gorgonia.org/gorgonia@v0.9.17/nn_test.go

package gorgonia

import (
	"fmt"
	"io/ioutil"
	"math/rand"
	"runtime"
	"testing"

	"github.com/stretchr/testify/assert"
	"github.com/stretchr/testify/require"
	"gorgonia.org/dawson"
	"gorgonia.org/tensor"
)

func TestDropout(t *testing.T) {
	var tests = []struct {
		dt       tensor.Dtype
		prob     float64
		rand     interface{}
		expected interface{}
	}{
		{Float64, 0.0, []float64{0.0, 0.2, 0.5, 0.8, 1.0}, []float64{1.0, 1.0, 1.0, 1.0, 1.0}},
		{Float64, 0.2, []float64{0.0, 0.2, 0.5, 0.8, 1.0}, []float64{1.25, 1.25, 1.25, 0.0, 0.0}},
		{Float64, 0.5, []float64{0.0, 0.2, 0.5, 0.8, 1.0}, []float64{2.0, 2.0, 0.0, 0.0, 0.0}},
		{Float32, 0.2, []float32{0.0, 0.2, 0.5, 0.8, 1.0}, []float32{1.25, 1.25, 1.25, 0.0, 0.0}},
		{Float32, 0.5, []float32{0.0, 0.2, 0.5, 0.8, 1.0}, []float32{2.0, 2.0, 0.0, 0.0, 0.0}},
	}

	for _, tt := range tests {
		name := fmt.Sprintf("%v-%.1f", tt.dt, tt.prob)
		t.Run(name, func(t *testing.T) {
			randFn := func(g *ExprGraph, dt tensor.Dtype, low, high float64, shape ...int) *Node {
				return NewVector(g, dt, WithShape(shape...), WithInit(func(dt tensor.Dtype, s ...int) interface{} {
					return tt.rand
				}))
			}
			g := NewGraph()
			x := NewVector(g, tt.dt, WithShape(5), WithName("x"), WithInit(Ones()))
			do := Must(dropout(x, tt.prob, randFn))

			m := NewTapeMachine(g, BindDualValues())
			defer m.Close()
			defer runtime.GC()

			assert.NoError(t, m.RunAll())
			assert.Equal(t, tt.expected, do.Value().Data())
		})
	}
}

func dropoutTest(t *testing.T, dt tensor.Dtype) error {
	g := NewGraph()
	x := NewVector(g, dt, WithShape(10), WithName("x"), WithInit(RangedFrom(0)))
	w := NewMatrix(g, dt, WithShape(20, 10), WithName("w"), WithInit(RangedFrom(0)))
	w2 := NewMatrix(g, dt, WithShape(10, 20), WithName("w2"), WithInit(RangedFrom(0)))
	wx := Must(Mul(w, x))
	act := Must(Cube(wx))
	do := Must(Dropout(act, 0.5))

	act2 := Must(Cube(Must(Mul(w2, do))))
	do2 := Must(Dropout(act2, 0.1))
	cost := Must(Sum(do2))

	_, err := Grad(cost, x, w, w2)

	if err != nil {
		ioutil.WriteFile("fullGraph.dot", []byte(g.ToDot()), 0644)
		// t.Fatalf("%+v", err)
		return err
	}

	// logger := log.New(os.Stderr, "", 0)

	// m := NewTapeMachine(g, TraceExec(), BindDualValues(), WithLogger(logger), WithWatchlist())
	m := NewTapeMachine(g, TraceExec(), BindDualValues())
	defer m.Close()
	cudaLogf("%v", m.Prog())
	defer runtime.GC()
	if err := m.RunAll(); err != nil {
		return err
	}
	return nil
}

func TestDropout_integration(t *testing.T) {
	// t.Skip()

	if err := dropoutTest(t, Float64); err != nil {
		t.Errorf("%+v", err)
	}

	if err := dropoutTest(t, Float32); err != nil {
		t.Errorf("%+v", err)
	}

	// visual inspection
	// ioutil.WriteFile("fullGraph.dot", []byte(g.ToDot()), 0644)
}

var im2colTests = []struct {
	kernel   tensor.Shape
	pad      tensor.Shape
	stride   tensor.Shape
	dilation tensor.Shape
}{
	{tensor.Shape{4, 4}, tensor.Shape{0, 0}, tensor.Shape{1, 1}, tensor.Shape{1, 1}},
	{tensor.Shape{3, 3}, tensor.Shape{1, 1}, tensor.Shape{2, 2}, tensor.Shape{1, 1}},
	{tensor.Shape{3, 3}, tensor.Shape{1, 1}, tensor.Shape{3, 3}, tensor.Shape{1, 1}},
}

func im2colTest(t *testing.T, dt tensor.Dtype, kernel, pad, stride, dilation tensor.Shape) {
	assert := assert.New(t)
	g := NewGraph()
	x := NewTensor(g, dt, 4, WithShape(2, 1, 28, 28), WithInit(RangedFrom(0))) // mnist, in batches of 10
	y, err := Im2Col(x, kernel, pad, stride, dilation)
	if err != nil {
		t.Error(err)
		return
	}
	cost := Must(Sum(y))

	grads, err := Grad(cost, x)
	if err != nil {
		t.Errorf("error while Grad(): %v", err)
		return
	}

	m := NewTapeMachine(g, BindDualValues())
	defer m.Close()
	if err := m.RunAll(); err != nil {
		t.Error(err)
		return
	}
	// t.Logf("x: %v", x.Value())
	// t.Logf("c: %3.3f", cost.Value())
	// t.Logf("xG: %v", grads[0].Value())

	h := NewGraph()
	a := NewTensor(h, dt, 4, WithShape(2, 1, 28, 28), WithInit(RangedFrom(0)))
	b, err := Im2Col(a, kernel, pad, stride, dilation)
	if err != nil {
		t.Error(err)
		return
	}
	cost2 := Must(Sum(b))
	n := NewLispMachine(h)
	defer n.Close()
	if err = n.RunAll(); err != nil {
		t.Error(err)
		return
	}
	aG, err := a.Grad()
	if err != nil {
		t.Error(err)
		return
	}

	// t.Logf("a: %v", a.Value())
	// t.Logf("c: %3.3f", cost2.Value())
	// t.Logf("aG: %v", aG)

	assert.Equal(x.Value().Data(), a.Value().Data())
	assert.Equal(grads[0].Value().Data(), aG.Data())
	assert.Equal(cost.Value().Data(), cost2.Value().Data())
}

func TestIm2Col(t *testing.T) {
	// assert := assert.New(t)
	dts := []tensor.Dtype{tensor.Float64, tensor.Float32}
	for _, dt := range dts {
		for _, i2ct := range im2colTests {
			im2colTest(t, dt, i2ct.kernel, i2ct.pad, i2ct.stride, i2ct.dilation)
		}
	}
}

func TestMaxPool2D_asymmetricPadding(t *testing.T) {
	assert := assert.New(t)
	tsts := []struct {
		inputT  tensor.Tensor
		outputT tensor.Tensor
		padding []int
	}{
		{
			inputT: tensor.New(tensor.WithShape(1, 3, 32, 32),
				tensor.WithBacking([]float32{1.7640524, 0.4001572, 0.978738, 2.2408931, 1.867558, -0.9772779, 0.95008844, -0.1513572, -0.10321885, 0.41059852, 0.14404356, 1.4542735, 0.7610377, 0.121675014, 0.44386324, 0.33367434, 1.4940791, -0.20515826, 0.3130677, -0.85409576, -2.5529897, 0.6536186, 0.8644362, -0.742165, 2.2697546, -1.4543657, 0.045758516, -0.18718386, 1.5327792, 1.4693588, 0.15494743, 0.37816253, -0.88778573, -1.9807965, -0.34791216, 0.15634897, 1.2302907, 1.2023798, -0.3873268, -0.30230275, -1.048553, -1.420018, -1.7062702, 1.9507754, -0.5096522, -0.4380743, -1.2527953, 0.7774904, -1.6138978, -0.21274029, -0.89546657, 0.3869025, -0.51080513, -1.1806322, -0.028182229, 0.42833188, 0.06651722, 0.3024719, -0.6343221, -0.36274117, -0.67246044, -0.35955316, -0.8131463, -1.7262826, 0.17742614, -0.40178093, -1.6301984, 0.46278226, -0.9072984, 0.051945396, 0.7290906, 0.12898292, 1.1394007, -1.2348258, 0.40234163, -0.6848101, -0.87079716, -0.5788497, -0.31155252, 0.05616534, -1.1651498, 0.9008265, 0.46566245, -1.5362437, 1.4882522, 1.8958892, 1.1787796, -0.17992483, -1.0707526, 1.0544517, -0.40317693, 1.222445, 0.20827498, 0.97663903, 0.3563664, 0.7065732, 0.01050002, 1.7858706, 0.12691209, 0.40198937, 1.8831507, -1.347759, -1.270485, 0.9693967, -1.1731234, 1.9436212, -0.41361898, -0.7474548, 1.922942, 1.4805148, 1.867559, 0.90604466, -0.86122566, 1.9100649, -0.26800337, 0.8024564, 0.947252, -0.15501009, 0.61407936, 0.9222067, 0.37642553, -1.0994008, 0.2982382, 1.3263859, -0.69456786, -0.14963454, -0.43515354, 1.8492638, 0.67229474, 0.40746182, -0.76991606, 0.5392492, -0.6743327, 0.031830557, -0.6358461, 0.67643327, 0.57659084, -0.20829876, 0.3960067, -1.0930616, -1.4912575, 0.4393917, 0.1666735, 0.63503146, 2.3831449, 0.94447947, -0.91282225, 1.1170163, -1.3159074, -0.4615846, -0.0682416, 1.7133427, -0.74475485, -0.82643855, -0.09845252, -0.6634783, 1.1266359, -1.0799315, -1.1474687, -0.43782005, -0.49803245, 1.929532, 0.9494208, 0.08755124, -1.2254355, 0.844363, -1.0002153, -1.5447711, 1.1880298, 0.3169426, 0.9208588, 0.31872764, 0.8568306, -0.6510256, -1.0342429, 0.6815945, -0.80340964, -0.6895498, -0.4555325, 0.017479159, -0.35399392, -1.3749512, -0.6436184, -2.2234032, 0.62523144, -1.6020577, -1.1043833, 0.05216508, -0.739563, 1.5430146, -1.2928569, 0.26705086, -0.039282817, -1.1680934, 0.5232767, -0.17154633, 0.77179056, 0.82350415, 2.163236, 1.336528, -0.36918184, -0.23937918, 1.0996596, 0.6552637, 0.64013153, -1.616956, -0.024326125, -0.7380309, 0.2799246, -0.09815039, 0.9101789, 0.3172182, 0.78632796, -0.4664191, -0.94444627, -0.4100497, -0.017020414, 0.37915173, 2.259309, -0.042257152, -0.955945, -0.34598178, -0.463596, 0.48148146, -1.540797, 0.06326199, 0.15650654, 0.23218104, -0.5973161, -0.23792173, -1.424061, -0.49331987, -0.54286146, 0.41605005, -1.1561824, 0.7811981, 1.4944845, -2.069985, 0.42625874, 0.676908, -0.63743705, -0.3972718, -0.13288058, -0.29779088, -0.30901298, -1.6760038, 1.1523316, 1.0796186, -0.81336427, -1.4664243, 0.5210649, -0.57578796, 0.14195317, -0.31932843, 0.69153875, 0.6947491, -0.7255974, -1.383364, -1.5829384, 0.6103794, -1.1888592, -0.5068163, -0.596314, -0.052567296, -1.9362798, 0.1887786, 0.52389103, 0.08842209, -0.31088617, 0.097400166, 0.39904633, -2.7725928, 1.9559124, 0.39009333, -0.6524086, -0.39095336, 0.49374178, -0.11610394, -2.0306845, 2.064493, -0.11054066, 1.0201727, -0.69204986, 1.5363771, 0.2863437, 0.60884386, -1.0452534, 1.2111453, 0.68981814, 1.3018463, -0.6280876, -0.48102713, 2.3039167, -1.0600158, -0.1359497, 1.1368914, 0.09772497, 0.5829537, -0.39944902, 0.37005588, -1.3065269, 1.6581306, -0.11816405, -0.6801782, 0.6663831, -0.4607198, -1.3342584, -1.3467175, 0.69377315, -0.15957344, -0.13370156, 1.0777438, -1.1268258, -0.7306777, -0.3848798, 0.09435159, -0.042171452, -0.2868872, -0.0616264, -0.10730527, -0.7196044, -0.812993, 0.27451634, -0.8909151, -1.1573553, -0.31229225, -0.15766701, 2.2567234, -0.7047003, 0.9432607, 0.7471883, -1.1889449, 0.77325296, -1.1838807, -2.6591723, 0.60631955, -1.7558906, 0.45093447, -0.6840109, 1.6595508, 1.0685093, -0.4533858, -0.6878376, -1.2140774, -0.44092262, -0.28035548, -0.36469355, 0.15670386, 0.5785215, 0.34965447, -0.76414394, -1.4377915, 1.3645319, -0.6894492, -0.6522936, -0.52118933, -1.8430696, -0.477974, -0.4796558, 0.6203583, 0.6984571, 0.003770889, 0.93184835, 0.339965, -0.015682112, 0.16092817, -0.19065349, -0.3948495, -0.26773354, -1.1280113, 0.2804417, -0.9931236, 0.8416313, -0.24945858, 0.04949498, 0.4938368, 0.6433145, -1.5706234, -0.20690368, 0.8801789, -1.6981058, 0.38728046, -2.2555642, -1.0225068, 0.038630553, -1.6567152, -0.98551077, -1.471835, 1.648135, 0.16422775, 0.5672903, -0.2226751, -0.35343176, -1.6164742, -0.29183736, -0.7614922, 0.8579239, 1.1411018, 1.4665787, 0.85255194, -0.5986539, -1.1158969, 0.7666632, 0.3562928, -1.7685385, 0.3554818, 0.8145198, 0.058925588, -0.18505368, -0.8076485, -1.4465348, 0.800298, -0.30911446, -0.23346666, 1.7327212, 0.6845011, 0.370825, 0.1420618, 1.5199949, 1.7195894, 0.9295051, 0.5822246, -2.094603, 0.12372191, -0.13010696, 0.09395323, 0.9430461, -2.7396772, -0.56931204, 0.26990435, -0.46684554, -1.4169061, 0.8689635, 0.27687192, -0.97110456, 0.3148172, 0.8215857, 0.005292646, 0.8005648, 0.078260176, -0.39522898, -1.1594205, -0.085930765, 0.19429293, 0.87583274, -0.11510747, 0.4574156, -0.964612, -0.78262913, -0.1103893, -1.0546285, 0.8202478, 0.46313033, 0.27909577, 0.3389041, 2.0210435, -0.4688642, -2.2014413, 0.1993002, -0.050603542, -0.51751906, -0.97882986, -0.43918952, 0.18133843, -0.5028167, 2.4124537, -0.96050435, -0.79311734, -2.28862, 0.25148442, -2.0164065, -0.53945464, -0.27567053, -0.70972794, 1.7388726, 0.99439436, 1.3191369, -0.8824188, 1.128594, 0.49600095, 0.77140594, 1.0294389, -0.90876323, -0.42431763, 0.86259604, -2.6556191, 1.5133281, 0.55313206, -0.045703962, 0.22050765, -1.0299352, -0.34994337, 1.1002843, 1.298022, 2.696224, -0.07392467, -0.65855294, -0.51423395, -1.0180418, -0.07785475, 0.38273242, -0.03424228, 1.0963469, -0.2342158, -0.34745064, -0.5812685, -1.6326345, -1.5677677, -1.179158, 1.3014281, 0.8952603, 1.3749641, -1.3322116, -1.9686247, -0.6600563, 0.17581895, 0.49869028, 1.0479722, 0.28427967, 1.7426687, -0.22260568, -0.9130792, -1.6812183, -0.8889713, 0.24211796, -0.8887203, 0.9367425, 1.4123276, -2.369587, 0.8640523, -2.239604, 0.40149906, 1.2248706, 0.064856105, -1.2796892, -0.5854312, -0.26164544, -0.18224478, -0.20289683, -0.10988278, 0.21348006, -1.2085737, -0.24201983, 1.5182612, -0.38464543, -0.4438361, 1.0781974, -2.5591846, 1.1813786, -0.63190377, 0.16392857, 0.09632136, 0.9424681, -0.26759475, -0.6780258, 1.2978458, -2.364174, 0.020334182, -1.3479254, -0.7615734, 2.0112567, -0.044595428, 0.1950697, -1.7815628, -0.7290447, 0.1965574, 0.3547577, 0.61688656, 0.008627899, 0.5270042, 0.4537819, -1.8297404, 0.037005723, 0.76790243, 0.5898798, -0.36385882, -0.8056265, -1.1183119, -0.13105401, 1.1330799, -1.9518042, -0.6598917, -1.1398025, 0.7849575, -0.5543096, -0.47063765, -0.21694957, 0.44539326, -0.392389, -3.046143, 0.5433119, 0.43904296, -0.21954103, -1.0840366, 0.35178012, 0.37923554, -0.47003287, -0.21673147, -0.9301565, -0.17858909, -1.5504293, 0.41731882, -0.9443685, 0.23810315, -1.405963, -0.5900577, -0.110489406, -1.6606998, 0.115147874, -0.37914756, -1.7423562, -1.3032428, 0.60512006, 0.895556, -0.13190864, 0.40476182, 0.22384356, 0.32962298, 1.285984, -1.5069984, 0.67646074, -0.38200897, -0.22425893, -0.30224973, -0.3751471, -1.2261962, 0.1833392, 1.670943, -0.05613302, -0.0013850428, -0.687299, -0.11747455, 0.46616644, -0.37024245, -0.45380405, 0.40326455, -0.91800475, 0.25249663, 0.8203218, 1.3599485, -0.09038201, 1.3675972, 1.0344099, -0.99621266, -1.2179385, -0.30496365, 1.0289356, -0.07228701, -0.6006576, 1.5522432, 0.28690448, -2.3205943, 0.31716064, 0.52004063, 0.22560866, 0.4497121, -0.067275606, -1.3183959, -0.370704, -0.94561577, -0.9327409, -1.2630683, 0.45248908, 0.097896144, -0.44816536, 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0.40955177, 0.40955177, 1.5116394, 1.7064682, 1.7064682, 0.70178336, 0.07328543, 0.5298338, 1.7108365, 1.7108365, 1.414415, -0.063661486, -0.11244375, 0.89786416, 0.89786416, 1.400689, 1.400689, 0.50481546, 1.3031809, 1.3031809, 1.0659268, 0.063896194, -0.9659539, 0.41609964, 0.41609964, 0.12253132, 0.12253132, 0.66414404, 0.66414404, 1.1279566, 1.8961822, 1.8961822, -0.28157544, -0.28157544, 0.44478136, 1.0223923, 1.0223923, 2.2856011, 2.2856011, 0.5298338, 0.8097407, 1.0799313, 1.0799313, 1.094167, -0.11244375, 1.131812, 1.131812, 0.29507577, -0.49941677, 0.1746801, 0.1746801, 1.2505698, 1.2505698, 0.063896194, -0.8633556, 0.14166717, 0.14166717, 1.3440744, 1.3440744, 0.5000167, -0.6289807, 1.1279566, 1.1279566, 1.7345722, 1.7345722, 0.57261336, 0.57261336, 0.87550914, 1.0985817, 2.2856011, 2.2856011, 0.81345224, 0.8097407, 1.0799313, 1.0799313, 1.094167, 0.42425263, 1.6295, 1.6295, -0.3000036, -0.3000036, -0.9325702, 1.7969185, 1.7969185, 1.2505698, -0.19871506, 1.4198639, 1.4198639, 0.6795679, 1.3440744, 1.3440744, 0.5670582, 0.9882406, 1.0700725, 1.0700725, 1.7345722, 1.7345722, 1.1035038, 0.57261336, 1.4099522, 1.4099522, 1.0985817, 0.81345224, 0.81345224, 0.75386566, 0.75386566, 0.36559147, 0.5880377, 1.55701, 1.6295, 1.6295, -0.3000036, -0.18779492, 0.4869373, 1.7969185, 1.7969185, 0.5953003, 1.1641518, 1.4198639, 1.4198639, 0.7289299, 0.6795679, -0.32125893, 0.5670582, 0.9882406, 1.0137506, 1.0137506, 0.50666904, 1.1035038, 1.1035038, 0.5569641, 1.4099522, 1.4099522, 0.77946055, 0.77946055, 0.28791544, 0.28791544, 0.36559147, 0.36559147, 0.924434, 1.55701, 1.55701, 1.0121332, 1.6184593, 1.6184593, 0.57110983, 0.4869373, 0.20579681, 0.5953003, 1.1641518, 1.1641518, 0.7289299, 0.7289299, -0.034475047, 0.072677895, 0.072677895, 0.3327982, 1.0137506, 1.0137506, 0.50666904, 2.4643219, 2.4643219, 1.1320051, 1.1320051, 0.6530373, -0.18844908, 0.2872573, 0.2872573, 1.3363862, 1.3363862, -0.0842233, 0.924434, 0.924434, 1.0121332, 1.7140515, 1.7140515, 1.6184593, 0.7669904, -0.45962644, 0.20579681, 0.3263751, 1.4831287, 1.4831287, 0.23908707, 0.650042, 0.650042, 0.23868157, 0.23868157, -0.11622244, 0.85464275, 0.85464275, 0.92660475, 2.4643219, 2.4643219, 1.1320051, 1.1320051, 0.409295, 0.39710623, 0.49964696, 0.49964696, 1.3363862, 1.3363862, -0.120041125, 0.46146888, -0.046481155, 0.037996013, 1.7140515, 1.7140515, 0.7669904, 0.86214805, 0.86214805, 0.4883706, 0.9659361, 1.4831287, 1.9612269, 1.9612269, 0.650042, 0.650042, 1.0616002, 1.0616002, -0.11622244, 0.830386, 0.830386, 0.92660475, 1.8004627, 1.8004627, 0.51650745, 0.51650745, 0.409295, 0.39710623, 0.49964696, 0.49964696, 0.24622276, 0.24622276, 0.09082593, 1.3970653, 1.497715, 1.497715, 0.5652672, 0.54985684, 0.40713033, 0.86214805, 0.86214805, 0.8989674, 0.9659361, 1.4226048, 1.9612269, 1.9612269, 1.3752254, 1.4994915, 1.4994915, 1.0616002, -0.86050975, 0.830386, 0.830386, 0.8576619, 0.8576619, 0.40807465, 1.0770375, 1.0770375, 0.016491488, 0.5746089, 0.5746089, 0.65880215, 0.65880215, 0.16987796, 0.09082593, 1.3970653, 1.497715, 1.497715, 0.81547195, 0.48096985, 0.48096985, 0.85282975, 0.85282975, 1.0044192, 1.0044192, 1.3141544, 1.3141544, 1.3752254, 1.3752254, 1.9258057, 1.951761, 1.951761, 1.2399405, 0.93858516, 0.5125622, 0.8576619, 0.8576619, -0.1105905, 1.0770375, 1.0770375, -0.22482656, 0.7920415, 0.7920415, 0.65880215, 0.65880215, 0.1118724, -0.22295918, 0.3105647, 0.3105647, 0.81547195, 0.81547195, 0.48096985, 0.69865024, 0.85282975, 0.85282975, 1.0044192, 1.0044192, 0.7315516, 0.6335339, 0.6335339, 0.8927082, 1.9258057, 1.951761, 1.951761, 1.9093426, 1.9093426, 1.6638731, 0.90082276, -0.35911658, -0.50900584, 1.6531498, 1.6531498, 0.38616636, 0.7920415, 0.7920415, 0.38182402, 0.3982961, 0.3982961, -0.67570317, 1.2228775, 0.3105647, 0.9719703, 0.9719703, 0.35026592, 0.69865024, 0.69865024, 0.5216252, 0.49689314, 0.7315516, 0.7315516, 0.3196498, -0.40985453, 0.8927082, 0.8927082, 0.63438666, 1.6292758, 1.9093426, 1.9093426, 1.6638731, 0.90082276, 0.7046427, 0.7046427, 1.6531498, 1.6531498, 0.7703309, 0.47348827, 1.8552462, 1.8552462, 1.4156562, 0.98967946, 0.98967946, 1.2228775, 1.1363881, 0.9719703, 0.9719703, 0.572627, 1.9026182, 1.9026182, -0.09302414, -0.18808974, 1.1778295, 1.1778295, -0.40176374, 0.3750199, 0.7979132, 0.7979132, 0.63438666, 1.6292758, 1.6292758, 0.13906415, -0.8576702, 0.88833886, 0.88833886, 0.7046427, 0.93679523, 0.93679523, 1.3289608, 1.3289608, 1.8552462, 1.8552462, 1.4156562, 0.98967946, 0.98967946, 1.1363881, 1.1363881, 0.67161655, 0.2501139, 0.572627, 1.9026182, 1.9026182, 0.64221096, 1.040239, 1.1778295, 1.1778295, 1.1445535, 1.1445535, 1.283455, 1.283455, 2.1886187, 2.1886187, 0.90072393, 0.90072393, 0.8913641, 0.88833886, 0.88833886, -0.52474195, 0.1265688, 0.7930071, 1.3289608, 1.3289608, 0.86301714, 0.86301714, 0.7271749, 0.7375215, 0.7375215, 0.43311873, 1.3207943, 1.3207943, 0.2501139, -0.28339776, 0.8165349, 0.8165349, 0.64221096, 1.040239, 1.0875463, 1.0875463, 1.1445535, 1.1445535, 1.283455, 1.283455, 2.1886187, 2.1886187, 0.90072393, 0.90072393, 0.8913641, -0.17248231, 0.29577908, 0.8425888, 0.8425888, 0.7930071, 0.7930071, 1.0061071, 1.0061071, 1.9595621, 1.9595621, 0.9423143, 0.7375215, 0.7550497, 1.3207943, 1.3207943, -0.09406245, 0.39756522, 0.8165349, 0.8165349, 0.6006052, 0.6006052, 1.0875463, 1.0875463, 0.73930454, 0.61915493, -0.8907292, 0.9219348, 0.9219348, 0.5225576, 0.5225576, 0.45028883, 0.45028883, 1.2341441, 1.4498476, 1.4498476, 0.8425888, 0.24561642, -0.03299648, 1.0061071, 1.0061071, 2.0118642, 2.0118642, 1.336235, 1.336235, 1.3423537, 0.6021635, 0.8732731, 1.9741, 1.9741, 0.47780856, -0.060137887, 0.6006052, 1.0241649, 1.0241649, 0.24461035, 0.089076206, 0.089076206, 0.26473877, 0.9219348, 0.9219348, 0.59542316, 0.59542316, 0.45028883, 0.45028883, 1.2341441, 1.4498476, 1.4498476, -0.24508175, -0.24508175, 0.3140257, 0.37287602, 0.8862932, 2.0118642, 2.0118642, 1.336235, 1.336235, 1.3423537, 0.6498296, 0.8732731, 1.9741, 1.9741, 1.8571023, 1.8571023, 0.41467425, 1.0241649, 1.0241649, 0.24461035, 0.6500845, 1.8209393, 1.8209393, 1.4540358, 1.4540358, 0.59542316, 1.0703601, 1.0703601, 1.2424939, 1.2424939, 1.060715, 1.060715, 0.66200536, 0.66200536, 0.3140257, 0.37287602, 0.8862932, 0.8862932, 0.7312936, 0.07077408, 0.07077408, 0.8188394, 0.6498296, 0.6498296, 0.4347888, 0.09725088, 1.8571023, 1.8571023, 0.66939247, 0.73860705, 1.3042139, 1.3042139, 0.6500845, 1.8209393, 1.8209393, 1.4540358, 1.4540358, 0.2934714, 1.0703601, 1.0703601, 1.2424939, 1.2424939, 0.65592444, -0.038476657, 0.66200536, 0.66200536, 0.50434357, -0.21653287, -0.21653287, 0.7312936, 0.8123336, 0.8123336, 0.54627186, 0.51226676, 0.51226676, 0.3251987, 0.3251987, 0.09725088, 0.679944, 0.679944, 0.66939247, 2.2542837, 2.2542837, 1.3042139, 0.2598325, -0.6783298, -0.083288394, 1.6028637, 1.6028637, 0.4655892, 1.176787, 1.176787, 1.6973464, 1.6973464, 0.65592444, 0.17462958, 0.982327, 1.0374448, 1.0374448, 0.15919177, -0.5053407, -0.5053407, 0.8123336, 0.8123336, 0.54627186, 0.51226676, 0.51226676, 0.08584311, 0.7510253, 0.7510253, 0.8375479, 1.9608808, 1.9608808, 2.2542837, 2.2542837, 1.2098372, 1.2098372, -0.6783298, -0.083288394, 1.6028637, 1.6028637, 0.78990495, 1.176787, 1.176787, 1.6973464, 1.6973464, 0.7591567, 0.17462958, 0.982327, 1.0374448, 1.0374448, 0.5769559, 0.5769559, 0.23497719, 0.6289996, 0.6289996, 0.31403384, 1.0122606, 1.0122606, 0.92874193, 1.081056, 1.5723304, 1.5723304, 1.9608808, 1.9608808, 0.79388034, 0.04399551, 1.2098372, 1.2098372, 0.32099947, 0.32099947, 0.5922057, 0.5922057, 0.78990495, 1.1022825, 1.1022825, 0.5769346, 0.8183537, 0.8183537, 0.100564204, 0.1066523, 1.5190033, 1.5190033, 1.90134, 1.90134, 0.27441698, 0.6289996, 0.6289996, 0.31403384, 1.0122606, 1.0122606, 0.92874193, 1.081056, 1.5723304, 1.5723304, 0.13998847, 0.79388034, 0.79388034, 0.04399551, 0.49097106, 0.8655252, 1.2740445, 1.2740445, 0.5922057, 0.5922057, 0.37317473, 1.0826722, 1.0826722, 1.072636, 1.072636, 0.8183537, -0.13580984, 1.0222104, 1.5190033, 1.5190033, 1.90134, 1.90134, 1.2466952, 1.2466952, 0.70068014, 0.6966507, -0.20697448, 0.6772459, 0.6772459, 0.022212664, 0.8982406, 0.8982406, 0.13998847, 0.9091885, 0.9091885, 0.08071989, 0.49097106, 0.8655252, 2.3374097, 2.3374097, 0.4693722, 1.5237712, 1.5237712, 1.0826722, 1.0826722, 1.226664, 1.226664, 0.8261257, 0.8261257, 1.0222104, 1.0222104, 0.13603121, 0.13603121, 0.5614499, 1.2466952, 1.2466952, 0.70068014, 0.6966507, -0.20697448, 0.6772459, 0.6772459, 1.3257079, 1.3257079, 0.8982406, 0.09152195, 1.0534397, 1.0534397, 1.0147377, 1.4403037, 1.4403037, 2.3374097, 2.3374097, 1.5148823, 1.6043264, 1.6043264, 0.20806953, -0.4292239, 1.226664, 1.226664, 0.8261257, 0.8261257, -0.057756558, -0.21716312, 0.13603121, 0.13603121, 0.5614499, 0.5614499, -0.33275875, -0.20400788, -0.20400788, 1.2598753, 1.2598753, 0.44786966, 1.3257079, 1.3257079, 1.0121683, 1.3841867, 1.3841867, 1.252002, 1.0147377, 1.4403037, 1.4403037, 1.6264315, 1.6264315, 1.5148823, 1.6043264, 1.6043264, 0.6941287, 0.66396505, 0.8220764, 0.8220764, -0.21321523, -0.15279447, 0.59172696, 0.59172696, -0.13022317, 0.61295235, 0.61295235, 0.12955453, 1.17942, 1.17942, 0.836636, 1.2598753, 1.2598753, 0.24099673, -0.28044778, 0.40508026, 1.0121683, 1.3841867, 1.3841867, 1.252002, 0.71178526, 0.27372944, 0.5620131, 0.7955496, 0.7955496, 0.49585068, 0.49585068, 0.6941287, 0.6941287, 0.66396505, 0.8220764, 0.8220764, 1.2412993, 1.2412993, 0.59172696, 0.59172696, -0.05872619, 0.61295235, 0.61295235, 0.12955453, 1.17942, 1.9457614, 1.9457614, 1.0723007, 1.0723007, -0.3070685, -0.3070685, 0.8528891, 0.8528891, 0.16491273, 0.16491273, 0.71178526, 1.9677297, 1.9677297, 0.44768322, 0.7955496, 0.7955496, 0.49585068, 0.49585068, 0.49908426, 0.49908426, 2.0791767, 2.0791767, 0.31263396, 1.2412993, 1.2412993, 1.5932736, 1.5932736, 0.5687224, 0.25163025, 0.25163025, -0.17724662, 0.19939707, 1.9457614, 1.9457614, 1.0723007, 1.0723007, 0.2791965, 0.004174999, 0.8528891, 0.8528891, 0.13468726, 0.13468726, 0.10233585, 1.9677297, 1.9677297, 0.44768322, -0.15065365, -0.15065365, -0.23469436, 0.8997308, 0.8997308, 0.32003194, 2.0791767, 2.0791767, 0.4723279, 0.9663058, 0.9663058, 1.5932736, 1.5932736, 0.74380016, 0.74380016, 0.5921491, -0.3937337, 0.085252576, 1.6616518, 1.6616518, 2.3602285, 2.3602285, 0.5555456, 0.43952233, 0.99914986, 0.99914986, 2.1600132, 2.1600132, -0.01155552, 0.83949065, 1.0923389, 1.0923389, 0.5668694, 0.5668694, -0.23469436, 0.8997308, 0.8997308, 0.76354146, 1.5703039, 1.5703039, 0.4723279, 0.9663058, 0.9663058, 0.21023144, 0.8961672, 0.8961672, 0.74380016, 1.1781894, 1.1781894, -0.94154567, 1.6616518, 1.6616518, 2.3602285, 2.3602285, 0.5555456, 0.43952233, 0.99914986, 0.99914986, 2.1600132, 2.1600132, -0.100301705, 0.302561, 1.0923389, 1.0923389, 0.5668694, 0.5668694, -0.5062735, -0.48948243, 0.76354146, 0.76354146, 0.1926161, 0.1926161, -0.34341785, -0.84721017, -1.2028884, -1.2028884, 0.8961672, 0.8961672, 0.15865193, 1.1781894, 1.1781894, -0.94154567, 0.25471553, 0.25471553}),
			),
		},
	}

	for _, tst := range tsts {
		g := NewGraph()
		x := NodeFromAny(g, tst.inputT)
		y, err := MaxPool2D(x, tensor.Shape{2, 2}, []int{1, 0, 1, 0}, []int{1, 1})
		if err != nil {
			t.Fatal(err)
		}
		m := NewTapeMachine(g, BindDualValues())
		if err := m.RunAll(); err != nil {
			t.Fatal(err)
		}
		m.Close()
		if len(y.Shape()) != len(tst.outputT.Shape()) {
			t.Fatalf("Maxpool: expected shape %v, got %v", tst.outputT.Shape(), y.Shape())
		}
		for i, v := range y.Shape() {
			if v != tst.outputT.Shape()[i] {
				t.Fatalf("Maxpool: expected shape %v, got %v", tst.outputT.Shape(), y.Shape())
			}
		}
		assert.Equal(y.Value().Data(), tst.outputT.Data())
	}
}

func TestMaxPool2D(t *testing.T) {
	assert := assert.New(t)
	dts := []tensor.Dtype{tensor.Float64, tensor.Float32}
	for _, dt := range dts {
		g := NewGraph()
		x := NewTensor(g, dt, 4, WithShape(1, 2, 3, 4), WithInit(RangedFrom(0)))
		y, err := MaxPool2D(x, tensor.Shape{2, 2}, []int{0, 0}, []int{1, 1})
		if err != nil {
			t.Fatal(err)
		}
		cost := Must(Sum(y))
		grads, err := Grad(cost, x)
		if err != nil {
			t.Fatal(err)
		}

		m := NewTapeMachine(g, BindDualValues())
		if err := m.RunAll(); err != nil {
			t.Fatal(err)
		}
		// t.Logf("x %v", x.Value())
		// t.Logf("y: %v", y.Value())
		// t.Logf("c: %v", cost.Value())
		// t.Logf("xG: %v", grads[0])

		h := NewGraph()
		a := NewTensor(h, dt, 4, WithShape(1, 2, 3, 4), WithInit(RangedFrom(0)))
		b, err := MaxPool2D(a, tensor.Shape{2, 2}, []int{0, 0}, []int{1, 1})
		if err != nil {
			t.Fatal(err)
		}
		cost2 := Must(Sum(b))
		if err != nil {
			t.Fatal(err)
		}

		m2 := NewLispMachine(h)
		if err = m2.RunAll(); err != nil {
			t.Fatal(err)
		}
		aG, err := a.Grad()
		if err != nil {
			t.Error(err)
			return
		}

		assert.Equal(x.Value().Data(), a.Value().Data())
		assert.Equal(grads[0].Value().Data(), aG.Data())
		assert.Equal(cost.Value().Data(), cost2.Value().Data())

		m.Close()
		m2.Close()
	}

}

func TestBatchNorm1d(t *testing.T) {
	generator := func(start float64, increment float64) InitWFn {
		return func(dt tensor.Dtype, s ...int) interface{} {
			totalSize := tensor.Shape(s).TotalSize()

			if dt == tensor.Float32 {
				result := make([]float32, totalSize)

				for i := 0; i < totalSize; i++ {
					result[i] = float32(start)
					start += increment
				}

				return result
			}

			result := make([]float64, totalSize)

			for i := 0; i < totalSize; i++ {
				result[i] = start
				start += increment
			}

			return result
		}
	}

	testCases := []struct {
		desc  string
		Dtype tensor.Dtype

		XInit  InitWFn
		XShape tensor.Shape

		ScaleInit  InitWFn
		ScaleShape tensor.Shape

		BiasInit  InitWFn
		BiasShape tensor.Shape

		ExpectedResult interface{}
	}{
		{
			desc:           "Example1",
			Dtype:          tensor.Float64,
			XInit:          generator(0.5, 0.01),
			XShape:         tensor.Shape{3, 2},
			ScaleInit:      Ones(),
			ScaleShape:     tensor.Shape{3, 1},
			BiasInit:       Zeroes(),
			BiasShape:      tensor.Shape{3, 1},
			ExpectedResult: []float64{-1.2024072240843036, -1.2024072240843036, 0, 0, 1.2024072240843036, 1.2024072240843036},
		},
		{
			desc:           "Example2",
			Dtype:          tensor.Float64,
			XInit:          generator(0.5, 0.01),
			XShape:         tensor.Shape{3, 2},
			ScaleInit:      Ones(),
			ScaleShape:     tensor.Shape{3, 2},
			BiasInit:       Zeroes(),
			BiasShape:      tensor.Shape{3, 2},
			ExpectedResult: []float64{-1.2024072240843036, -1.2024072240843036, 0, 0, 1.2024072240843036, 1.2024072240843036},
		},
		{
			desc:           "Example3",
			Dtype:          tensor.Float32,
			XInit:          generator(0.1, 0.001),
			XShape:         tensor.Shape{3, 2},
			ScaleInit:      Ones(),
			ScaleShape:     tensor.Shape{3, 2},
			BiasInit:       Zeroes(),
			BiasShape:      tensor.Shape{3, 2},
			ExpectedResult: []float32{-0.5619547, -0.56195074, -4.1868648e-06, 0, 0.5619484, 0.56195074},
		},
	}
	for _, tC := range testCases {
		t.Run(tC.desc, func(t *testing.T) {
			rand.Seed(0)

			c := require.New(t)

			g := NewGraph()

			x := NewTensor(g, tC.Dtype, tC.XShape.Dims(), WithShape(tC.XShape...), WithInit(tC.XInit), WithName("x"))
			scale := NewTensor(g, tC.Dtype, tC.ScaleShape.Dims(), WithShape(tC.ScaleShape...), WithInit(tC.ScaleInit), WithName("scale"))
			bias := NewTensor(g, tC.Dtype, tC.BiasShape.Dims(), WithShape(tC.BiasShape...), WithInit(tC.BiasInit), WithName("bias"))

			y, _, _, op, err := BatchNorm(x, scale, bias, 0.9, 1e-5)
			c.NoError(err)

			op.SetTraining()

			var yVal, scaleVal Value
			Read(y, &yVal)
			Read(scale, &scaleVal)

			cost, _ := Mean(y)

			if _, err := Grad(cost, x, scale, bias); err != nil {
				t.Fatal(err)
			}

			m := NewTapeMachine(g, BindDualValues(x, scale, bias), TraceExec())

			err = m.RunAll()
			c.NoError(err)

			c.NoError(m.Close())

			c.Equal(tC.ExpectedResult, yVal.Data())
		})
	}
}

func TestBatchNorm_F64(t *testing.T) {
	g := NewGraph()
	x := NewTensor(g, Float64, 4, WithShape(5, 2, 3, 4), WithInit(Gaussian(0, 1)), WithName("x"))
	scale := NewTensor(g, Float64, 4, WithShape(5, 2, 3, 4), WithInit(Ones()), WithName("scale"))
	bias := NewTensor(g, Float64, 4, WithShape(5, 2, 3, 4), WithInit(Zeroes()), WithName("bias"))
	y, _, _, op, err := BatchNorm(x, scale, bias, 0.9, 1e-5)
	if err != nil {
		t.Fatal(err)
	}

	var yVal Value
	Read(y, &yVal)

	cost, _ := Mean(y)

	if _, err := Grad(cost, x); err != nil {
		t.Fatal(err)
	}

	m := NewTapeMachine(g, BindDualValues(x), TraceExec())
	if err := m.RunAll(); err != nil {
		t.Fatal(err)
	}
	m.Close()

	shape := x.Shape()
	n, c, h, w := shape[0], shape[1], shape[2], shape[3]

	yVT := yVal.(*tensor.Dense)
	for j := 0; j < c; j++ {
		var sum, variance float64
		for i := 0; i < n; i++ {
			for k := 0; k < h; k++ {
				for l := 0; l < w; l++ {
					at, err := yVT.At(i, j, k, l)
					if err != nil {
						t.Fatal(err)
					}
					atf := at.(float64)
					sum += atf
					variance += atf * atf
				}
			}
		}
		sum /= float64(h * w * n)
		variance /= float64(h * w * n)

		if !dawson.ToleranceF64(sum, 0, 0.00001) {
			t.Errorf("channel %d: Expected sum to be near 0. Got %v", j, sum)
		}

		if !dawson.ToleranceF64(variance, 1, 0.0001) {
			t.Errorf("channel %d: Expected variance to be near 1. Got %v", j, variance)
		}
	}

	op.SetTesting()
	m = NewTapeMachine(g, BindDualValues(x))
	if err := m.RunAll(); err != nil {
		t.Fatal(err)
	}
	m.Close()
	yVT = yVal.(*tensor.Dense)
	for j := 0; j < c; j++ {
		var sum, variance float64
		for i := 0; i < n; i++ {
			for k := 0; k < h; k++ {
				for l := 0; l < w; l++ {
					at, err := yVT.At(i, j, k, l)
					if err != nil {
						t.Fatal(err)
					}
					atf := at.(float64)
					sum += atf
					variance += atf * atf
				}
			}
		}
		sum /= float64(h * w * n)
		variance /= float64(h * w * n)

		if !dawson.ToleranceF64(sum, 0, 0.00001) {
			t.Errorf("channel %d: Expected sum to be near 0. Got %v", j, sum)
		}

		if !dawson.ToleranceF64(variance, 0.9833, 0.0001) {
			t.Errorf("channel %d: Expected variance to be near 0.98. Got %v", j, variance)
		}
	}

}

func TestBatchNorm_F32(t *testing.T) {
	g := NewGraph()
	x := NewTensor(g, Float32, 4, WithShape(5, 2, 3, 4), WithInit(Gaussian(0, 1)))
	scale := NewTensor(g, Float32, 4, WithShape(5, 2, 3, 4), WithInit(Ones()), WithName("scale"))
	bias := NewTensor(g, Float32, 4, WithShape(5, 2, 3, 4), WithInit(Zeroes()), WithName("bias"))
	y, _, _, op, err := BatchNorm(x, scale, bias, 0.9, 1e-5)
	if err != nil {
		t.Fatal(err)
	}

	var yVal Value
	Read(y, &yVal)

	cost, _ := Mean(y)

	if _, err := Grad(cost, x); err != nil {
		ioutil.WriteFile("foo.dot", []byte(g.ToDot()), 0644)
		t.Fatal(err)
	}

	m := NewTapeMachine(g, BindDualValues(x))
	if err := m.RunAll(); err != nil {
		t.Fatal(err)
	}
	m.Close()

	shape := x.Shape()
	n, c, h, w := shape[0], shape[1], shape[2], shape[3]

	yVT := yVal.(*tensor.Dense)
	for j := 0; j < c; j++ {
		var sum, variance float32
		for i := 0; i < n; i++ {
			for k := 0; k < h; k++ {
				for l := 0; l < w; l++ {
					at, err := yVT.At(i, j, k, l)
					if err != nil {
						t.Fatal(err)
					}
					atf := at.(float32)
					sum += atf
					variance += atf * atf
				}
			}
		}
		sum /= float32(h * w * n)
		variance /= float32(h * w * n)

		if !dawson.ToleranceF32(sum, 0, 0.001) {
			t.Errorf("channel %d: Expected sum to be near 0. Got %v", j, sum)
		}

		if !dawson.ToleranceF32(variance, 1, 0.001) {
			t.Errorf("channel %d: Expected variance to be near 1. Got %v", j, variance)
		}
	}

	op.SetTesting()
	m = NewTapeMachine(g, BindDualValues(x))
	if err := m.RunAll(); err != nil {
		t.Fatal(err)
	}
	m.Close()
	yVT = yVal.(*tensor.Dense)
	for j := 0; j < c; j++ {
		var sum, variance float32
		for i := 0; i < n; i++ {
			for k := 0; k < h; k++ {
				for l := 0; l < w; l++ {
					at, err := yVT.At(i, j, k, l)
					if err != nil {
						t.Fatal(err)
					}
					atf := at.(float32)
					sum += atf
					variance += atf * atf
				}
			}
		}
		sum /= float32(h * w * n)
		variance /= float32(h * w * n)

		if !dawson.ToleranceF32(sum, 0, 0.001) {
			t.Errorf("channel %d: Expected sum to be near 0. Got %v", j, sum)
		}

		if !dawson.ToleranceF32(variance, 0.9833, 0.001) {
			t.Errorf("channel %d: Expected variance to be near 0.98. Got %v", j, variance)
		}
	}

}

func TestLeakyRelu(t *testing.T) {
	tests := []struct {
		name  string
		alpha float64
		xT    tensor.Tensor
		yT    tensor.Tensor
	}{
		{
			name:  "simple float32",
			alpha: 0.1,
			xT: tensor.New(
				tensor.WithShape(3, 4, 5),
				tensor.WithBacking([]float32{1.7640524, 0.4001572, 0.978738, 2.2408931, 1.867558, -0.9772779, 0.95008844, -0.1513572, -0.10321885, 0.41059852, 0.14404356, 1.4542735, 0.7610377, 0.121675014, 0.44386324, 0.33367434, 1.4940791, -0.20515826, 0.3130677, -0.85409576, -2.5529897, 0.6536186, 0.8644362, -0.742165, 2.2697546, -1.4543657, 0.045758516, -0.18718386, 1.5327792, 1.4693588, 0.15494743, 0.37816253, -0.88778573, -1.9807965, -0.34791216, 0.15634897, 1.2302907, 1.2023798, -0.3873268, -0.30230275, -1.048553, -1.420018, -1.7062702, 1.9507754, -0.5096522, -0.4380743, -1.2527953, 0.7774904, -1.6138978, -0.21274029, -0.89546657, 0.3869025, -0.51080513, -1.1806322, -0.028182229, 0.42833188, 0.06651722, 0.3024719, -0.6343221, -0.36274117}),
			),
			yT: tensor.New(
				tensor.WithShape(3, 4, 5),
				tensor.WithBacking([]float32{1.7640524, 0.4001572, 0.978738, 2.2408931, 1.867558, -0.09772779, 0.95008844, -0.01513572, -0.010321885, 0.41059852, 0.14404356, 1.4542735, 0.7610377, 0.121675014, 0.44386324, 0.33367434, 1.4940791, -0.020515827, 0.3130677, -0.085409574, -0.25529897, 0.6536186, 0.8644362, -0.07421651, 2.2697546, -0.14543657, 0.045758516, -0.018718386, 1.5327792, 1.4693588, 0.15494743, 0.37816253, -0.08877858, -0.19807965, -0.034791216, 0.15634897, 1.2302907, 1.2023798, -0.03873268, -0.030230274, -0.1048553, -0.1420018, -0.17062703, 1.9507754, -0.05096522, -0.04380743, -0.12527953, 0.7774904, -0.16138978, -0.021274028, -0.08954666, 0.3869025, -0.051080514, -0.11806323, -0.0028182229, 0.42833188, 0.06651722, 0.3024719, -0.06343221, -0.036274116}),
			),
		},
		{
			name:  "simple float64",
			alpha: 0.1,
			xT: tensor.New(
				tensor.WithShape(3, 4, 5),
				tensor.WithBacking([]float64{1.7640524, 0.4001572, 0.978738, 2.2408931, 1.867558, -0.9772779, 0.95008844, -0.1513572, -0.10321885, 0.41059852, 0.14404356, 1.4542735, 0.7610377, 0.121675014, 0.44386324, 0.33367434, 1.4940791, -0.20515826, 0.3130677, -0.85409576, -2.5529897, 0.6536186, 0.8644362, -0.742165, 2.2697546, -1.4543657, 0.045758516, -0.18718386, 1.5327792, 1.4693588, 0.15494743, 0.37816253, -0.88778573, -1.9807965, -0.34791216, 0.15634897, 1.2302907, 1.2023798, -0.3873268, -0.30230275, -1.048553, -1.420018, -1.7062702, 1.9507754, -0.5096522, -0.4380743, -1.2527953, 0.7774904, -1.6138978, -0.21274029, -0.89546657, 0.3869025, -0.51080513, -1.1806322, -0.028182229, 0.42833188, 0.06651722, 0.3024719, -0.6343221, -0.36274117}),
			),
			yT: tensor.New(
				tensor.WithShape(3, 4, 5),
				tensor.WithBacking([]float64{1.7640524, 0.4001572, 0.978738, 2.2408931, 1.867558, -0.09772779, 0.95008844, -0.01513572, -0.010321885, 0.41059852, 0.14404356, 1.4542735, 0.7610377, 0.121675014, 0.44386324, 0.33367434, 1.4940791, -0.020515827, 0.3130677, -0.085409574, -0.25529897, 0.6536186, 0.8644362, -0.07421651, 2.2697546, -0.14543657, 0.045758516, -0.018718386, 1.5327792, 1.4693588, 0.15494743, 0.37816253, -0.08877858, -0.19807965, -0.034791216, 0.15634897, 1.2302907, 1.2023798, -0.03873268, -0.030230274, -0.1048553, -0.1420018, -0.17062703, 1.9507754, -0.05096522, -0.04380743, -0.12527953, 0.7774904, -0.16138978, -0.021274028, -0.08954666, 0.3869025, -0.051080514, -0.11806323, -0.0028182229, 0.42833188, 0.06651722, 0.3024719, -0.06343221, -0.036274116}),
			),
		},
	}
	for _, tst := range tests {
		t.Run(tst.name, func(t *testing.T) {
			g := NewGraph()
			assert := assert.New(t)
			x := NodeFromAny(g, tst.xT)
			output, err := LeakyRelu(x, tst.alpha)

			if err != nil {
				t.Fatalf("%+v", err)
			}
			m := NewTapeMachine(g)
			if err := m.RunAll(); err != nil {
				t.Fatalf("%+v", err)
			}
			defer m.Close()
			assert.InDeltaSlice(tst.yT.Data(), output.Value().Data(), 1e-6, "the two tensors should be equal.")
		})
	}
}

func TestGlobalAveragePool2D_fwdPass(t *testing.T) {
	for _, tst := range []struct {
		inputT         tensor.Tensor
		expectedOutput tensor.Tensor
	}{
		{
			inputT: tensor.New(
				tensor.WithShape(1, 3, 5, 5),
				tensor.WithBacking([]float32{
					1.7640524, 0.4001572, 0.978738, 2.2408931, 1.867558,
					-0.9772779, 0.95008844, -0.1513572, -0.10321885, 0.41059852,
					0.14404356, 1.4542735, 0.7610377, 0.121675014, 0.44386324,
					0.33367434, 1.4940791, -0.20515826, 0.3130677, -0.85409576,
					-2.5529897, 0.6536186, 0.8644362, -0.742165, 2.2697546,

					-1.4543657, 0.045758516, -0.18718386, 1.5327792, 1.4693588,
					0.15494743, 0.37816253, -0.88778573, -1.9807965, -0.34791216,
					0.15634897, 1.2302907, 1.2023798, -0.3873268, -0.30230275,
					-1.048553, -1.420018, -1.7062702, 1.9507754, -0.5096522,
					-0.4380743, -1.2527953, 0.7774904, -1.6138978, -0.21274029,

					-0.89546657, 0.3869025, -0.51080513, -1.1806322, -0.028182229,
					0.42833188, 0.06651722, 0.3024719, -0.6343221, -0.36274117,
					-0.67246044, -0.35955316, -0.8131463, -1.7262826, 0.17742614,
					-0.40178093, -1.6301984, 0.46278226, -0.9072984, 0.051945396,
					0.7290906, 0.12898292, 1.1394007, -1.2348258, 0.40234163})),
			expectedOutput: tensor.New(
				tensor.WithShape(1, 3, 1, 1),
				tensor.WithBacking([]float32{0.47517386, -0.1940553, -0.28326008})),
		},
		{
			inputT: tensor.New(
				tensor.WithShape(1, 3, 5, 5),
				tensor.WithBacking([]float64{
					1.7640524, 0.4001572, 0.978738, 2.2408931, 1.867558,
					-0.9772779, 0.95008844, -0.1513572, -0.10321885, 0.41059852,
					0.14404356, 1.4542735, 0.7610377, 0.121675014, 0.44386324,
					0.33367434, 1.4940791, -0.20515826, 0.3130677, -0.85409576,
					-2.5529897, 0.6536186, 0.8644362, -0.742165, 2.2697546,

					-1.4543657, 0.045758516, -0.18718386, 1.5327792, 1.4693588,
					0.15494743, 0.37816253, -0.88778573, -1.9807965, -0.34791216,
					0.15634897, 1.2302907, 1.2023798, -0.3873268, -0.30230275,
					-1.048553, -1.420018, -1.7062702, 1.9507754, -0.5096522,
					-0.4380743, -1.2527953, 0.7774904, -1.6138978, -0.21274029,

					-0.89546657, 0.3869025, -0.51080513, -1.1806322, -0.028182229,
					0.42833188, 0.06651722, 0.3024719, -0.6343221, -0.36274117,
					-0.67246044, -0.35955316, -0.8131463, -1.7262826, 0.17742614,
					-0.40178093, -1.6301984, 0.46278226, -0.9072984, 0.051945396,
					0.7290906, 0.12898292, 1.1394007, -1.2348258, 0.40234163})),
			expectedOutput: tensor.New(
				tensor.WithShape(1, 3, 1, 1),
				tensor.WithBacking([]float64{0.47517386, -0.1940553, -0.28326008})),
		},
	} {
		inputT := tst.inputT
		expectedOutput := tst.expectedOutput
		g := NewGraph()
		assert := assert.New(t)
		x := NodeFromAny(g, inputT)
		output, err := GlobalAveragePool2D(x)

		if err != nil {
			t.Fatal(err)
		}
		m := NewTapeMachine(g)
		if err := m.RunAll(); err != nil {
			t.Fatalf("%+v", err)
		}
		defer m.Close()
		if len(output.Shape()) != len(expectedOutput.Shape()) {
			t.Fatalf("Bad output shape, expected %v, got %v", expectedOutput.Shape(), output.Shape())
		}
		for i, d := range output.Shape() {
			if expectedOutput.Shape()[i] != d {
				t.Fatalf("Bad output shape, expected %v, got %v", expectedOutput.Shape(), output.Shape())
			}
		}
		assert.InDeltaSlice(expectedOutput.Data(), output.Value().Data(), 1e-6, "the two tensors should be equal.")
	}
}

func TestConv2dErrors(t *testing.T) {
	g := NewGraph()

	testCases := []struct {
		desc                  string
		im                    *Node
		filter                *Node
		kernelShape           tensor.Shape
		pad, stride, dilation []int
		err                   string
		panics                bool
	}{
		{
			desc:        "Succesful",
			im:          NewTensor(g, tensor.Float64, 4, WithShape(1, 1, 28, 28)),
			filter:      NewTensor(g, tensor.Float64, 4, WithShape(32, 1, 3, 3)),
			kernelShape: tensor.Shape{3, 3},
			pad:         []int{1, 1},
			stride:      []int{1, 1},
			dilation:    []int{1, 1},
			err:         "",
		},
		{
			desc:        "5dIM",
			im:          NewTensor(g, tensor.Float64, 5, WithShape(1, 1, 1, 28, 28)),
			filter:      NewTensor(g, tensor.Float64, 4, WithShape(32, 1, 3, 3)),
			kernelShape: tensor.Shape{3, 3},
			pad:         []int{1, 1},
			stride:      []int{1, 1},
			dilation:    []int{1, 1},
			err:         "im should have 4 dims, got 5 dims",
		},
		{
			desc:        "5dFilter",
			im:          NewTensor(g, tensor.Float64, 4, WithShape(1, 1, 28, 28)),
			filter:      NewTensor(g, tensor.Float64, 5, WithShape(32, 1, 1, 3, 3)),
			kernelShape: tensor.Shape{3, 3},
			pad:         []int{1, 1},
			stride:      []int{1, 1},
			dilation:    []int{1, 1},
			err:         "filter should have 4 dims, got 5 dims",
		},
		{
			desc:        "Shapes",
			im:          NewTensor(g, tensor.Float64, 4, WithShape(1, 1, 28, 28)),
			filter:      NewTensor(g, tensor.Float64, 4, WithShape(32, 3, 3, 3)),
			kernelShape: tensor.Shape{3, 3},
			pad:         []int{1, 1},
			stride:      []int{1, 1},
			dilation:    []int{1, 1},
			err:         "3 (kernel) * 3 (width) * 3 (height) must be 9, got 27",
		},
	}

	for _, tC := range testCases {
		t.Run(tC.desc, func(t *testing.T) {
			c := assert.New(t)

			_, err := Conv2d(tC.im, tC.filter, tC.kernelShape, tC.pad, tC.stride, tC.dilation)
			if tC.err != "" {
				require.Error(t, err)
				c.Equal(tC.err, err.Error())
			} else {
				c.NoError(err)
			}
		})
	}
}