_fwd (:conv) {1, 144, 40, 40}"];
168["mobilenetv20_output_flatten0_reshape0 (:reshape) {1000}"];
117["mobilenetv20_features_linearbottleneck12_conv0_fwd (:conv) {1, 576, 10, 10}"];
12["mobilenetv20_features_linearbottleneck0_relu1_fwd (:relu) {1, 32, 80, 80}"];
157["mobilenetv20_features_linearbottleneck16_relu0_fwd (:relu) {1, 960, 5, 5}"];
132["mobilenetv20_features_linearbottleneck13_relu1_fwd (:relu) {1, 576, 5, 5}"];
73["mobilenetv20_features_linearbottleneck7_batchnorm1_fwd (:batch_norm) {1, 384, 20, 20}"];
44["mobilenetv20_features_linearbottleneck4_conv1_fwd (:conv) {1, 192, 20, 20}"];
124["mobilenetv20_features_linearbottleneck12_batchnorm2_fwd (:batch_norm) {1, 96, 10, 10}"];
23["mobilenetv20_features_linearbottleneck2_conv0_fwd (:conv) {1, 144, 40, 40}"];
29["mobilenetv20_features_linearbottleneck2_conv2_fwd (:conv) {1, 24, 40, 40}"];
47["mobilenetv20_features_linearbottleneck4_conv2_fwd (:conv) {1, 32, 20, 20}"];
89["mobilenetv20_features_linearbottleneck9_conv0_fwd (:conv) {1, 384, 20, 20}"];
61["mobilenetv20_features_linearbottleneck6_conv0_fwd (:conv) {1, 192, 20, 20}"];
30["mobilenetv20_features_linearbottleneck2_batchnorm2_fwd (:batch_norm) {1, 24, 40, 40}"];
43["mobilenetv20_features_linearbottleneck4_relu0_fwd (:relu) {1, 192, 20, 20}"];
163["mobilenetv20_features_conv1_fwd (:conv) {1, 1280, 5, 5}"];
39["mobilenetv20_features_linearbottleneck3_conv2_fwd (:conv) {1, 32, 20, 20}"];
131["mobilenetv20_features_linearbottleneck13_batchnorm1_fwd (:batch_norm) {1, 576, 5, 5}"];
45["mobilenetv20_features_linearbottleneck4_batchnorm1_fwd (:batch_norm) {1, 192, 20, 20}"];
48["mobilenetv20_features_linearbottleneck4_batchnorm2_fwd (:batch_norm) {1, 32, 20, 20}"];
145["mobilenetv20_features_linearbottleneck15_conv0_fwd (:conv) {1, 960, 5, 5}"];
57["mobilenetv20_features_linearbottleneck5_conv2_fwd (:conv) {1, 32, 20, 20}"];
143["container_8 (:container) {{1, 160, 5, 5}, {1, 160, 5, 5}}"];
113["mobilenetv20_features_linearbottleneck11_conv2_fwd (:conv) {1, 96, 10, 10}"];
26["mobilenetv20_features_linearbottleneck2_conv1_fwd (:conv) {1, 144, 40, 40}"];
69["mobilenetv20_features_linearbottleneck7_conv0_fwd (:conv) {1, 384, 20, 20}"];
88["add_4 (:add) {1, 64, 20, 20}"];
166["mobilenetv20_features_pool0_fwd (:global_avg_pool) {1, 1280, 1, 1}"];
144["add_8 (:add) {1, 160, 5, 5}"];
141["mobilenetv20_features_linearbottleneck14_conv2_fwd (:conv) {1, 160, 5, 5}"];
152["mobilenetv20_features_linearbottleneck15_batchnorm2_fwd (:batch_norm) {1, 160, 5, 5}"];
63["mobilenetv20_features_linearbottleneck6_relu0_fwd (:relu) {1, 192, 20, 20}"];
165["mobilenetv20_features_relu1_fwd (:relu) {1, 1280, 5, 5}"];
100["mobilenetv20_features_linearbottleneck10_batchnorm0_fwd (:batch_norm) {1, 384, 20, 20}"];
71["mobilenetv20_features_linearbottleneck7_relu0_fwd (:relu) {1, 384, 20, 20}"];
133["mobilenetv20_features_linearbottleneck13_conv2_fwd (:conv) {1, 160, 5, 5}"];
46["mobilenetv20_features_linearbottleneck4_relu1_fwd (:relu) {1, 192, 20, 20}"];
31["container_0 (:container) {{1, 24, 40, 40}, {1, 24, 40, 40}}"];
158["mobilenetv20_features_linearbottleneck16_conv1_fwd (:conv) {1, 960, 5, 5}"];
167["mobilenetv20_output_pred_fwd (:conv) {1, 1000, 1, 1}"];
98["add_5 (:add) {1, 64, 20, 20}"];
81["mobilenetv20_features_linearbottleneck8_relu0_fwd (:relu) {1, 384, 20, 20}"];
11["mobilenetv20_features_linearbottleneck0_batchnorm1_fwd (:batch_norm) {1, 32, 80, 80}"];
37["mobilenetv20_features_linearbottleneck3_batchnorm1_fwd (:batch_norm) {1, 144, 20, 20}"];
153["container_9 (:container) {{1, 160, 5, 5}, {1, 160, 5, 5}}"];
9["mobilenetv20_features_linearbottleneck0_relu0_fwd (:relu) {1, 32, 80, 80}"];
76["mobilenetv20_features_linearbottleneck7_batchnorm2_fwd (:batch_norm) {1, 64, 20, 20}"];
32["add_0 (:add) {1, 24, 40, 40}"];
34["mobilenetv20_features_linearbottleneck3_batchnorm0_fwd (:batch_norm) {1, 144, 40, 40}"];
25["mobilenetv20_features_linearbottleneck2_relu0_fwd (:relu) {1, 144, 40, 40}"];
28["mobilenetv20_features_linearbottleneck2_relu1_fwd (:relu) {1, 144, 40, 40}"];
85["mobilenetv20_features_linearbottleneck8_conv2_fwd (:conv) {1, 64, 20, 20}"];
91["mobilenetv20_features_linearbottleneck9_relu0_fwd (:relu) {1, 384, 20, 20}"];
120["mobilenetv20_features_linearbottleneck12_conv1_fwd (:conv) {1, 576, 10, 10}"];
64["mobilenetv20_features_linearbottleneck6_conv1_fwd (:conv) {1, 192, 20, 20}"];
146["mobilenetv20_features_linearbottleneck15_batchnorm0_fwd (:batch_norm) {1, 960, 5, 5}"];
6["mobilenetv20_features_relu0_fwd (:relu) {1, 32, 80, 80}"];
109["mobilenetv20_features_linearbottleneck11_relu0_fwd (:relu) {1, 576, 10, 10}"];
72["mobilenetv20_features_linearbottleneck7_conv1_fwd (:conv) {1, 384, 20, 20}"];
110["mobilenetv20_features_linearbottleneck11_conv1_fwd (:conv) {1, 576, 10, 10}"];
68["mobilenetv20_features_linearbottleneck6_batchnorm2_fwd (:batch_norm) {1, 64, 20, 20}"];
86["mobilenetv20_features_linearbottleneck8_batchnorm2_fwd (:batch_norm) {1, 64, 20, 20}"];
116["add_6 (:add) {1, 96, 10, 10}"];
51["mobilenetv20_features_linearbottleneck5_conv0_fwd (:conv) {1, 192, 20, 20}"];
80["mobilenetv20_features_linearbottleneck8_batchnorm0_fwd (:batch_norm) {1, 384, 20, 20}"];
38["mobilenetv20_features_linearbottleneck3_relu1_fwd (:relu) {1, 144, 20, 20}"];
137["mobilenetv20_features_linearbottleneck14_relu0_fwd (:relu) {1, 960, 5, 5}"];
126["add_7 (:add) {1, 96, 10, 10}"];
13["mobilenetv20_features_linearbottleneck0_conv2_fwd (:conv) {1, 16, 80, 80}"];
59["container_2 (:container) {{1, 32, 20, 20}, {1, 32, 20, 20}}"];
40["mobilenetv20_features_linearbottleneck3_batchnorm2_fwd (:batch_norm) {1, 32, 20, 20}"];
123["mobilenetv20_features_linearbottleneck12_conv2_fwd (:conv) {1, 96, 10, 10}"];
77["container_3 (:container) {{1, 64, 20, 20}, {1, 64, 20, 20}}"];
95["mobilenetv20_features_linearbottleneck9_conv2_fwd (:conv) {1, 64, 20, 20}"];
130["mobilenetv20_features_linearbottleneck13_conv1_fwd (:conv) {1, 576, 5, 5}"];
41["mobilenetv20_features_linearbottleneck4_conv0_fwd (:conv) {1, 192, 20, 20}"];
20["mobilenetv20_features_linearbottleneck1_relu1_fwd (:relu) {1, 96, 40, 40}"];
78["add_3 (:add) {1, 64, 20, 20}"];
15["mobilenetv20_features_linearbottleneck1_conv0_fwd (:conv) {1, 96, 80, 80}"];
147["mobilenetv20_features_linearbottleneck15_relu0_fwd (:relu) {1, 960, 5, 5}"];
14["mobilenetv20_features_linearbottleneck0_batchnorm2_fwd (:batch_norm) {1, 16, 80, 80}"];
108["mobilenetv20_features_linearbottleneck11_batchnorm0_fwd (:batch_norm) {1, 576, 10, 10}"];
60["add_2 (:add) {1, 32, 20, 20}"];
162["mobilenetv20_features_linearbottleneck16_batchnorm2_fwd (:batch_norm) {1, 320, 5, 5}"];
103["mobilenetv20_features_linearbottleneck10_batchnorm1_fwd (:batch_norm) {1, 384, 10, 10}"];
7["mobilenetv20_features_linearbottleneck0_conv0_fwd (:conv) {1, 32, 80, 80}"];
122["mobilenetv20_features_linearbottleneck12_relu1_fwd (:relu) {1, 576, 10, 10}"];
140["mobilenetv20_features_linearbottleneck14_relu1_fwd (:relu) {1, 960, 5, 5}"];
83["mobilenetv20_features_linearbottleneck8_batchnorm1_fwd (:batch_norm) {1, 384, 20, 20}"];
74["mobilenetv20_features_linearbottleneck7_relu1_fwd (:relu) {1, 384, 20, 20}"];
139["mobilenetv20_features_linearbottleneck14_batchnorm1_fwd (:batch_norm) {1, 960, 5, 5}"];
99["mobilenetv20_features_linearbottleneck10_conv0_fwd (:conv) {1, 384, 20, 20}"];
112["mobilenetv20_features_linearbottleneck11_relu1_fwd (:relu) {1, 576, 10, 10}"];
97["container_5 (:container) {{1, 64, 20, 20}, {1, 64, 20, 20}}"];
149["mobilenetv20_features_linearbottleneck15_batchnorm1_fwd (:batch_norm) {1, 960, 5, 5}"];
104["mobilenetv20_features_linearbottleneck10_relu1_fwd (:relu) {1, 384, 10, 10}"];
8["mobilenetv20_features_linearbottleneck0_batchnorm0_fwd (:batch_norm) {1, 32, 80, 80}"];
134["mobilenetv20_features_linearbottleneck13_batchnorm2_fwd (:batch_norm) {1, 160, 5, 5}"];
3[/"data (:input) {1, 3, 160, 160}"/];
75["mobilenetv20_features_linearbottleneck7_conv2_fwd (:conv) {1, 64, 20, 20}"];
90["mobilenetv20_features_linearbottleneck9_batchnorm0_fwd (:batch_norm) {1, 384, 20, 20}"];
154["add_9 (:add) {1, 160, 5, 5}"];
151["mobilenetv20_features_linearbottleneck15_conv2_fwd (:conv) {1, 160, 5, 5}"];
58["mobilenetv20_features_linearbottleneck5_batchnorm2_fwd (:batch_norm) {1, 32, 20, 20}"];
135["mobilenetv20_features_linearbottleneck14_conv0_fwd (:conv) {1, 960, 5, 5}"];
55["mobilenetv20_features_linearbottleneck5_batchnorm1_fwd (:batch_norm) {1, 192, 20, 20}"];
17["mobilenetv20_features_linearbottleneck1_relu0_fwd (:relu) {1, 96, 80, 80}"];
22["mobilenetv20_features_linearbottleneck1_batchnorm2_fwd (:batch_norm) {1, 24, 40, 40}"];
52["mobilenetv20_features_linearbottleneck5_batchnorm0_fwd (:batch_norm) {1, 192, 20, 20}"];
111["mobilenetv20_features_linearbottleneck11_batchnorm1_fwd (:batch_norm) {1, 576, 10, 10}"];
155["mobilenetv20_features_linearbottleneck16_conv0_fwd (:conv) {1, 960, 5, 5}"];
87["container_4 (:container) {{1, 64, 20, 20}, {1, 64, 20, 20}}"];
21["mobilenetv20_features_linearbottleneck1_conv2_fwd (:conv) {1, 24, 40, 40}"];
96["mobilenetv20_features_linearbottleneck9_batchnorm2_fwd (:batch_norm) {1, 64, 20, 20}"];
4["mobilenetv20_features_conv0_fwd (:conv) {1, 32, 80, 80}"];
128["mobilenetv20_features_linearbottleneck13_batchnorm0_fwd (:batch_norm) {1, 576, 10, 10}"];
159["mobilenetv20_features_linearbottleneck16_batchnorm1_fwd (:batch_norm) {1, 960, 5, 5}"];
119["mobilenetv20_features_linearbottleneck12_relu0_fwd (:relu) {1, 576, 10, 10}"];
148["mobilenetv20_features_linearbottleneck15_conv1_fwd (:conv) {1, 960, 5, 5}"];
82["mobilenetv20_features_linearbottleneck8_conv1_fwd (:conv) {1, 384, 20, 20}"];
36["mobilenetv20_features_linearbottleneck3_conv1_fwd (:conv) {1, 144, 20, 20}"];
138["mobilenetv20_features_linearbottleneck14_conv1_fwd (:conv) {1, 960, 5, 5}"];
53["mobilenetv20_features_linearbottleneck5_relu0_fwd (:relu) {1, 192, 20, 20}"];
50["add_1 (:add) {1, 32, 20, 20}"];
66["mobilenetv20_features_linearbottleneck6_relu1_fwd (:relu) {1, 192, 20, 20}"];
24["mobilenetv20_features_linearbottleneck2_batchnorm0_fwd (:batch_norm) {1, 144, 40, 40}"];
10["mobilenetv20_features_linearbottleneck0_conv1_fwd (:conv) {1, 32, 80, 80}"];
150["mobilenetv20_features_linearbottleneck15_relu1_fwd (:relu) {1, 960, 5, 5}"];
35["mobilenetv20_features_linearbottleneck3_relu0_fwd (:relu) {1, 144, 40, 40}"];
70["mobilenetv20_features_linearbottleneck7_batchnorm0_fwd (:batch_norm) {1, 384, 20, 20}"];
56["mobilenetv20_features_linearbottleneck5_relu1_fwd (:relu) {1, 192, 20, 20}"];
161["mobilenetv20_features_linearbottleneck16_conv2_fwd (:conv) {1, 320, 5, 5}"];
102["mobilenetv20_features_linearbottleneck10_conv1_fwd (:conv) {1, 384, 10, 10}"];
142["mobilenetv20_features_linearbottleneck14_batchnorm2_fwd (:batch_norm) {1, 160, 5, 5}"];
62["mobilenetv20_features_linearbottleneck6_batchnorm0_fwd (:batch_norm) {1, 192, 20, 20}"];
105["mobilenetv20_features_linearbottleneck10_conv2_fwd (:conv) {1, 96, 10, 10}"];
127["mobilenetv20_features_linearbottleneck13_conv0_fwd (:conv) {1, 576, 10, 10}"];
121["mobilenetv20_features_linearbottleneck12_batchnorm1_fwd (:batch_norm) {1, 576, 10, 10}"];
49["container_1 (:container) {{1, 32, 20, 20}, {1, 32, 20, 20}}"];
125["container_7 (:container) {{1, 96, 10, 10}, {1, 96, 10, 10}}"];
160["mobilenetv20_features_linearbottleneck16_relu1_fwd (:relu) {1, 960, 5, 5}"];
27["mobilenetv20_features_linearbottleneck2_batchnorm1_fwd (:batch_norm) {1, 144, 40, 40}"];
84["mobilenetv20_features_linearbottleneck8_relu1_fwd (:relu) {1, 384, 20, 20}"];
94["mobilenetv20_features_linearbottleneck9_relu1_fwd (:relu) {1, 384, 20, 20}"];
92["mobilenetv20_features_linearbottleneck9_conv1_fwd (:conv) {1, 384, 20, 20}"];
42["mobilenetv20_features_linearbottleneck4_batchnorm0_fwd (:batch_norm) {1, 192, 20, 20}"];
19["mobilenetv20_features_linearbottleneck1_batchnorm1_fwd (:batch_norm) {1, 96, 40, 40}"];
67["mobilenetv20_features_linearbottleneck6_conv2_fwd (:conv) {1, 64, 20, 20}"];
93["mobilenetv20_features_linearbottleneck9_batchnorm1_fwd (:batch_norm) {1, 384, 20, 20}"];
106["mobilenetv20_features_linearbottleneck10_batchnorm2_fwd (:batch_norm) {1, 96, 10, 10}"];
65["mobilenetv20_features_linearbottleneck6_batchnorm1_fwd (:batch_norm) {1, 192, 20, 20}"];
101["mobilenetv20_features_linearbottleneck10_relu0_fwd (:relu) {1, 384, 20, 20}"];
156["mobilenetv20_features_linearbottleneck16_batchnorm0_fwd (:batch_norm) {1, 960, 5, 5}"];
129["mobilenetv20_features_linearbottleneck13_relu0_fwd (:relu) {1, 576, 10, 10}"];
114["mobilenetv20_features_linearbottleneck11_batchnorm2_fwd (:batch_norm) {1, 96, 10, 10}"];
118["mobilenetv20_features_linearbottleneck12_batchnorm0_fwd (:batch_norm) {1, 576, 10, 10}"];
5["mobilenetv20_features_batchnorm0_fwd (:batch_norm) {1, 32, 80, 80}"];
54["mobilenetv20_features_linearbottleneck5_conv1_fwd (:conv) {1, 192, 20, 20}"];
18["mobilenetv20_features_linearbottleneck1_conv1_fwd (:conv) {1, 96, 40, 40}"];
79["mobilenetv20_features_linearbottleneck8_conv0_fwd (:conv) {1, 384, 20, 20}"];
136["mobilenetv20_features_linearbottleneck14_batchnorm0_fwd (:batch_norm) {1, 960, 5, 5}"];
115["container_6 (:container) {{1, 96, 10, 10}, {1, 96, 10, 10}}"];
107["mobilenetv20_features_linearbottleneck11_conv0_fwd (:conv) {1, 576, 10, 10}"];
164["mobilenetv20_features_batchnorm1_fwd (:batch_norm) {1, 1280, 5, 5}"];
16["mobilenetv20_features_linearbottleneck1_batchnorm0_fwd (:batch_norm) {1, 96, 80, 80}"];
167 --> 168;
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3 --> 4;
{_popped, cnn_base} = cnn_base |> Axon.pop_node()
{_popped, cnn_base} = cnn_base |> Axon.pop_node()
Axon.Display.as_graph(cnn_base, input_template)
graph TD;
33["mobilenetv20_features_linearbottleneck3_conv0_fwd (:conv) {1, 144, 40, 40}"];
117["mobilenetv20_features_linearbottleneck12_conv0_fwd (:conv) {1, 576, 10, 10}"];
12["mobilenetv20_features_linearbottleneck0_relu1_fwd (:relu) {1, 32, 80, 80}"];
157["mobilenetv20_features_linearbottleneck16_relu0_fwd (:relu) {1, 960, 5, 5}"];
132["mobilenetv20_features_linearbottleneck13_relu1_fwd (:relu) {1, 576, 5, 5}"];
73["mobilenetv20_features_linearbottleneck7_batchnorm1_fwd (:batch_norm) {1, 384, 20, 20}"];
44["mobilenetv20_features_linearbottleneck4_conv1_fwd (:conv) {1, 192, 20, 20}"];
124["mobilenetv20_features_linearbottleneck12_batchnorm2_fwd (:batch_norm) {1, 96, 10, 10}"];
23["mobilenetv20_features_linearbottleneck2_conv0_fwd (:conv) {1, 144, 40, 40}"];
29["mobilenetv20_features_linearbottleneck2_conv2_fwd (:conv) {1, 24, 40, 40}"];
47["mobilenetv20_features_linearbottleneck4_conv2_fwd (:conv) {1, 32, 20, 20}"];
89["mobilenetv20_features_linearbottleneck9_conv0_fwd (:conv) {1, 384, 20, 20}"];
61["mobilenetv20_features_linearbottleneck6_conv0_fwd (:conv) {1, 192, 20, 20}"];
30["mobilenetv20_features_linearbottleneck2_batchnorm2_fwd (:batch_norm) {1, 24, 40, 40}"];
43["mobilenetv20_features_linearbottleneck4_relu0_fwd (:relu) {1, 192, 20, 20}"];
163["mobilenetv20_features_conv1_fwd (:conv) {1, 1280, 5, 5}"];
39["mobilenetv20_features_linearbottleneck3_conv2_fwd (:conv) {1, 32, 20, 20}"];
131["mobilenetv20_features_linearbottleneck13_batchnorm1_fwd (:batch_norm) {1, 576, 5, 5}"];
45["mobilenetv20_features_linearbottleneck4_batchnorm1_fwd (:batch_norm) {1, 192, 20, 20}"];
48["mobilenetv20_features_linearbottleneck4_batchnorm2_fwd (:batch_norm) {1, 32, 20, 20}"];
145["mobilenetv20_features_linearbottleneck15_conv0_fwd (:conv) {1, 960, 5, 5}"];
57["mobilenetv20_features_linearbottleneck5_conv2_fwd (:conv) {1, 32, 20, 20}"];
143["container_8 (:container) {{1, 160, 5, 5}, {1, 160, 5, 5}}"];
113["mobilenetv20_features_linearbottleneck11_conv2_fwd (:conv) {1, 96, 10, 10}"];
26["mobilenetv20_features_linearbottleneck2_conv1_fwd (:conv) {1, 144, 40, 40}"];
69["mobilenetv20_features_linearbottleneck7_conv0_fwd (:conv) {1, 384, 20, 20}"];
88["add_4 (:add) {1, 64, 20, 20}"];
166["mobilenetv20_features_pool0_fwd (:global_avg_pool) {1, 1280, 1, 1}"];
144["add_8 (:add) {1, 160, 5, 5}"];
141["mobilenetv20_features_linearbottleneck14_conv2_fwd (:conv) {1, 160, 5, 5}"];
152["mobilenetv20_features_linearbottleneck15_batchnorm2_fwd (:batch_norm) {1, 160, 5, 5}"];
63["mobilenetv20_features_linearbottleneck6_relu0_fwd (:relu) {1, 192, 20, 20}"];
165["mobilenetv20_features_relu1_fwd (:relu) {1, 1280, 5, 5}"];
100["mobilenetv20_features_linearbottleneck10_batchnorm0_fwd (:batch_norm) {1, 384, 20, 20}"];
71["mobilenetv20_features_linearbottleneck7_relu0_fwd (:relu) {1, 384, 20, 20}"];
133["mobilenetv20_features_linearbottleneck13_conv2_fwd (:conv) {1, 160, 5, 5}"];
46["mobilenetv20_features_linearbottleneck4_relu1_fwd (:relu) {1, 192, 20, 20}"];
31["container_0 (:container) {{1, 24, 40, 40}, {1, 24, 40, 40}}"];
158["mobilenetv20_features_linearbottleneck16_conv1_fwd (:conv) {1, 960, 5, 5}"];
98["add_5 (:add) {1, 64, 20, 20}"];
81["mobilenetv20_features_linearbottleneck8_relu0_fwd (:relu) {1, 384, 20, 20}"];
11["mobilenetv20_features_linearbottleneck0_batchnorm1_fwd (:batch_norm) {1, 32, 80, 80}"];
37["mobilenetv20_features_linearbottleneck3_batchnorm1_fwd (:batch_norm) {1, 144, 20, 20}"];
153["container_9 (:container) {{1, 160, 5, 5}, {1, 160, 5, 5}}"];
9["mobilenetv20_features_linearbottleneck0_relu0_fwd (:relu) {1, 32, 80, 80}"];
76["mobilenetv20_features_linearbottleneck7_batchnorm2_fwd (:batch_norm) {1, 64, 20, 20}"];
32["add_0 (:add) {1, 24, 40, 40}"];
34["mobilenetv20_features_linearbottleneck3_batchnorm0_fwd (:batch_norm) {1, 144, 40, 40}"];
25["mobilenetv20_features_linearbottleneck2_relu0_fwd (:relu) {1, 144, 40, 40}"];
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77["container_3 (:container) {{1, 64, 20, 20}, {1, 64, 20, 20}}"];
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147["mobilenetv20_features_linearbottleneck15_relu0_fwd (:relu) {1, 960, 5, 5}"];
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103["mobilenetv20_features_linearbottleneck10_batchnorm1_fwd (:batch_norm) {1, 384, 10, 10}"];
7["mobilenetv20_features_linearbottleneck0_conv0_fwd (:conv) {1, 32, 80, 80}"];
122["mobilenetv20_features_linearbottleneck12_relu1_fwd (:relu) {1, 576, 10, 10}"];
140["mobilenetv20_features_linearbottleneck14_relu1_fwd (:relu) {1, 960, 5, 5}"];
83["mobilenetv20_features_linearbottleneck8_batchnorm1_fwd (:batch_norm) {1, 384, 20, 20}"];
74["mobilenetv20_features_linearbottleneck7_relu1_fwd (:relu) {1, 384, 20, 20}"];
139["mobilenetv20_features_linearbottleneck14_batchnorm1_fwd (:batch_norm) {1, 960, 5, 5}"];
99["mobilenetv20_features_linearbottleneck10_conv0_fwd (:conv) {1, 384, 20, 20}"];
112["mobilenetv20_features_linearbottleneck11_relu1_fwd (:relu) {1, 576, 10, 10}"];
97["container_5 (:container) {{1, 64, 20, 20}, {1, 64, 20, 20}}"];
149["mobilenetv20_features_linearbottleneck15_batchnorm1_fwd (:batch_norm) {1, 960, 5, 5}"];
104["mobilenetv20_features_linearbottleneck10_relu1_fwd (:relu) {1, 384, 10, 10}"];
8["mobilenetv20_features_linearbottleneck0_batchnorm0_fwd (:batch_norm) {1, 32, 80, 80}"];
134["mobilenetv20_features_linearbottleneck13_batchnorm2_fwd (:batch_norm) {1, 160, 5, 5}"];
3[/"data (:input) {1, 3, 160, 160}"/];
75["mobilenetv20_features_linearbottleneck7_conv2_fwd (:conv) {1, 64, 20, 20}"];
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154["add_9 (:add) {1, 160, 5, 5}"];
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135["mobilenetv20_features_linearbottleneck14_conv0_fwd (:conv) {1, 960, 5, 5}"];
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17["mobilenetv20_features_linearbottleneck1_relu0_fwd (:relu) {1, 96, 80, 80}"];
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87["container_4 (:container) {{1, 64, 20, 20}, {1, 64, 20, 20}}"];
21["mobilenetv20_features_linearbottleneck1_conv2_fwd (:conv) {1, 24, 40, 40}"];
96["mobilenetv20_features_linearbottleneck9_batchnorm2_fwd (:batch_norm) {1, 64, 20, 20}"];
4["mobilenetv20_features_conv0_fwd (:conv) {1, 32, 80, 80}"];
128["mobilenetv20_features_linearbottleneck13_batchnorm0_fwd (:batch_norm) {1, 576, 10, 10}"];
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119["mobilenetv20_features_linearbottleneck12_relu0_fwd (:relu) {1, 576, 10, 10}"];
148["mobilenetv20_features_linearbottleneck15_conv1_fwd (:conv) {1, 960, 5, 5}"];
82["mobilenetv20_features_linearbottleneck8_conv1_fwd (:conv) {1, 384, 20, 20}"];
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138["mobilenetv20_features_linearbottleneck14_conv1_fwd (:conv) {1, 960, 5, 5}"];
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101["mobilenetv20_features_linearbottleneck10_relu0_fwd (:relu) {1, 384, 20, 20}"];
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114["mobilenetv20_features_linearbottleneck11_batchnorm2_fwd (:batch_norm) {1, 96, 10, 10}"];
118["mobilenetv20_features_linearbottleneck12_batchnorm0_fwd (:batch_norm) {1, 576, 10, 10}"];
5["mobilenetv20_features_batchnorm0_fwd (:batch_norm) {1, 32, 80, 80}"];
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16["mobilenetv20_features_linearbottleneck1_batchnorm0_fwd (:batch_norm) {1, 96, 80, 80}"];
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Building the Model from Mobilenet Model
model =
cnn_base
|> Axon.namespace("feature_extractor")
|> Axon.freeze()
|> Axon.global_avg_pool(channels: :first)
|> Axon.dropout(rate: 0.2)
|> Axon.dense(1)
#Axon<
inputs: %{"data" => {1, 3, 224, 224}}
outputs: "dense_0"
nodes: 168
>
Training the Model
loss =
&Axon.Losses.binary_cross_entropy(&1, &2,
reduction: :mean,
from_logits: true
)
optimizer = Polaris.Optimizers.adam(learning_rate: 1.0e-4)
trained_model_state =
model
|> Axon.Loop.trainer(loss, optimizer)
|> Axon.Loop.metric(:accuracy)
|> Axon.Loop.validate(model, val_pipeline)
|> Axon.Loop.early_stop("validation_loss", mode: :min, patience: 5)
|> Axon.Loop.run(
train_pipeline,
%{"feature_extractor" => cnn_base_params},
epochs: 100,
compiler: EXLA
)
18:00:45.817 [debug] Forwarding options: [compiler: EXLA] to JIT compiler
18:00:45.952 [warning] found unexpected key in the initial parameters map: "mobilenetv20_output_pred_fwd"
Epoch: 0, Batch: 700, accuracy: 0.7490190 loss: 0.4872290
Epoch: 1, Batch: 700, accuracy: 0.8607792 loss: 0.3941975
Epoch: 2, Batch: 700, accuracy: 0.8781650 loss: 0.3532889
Epoch: 3, Batch: 700, accuracy: 0.8869918 loss: 0.3276705
Epoch: 4, Batch: 700, accuracy: 0.8916724 loss: 0.3102300
Epoch: 5, Batch: 700, accuracy: 0.8947041 loss: 0.2979036
Epoch: 6, Batch: 700, accuracy: 0.8957738 loss: 0.2888097
Epoch: 7, Batch: 700, accuracy: 0.9012572 loss: 0.2812564
Epoch: 8, Batch: 700, accuracy: 0.8998307 loss: 0.2750721
Epoch: 9, Batch: 700, accuracy: 0.9046897 loss: 0.2699555
Epoch: 10, Batch: 700, accuracy: 0.9046898 loss: 0.2652446
Epoch: 11, Batch: 700, accuracy: 0.9021933 loss: 0.2615432
Epoch: 12, Batch: 700, accuracy: 0.9069632 loss: 0.2578678
Epoch: 13, Batch: 700, accuracy: 0.9043331 loss: 0.2548892
Epoch: 14, Batch: 700, accuracy: 0.9046451 loss: 0.2521417
Epoch: 15, Batch: 700, accuracy: 0.9068741 loss: 0.2496624
Epoch: 16, Batch: 700, accuracy: 0.9083008 loss: 0.2473769
Epoch: 17, Batch: 700, accuracy: 0.9061162 loss: 0.2456258
Epoch: 18, Batch: 700, accuracy: 0.9071863 loss: 0.2438199
Epoch: 19, Batch: 700, accuracy: 0.9093258 loss: 0.2419264
Epoch: 20, Batch: 700, accuracy: 0.9084345 loss: 0.2402944
Epoch: 21, Batch: 700, accuracy: 0.9063838 loss: 0.2390028
Epoch: 22, Batch: 700, accuracy: 0.9085682 loss: 0.2377930
Epoch: 23, Batch: 700, accuracy: 0.9099947 loss: 0.2364875
Epoch: 24, Batch: 700, accuracy: 0.9115102 loss: 0.2352752
Epoch: 25, Batch: 700, accuracy: 0.9088356 loss: 0.2342139
Epoch: 26, Batch: 700, accuracy: 0.9062501 loss: 0.2333108
Epoch: 27, Batch: 700, accuracy: 0.9084344 loss: 0.2323771
Epoch: 28, Batch: 700, accuracy: 0.9125357 loss: 0.2313402
Epoch: 29, Batch: 700, accuracy: 0.9106188 loss: 0.2304147
Epoch: 30, Batch: 700, accuracy: 0.9093260 loss: 0.2297038
Epoch: 31, Batch: 700, accuracy: 0.9107079 loss: 0.2288840
Epoch: 32, Batch: 700, accuracy: 0.9102621 loss: 0.2282075
Epoch: 33, Batch: 700, accuracy: 0.9108863 loss: 0.2274254
Epoch: 34, Batch: 700, accuracy: 0.9124019 loss: 0.2267024
Epoch: 35, Batch: 700, accuracy: 0.9111538 loss: 0.2260765
Epoch: 36, Batch: 700, accuracy: 0.9125357 loss: 0.2254625
Epoch: 37, Batch: 700, accuracy: 0.9126249 loss: 0.2248283
Epoch: 38, Batch: 700, accuracy: 0.9089692 loss: 0.2243719
Epoch: 39, Batch: 700, accuracy: 0.9092367 loss: 0.2239486
Epoch: 40, Batch: 700, accuracy: 0.9117332 loss: 0.2234031
Epoch: 41, Batch: 700, accuracy: 0.9137395 loss: 0.2228346
Epoch: 42, Batch: 700, accuracy: 0.9102621 loss: 0.2224314
Batch: 6, accuracy: 0.9464286 loss: 0.1156730
...
EXLA.Backend
[0.1144828125834465, 0.29103899002075195, 0.19599959254264832, 0.11088918149471283, 0.11658263951539993, 0.2404186874628067, 0.05210668221116066, 0.279130220413208, 0.16053666174411774, 0.10330193489789963, 0.17037393152713776, 0.42853739857673645, 0.2756047546863556, 0.3480927646160126, 0.41298505663871765, 0.3465864956378937, 0.2918163239955902, 0.2078855335712433, 0.19570542871952057, 0.3232663571834564, 0.20460295677185059, 0.21620818972587585, 0.06636220216751099, 0.16237126290798187, 0.11230707168579102, 0.20659472048282623, 0.04134644940495491, 0.16707743704319, 0.18970169126987457, 0.08170202374458313, ...]
>,
"mean" => #Nx.Tensor<
f32[96]
EXLA.Backend
[7.723511662334204e-4, -3.091116959694773e-4, -9.241990483133122e-5, -6.965994543861598e-5, 2.6191226424998604e-5, -3.1657828367315233e-4, 1.1303959036013111e-4, 1.4945304428692907e-4, 4.511853330768645e-4, -4.4339807936921716e-4, -1.6256280650850385e-4, 1.3352044334169477e-4, 7.505110261263326e-5, 5.360154318623245e-4, 0.001765010878443718, -3.995028673671186e-4, -1.3021344784647226e-5, 7.265885942615569e-4, 4.0445878403261304e-4, -2.8110373023082502e-5, 1.516808697488159e-4, 1.1108114995295182e-4, -1.617355301277712e-4, 4.557485517580062e-4, 1.0374527482781559e-4, -6.70250374241732e-5, 1.5719550719950348e-4, 3.4182274248450994e-5, -4.4115696800872684e-4, ...]
>,
"var" => #Nx.Tensor<
f32[96]
EXLA.Backend
[0.09834592044353485, 0.1735430806875229, 0.38497281074523926, 0.17505548894405365, 0.19528056681156158, 0.1011020764708519, 0.18303343653678894, 0.20173640549182892, 0.09403466433286667, 0.06812598556280136, 0.14213919639587402, 0.43439722061157227, 0.1752435863018036, 0.4183683693408966, 0.3139973282814026, 0.3875087797641754, 0.09094872325658798, 0.10979244112968445, 0.3352102041244507, 0.11777494847774506, 0.193935826420784, 0.1976424902677536, 0.1345539391040802, 0.2652271091938019, 0.16767887771129608, 0.1588459610939026, 0.09324903041124344, 0.2787035405635834, ...]
>
},
"mobilenetv20_features_linearbottleneck11_batchnorm0_fwd" => %{
"beta" => #Nx.Tensor<
f32[576]
EXLA.Backend
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EXLA.Backend
[0.5097491145133972, 0.6192775368690491, 0.657399594783783, 0.5558041930198669, 0.6164559721946716, 0.6381960511207581, 0.6152222156524658, 0.4946329891681671, 0.5616724491119385, 0.6567564606666565, 0.5526584982872009, 0.42007970809936523, 0.4637764096260071, 0.5626768469810486, 0.6189385056495667, 0.6354565620422363]
>,
"mean" => #Nx.Tensor<
f32[16]
EXLA.Backend
[0.021046694368124008, -0.4771552085876465, -0.4624612629413605, 1.7339810132980347, 0.3934212327003479, -0.845706045627594, 1.2445530891418457, -0.4817613959312439, 0.8393087983131409, -2.1945111751556396, 2.2859015464782715, 0.042962949723005295, -1.1515218019485474, -0.37790021300315857, -3.0484421253204346, -0.784759521484375]
>,
"var" => #Nx.Tensor<
f32[16]
EXLA.Backend
[0.015569682233035564, 0.057801131159067154, 0.05658106878399849, 0.018852993845939636, 0.038473501801490784, 0.0927843227982521, 0.05347060412168503, 0.04166773706674576, 0.037543755024671555, 0.14158287644386292, 0.04085716977715492, 0.060751400887966156, 0.034666694700717926, 0.056090183556079865, 0.04101115092635155, 0.052235011011362076]
>
},
"mobilenetv20_features_linearbottleneck7_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[384][1][3][3]
EXLA.Backend
[
[
[
[0.09069628268480301, 0.011556080542504787, -0.044405367225408554],
[0.1974998563528061, -0.05144780874252319, -0.15406860411167145],
[0.03294772282242775, -0.08055609464645386, -0.10401368886232376]
]
],
[
[
[0.004530614707618952, -0.016135336831212044, 0.010837683454155922],
[-0.013911036774516106, -0.17941752076148987, 0.0742872878909111],
[0.03701423481106758, 0.17489252984523773, 0.049539677798748016]
]
],
[
[
[-0.03442509472370148, -0.04338786378502846, -8.070958429016173e-4],
...
]
],
...
]
>
},
"mobilenetv20_features_linearbottleneck1_conv0_fwd" => %{
"kernel" => #Nx.Tensor<
f32[96][16][1][1]
EXLA.Backend
[
[
[
[-0.26627230644226074]
],
[
[0.1223592683672905]
],
[
[-0.17675793170928955]
],
[
[-0.14175795018672943]
],
[
[-0.04847275838255882]
],
[
[0.02572016417980194]
],
[
[0.18247880041599274]
],
[
[-0.22272470593452454]
],
[
[-0.0375950001180172]
],
[
[-0.12036115676164627]
],
[
[-0.42806223034858704]
],
[
[0.2049194574356079]
],
[
[0.025416148826479912]
],
[
[-0.20868347585201263]
],
[
[-0.00869692862033844]
],
[
[0.10014521330595016]
]
],
[
[
[0.1901395469903946]
],
[
[-0.15854698419570923]
],
[
[-0.05858785659074783]
],
[
[-0.016011832281947136]
],
...
],
...
]
>
},
"mobilenetv20_features_linearbottleneck2_batchnorm2_fwd" => %{
"beta" => #Nx.Tensor<
f32[24]
EXLA.Backend
[-6.408389890566468e-5, -1.0128262510988861e-4, -4.3479518353706226e-5, -1.589354214956984e-4, -7.557999197160825e-5, -2.3196584152174182e-5, -2.216532493548584e-6, 9.525781933916733e-6, -8.600373985245824e-5, -2.3008385323919356e-4, -2.3174902889877558e-4, -2.012878976529464e-4, -9.838923870120198e-5, -1.22729703434743e-4, -9.646145917940885e-5, -2.797471643134486e-5, -1.039204653352499e-4, -1.2249314750079066e-4, -8.16335596027784e-5, ...]
>,
"gamma" => #Nx.Tensor<
f32[24]
EXLA.Backend
[0.5065965056419373, 0.44786974787712097, 0.5534567832946777, 0.3781253695487976, 0.44674113392829895, 0.28593751788139343, 0.5029821395874023, 0.19650447368621826, 0.3918337821960449, 0.33050450682640076, 0.31709131598472595, 0.1402808576822281, 0.4198921024799347, 0.02611447498202324, 0.5337468981742859, 0.49816271662712097, 0.32386210560798645, 0.40961867570877075, ...]
>,
"mean" => #Nx.Tensor<
f32[24]
EXLA.Backend
[-0.49676281213760376, -0.5412184000015259, 0.2777172029018402, 0.30402812361717224, -0.038367077708244324, 0.19269412755966187, -0.20861922204494476, 0.3330950438976288, -0.35258007049560547, -0.0413212850689888, -0.44278526306152344, 0.43556883931159973, -0.007557356264442205, -0.049789782613515854, -0.1810888946056366, -0.1666330248117447, 0.15477608144283295, ...]
>,
"var" => #Nx.Tensor<
f32[24]
EXLA.Backend
[0.0421639047563076, 0.03801903501152992, 0.04582618921995163, 0.028266852721571922, 0.025787167251110077, 0.026369646191596985, 0.03499341756105423, 0.02089923992753029, 0.02381058968603611, 0.04512317106127739, 0.018083490431308746, 0.02251904457807541, 0.023619014769792557, 0.005619029048830271, 0.04942920058965683, 0.02654757723212242, ...]
>
},
"mobilenetv20_features_linearbottleneck8_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[384][1][3][3]
EXLA.Backend
[
[
[
[-0.0020327651873230934, -0.03829871118068695, -0.013563335873186588],
[-0.07023981958627701, -0.0530773401260376, -0.05999664217233658],
[-0.02172546088695526, -0.05160605534911156, -0.02340804785490036]
]
],
[
[
[-0.06414368003606796, -0.09891236573457718, 0.09147416055202484],
[-0.035863131284713745, -0.026917265728116035, 0.07736452668905258],
[0.08244364708662033, 0.10789858549833298, -0.16784991323947906]
]
],
...
]
>
},
"mobilenetv20_features_linearbottleneck11_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[576][1][3][3]
EXLA.Backend
[
[
[
[0.0561985969543457, -0.08534234762191772, -0.0728735700249672],
[0.20427849888801575, -0.03384411707520485, -0.18976956605911255],
[0.10962294042110443, 0.006988390814512968, -0.03254517540335655]
]
],
[
[
[-0.007281317375600338, -0.10424316674470901, -0.0011419359361752868],
[-0.0659799799323082, -0.2147035449743271, -0.10787361860275269],
[0.004407626111060381, 0.44921964406967163, ...]
]
],
...
]
>
},
"mobilenetv20_features_linearbottleneck14_conv0_fwd" => %{
"kernel" => #Nx.Tensor<
f32[960][160][1][1]
EXLA.Backend
[
[
[
[0.037727002054452896]
],
[
[-0.07920914888381958]
],
[
[-0.0031863711774349213]
],
[
[-0.017933860421180725]
],
[
[-0.012083934620022774]
],
[
[-0.03305855393409729]
],
[
[0.010104630142450333]
],
[
[-0.01614421419799328]
],
[
[0.0415346585214138]
],
[
[-0.05910014733672142]
],
[
[3.380556881893426e-4]
],
[
[0.02735193446278572]
],
[
[-0.0788668617606163]
],
[
[-0.028101010248064995]
],
[
[-0.01531447283923626]
],
[
[-0.04052167013287544]
],
...
],
...
]
>
},
"mobilenetv20_features_linearbottleneck15_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[960][1][3][3]
EXLA.Backend
[
[
[
[0.008491688407957554, 0.01882096938788891, -0.001548346015624702],
[0.009763970039784908, 0.2359771430492401, 0.03868749365210533],
[-0.05791253224015236, 0.028106149286031723, -0.07315897196531296]
]
],
[
[
[-0.01212818082422018, -0.16292721033096313, -0.033156707882881165],
[0.005929793696850538, 0.2561517059803009, 0.014003193937242031],
...
]
],
...
]
>
},
"mobilenetv20_features_linearbottleneck7_conv2_fwd" => %{
"kernel" => #Nx.Tensor<
f32[64][384][1][1]
EXLA.Backend
[
[
[
[-0.03408721089363098]
],
[
[-0.017362290993332863]
],
[
[0.01995295286178589]
],
[
[-0.051705457270145416]
],
[
[0.0689198449254036]
],
[
[0.08685684949159622]
],
[
[-0.09933704882860184]
],
[
[0.07178854197263718]
],
[
[0.044526729732751846]
],
[
[-0.018590005114674568]
],
[
[0.013196618296205997]
],
[
[-0.0795169249176979]
],
[
[-0.10800802707672119]
],
[
[-0.042785294353961945]
],
...
],
...
]
>
},
"mobilenetv20_features_linearbottleneck4_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[192][1][3][3]
EXLA.Backend
[
[
[
[-0.031621839851140976, 0.18290314078330994, 0.040609210729599],
[-0.19228996336460114, 0.031516727060079575, 0.09460115432739258],
[-0.026457147672772408, 0.05563095211982727, 0.0026122310664504766]
]
],
[
[
[-0.00655049504712224, -0.004564541857689619, -0.01380461547523737],
[-0.002132135210558772, ...],
...
]
],
...
]
>
},
"mobilenetv20_features_linearbottleneck16_conv0_fwd" => %{
"kernel" => #Nx.Tensor<
f32[960][160][1][1]
EXLA.Backend
[
[
[
[0.04804954305291176]
],
[
[-0.0361880324780941]
],
[
[0.09869731217622757]
],
[
[0.03218498453497887]
],
[
[-0.05613848939538002]
],
[
[0.03235843405127525]
],
[
[0.035072293132543564]
],
[
[0.04535597562789917]
],
[
[0.031406670808792114]
],
[
[0.003971233498305082]
],
[
[0.016072509810328484]
],
[
[-0.041956160217523575]
],
...
],
...
]
>
},
"mobilenetv20_features_linearbottleneck10_batchnorm2_fwd" => %{
"beta" => #Nx.Tensor<
f32[96]
EXLA.Backend
[2.1335902999908285e-7, 3.535939754328865e-7, 5.073925990473072e-7, 5.08670098042785e-7, 3.3390904263796983e-7, 4.946827516505437e-7, 4.795706445293035e-7, 3.743390948329761e-7, 5.272184466775798e-7, 5.93354513966915e-7, 2.1419212714590685e-7, ...]
>,
"gamma" => #Nx.Tensor<
f32[96]
EXLA.Backend
[0.2979993522167206, 0.3140060007572174, 0.2445983588695526, 0.2865447998046875, 0.35691216588020325, 0.3592449426651001, 0.27936986088752747, 0.25922414660453796, 0.2633695900440216, 0.2901868522167206, ...]
>,
"mean" => #Nx.Tensor<
f32[96]
EXLA.Backend
[-0.029136953875422478, -0.5148138999938965, 0.05061722546815872, -0.3127302825450897, -0.029322732239961624, 0.2176133394241333, 0.10727949440479279, -0.23169416189193726, -0.4668867290019989, ...]
>,
"var" => #Nx.Tensor<
f32[96]
EXLA.Backend
[0.11043474823236465, 0.09818199276924133, 0.06050458922982216, 0.09404341876506805, 0.19909954071044922, 0.13264349102973938, 0.06298568099737167, 0.07344550639390945, ...]
>
},
"mobilenetv20_features_linearbottleneck14_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[960][1][3][3]
EXLA.Backend
[
[
[
[0.03072543255984783, 0.024941517040133476, -0.044987138360738754],
[0.2105334848165512, -0.09797866642475128, -0.12514935433864594],
[0.049303051084280014, -0.004124515224248171, -0.0421803742647171]
]
],
[
[
[0.027953263372182846, ...],
...
]
],
...
]
>
},
"mobilenetv20_features_linearbottleneck12_conv0_fwd" => %{
"kernel" => #Nx.Tensor<
f32[576][96][1][1]
EXLA.Backend
[
[
[
[-0.020311787724494934]
],
[
[0.00342985475435853]
],
[
[-0.04346230626106262]
],
[
[-0.016626032069325447]
],
[
[-0.012114123441278934]
],
[
[-0.012785198166966438]
],
[
[0.056096695363521576]
],
[
[0.09021388739347458]
],
[
[0.16139942407608032]
],
...
],
...
]
>
},
"mobilenetv20_features_linearbottleneck0_batchnorm1_fwd" => %{
"beta" => #Nx.Tensor<
f32[32]
EXLA.Backend
[1.037217140197754, 1.2865849733352661, 0.7645959258079529, 0.6374938488006592, 1.069508671760559, 0.6837575435638428, 0.9550023674964905, 0.9839419722557068, ...]
>,
"gamma" => #Nx.Tensor<
f32[32]
EXLA.Backend
[0.2217620462179184, 0.11614178121089935, 0.1586529165506363, 0.15199987590312958, 0.23966756463050842, 0.17114117741584778, 0.18509696424007416, ...]
>,
"mean" => #Nx.Tensor<
f32[32]
EXLA.Backend
[0.6259635090827942, -0.08774495124816895, 1.0720069408416748, 2.3868372440338135, -5.589635848999023, 3.4053637981414795, ...]
>,
"var" => #Nx.Tensor<
f32[32]
EXLA.Backend
[0.3122387230396271, 0.008777356706559658, 0.010774576105177402, 0.3379133641719818, 2.11269211769104, ...]
>
},
"mobilenetv20_features_linearbottleneck6_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[192][1][3][3]
EXLA.Backend
[
[
[
[0.014363789930939674, 0.13237811625003815, 0.09334706515073776],
[0.12919868528842926, -0.35225632786750793, -0.1466313749551773],
[0.06540388613939285, ...]
]
],
...
]
>
},
"mobilenetv20_features_linearbottleneck16_batchnorm2_fwd" => %{
"beta" => #Nx.Tensor<
f32[320]
EXLA.Backend
[-5.189255602999765e-7, -2.7314237627251714e-7, -2.1782213366350334e-7, 1.9330661871208576e-7, 2.6874428016299134e-8, -2.4903325623881756e-8, ...]
>,
"gamma" => #Nx.Tensor<
f32[320]
EXLA.Backend
[0.25196632742881775, 0.2486555427312851, 0.24830971658229828, 0.24681176245212555, 0.24941202998161316, ...]
>,
"mean" => #Nx.Tensor<
f32[320]
EXLA.Backend
[-0.1392788290977478, -0.05395103245973587, -0.14208653569221497, 0.18811774253845215, ...]
>,
"var" => #Nx.Tensor<
f32[320]
EXLA.Backend
[0.008379261940717697, 0.008914480917155743, 0.007564138621091843, ...]
>
},
"mobilenetv20_features_linearbottleneck10_conv2_fwd" => %{
"kernel" => #Nx.Tensor<
f32[96][384][1][1]
EXLA.Backend
[
[
[
[0.01824713684618473]
],
[
[0.08710300177335739]
],
[
[-0.10191874951124191]
],
[
[-0.07710960507392883]
],
[
[-0.042343202978372574]
],
...
],
...
]
>
},
"mobilenetv20_features_linearbottleneck8_conv0_fwd" => %{
"kernel" => #Nx.Tensor<
f32[384][64][1][1]
EXLA.Backend
[
[
[
[0.015875784680247307]
],
[
[0.00680553587153554]
],
[
[0.09295158088207245]
],
[
[-0.05411294475197792]
],
...
],
...
]
>
},
"mobilenetv20_features_conv0_fwd" => %{
"kernel" => #Nx.Tensor<
f32[32][3][3][3]
EXLA.Backend
[
[
[
[-0.11137320101261139, -0.22142910957336426, 0.10717468708753586],
...
],
...
],
...
]
>
},
"mobilenetv20_features_linearbottleneck11_batchnorm2_fwd" => %{
"beta" => #Nx.Tensor<
f32[96]
EXLA.Backend
[3.9526614159512974e-7, 5.761955890193349e-7, ...]
>,
"gamma" => #Nx.Tensor<
f32[96]
EXLA.Backend
[0.21182486414909363, ...]
>,
"mean" => #Nx.Tensor<
f32[96]
EXLA.Backend
[...]
>,
...
},
"mobilenetv20_features_linearbottleneck6_conv2_fwd" => %{
"kernel" => #Nx.Tensor<
f32[64][192][1][1]
EXLA.Backend
[
[
[
[0.07365027070045471]
],
...
],
...
]
>
},
"mobilenetv20_features_linearbottleneck3_conv2_fwd" => %{
"kernel" => #Nx.Tensor<
f32[32][144][1][1]
EXLA.Backend
[
...
]
>
},
"mobilenetv20_features_linearbottleneck4_conv0_fwd" => %{...},
...
}
}
model
|> Axon.sigmoid()
|> Axon.Loop.evaluator()
|> Axon.Loop.metric(:accuracy)
|> Axon.Loop.run(test_pipeline, trained_model_state, compiler: EXLA)
08:16:14.945 [debug] Forwarding options: [compiler: EXLA] to JIT compiler
Batch: 22, accuracy: 0.9592391
%{
0 => %{
"accuracy" => #Nx.Tensor<
f32
EXLA.Backend
0.95923912525177
>
}
}
model = model |> Axon.unfreeze(up: 50)
loss =
&Axon.Losses.binary_cross_entropy(&1, &2,
reduction: :mean,
from_logits: true
)
optimizer = Polaris.Optimizers.rmsprop(learning_rate: 1.0e-5)
trained_model_state =
model
|> Axon.Loop.trainer(loss, optimizer)
|> Axon.Loop.metric(:accuracy)
|> Axon.Loop.validate(model, val_pipeline)
|> Axon.Loop.early_stop("validation_loss", mode: :min, patience: 5)
|> Axon.Loop.run(
train_pipeline,
trained_model_state,
epochs: 100,
compiler: EXLA
)
08:17:43.369 [debug] Forwarding options: [compiler: EXLA] to JIT compiler
Epoch: 0, Batch: 700, accuracy: 0.9254190 loss: 0.1792496
Epoch: 1, Batch: 700, accuracy: 0.9409326 loss: 0.1616345
Epoch: 2, Batch: 700, accuracy: 0.9496701 loss: 0.1484112
Epoch: 3, Batch: 700, accuracy: 0.9557329 loss: 0.1378420
Epoch: 4, Batch: 700, accuracy: 0.9598787 loss: 0.1302559
Epoch: 5, Batch: 700, accuracy: 0.9650499 loss: 0.1230860
Epoch: 6, Batch: 700, accuracy: 0.9674572 loss: 0.1170269
Epoch: 7, Batch: 700, accuracy: 0.9712464 loss: 0.1114469
Batch: 6, accuracy: 0.9642857 loss: 0.1175858
...
EXLA.Backend
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>,
"mean" => #Nx.Tensor<
f32[160]
EXLA.Backend
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>,
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f32[160]
EXLA.Backend
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>
},
"mobilenetv20_features_linearbottleneck0_batchnorm2_fwd" => %{
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f32[16]
EXLA.Backend
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"gamma" => #Nx.Tensor<
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EXLA.Backend
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EXLA.Backend
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f32[16]
EXLA.Backend
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},
"mobilenetv20_features_linearbottleneck7_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[384][1][3][3]
EXLA.Backend
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>
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"mobilenetv20_features_linearbottleneck1_conv0_fwd" => %{
"kernel" => #Nx.Tensor<
f32[96][16][1][1]
EXLA.Backend
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>
},
"mobilenetv20_features_linearbottleneck2_batchnorm2_fwd" => %{
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f32[24]
EXLA.Backend
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>,
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EXLA.Backend
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EXLA.Backend
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f32[24]
EXLA.Backend
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"mobilenetv20_features_linearbottleneck8_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[384][1][3][3]
EXLA.Backend
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>
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"mobilenetv20_features_linearbottleneck11_conv1_fwd" => %{
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f32[576][1][3][3]
EXLA.Backend
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"mobilenetv20_features_linearbottleneck14_conv0_fwd" => %{
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f32[960][160][1][1]
EXLA.Backend
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>
},
"mobilenetv20_features_linearbottleneck15_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[960][1][3][3]
EXLA.Backend
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},
"mobilenetv20_features_linearbottleneck7_conv2_fwd" => %{
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f32[64][384][1][1]
EXLA.Backend
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>
},
"mobilenetv20_features_linearbottleneck4_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[192][1][3][3]
EXLA.Backend
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},
"mobilenetv20_features_linearbottleneck16_conv0_fwd" => %{
"kernel" => #Nx.Tensor<
f32[960][160][1][1]
EXLA.Backend
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},
"mobilenetv20_features_linearbottleneck10_batchnorm2_fwd" => %{
"beta" => #Nx.Tensor<
f32[96]
EXLA.Backend
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>,
"gamma" => #Nx.Tensor<
f32[96]
EXLA.Backend
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>,
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EXLA.Backend
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>,
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EXLA.Backend
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},
"mobilenetv20_features_linearbottleneck14_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[960][1][3][3]
EXLA.Backend
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>
},
"mobilenetv20_features_linearbottleneck12_conv0_fwd" => %{
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f32[576][96][1][1]
EXLA.Backend
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},
"mobilenetv20_features_linearbottleneck0_batchnorm1_fwd" => %{
"beta" => #Nx.Tensor<
f32[32]
EXLA.Backend
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>,
"gamma" => #Nx.Tensor<
f32[32]
EXLA.Backend
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>,
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f32[32]
EXLA.Backend
[0.6259415745735168, -0.08773765712976456, 1.0720137357711792, 2.3871850967407227, -5.589552402496338, 3.4055421352386475, ...]
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f32[32]
EXLA.Backend
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},
"mobilenetv20_features_linearbottleneck6_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[192][1][3][3]
EXLA.Backend
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>
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"mobilenetv20_features_linearbottleneck16_batchnorm2_fwd" => %{
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f32[320]
EXLA.Backend
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>,
"gamma" => #Nx.Tensor<
f32[320]
EXLA.Backend
[0.25120291113853455, 0.24765633046627045, 0.2482394427061081, 0.2461756020784378, 0.2500693202018738, ...]
>,
"mean" => #Nx.Tensor<
f32[320]
EXLA.Backend
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>,
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f32[320]
EXLA.Backend
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>
},
"mobilenetv20_features_linearbottleneck10_conv2_fwd" => %{
"kernel" => #Nx.Tensor<
f32[96][384][1][1]
EXLA.Backend
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},
"mobilenetv20_features_linearbottleneck8_conv0_fwd" => %{
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f32[384][64][1][1]
EXLA.Backend
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>
},
"mobilenetv20_features_conv0_fwd" => %{
"kernel" => #Nx.Tensor<
f32[32][3][3][3]
EXLA.Backend
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},
"mobilenetv20_features_linearbottleneck11_batchnorm2_fwd" => %{
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f32[96]
EXLA.Backend
[3.9526614159512974e-7, 5.761955890193349e-7, ...]
>,
"gamma" => #Nx.Tensor<
f32[96]
EXLA.Backend
[0.21182486414909363, ...]
>,
"mean" => #Nx.Tensor<
f32[96]
EXLA.Backend
[...]
>,
...
},
"mobilenetv20_features_linearbottleneck6_conv2_fwd" => %{
"kernel" => #Nx.Tensor<
f32[64][192][1][1]
EXLA.Backend
[
[
[
[0.07365027070045471]
],
...
],
...
]
>
},
"mobilenetv20_features_linearbottleneck3_conv2_fwd" => %{
"kernel" => #Nx.Tensor<
f32[32][144][1][1]
EXLA.Backend
[
...
]
>
},
"mobilenetv20_features_linearbottleneck4_conv0_fwd" => %{...},
...
}
}
model
|> Axon.sigmoid()
|> Axon.Loop.evaluator()
|> Axon.Loop.metric(:accuracy)
|> Axon.Loop.run(test_pipeline, trained_model_state, compiler: EXLA)
16:58:36.174 [debug] Forwarding options: [compiler: EXLA] to JIT compiler
Batch: 22, accuracy: 0.9755435
%{
0 => %{
"accuracy" => #Nx.Tensor<
f32
EXLA.Backend
0.9755434989929199
>
}
}
