gorgonia.org/gorgonia@v0.9.17/nn_test.go (about)

     1  package gorgonia
     2  
     3  import (
     4  	"fmt"
     5  	"io/ioutil"
     6  	"math/rand"
     7  	"runtime"
     8  	"testing"
     9  
    10  	"github.com/stretchr/testify/assert"
    11  	"github.com/stretchr/testify/require"
    12  	"gorgonia.org/dawson"
    13  	"gorgonia.org/tensor"
    14  )
    15  
    16  func TestDropout(t *testing.T) {
    17  	var tests = []struct {
    18  		dt       tensor.Dtype
    19  		prob     float64
    20  		rand     interface{}
    21  		expected interface{}
    22  	}{
    23  		{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}},
    24  		{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}},
    25  		{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}},
    26  		{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}},
    27  		{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}},
    28  	}
    29  
    30  	for _, tt := range tests {
    31  		name := fmt.Sprintf("%v-%.1f", tt.dt, tt.prob)
    32  		t.Run(name, func(t *testing.T) {
    33  			randFn := func(g *ExprGraph, dt tensor.Dtype, low, high float64, shape ...int) *Node {
    34  				return NewVector(g, dt, WithShape(shape...), WithInit(func(dt tensor.Dtype, s ...int) interface{} {
    35  					return tt.rand
    36  				}))
    37  			}
    38  			g := NewGraph()
    39  			x := NewVector(g, tt.dt, WithShape(5), WithName("x"), WithInit(Ones()))
    40  			do := Must(dropout(x, tt.prob, randFn))
    41  
    42  			m := NewTapeMachine(g, BindDualValues())
    43  			defer m.Close()
    44  			defer runtime.GC()
    45  
    46  			assert.NoError(t, m.RunAll())
    47  			assert.Equal(t, tt.expected, do.Value().Data())
    48  		})
    49  	}
    50  }
    51  
    52  func dropoutTest(t *testing.T, dt tensor.Dtype) error {
    53  	g := NewGraph()
    54  	x := NewVector(g, dt, WithShape(10), WithName("x"), WithInit(RangedFrom(0)))
    55  	w := NewMatrix(g, dt, WithShape(20, 10), WithName("w"), WithInit(RangedFrom(0)))
    56  	w2 := NewMatrix(g, dt, WithShape(10, 20), WithName("w2"), WithInit(RangedFrom(0)))
    57  	wx := Must(Mul(w, x))
    58  	act := Must(Cube(wx))
    59  	do := Must(Dropout(act, 0.5))
    60  
    61  	act2 := Must(Cube(Must(Mul(w2, do))))
    62  	do2 := Must(Dropout(act2, 0.1))
    63  	cost := Must(Sum(do2))
    64  
    65  	_, err := Grad(cost, x, w, w2)
    66  
    67  	if err != nil {
    68  		ioutil.WriteFile("fullGraph.dot", []byte(g.ToDot()), 0644)
    69  		// t.Fatalf("%+v", err)
    70  		return err
    71  	}
    72  
    73  	// logger := log.New(os.Stderr, "", 0)
    74  
    75  	// m := NewTapeMachine(g, TraceExec(), BindDualValues(), WithLogger(logger), WithWatchlist())
    76  	m := NewTapeMachine(g, TraceExec(), BindDualValues())
    77  	defer m.Close()
    78  	cudaLogf("%v", m.Prog())
    79  	defer runtime.GC()
    80  	if err := m.RunAll(); err != nil {
    81  		return err
    82  	}
    83  	return nil
    84  }
    85  
    86  func TestDropout_integration(t *testing.T) {
    87  	// t.Skip()
    88  
    89  	if err := dropoutTest(t, Float64); err != nil {
    90  		t.Errorf("%+v", err)
    91  	}
    92  
    93  	if err := dropoutTest(t, Float32); err != nil {
    94  		t.Errorf("%+v", err)
    95  	}
    96  
    97  	// visual inspection
    98  	// ioutil.WriteFile("fullGraph.dot", []byte(g.ToDot()), 0644)
    99  }
   100  
   101  var im2colTests = []struct {
   102  	kernel   tensor.Shape
   103  	pad      tensor.Shape
   104  	stride   tensor.Shape
   105  	dilation tensor.Shape
   106  }{
   107  	{tensor.Shape{4, 4}, tensor.Shape{0, 0}, tensor.Shape{1, 1}, tensor.Shape{1, 1}},
   108  	{tensor.Shape{3, 3}, tensor.Shape{1, 1}, tensor.Shape{2, 2}, tensor.Shape{1, 1}},
   109  	{tensor.Shape{3, 3}, tensor.Shape{1, 1}, tensor.Shape{3, 3}, tensor.Shape{1, 1}},
   110  }
   111  
   112  func im2colTest(t *testing.T, dt tensor.Dtype, kernel, pad, stride, dilation tensor.Shape) {
   113  	assert := assert.New(t)
   114  	g := NewGraph()
   115  	x := NewTensor(g, dt, 4, WithShape(2, 1, 28, 28), WithInit(RangedFrom(0))) // mnist, in batches of 10
   116  	y, err := Im2Col(x, kernel, pad, stride, dilation)
   117  	if err != nil {
   118  		t.Error(err)
   119  		return
   120  	}
   121  	cost := Must(Sum(y))
   122  
   123  	grads, err := Grad(cost, x)
   124  	if err != nil {
   125  		t.Errorf("error while Grad(): %v", err)
   126  		return
   127  	}
   128  
   129  	m := NewTapeMachine(g, BindDualValues())
   130  	defer m.Close()
   131  	if err := m.RunAll(); err != nil {
   132  		t.Error(err)
   133  		return
   134  	}
   135  	// t.Logf("x: %v", x.Value())
   136  	// t.Logf("c: %3.3f", cost.Value())
   137  	// t.Logf("xG: %v", grads[0].Value())
   138  
   139  	h := NewGraph()
   140  	a := NewTensor(h, dt, 4, WithShape(2, 1, 28, 28), WithInit(RangedFrom(0)))
   141  	b, err := Im2Col(a, kernel, pad, stride, dilation)
   142  	if err != nil {
   143  		t.Error(err)
   144  		return
   145  	}
   146  	cost2 := Must(Sum(b))
   147  	n := NewLispMachine(h)
   148  	defer n.Close()
   149  	if err = n.RunAll(); err != nil {
   150  		t.Error(err)
   151  		return
   152  	}
   153  	aG, err := a.Grad()
   154  	if err != nil {
   155  		t.Error(err)
   156  		return
   157  	}
   158  
   159  	// t.Logf("a: %v", a.Value())
   160  	// t.Logf("c: %3.3f", cost2.Value())
   161  	// t.Logf("aG: %v", aG)
   162  
   163  	assert.Equal(x.Value().Data(), a.Value().Data())
   164  	assert.Equal(grads[0].Value().Data(), aG.Data())
   165  	assert.Equal(cost.Value().Data(), cost2.Value().Data())
   166  }
   167  
   168  func TestIm2Col(t *testing.T) {
   169  	// assert := assert.New(t)
   170  	dts := []tensor.Dtype{tensor.Float64, tensor.Float32}
   171  	for _, dt := range dts {
   172  		for _, i2ct := range im2colTests {
   173  			im2colTest(t, dt, i2ct.kernel, i2ct.pad, i2ct.stride, i2ct.dilation)
   174  		}
   175  	}
   176  }
   177  
   178  func TestMaxPool2D_asymmetricPadding(t *testing.T) {
   179  	assert := assert.New(t)
   180  	tsts := []struct {
   181  		inputT  tensor.Tensor
   182  		outputT tensor.Tensor
   183  		padding []int
   184  	}{
   185  		{
   186  			inputT: tensor.New(tensor.WithShape(1, 3, 32, 32),
   187  				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, 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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, 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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}),
   199  			),
   200  		},
   201  	}
   202  
   203  	for _, tst := range tsts {
   204  		g := NewGraph()
   205  		x := NodeFromAny(g, tst.inputT)
   206  		y, err := MaxPool2D(x, tensor.Shape{2, 2}, []int{1, 0, 1, 0}, []int{1, 1})
   207  		if err != nil {
   208  			t.Fatal(err)
   209  		}
   210  		m := NewTapeMachine(g, BindDualValues())
   211  		if err := m.RunAll(); err != nil {
   212  			t.Fatal(err)
   213  		}
   214  		m.Close()
   215  		if len(y.Shape()) != len(tst.outputT.Shape()) {
   216  			t.Fatalf("Maxpool: expected shape %v, got %v", tst.outputT.Shape(), y.Shape())
   217  		}
   218  		for i, v := range y.Shape() {
   219  			if v != tst.outputT.Shape()[i] {
   220  				t.Fatalf("Maxpool: expected shape %v, got %v", tst.outputT.Shape(), y.Shape())
   221  			}
   222  		}
   223  		assert.Equal(y.Value().Data(), tst.outputT.Data())
   224  	}
   225  }
   226  
   227  func TestMaxPool2D(t *testing.T) {
   228  	assert := assert.New(t)
   229  	dts := []tensor.Dtype{tensor.Float64, tensor.Float32}
   230  	for _, dt := range dts {
   231  		g := NewGraph()
   232  		x := NewTensor(g, dt, 4, WithShape(1, 2, 3, 4), WithInit(RangedFrom(0)))
   233  		y, err := MaxPool2D(x, tensor.Shape{2, 2}, []int{0, 0}, []int{1, 1})
   234  		if err != nil {
   235  			t.Fatal(err)
   236  		}
   237  		cost := Must(Sum(y))
   238  		grads, err := Grad(cost, x)
   239  		if err != nil {
   240  			t.Fatal(err)
   241  		}
   242  
   243  		m := NewTapeMachine(g, BindDualValues())
   244  		if err := m.RunAll(); err != nil {
   245  			t.Fatal(err)
   246  		}
   247  		// t.Logf("x %v", x.Value())
   248  		// t.Logf("y: %v", y.Value())
   249  		// t.Logf("c: %v", cost.Value())
   250  		// t.Logf("xG: %v", grads[0])
   251  
   252  		h := NewGraph()
   253  		a := NewTensor(h, dt, 4, WithShape(1, 2, 3, 4), WithInit(RangedFrom(0)))
   254  		b, err := MaxPool2D(a, tensor.Shape{2, 2}, []int{0, 0}, []int{1, 1})
   255  		if err != nil {
   256  			t.Fatal(err)
   257  		}
   258  		cost2 := Must(Sum(b))
   259  		if err != nil {
   260  			t.Fatal(err)
   261  		}
   262  
   263  		m2 := NewLispMachine(h)
   264  		if err = m2.RunAll(); err != nil {
   265  			t.Fatal(err)
   266  		}
   267  		aG, err := a.Grad()
   268  		if err != nil {
   269  			t.Error(err)
   270  			return
   271  		}
   272  
   273  		assert.Equal(x.Value().Data(), a.Value().Data())
   274  		assert.Equal(grads[0].Value().Data(), aG.Data())
   275  		assert.Equal(cost.Value().Data(), cost2.Value().Data())
   276  
   277  		m.Close()
   278  		m2.Close()
   279  	}
   280  
   281  }
   282  
   283  func TestBatchNorm1d(t *testing.T) {
   284  	generator := func(start float64, increment float64) InitWFn {
   285  		return func(dt tensor.Dtype, s ...int) interface{} {
   286  			totalSize := tensor.Shape(s).TotalSize()
   287  
   288  			if dt == tensor.Float32 {
   289  				result := make([]float32, totalSize)
   290  
   291  				for i := 0; i < totalSize; i++ {
   292  					result[i] = float32(start)
   293  					start += increment
   294  				}
   295  
   296  				return result
   297  			}
   298  
   299  			result := make([]float64, totalSize)
   300  
   301  			for i := 0; i < totalSize; i++ {
   302  				result[i] = start
   303  				start += increment
   304  			}
   305  
   306  			return result
   307  		}
   308  	}
   309  
   310  	testCases := []struct {
   311  		desc  string
   312  		Dtype tensor.Dtype
   313  
   314  		XInit  InitWFn
   315  		XShape tensor.Shape
   316  
   317  		ScaleInit  InitWFn
   318  		ScaleShape tensor.Shape
   319  
   320  		BiasInit  InitWFn
   321  		BiasShape tensor.Shape
   322  
   323  		ExpectedResult interface{}
   324  	}{
   325  		{
   326  			desc:           "Example1",
   327  			Dtype:          tensor.Float64,
   328  			XInit:          generator(0.5, 0.01),
   329  			XShape:         tensor.Shape{3, 2},
   330  			ScaleInit:      Ones(),
   331  			ScaleShape:     tensor.Shape{3, 1},
   332  			BiasInit:       Zeroes(),
   333  			BiasShape:      tensor.Shape{3, 1},
   334  			ExpectedResult: []float64{-1.2024072240843036, -1.2024072240843036, 0, 0, 1.2024072240843036, 1.2024072240843036},
   335  		},
   336  		{
   337  			desc:           "Example2",
   338  			Dtype:          tensor.Float64,
   339  			XInit:          generator(0.5, 0.01),
   340  			XShape:         tensor.Shape{3, 2},
   341  			ScaleInit:      Ones(),
   342  			ScaleShape:     tensor.Shape{3, 2},
   343  			BiasInit:       Zeroes(),
   344  			BiasShape:      tensor.Shape{3, 2},
   345  			ExpectedResult: []float64{-1.2024072240843036, -1.2024072240843036, 0, 0, 1.2024072240843036, 1.2024072240843036},
   346  		},
   347  		{
   348  			desc:           "Example3",
   349  			Dtype:          tensor.Float32,
   350  			XInit:          generator(0.1, 0.001),
   351  			XShape:         tensor.Shape{3, 2},
   352  			ScaleInit:      Ones(),
   353  			ScaleShape:     tensor.Shape{3, 2},
   354  			BiasInit:       Zeroes(),
   355  			BiasShape:      tensor.Shape{3, 2},
   356  			ExpectedResult: []float32{-0.5619547, -0.56195074, -4.1868648e-06, 0, 0.5619484, 0.56195074},
   357  		},
   358  	}
   359  	for _, tC := range testCases {
   360  		t.Run(tC.desc, func(t *testing.T) {
   361  			rand.Seed(0)
   362  
   363  			c := require.New(t)
   364  
   365  			g := NewGraph()
   366  
   367  			x := NewTensor(g, tC.Dtype, tC.XShape.Dims(), WithShape(tC.XShape...), WithInit(tC.XInit), WithName("x"))
   368  			scale := NewTensor(g, tC.Dtype, tC.ScaleShape.Dims(), WithShape(tC.ScaleShape...), WithInit(tC.ScaleInit), WithName("scale"))
   369  			bias := NewTensor(g, tC.Dtype, tC.BiasShape.Dims(), WithShape(tC.BiasShape...), WithInit(tC.BiasInit), WithName("bias"))
   370  
   371  			y, _, _, op, err := BatchNorm(x, scale, bias, 0.9, 1e-5)
   372  			c.NoError(err)
   373  
   374  			op.SetTraining()
   375  
   376  			var yVal, scaleVal Value
   377  			Read(y, &yVal)
   378  			Read(scale, &scaleVal)
   379  
   380  			cost, _ := Mean(y)
   381  
   382  			if _, err := Grad(cost, x, scale, bias); err != nil {
   383  				t.Fatal(err)
   384  			}
   385  
   386  			m := NewTapeMachine(g, BindDualValues(x, scale, bias), TraceExec())
   387  
   388  			err = m.RunAll()
   389  			c.NoError(err)
   390  
   391  			c.NoError(m.Close())
   392  
   393  			c.Equal(tC.ExpectedResult, yVal.Data())
   394  		})
   395  	}
   396  }
   397  
   398  func TestBatchNorm_F64(t *testing.T) {
   399  	g := NewGraph()
   400  	x := NewTensor(g, Float64, 4, WithShape(5, 2, 3, 4), WithInit(Gaussian(0, 1)), WithName("x"))
   401  	scale := NewTensor(g, Float64, 4, WithShape(5, 2, 3, 4), WithInit(Ones()), WithName("scale"))
   402  	bias := NewTensor(g, Float64, 4, WithShape(5, 2, 3, 4), WithInit(Zeroes()), WithName("bias"))
   403  	y, _, _, op, err := BatchNorm(x, scale, bias, 0.9, 1e-5)
   404  	if err != nil {
   405  		t.Fatal(err)
   406  	}
   407  
   408  	var yVal Value
   409  	Read(y, &yVal)
   410  
   411  	cost, _ := Mean(y)
   412  
   413  	if _, err := Grad(cost, x); err != nil {
   414  		t.Fatal(err)
   415  	}
   416  
   417  	m := NewTapeMachine(g, BindDualValues(x), TraceExec())
   418  	if err := m.RunAll(); err != nil {
   419  		t.Fatal(err)
   420  	}
   421  	m.Close()
   422  
   423  	shape := x.Shape()
   424  	n, c, h, w := shape[0], shape[1], shape[2], shape[3]
   425  
   426  	yVT := yVal.(*tensor.Dense)
   427  	for j := 0; j < c; j++ {
   428  		var sum, variance float64
   429  		for i := 0; i < n; i++ {
   430  			for k := 0; k < h; k++ {
   431  				for l := 0; l < w; l++ {
   432  					at, err := yVT.At(i, j, k, l)
   433  					if err != nil {
   434  						t.Fatal(err)
   435  					}
   436  					atf := at.(float64)
   437  					sum += atf
   438  					variance += atf * atf
   439  				}
   440  			}
   441  		}
   442  		sum /= float64(h * w * n)
   443  		variance /= float64(h * w * n)
   444  
   445  		if !dawson.ToleranceF64(sum, 0, 0.00001) {
   446  			t.Errorf("channel %d: Expected sum to be near 0. Got %v", j, sum)
   447  		}
   448  
   449  		if !dawson.ToleranceF64(variance, 1, 0.0001) {
   450  			t.Errorf("channel %d: Expected variance to be near 1. Got %v", j, variance)
   451  		}
   452  	}
   453  
   454  	op.SetTesting()
   455  	m = NewTapeMachine(g, BindDualValues(x))
   456  	if err := m.RunAll(); err != nil {
   457  		t.Fatal(err)
   458  	}
   459  	m.Close()
   460  	yVT = yVal.(*tensor.Dense)
   461  	for j := 0; j < c; j++ {
   462  		var sum, variance float64
   463  		for i := 0; i < n; i++ {
   464  			for k := 0; k < h; k++ {
   465  				for l := 0; l < w; l++ {
   466  					at, err := yVT.At(i, j, k, l)
   467  					if err != nil {
   468  						t.Fatal(err)
   469  					}
   470  					atf := at.(float64)
   471  					sum += atf
   472  					variance += atf * atf
   473  				}
   474  			}
   475  		}
   476  		sum /= float64(h * w * n)
   477  		variance /= float64(h * w * n)
   478  
   479  		if !dawson.ToleranceF64(sum, 0, 0.00001) {
   480  			t.Errorf("channel %d: Expected sum to be near 0. Got %v", j, sum)
   481  		}
   482  
   483  		if !dawson.ToleranceF64(variance, 0.9833, 0.0001) {
   484  			t.Errorf("channel %d: Expected variance to be near 0.98. Got %v", j, variance)
   485  		}
   486  	}
   487  
   488  }
   489  
   490  func TestBatchNorm_F32(t *testing.T) {
   491  	g := NewGraph()
   492  	x := NewTensor(g, Float32, 4, WithShape(5, 2, 3, 4), WithInit(Gaussian(0, 1)))
   493  	scale := NewTensor(g, Float32, 4, WithShape(5, 2, 3, 4), WithInit(Ones()), WithName("scale"))
   494  	bias := NewTensor(g, Float32, 4, WithShape(5, 2, 3, 4), WithInit(Zeroes()), WithName("bias"))
   495  	y, _, _, op, err := BatchNorm(x, scale, bias, 0.9, 1e-5)
   496  	if err != nil {
   497  		t.Fatal(err)
   498  	}
   499  
   500  	var yVal Value
   501  	Read(y, &yVal)
   502  
   503  	cost, _ := Mean(y)
   504  
   505  	if _, err := Grad(cost, x); err != nil {
   506  		ioutil.WriteFile("foo.dot", []byte(g.ToDot()), 0644)
   507  		t.Fatal(err)
   508  	}
   509  
   510  	m := NewTapeMachine(g, BindDualValues(x))
   511  	if err := m.RunAll(); err != nil {
   512  		t.Fatal(err)
   513  	}
   514  	m.Close()
   515  
   516  	shape := x.Shape()
   517  	n, c, h, w := shape[0], shape[1], shape[2], shape[3]
   518  
   519  	yVT := yVal.(*tensor.Dense)
   520  	for j := 0; j < c; j++ {
   521  		var sum, variance float32
   522  		for i := 0; i < n; i++ {
   523  			for k := 0; k < h; k++ {
   524  				for l := 0; l < w; l++ {
   525  					at, err := yVT.At(i, j, k, l)
   526  					if err != nil {
   527  						t.Fatal(err)
   528  					}
   529  					atf := at.(float32)
   530  					sum += atf
   531  					variance += atf * atf
   532  				}
   533  			}
   534  		}
   535  		sum /= float32(h * w * n)
   536  		variance /= float32(h * w * n)
   537  
   538  		if !dawson.ToleranceF32(sum, 0, 0.001) {
   539  			t.Errorf("channel %d: Expected sum to be near 0. Got %v", j, sum)
   540  		}
   541  
   542  		if !dawson.ToleranceF32(variance, 1, 0.001) {
   543  			t.Errorf("channel %d: Expected variance to be near 1. Got %v", j, variance)
   544  		}
   545  	}
   546  
   547  	op.SetTesting()
   548  	m = NewTapeMachine(g, BindDualValues(x))
   549  	if err := m.RunAll(); err != nil {
   550  		t.Fatal(err)
   551  	}
   552  	m.Close()
   553  	yVT = yVal.(*tensor.Dense)
   554  	for j := 0; j < c; j++ {
   555  		var sum, variance float32
   556  		for i := 0; i < n; i++ {
   557  			for k := 0; k < h; k++ {
   558  				for l := 0; l < w; l++ {
   559  					at, err := yVT.At(i, j, k, l)
   560  					if err != nil {
   561  						t.Fatal(err)
   562  					}
   563  					atf := at.(float32)
   564  					sum += atf
   565  					variance += atf * atf
   566  				}
   567  			}
   568  		}
   569  		sum /= float32(h * w * n)
   570  		variance /= float32(h * w * n)
   571  
   572  		if !dawson.ToleranceF32(sum, 0, 0.001) {
   573  			t.Errorf("channel %d: Expected sum to be near 0. Got %v", j, sum)
   574  		}
   575  
   576  		if !dawson.ToleranceF32(variance, 0.9833, 0.001) {
   577  			t.Errorf("channel %d: Expected variance to be near 0.98. Got %v", j, variance)
   578  		}
   579  	}
   580  
   581  }
   582  
   583  func TestLeakyRelu(t *testing.T) {
   584  	tests := []struct {
   585  		name  string
   586  		alpha float64
   587  		xT    tensor.Tensor
   588  		yT    tensor.Tensor
   589  	}{
   590  		{
   591  			name:  "simple float32",
   592  			alpha: 0.1,
   593  			xT: tensor.New(
   594  				tensor.WithShape(3, 4, 5),
   595  				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}),
   596  			),
   597  			yT: tensor.New(
   598  				tensor.WithShape(3, 4, 5),
   599  				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}),
   600  			),
   601  		},
   602  		{
   603  			name:  "simple float64",
   604  			alpha: 0.1,
   605  			xT: tensor.New(
   606  				tensor.WithShape(3, 4, 5),
   607  				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}),
   608  			),
   609  			yT: tensor.New(
   610  				tensor.WithShape(3, 4, 5),
   611  				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}),
   612  			),
   613  		},
   614  	}
   615  	for _, tst := range tests {
   616  		t.Run(tst.name, func(t *testing.T) {
   617  			g := NewGraph()
   618  			assert := assert.New(t)
   619  			x := NodeFromAny(g, tst.xT)
   620  			output, err := LeakyRelu(x, tst.alpha)
   621  
   622  			if err != nil {
   623  				t.Fatalf("%+v", err)
   624  			}
   625  			m := NewTapeMachine(g)
   626  			if err := m.RunAll(); err != nil {
   627  				t.Fatalf("%+v", err)
   628  			}
   629  			defer m.Close()
   630  			assert.InDeltaSlice(tst.yT.Data(), output.Value().Data(), 1e-6, "the two tensors should be equal.")
   631  		})
   632  	}
   633  }
   634  
   635  func TestGlobalAveragePool2D_fwdPass(t *testing.T) {
   636  	for _, tst := range []struct {
   637  		inputT         tensor.Tensor
   638  		expectedOutput tensor.Tensor
   639  	}{
   640  		{
   641  			inputT: tensor.New(
   642  				tensor.WithShape(1, 3, 5, 5),
   643  				tensor.WithBacking([]float32{
   644  					1.7640524, 0.4001572, 0.978738, 2.2408931, 1.867558,
   645  					-0.9772779, 0.95008844, -0.1513572, -0.10321885, 0.41059852,
   646  					0.14404356, 1.4542735, 0.7610377, 0.121675014, 0.44386324,
   647  					0.33367434, 1.4940791, -0.20515826, 0.3130677, -0.85409576,
   648  					-2.5529897, 0.6536186, 0.8644362, -0.742165, 2.2697546,
   649  
   650  					-1.4543657, 0.045758516, -0.18718386, 1.5327792, 1.4693588,
   651  					0.15494743, 0.37816253, -0.88778573, -1.9807965, -0.34791216,
   652  					0.15634897, 1.2302907, 1.2023798, -0.3873268, -0.30230275,
   653  					-1.048553, -1.420018, -1.7062702, 1.9507754, -0.5096522,
   654  					-0.4380743, -1.2527953, 0.7774904, -1.6138978, -0.21274029,
   655  
   656  					-0.89546657, 0.3869025, -0.51080513, -1.1806322, -0.028182229,
   657  					0.42833188, 0.06651722, 0.3024719, -0.6343221, -0.36274117,
   658  					-0.67246044, -0.35955316, -0.8131463, -1.7262826, 0.17742614,
   659  					-0.40178093, -1.6301984, 0.46278226, -0.9072984, 0.051945396,
   660  					0.7290906, 0.12898292, 1.1394007, -1.2348258, 0.40234163})),
   661  			expectedOutput: tensor.New(
   662  				tensor.WithShape(1, 3, 1, 1),
   663  				tensor.WithBacking([]float32{0.47517386, -0.1940553, -0.28326008})),
   664  		},
   665  		{
   666  			inputT: tensor.New(
   667  				tensor.WithShape(1, 3, 5, 5),
   668  				tensor.WithBacking([]float64{
   669  					1.7640524, 0.4001572, 0.978738, 2.2408931, 1.867558,
   670  					-0.9772779, 0.95008844, -0.1513572, -0.10321885, 0.41059852,
   671  					0.14404356, 1.4542735, 0.7610377, 0.121675014, 0.44386324,
   672  					0.33367434, 1.4940791, -0.20515826, 0.3130677, -0.85409576,
   673  					-2.5529897, 0.6536186, 0.8644362, -0.742165, 2.2697546,
   674  
   675  					-1.4543657, 0.045758516, -0.18718386, 1.5327792, 1.4693588,
   676  					0.15494743, 0.37816253, -0.88778573, -1.9807965, -0.34791216,
   677  					0.15634897, 1.2302907, 1.2023798, -0.3873268, -0.30230275,
   678  					-1.048553, -1.420018, -1.7062702, 1.9507754, -0.5096522,
   679  					-0.4380743, -1.2527953, 0.7774904, -1.6138978, -0.21274029,
   680  
   681  					-0.89546657, 0.3869025, -0.51080513, -1.1806322, -0.028182229,
   682  					0.42833188, 0.06651722, 0.3024719, -0.6343221, -0.36274117,
   683  					-0.67246044, -0.35955316, -0.8131463, -1.7262826, 0.17742614,
   684  					-0.40178093, -1.6301984, 0.46278226, -0.9072984, 0.051945396,
   685  					0.7290906, 0.12898292, 1.1394007, -1.2348258, 0.40234163})),
   686  			expectedOutput: tensor.New(
   687  				tensor.WithShape(1, 3, 1, 1),
   688  				tensor.WithBacking([]float64{0.47517386, -0.1940553, -0.28326008})),
   689  		},
   690  	} {
   691  		inputT := tst.inputT
   692  		expectedOutput := tst.expectedOutput
   693  		g := NewGraph()
   694  		assert := assert.New(t)
   695  		x := NodeFromAny(g, inputT)
   696  		output, err := GlobalAveragePool2D(x)
   697  
   698  		if err != nil {
   699  			t.Fatal(err)
   700  		}
   701  		m := NewTapeMachine(g)
   702  		if err := m.RunAll(); err != nil {
   703  			t.Fatalf("%+v", err)
   704  		}
   705  		defer m.Close()
   706  		if len(output.Shape()) != len(expectedOutput.Shape()) {
   707  			t.Fatalf("Bad output shape, expected %v, got %v", expectedOutput.Shape(), output.Shape())
   708  		}
   709  		for i, d := range output.Shape() {
   710  			if expectedOutput.Shape()[i] != d {
   711  				t.Fatalf("Bad output shape, expected %v, got %v", expectedOutput.Shape(), output.Shape())
   712  			}
   713  		}
   714  		assert.InDeltaSlice(expectedOutput.Data(), output.Value().Data(), 1e-6, "the two tensors should be equal.")
   715  	}
   716  }
   717  
   718  func TestConv2dErrors(t *testing.T) {
   719  	g := NewGraph()
   720  
   721  	testCases := []struct {
   722  		desc                  string
   723  		im                    *Node
   724  		filter                *Node
   725  		kernelShape           tensor.Shape
   726  		pad, stride, dilation []int
   727  		err                   string
   728  		panics                bool
   729  	}{
   730  		{
   731  			desc:        "Succesful",
   732  			im:          NewTensor(g, tensor.Float64, 4, WithShape(1, 1, 28, 28)),
   733  			filter:      NewTensor(g, tensor.Float64, 4, WithShape(32, 1, 3, 3)),
   734  			kernelShape: tensor.Shape{3, 3},
   735  			pad:         []int{1, 1},
   736  			stride:      []int{1, 1},
   737  			dilation:    []int{1, 1},
   738  			err:         "",
   739  		},
   740  		{
   741  			desc:        "5dIM",
   742  			im:          NewTensor(g, tensor.Float64, 5, WithShape(1, 1, 1, 28, 28)),
   743  			filter:      NewTensor(g, tensor.Float64, 4, WithShape(32, 1, 3, 3)),
   744  			kernelShape: tensor.Shape{3, 3},
   745  			pad:         []int{1, 1},
   746  			stride:      []int{1, 1},
   747  			dilation:    []int{1, 1},
   748  			err:         "im should have 4 dims, got 5 dims",
   749  		},
   750  		{
   751  			desc:        "5dFilter",
   752  			im:          NewTensor(g, tensor.Float64, 4, WithShape(1, 1, 28, 28)),
   753  			filter:      NewTensor(g, tensor.Float64, 5, WithShape(32, 1, 1, 3, 3)),
   754  			kernelShape: tensor.Shape{3, 3},
   755  			pad:         []int{1, 1},
   756  			stride:      []int{1, 1},
   757  			dilation:    []int{1, 1},
   758  			err:         "filter should have 4 dims, got 5 dims",
   759  		},
   760  		{
   761  			desc:        "Shapes",
   762  			im:          NewTensor(g, tensor.Float64, 4, WithShape(1, 1, 28, 28)),
   763  			filter:      NewTensor(g, tensor.Float64, 4, WithShape(32, 3, 3, 3)),
   764  			kernelShape: tensor.Shape{3, 3},
   765  			pad:         []int{1, 1},
   766  			stride:      []int{1, 1},
   767  			dilation:    []int{1, 1},
   768  			err:         "3 (kernel) * 3 (width) * 3 (height) must be 9, got 27",
   769  		},
   770  	}
   771  
   772  	for _, tC := range testCases {
   773  		t.Run(tC.desc, func(t *testing.T) {
   774  			c := assert.New(t)
   775  
   776  			_, err := Conv2d(tC.im, tC.filter, tC.kernelShape, tC.pad, tC.stride, tC.dilation)
   777  			if tC.err != "" {
   778  				require.Error(t, err)
   779  				c.Equal(tC.err, err.Error())
   780  			} else {
   781  				c.NoError(err)
   782  			}
   783  		})
   784  	}
   785  }