Hands On: Minesweeper
Mix.install(
[
{:nx, "~> 0.6.3"},
{:exla, "~> 0.6.4"},
{:nimble_csv, "~> 1.2"}
],
config: [nx: [default_backend: EXLA.Backend]]
)Importing & Preparing Dataset
defmodule C7.Sonar do
@doc """
Inserts a column of 1's into position 0 of tensor X along the the x-axis
"""
def prepend_bias(x) do
{row, _col} = Nx.shape(x)
bias = Nx.broadcast(Nx.tensor(1), {row, 1})
Nx.concatenate([bias, x], axis: 1)
end
@doc "Flip hot values to 1"
def one_hot_encode(y) do
Nx.equal(y, Nx.tensor([0, 1]))
end
end{:module, C7.Sonar, <<70, 79, 82, 49, 0, 0, 9, ...>>, {:one_hot_encode, 1}}NimbleCSV.define(SonarParser, separator: ",", escape: "\"")
file = __DIR__ |> Path.join("/files") |> Path.join("sonar.all-data")
# Parse data from csv:
# Convert all labels to 0 and 1 for mines and rocks, respectively
# then convert all data to float
dataset =
file
|> File.stream!()
|> SonarParser.parse_stream()
|> Stream.map(fn list ->
list
|> Stream.map(fn
"M" -> 1
"R" -> 0
data -> String.to_float(data)
end)
|> Enum.to_list()
end)
|> Enum.to_list()
|> Nx.tensor(type: :f32)
# Shuffle data because sonar data contains rock examples first
{shuffled, _} = Nx.Random.shuffle(Nx.Random.key(999_999), dataset)
# Dataset has 208 examples, we'll use 48 of them for testing (~23%)
{training_set, test_set} = Nx.split(shuffled, 0.77)
# x: Get all columns except the last one (label)
x_train = training_set[[.., 0..-2//1]] |> C7.Sonar.prepend_bias()
x_test = test_set[[.., 0..-2//1]] |> C7.Sonar.prepend_bias()
# y: Get last column (label), one-hot encode only y_train because
# the classify/1 function will convert it back to decoded format
# which is either 1 or 0
y_train = training_set[[.., -1..-1//1]] |> C7.Sonar.one_hot_encode()
y_test = test_set[[.., -1..-1//1]]#Nx.Tensor<
f32[47][1]
EXLA.Backend
[
[1.0],
[1.0],
[0.0],
[0.0],
[1.0],
[1.0],
[1.0],
[1.0],
[0.0],
[1.0],
[0.0],
[1.0],
[0.0],
[1.0],
[1.0],
[0.0],
[1.0],
[1.0],
[1.0],
[0.0],
[0.0],
[1.0],
[0.0],
[1.0],
[1.0],
[1.0],
[0.0],
[1.0],
[0.0],
[1.0],
[1.0],
[0.0],
[0.0],
[1.0],
[1.0],
[1.0],
[0.0],
[1.0],
[0.0],
[0.0],
[1.0],
[1.0],
[0.0],
[0.0],
[1.0],
[1.0],
[1.0]
]
>Sonar Classifier
defmodule C7.SonarClassifier do
@moduledoc "Same as C7.MNISTClassifier"
import Nx.Defn
defn sigmoid(z) do
1 / (1 + Nx.exp(-z))
end
# renaming predict function to forward
def forward(x, w) do
weighted_sum = Nx.dot(x, w)
sigmoid(weighted_sum)
end
def classify(x, w) do
x
|> forward(w)
|> Nx.argmax(axis: 1)
|> Nx.reshape({:auto, 1})
end
def loss(x, y, w) do
y_hat = forward(x, w)
first_term = Nx.multiply(y, Nx.log(y_hat))
second_term = Nx.multiply(Nx.subtract(1, y), Nx.log(Nx.subtract(1, y_hat)))
first_term
|> Nx.add(second_term)
|> Nx.mean()
|> Nx.multiply(-1)
end
def gradient(x, y, w) do
{num_samples, _} = Nx.shape(x)
x
|> forward(w)
|> Nx.subtract(y)
|> then(&Nx.dot(Nx.transpose(x), &1))
|> Nx.divide(num_samples)
end
def train(x_train, y_train, x_test, y_test, iterations, lr) do
{_, x_cols} = Nx.shape(x_train)
{_, y_cols} = Nx.shape(y_train)
w = Nx.broadcast(0, {x_cols, y_cols})
Enum.reduce(0..iterations, w, fn i, w ->
gradient = gradient(x_train, y_train, w)
report(i, x_train, y_train, x_test, y_test, w)
Nx.subtract(w, Nx.multiply(gradient, lr))
end)
end
def report(iteration, x_train, y_train, x_test, y_test, w) do
matches =
x_test
|> classify(w)
|> Nx.equal(y_test)
|> Nx.sum()
{n_test_examples, _} = Nx.shape(x_test)
match_percentage = Nx.to_number(matches) * 100 / n_test_examples
training_loss = loss(x_train, y_train, w)
IO.puts("#{iteration} - Loss: #{Nx.to_number(training_loss)}, #{match_percentage}")
end
end{:module, C7.SonarClassifier, <<70, 79, 82, 49, 0, 0, 24, ...>>, {:report, 6}}Training
C7.SonarClassifier.train(x_train, y_train, x_test, y_test, 100_000, 0.01)...
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99870 - Loss: 0.34724587202072144, 78.72340425531915
99871 - Loss: 0.34724539518356323, 78.72340425531915
99872 - Loss: 0.3472449779510498, 78.72340425531915
99873 - Loss: 0.3472445607185364, 78.72340425531915
99874 - Loss: 0.3472440838813782, 78.72340425531915
99875 - Loss: 0.34724369645118713, 78.72340425531915
99876 - Loss: 0.34724321961402893, 78.72340425531915
99877 - Loss: 0.3472428619861603, 78.72340425531915
99878 - Loss: 0.34724241495132446, 78.72340425531915
99879 - Loss: 0.34724196791648865, 78.72340425531915
99880 - Loss: 0.3472415506839752, 78.72340425531915
99881 - Loss: 0.3472411334514618, 78.72340425531915
99882 - Loss: 0.34724071621894836, 78.72340425531915
99883 - Loss: 0.34724023938179016, 78.72340425531915
99884 - Loss: 0.3472398519515991, 78.72340425531915
99885 - Loss: 0.3472393751144409, 78.72340425531915
99886 - Loss: 0.3472389280796051, 78.72340425531915
99887 - Loss: 0.3472385108470917, 78.72340425531915
99888 - Loss: 0.34723812341690063, 78.72340425531915
99889 - Loss: 0.3472377359867096, 78.72340425531915
99890 - Loss: 0.347237229347229, 78.72340425531915
99891 - Loss: 0.34723684191703796, 78.72340425531915
99892 - Loss: 0.34723639488220215, 78.72340425531915
99893 - Loss: 0.3472359776496887, 78.72340425531915
99894 - Loss: 0.3472355306148529, 78.72340425531915
99895 - Loss: 0.3472351133823395, 78.72340425531915
99896 - Loss: 0.34723466634750366, 78.72340425531915
99897 - Loss: 0.34723424911499023, 78.72340425531915
99898 - Loss: 0.3472338020801544, 78.72340425531915
99899 - Loss: 0.3472333550453186, 78.72340425531915
99900 - Loss: 0.34723299741744995, 78.72340425531915
99901 - Loss: 0.34723252058029175, 78.72340425531915
99902 - Loss: 0.34723204374313354, 78.72340425531915
99903 - Loss: 0.3472316265106201, 78.72340425531915
99904 - Loss: 0.3472312092781067, 78.72340425531915
99905 - Loss: 0.34723085165023804, 78.72340425531915
99906 - Loss: 0.34723037481307983, 78.72340425531915
99907 - Loss: 0.347229927778244, 78.72340425531915
99908 - Loss: 0.347229540348053, 78.72340425531915
99909 - Loss: 0.3472290635108948, 78.72340425531915
99910 - Loss: 0.34722861647605896, 78.72340425531915
99911 - Loss: 0.34722819924354553, 78.72340425531915
99912 - Loss: 0.3472277522087097, 78.72340425531915
99913 - Loss: 0.3472273051738739, 78.72340425531915
99914 - Loss: 0.3472268879413605, 78.72340425531915
99915 - Loss: 0.34722650051116943, 78.72340425531915
99916 - Loss: 0.347226083278656, 78.72340425531915
99917 - Loss: 0.3472255766391754, 78.72340425531915
99918 - Loss: 0.34722521901130676, 78.72340425531915
99919 - Loss: 0.34722477197647095, 78.72340425531915
99920 - Loss: 0.3472243547439575, 78.72340425531915
99921 - Loss: 0.3472239375114441, 78.72340425531915
99922 - Loss: 0.34722352027893066, 78.72340425531915
99923 - Loss: 0.34722307324409485, 78.72340425531915
99924 - Loss: 0.34722262620925903, 78.72340425531915
99925 - Loss: 0.3472221791744232, 78.72340425531915
99926 - Loss: 0.3472217619419098, 78.72340425531915
99927 - Loss: 0.34722137451171875, 78.72340425531915
99928 - Loss: 0.34722092747688293, 78.72340425531915
99929 - Loss: 0.3472205102443695, 78.72340425531915
99930 - Loss: 0.3472200930118561, 78.72340425531915
99931 - Loss: 0.3472195863723755, 78.72340425531915
99932 - Loss: 0.34721916913986206, 78.72340425531915
99933 - Loss: 0.34721875190734863, 78.72340425531915
99934 - Loss: 0.3472183346748352, 78.72340425531915
99935 - Loss: 0.3472178876399994, 78.72340425531915
99936 - Loss: 0.34721747040748596, 78.72340425531915
99937 - Loss: 0.34721702337265015, 78.72340425531915
99938 - Loss: 0.34721654653549194, 78.72340425531915
99939 - Loss: 0.3472161293029785, 78.72340425531915
99940 - Loss: 0.3472157120704651, 78.72340425531915
99941 - Loss: 0.34721532464027405, 78.72340425531915
99942 - Loss: 0.34721481800079346, 78.72340425531915
99943 - Loss: 0.3472144305706024, 78.72340425531915
99944 - Loss: 0.347214013338089, 78.72340425531915
99945 - Loss: 0.3472135663032532, 78.72340425531915
99946 - Loss: 0.3472132086753845, 78.72340425531915
99947 - Loss: 0.3472127318382263, 78.72340425531915
99948 - Loss: 0.3472122848033905, 78.72340425531915
99949 - Loss: 0.34721189737319946, 78.72340425531915
99950 - Loss: 0.34721142053604126, 78.72340425531915
99951 - Loss: 0.34721100330352783, 78.72340425531915
99952 - Loss: 0.3472105860710144, 78.72340425531915
99953 - Loss: 0.347210168838501, 78.72340425531915
99954 - Loss: 0.34720975160598755, 78.72340425531915
99955 - Loss: 0.34720927476882935, 78.72340425531915
99956 - Loss: 0.34720882773399353, 78.72340425531915
99957 - Loss: 0.3472084403038025, 78.72340425531915
99958 - Loss: 0.34720802307128906, 78.72340425531915
99959 - Loss: 0.34720760583877563, 78.72340425531915
99960 - Loss: 0.34720712900161743, 78.72340425531915
99961 - Loss: 0.3472066819667816, 78.72340425531915
99962 - Loss: 0.3472062945365906, 78.72340425531915
99963 - Loss: 0.34720587730407715, 78.72340425531915
99964 - Loss: 0.3472054600715637, 78.72340425531915
99965 - Loss: 0.3472050130367279, 78.72340425531915
99966 - Loss: 0.3472045660018921, 78.72340425531915
99967 - Loss: 0.34720414876937866, 78.72340425531915
99968 - Loss: 0.34720373153686523, 78.72340425531915
99969 - Loss: 0.3472033143043518, 78.72340425531915
99970 - Loss: 0.3472028374671936, 78.72340425531915
99971 - Loss: 0.34720245003700256, 78.72340425531915
99972 - Loss: 0.34720200300216675, 78.72340425531915
99973 - Loss: 0.34720155596733093, 78.72340425531915
99974 - Loss: 0.3472011685371399, 78.72340425531915
99975 - Loss: 0.3472006916999817, 78.72340425531915
99976 - Loss: 0.3472002446651459, 78.72340425531915
99977 - Loss: 0.34719985723495483, 78.72340425531915
99978 - Loss: 0.347199410200119, 78.72340425531915
99979 - Loss: 0.3471989631652832, 78.72340425531915
99980 - Loss: 0.3471985459327698, 78.72340425531915
99981 - Loss: 0.34719815850257874, 78.72340425531915
99982 - Loss: 0.34719768166542053, 78.72340425531915
99983 - Loss: 0.3471972644329071, 78.72340425531915
99984 - Loss: 0.3471968173980713, 78.72340425531915
99985 - Loss: 0.34719642996788025, 78.72340425531915
99986 - Loss: 0.3471960425376892, 78.72340425531915
99987 - Loss: 0.347195565700531, 78.72340425531915
99988 - Loss: 0.3471951484680176, 78.72340425531915
99989 - Loss: 0.34719470143318176, 78.72340425531915
99990 - Loss: 0.34719425439834595, 78.72340425531915
99991 - Loss: 0.3471938371658325, 78.72340425531915
99992 - Loss: 0.3471933901309967, 78.72340425531915
99993 - Loss: 0.3471929430961609, 78.72340425531915
99994 - Loss: 0.34719252586364746, 78.72340425531915
99995 - Loss: 0.34719210863113403, 78.72340425531915
99996 - Loss: 0.3471916615962982, 78.72340425531915
99997 - Loss: 0.3471912443637848, 78.72340425531915
99998 - Loss: 0.347190797328949, 78.72340425531915
99999 - Loss: 0.34719038009643555, 78.72340425531915
100000 - Loss: 0.3471899628639221, 78.72340425531915#Nx.Tensor<
f32[61][2]
EXLA.Backend
[
[1.7620571851730347, -1.7620567083358765],
[-1.2212328910827637, 1.2212327718734741],
[-1.4051320552825928, 1.4051320552825928],
[-0.7617281675338745, 0.7617281675338745],
[-2.203519105911255, 2.203519105911255],
[-1.4716229438781738, 1.4716224670410156],
[0.47121602296829224, -0.47121602296829224],
[1.2278388738632202, -1.2278385162353516],
[1.8315479755401611, -1.8315479755401611],
[-1.6977713108062744, 1.6977708339691162],
[-1.6527485847473145, 1.6527485847473145],
[-3.0582785606384277, 3.058279037475586],
[-2.472179889678955, 2.472180128097534],
[-1.2199965715408325, 1.2199959754943848],
[0.6713770627975464, -0.6713771224021912],
[1.1136888265609741, -1.1136888265609741],
[1.4667856693267822, -1.4667855501174927],
[0.10807175934314728, -0.10807185620069504],
[0.2797262370586395, -0.2797263562679291],
[0.3058620095252991, -0.3058619797229767],
[-0.3211183547973633, 0.3211185336112976],
[-1.2587522268295288, 1.258752465248108],
[-1.427368402481079, 1.427367925643921],
[0.12842321395874023, -0.12842316925525665],
[-1.1204497814178467, 1.1204497814178467],
...
]
>Conclusion
With 100,000 iterations and learning rate of 0.01, the classifier can achieve a 78.72% accuracy