diff --git a/Notebooks/nikolai/.ipynb_checkpoints/GINN_P2-checkpoint.ipynb b/Notebooks/nikolai/.ipynb_checkpoints/GINN_P2-checkpoint.ipynb
index 6289b33a39a12cd8638a07400e2b8155cee54431..69c2826ba6b122dabc685f6e5f7d85ceec09adf7 100644
--- a/Notebooks/nikolai/.ipynb_checkpoints/GINN_P2-checkpoint.ipynb
+++ b/Notebooks/nikolai/.ipynb_checkpoints/GINN_P2-checkpoint.ipynb
@@ -11,7 +11,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 6,
"metadata": {
"id": "51F8EYtcEv9h"
},
@@ -99,7 +99,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 7,
"metadata": {
"id": "WDYEYPt9rD_N"
},
@@ -1084899,7 +1084899,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 8,
"metadata": {
"id": "eBkpFXnXYhCU"
},
@@ -1084924,7 +1084924,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -1084973,7 +1084973,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -1084995,6 +1084995,8 @@
"\n",
" def loss(self, y, y_hat):\n",
" #euclidean distance\n",
+ " #loss = 0.5*(math.dist(y_hat,y))^2\n",
+ " #return loss\n",
" return y_hat - y\n",
" \n",
" def train(self,inputx, inputy, epochs, learning_rate):\n",
@@ -1085022,18 +1085024,18 @@
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Array 1: [ 3.80136868 -4.57378979 0.77749917 -2.95213157] with [0.84895904 0.99315748 0.87254225]\n",
- "Array 2: [ 0.83132719 -1.53500508 2.19807731 0.33278122] with [0.86729135 0.99855835 0.99997735]\n",
- "Array 3: [ 0.02081301 1.35818493 -0.38164287 2.86113188] with [0.93565786 0.99967525 0.99675767]\n",
- "Array 4: [ 1.45585566 4.40245284 -3.67094517 3.15337779] with [0.9966761 0.99993013 0.99014505]\n",
- "Array 5: [-3.38279044 3.80924278 -1.74817594 -4.1655153 ] with [0.95179912 0.99381633 0.04022942]\n"
+ "Array 1: [-1.00889441 2.73412163 -1.24156425 0.00519685] with [0.03777313 0.96062285 0.00135379]\n",
+ "Array 2: [-1.94574051 -4.80111725 2.84117176 0.37620081] with [0.99872667 0.00109461 0.99923719]\n",
+ "Array 3: [-4.49217921 -2.10821113 -2.28339569 -3.90504663] with [9.96202688e-01 3.11271974e-07 4.57072118e-09]\n",
+ "Array 4: [ 3.79813515 2.24646041 0.19663997 -4.50589303] with [0.94838419 0.99975979 0.00998604]\n",
+ "Array 5: [-4.52773054 -2.07664086 -4.85881148 -4.02403412] with [9.99694720e-01 7.42225206e-08 2.08625883e-09]\n"
]
}
],
@@ -1085045,10 +1085047,10 @@
"b2 = np.array([-2,1.2,1])\n",
"\n",
"\n",
- "data_amount = 1000\n",
+ "data_amount = 10\n",
"x = [np.random.uniform(-5, 5, (4)) for _ in range(data_amount)]\n",
- "y = [sigmoid(( sigmoid(( (u @ W1) + b1)) @ W2) + b2) for u in x]\n",
- "#y = [(u @ [[1,2,3],[-2,1.4,-2],[-1,.5,.1],[-.4,1,3]]) + [2,2,2] for u in x]\n",
+ "#y = [sigmoid(( sigmoid(( (u @ W1) + b1)) @ W2) + b2) for u in x]\n",
+ "y = [sigmoid((u @ [[1,2,3],[-2,1.4,-2],[-1,.5,.1],[-.4,1,3]]) + [2,2,2]) for u in x]\n",
"\n",
"for i in range(min(data_amount, 5)):\n",
" print(f\"Array {i + 1}: {x[i]} with {y[i]}\")"
@@ -1085056,9 +1085058,9016 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": 11,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[0.52606006 0.3552244 0.68514531]\n",
+ "[0.59565952 0.33420544 0.7030434 ]\n",
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+ "[0.47734859 0.40044043 0.69900559]\n",
+ "[0.51629389 0.37157482 0.65932863]\n",
+ "[0.52652572 0.34445464 0.70395118]\n",
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+ "[0.52766757 0.37916999 0.65717457]\n",
+ "[0.63511124 0.32975085 0.69194975]\n",
+ "[0.52546848 0.36622937 0.66643666]\n",
+ "[0.60196932 0.34061659 0.69025406]\n",
+ "[0.52482708 0.37940547 0.64778697]\n",
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+ "[0.52887474 0.35872416 0.68547904]\n",
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+ "[0.60793797 0.34691804 0.67734214]\n",
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+ "[0.89841266 0.05330691 0.04982122]\n",
+ "[0.88662459 0.92993742 0.07284727]\n",
+ "[0.93239335 0.04851926 0.0614575 ]\n",
+ "[0.87431868 0.8993469 0.91173849]\n",
+ "[0.07996352 0.89115394 0.0548609 ]\n",
+ "[0.06674081 0.90637943 0.05585207]\n",
+ "[0.53001333 0.13024859 0.03204277]\n",
+ "[0.16104754 0.6867294 0.91070454]\n"
+ ]
+ }
+ ],
"source": [
"layer_model = model()\n",
"layer_model.train(x, y, 900, 0.05)"
@@ -1085066,24 +1094075,36 @@
},
{
"cell_type": "code",
- "execution_count": 58,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "failure at index: 34: [0.86137119 0.98033212 0.17835986] vs [0.88296932 0.98651723 0.14647191]\n",
- "failure at index: 64: [0.86098567 0.97443643 0.46118326] vs [0.87126406 0.98221484 0.41380405]\n",
- "failure at index: 69: [0.73191424 0.98963623 0.99958056] vs [0.70060927 0.98923262 0.99959592]\n",
- "failure at index: 204: [0.33874672 0.96572138 0.98408441] vs [0.30853544 0.96270177 0.98415545]\n",
- "failure at index: 315: [0.65114339 0.99094295 0.99937111] vs [0.61956672 0.99460339 0.99936677]\n",
- "failure at index: 344: [0.59121444 0.97866749 0.08373359] vs [0.55817934 0.97621698 0.09353375]\n",
- "failure at index: 682: [0.76310881 0.98720785 0.23981463] vs [0.72551516 0.98551648 0.21900081]\n",
- "failure at index: 972: [0.69745141 0.99155537 0.99934323] vs [0.666537 0.99518072 0.99934831]\n"
+ "y: [0.94531382 0.99999432 0.99999352] vs calculated: [0.93749625 0.99241787 0.99450946]\n",
+ "y: [1.76669989e-01 1.40947288e-03 1.93882129e-12] vs calculated: [0.16631415 0.01474774 0.0053406 ]\n",
+ "y: [2.34739214e-05 5.88152065e-01 1.92932525e-10] vs calculated: [0.00646448 0.60113908 0.0052746 ]\n",
+ "y: [2.95491068e-05 8.36812628e-01 3.78530976e-03] vs calculated: [0.00658996 0.84228089 0.01328734]\n",
+ "y: [0.00221489 0.99999956 0.01497311] vs calculated: [0.01286053 0.99331561 0.03011881]\n",
+ "y: [2.15726852e-01 1.99271118e-02 4.66271761e-07] vs calculated: [0.2019411 0.03421361 0.00551063]\n",
+ "y: [0.99451797 0.99999243 1. ] vs calculated: [0.98428141 0.99148869 0.99502296]\n",
+ "y: [7.14238687e-07 9.99998044e-01 9.03924525e-01] vs calculated: [0.00597712 0.99339668 0.90494503]\n",
+ "y: [0.98700232 0.99955495 0.99116532] vs calculated: [0.97646911 0.99010785 0.98198618]\n",
+ "y: [0.94448469 0.514743 0.90184849] vs calculated: [0.94100646 0.50947251 0.90905395]\n"
]
}
],
+ "source": [
+ "for i in range(10):\n",
+ " print(f\"y: {y[i]} vs calculated: {layer_model.call(x[i])}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
"source": [
"treshold = 0.03\n",
"for i in range(len(x)):\n",
@@ -1085127,6 +1094148,20 @@
"\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Start:\n",
+ "700 EP: Die Validation Accuracy ist etwa 0.75, der Loss ist etwa bei 0.85\n",
+ " \n",
+ "800 EP: Die Validation Accuracy ist im max bei etwa 90%, der loss bei 0.85\n",
+ "\n",
+ "Der Verlauf der \n",
+ "### Austausch durch Softmax\n",
+ "700 Ep: Die Accurracy steigt auf vielleicht 90%, der Loss ist auf 0.8"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {
@@ -1085147,7 +1094182,7 @@
},
{
"cell_type": "code",
- "execution_count": 59,
+ "execution_count": 62,
"metadata": {
"id": "Lflgq-Kaux4E"
},
@@ -1085183,7 +1094218,7 @@
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": 68,
"metadata": {
"id": "XeGT-a2Hu4b9"
},
@@ -1085202,12 +1094237,12 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 700/700 [00:00<00:00, 834.98it/s]\n"
+ "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 700/700 [00:00<00:00, 1627.36it/s]\n"
]
},
{
"data": {
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",
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",
"text/plain": [
"<Figure size 1200x600 with 2 Axes>"
]
@@ -1085269,6 +1094304,13 @@
"ax2.set_xlabel(\"epochs\");"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": null,
diff --git a/Notebooks/nikolai/GINN_P2.ipynb b/Notebooks/nikolai/GINN_P2.ipynb
index 6289b33a39a12cd8638a07400e2b8155cee54431..69c2826ba6b122dabc685f6e5f7d85ceec09adf7 100644
--- a/Notebooks/nikolai/GINN_P2.ipynb
+++ b/Notebooks/nikolai/GINN_P2.ipynb
@@ -11,7 +11,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 6,
"metadata": {
"id": "51F8EYtcEv9h"
},
@@ -99,7 +99,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 7,
"metadata": {
"id": "WDYEYPt9rD_N"
},
@@ -1084899,7 +1084899,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 8,
"metadata": {
"id": "eBkpFXnXYhCU"
},
@@ -1084924,7 +1084924,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -1084973,7 +1084973,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -1084995,6 +1084995,8 @@
"\n",
" def loss(self, y, y_hat):\n",
" #euclidean distance\n",
+ " #loss = 0.5*(math.dist(y_hat,y))^2\n",
+ " #return loss\n",
" return y_hat - y\n",
" \n",
" def train(self,inputx, inputy, epochs, learning_rate):\n",
@@ -1085022,18 +1085024,18 @@
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Array 1: [ 3.80136868 -4.57378979 0.77749917 -2.95213157] with [0.84895904 0.99315748 0.87254225]\n",
- "Array 2: [ 0.83132719 -1.53500508 2.19807731 0.33278122] with [0.86729135 0.99855835 0.99997735]\n",
- "Array 3: [ 0.02081301 1.35818493 -0.38164287 2.86113188] with [0.93565786 0.99967525 0.99675767]\n",
- "Array 4: [ 1.45585566 4.40245284 -3.67094517 3.15337779] with [0.9966761 0.99993013 0.99014505]\n",
- "Array 5: [-3.38279044 3.80924278 -1.74817594 -4.1655153 ] with [0.95179912 0.99381633 0.04022942]\n"
+ "Array 1: [-1.00889441 2.73412163 -1.24156425 0.00519685] with [0.03777313 0.96062285 0.00135379]\n",
+ "Array 2: [-1.94574051 -4.80111725 2.84117176 0.37620081] with [0.99872667 0.00109461 0.99923719]\n",
+ "Array 3: [-4.49217921 -2.10821113 -2.28339569 -3.90504663] with [9.96202688e-01 3.11271974e-07 4.57072118e-09]\n",
+ "Array 4: [ 3.79813515 2.24646041 0.19663997 -4.50589303] with [0.94838419 0.99975979 0.00998604]\n",
+ "Array 5: [-4.52773054 -2.07664086 -4.85881148 -4.02403412] with [9.99694720e-01 7.42225206e-08 2.08625883e-09]\n"
]
}
],
@@ -1085045,10 +1085047,10 @@
"b2 = np.array([-2,1.2,1])\n",
"\n",
"\n",
- "data_amount = 1000\n",
+ "data_amount = 10\n",
"x = [np.random.uniform(-5, 5, (4)) for _ in range(data_amount)]\n",
- "y = [sigmoid(( sigmoid(( (u @ W1) + b1)) @ W2) + b2) for u in x]\n",
- "#y = [(u @ [[1,2,3],[-2,1.4,-2],[-1,.5,.1],[-.4,1,3]]) + [2,2,2] for u in x]\n",
+ "#y = [sigmoid(( sigmoid(( (u @ W1) + b1)) @ W2) + b2) for u in x]\n",
+ "y = [sigmoid((u @ [[1,2,3],[-2,1.4,-2],[-1,.5,.1],[-.4,1,3]]) + [2,2,2]) for u in x]\n",
"\n",
"for i in range(min(data_amount, 5)):\n",
" print(f\"Array {i + 1}: {x[i]} with {y[i]}\")"
@@ -1085056,9 +1085058,9016 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": 11,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[0.52606006 0.3552244 0.68514531]\n",
+ "[0.59565952 0.33420544 0.7030434 ]\n",
+ "[0.52025161 0.37229347 0.66709888]\n",
+ "[0.47734859 0.40044043 0.69900559]\n",
+ "[0.51629389 0.37157482 0.65932863]\n",
+ "[0.52652572 0.34445464 0.70395118]\n",
+ "[0.53698525 0.36064175 0.67346555]\n",
+ "[0.56157413 0.36082908 0.68368401]\n",
+ "[0.52766757 0.37916999 0.65717457]\n",
+ "[0.63511124 0.32975085 0.69194975]\n",
+ "[0.52546848 0.36622937 0.66643666]\n",
+ "[0.60196932 0.34061659 0.69025406]\n",
+ "[0.52482708 0.37940547 0.64778697]\n",
+ "[0.48270022 0.41352161 0.67970063]\n",
+ "[0.52118931 0.37970062 0.63777006]\n",
+ "[0.52887474 0.35872416 0.68547904]\n",
+ "[0.5362025 0.37114251 0.65500436]\n",
+ "[0.55797864 0.37286303 0.666192 ]\n",
+ "[0.52986734 0.3873628 0.63849786]\n",
+ "[0.63595845 0.33767936 0.67828292]\n",
+ "[0.5249094 0.37671669 0.64803433]\n",
+ "[0.60793797 0.34691804 0.67734214]\n",
+ "[0.52915881 0.38601402 0.6286495 ]\n",
+ "[0.48789498 0.42623337 0.66041032]\n",
+ "[0.52584222 0.38724793 0.61645951]\n",
+ "[0.53134594 0.37266521 0.66716815]\n",
+ "[0.53546038 0.38111391 0.63695859]\n",
+ "[0.55458835 0.38428101 0.64927148]\n",
+ "[0.53197978 0.39501563 0.6202011 ]\n",
+ "[0.63677769 0.34528789 0.66482122]\n",
+ "[0.52438819 0.38667825 0.63008855]\n",
+ "[0.61358103 0.35307129 0.66446806]\n",
+ "[0.53326472 0.39212893 0.60984162]\n",
+ "[0.49294074 0.43854505 0.64132831]\n",
+ "[0.53027309 0.39423269 0.59557487]\n",
+ "[0.53393017 0.38623246 0.64920081]\n",
+ "[0.53476355 0.39055557 0.61945928]\n",
+ "[0.55140324 0.39508163 0.63303205]\n",
+ "[0.53401753 0.40213928 0.60240297]\n",
+ "[0.63756289 0.35256076 0.65168746]\n",
+ "[0.52390706 0.39611706 0.61272005]\n",
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+ "[0.16384536 0.68742576 0.90891127]\n",
+ "[0.08408373 0.89137359 0.05786462]\n",
+ "[0.96233233 0.07056911 0.93509967]\n",
+ "[0.89651422 0.05418731 0.05068899]\n",
+ "[0.88478304 0.92829643 0.07392845]\n",
+ "[0.93075971 0.04915619 0.06225312]\n",
+ "[0.87284251 0.89815646 0.91044214]\n",
+ "[0.08134992 0.89008372 0.05573576]\n",
+ "[0.0678737 0.9055006 0.05670594]\n",
+ "[0.53162347 0.13217289 0.03267698]\n",
+ "[0.16372576 0.6873966 0.90898784]\n",
+ "[0.08401958 0.89142203 0.05782431]\n",
+ "[0.96236694 0.07051238 0.93514116]\n",
+ "[0.89659868 0.05414816 0.05065051]\n",
+ "[0.88486496 0.92836947 0.07388028]\n",
+ "[0.93083271 0.04912778 0.06221809]\n",
+ "[0.87290859 0.89820895 0.91049973]\n",
+ "[0.08128793 0.89013112 0.0556969 ]\n",
+ "[0.06782305 0.90553949 0.05666799]\n",
+ "[0.53155112 0.13208764 0.03264879]\n",
+ "[0.16360643 0.68736745 0.90906425]\n",
+ "[0.0839556 0.89147038 0.05778408]\n",
+ "[0.96240147 0.07045577 0.93518259]\n",
+ "[0.89668295 0.0541091 0.0506121 ]\n",
+ "[0.8849467 0.92844236 0.07383221]\n",
+ "[0.93090553 0.04909944 0.06218309]\n",
+ "[0.87297449 0.89826138 0.9105572 ]\n",
+ "[0.08122611 0.89017845 0.05565813]\n",
+ "[0.06777254 0.90557832 0.05663011]\n",
+ "[0.53147899 0.13200254 0.03262067]\n",
+ "[0.16348736 0.68733831 0.9091405 ]\n",
+ "[0.08389179 0.89151865 0.05774393]\n",
+ "[0.96243592 0.07039928 0.93522395]\n",
+ "[0.89676705 0.05407011 0.05057377]\n",
+ "[0.88502828 0.92851509 0.07378426]\n",
+ "[0.93097816 0.04907116 0.06214814]\n",
+ "[0.87304021 0.89831373 0.91061455]\n",
+ "[0.08116444 0.89022569 0.05561942]\n",
+ "[0.06772215 0.90561709 0.05659232]\n",
+ "[0.53140707 0.13191758 0.0325926 ]\n",
+ "[0.16336857 0.68730918 0.90921658]\n",
+ "[0.08382814 0.89156684 0.05770386]\n",
+ "[0.96247028 0.07034291 0.93526524]\n",
+ "[0.89685098 0.05403121 0.05053549]\n",
+ "[0.88510968 0.92858766 0.07373641]\n",
+ "[0.93105061 0.04904295 0.06211323]\n",
+ "[0.87310576 0.89836602 0.9106718 ]\n",
+ "[0.08110293 0.89027286 0.05558079]\n",
+ "[0.0676719 0.90565579 0.0565546 ]\n",
+ "[0.53133538 0.13183277 0.03256458]\n",
+ "[0.16325004 0.68728007 0.9092925 ]\n",
+ "[0.08376465 0.89161494 0.05766386]\n",
+ "[0.96250455 0.07028666 0.93530647]\n",
+ "[0.89693472 0.05399238 0.05049729]\n",
+ "[0.88519092 0.92866007 0.07368867]\n",
+ "[0.93112288 0.04901481 0.06207835]\n",
+ "[0.87317113 0.89841824 0.91072893]\n",
+ "[0.08104158 0.89031994 0.05554224]\n",
+ "[0.06762177 0.90569444 0.05651695]\n",
+ "[0.5312639 0.13174811 0.03253662]\n",
+ "[0.16313178 0.68725097 0.90936825]\n",
+ "[0.08370132 0.89166297 0.05762394]\n",
+ "[0.96253874 0.07023053 0.93534763]\n",
+ "[0.89701829 0.05395364 0.05045916]\n",
+ "[0.88527198 0.92873233 0.07364104]\n",
+ "[0.93119497 0.04898673 0.06204352]\n",
+ "[0.87323633 0.89847039 0.91078595]\n",
+ "[0.08098039 0.89036694 0.05550376]\n",
+ "[0.06757177 0.90573301 0.05647938]\n",
+ "[0.53119263 0.13166359 0.03250872]\n",
+ "[0.16301378 0.68722188 0.90944384]\n",
+ "[0.08363815 0.89171091 0.0575841 ]\n",
+ "[0.96257285 0.07017452 0.93538872]\n",
+ "[0.89710169 0.05391498 0.05042109]\n",
+ "[0.88535288 0.92880444 0.07359351]\n",
+ "[0.93126688 0.04895872 0.06200873]\n",
+ "[0.87330136 0.89852248 0.91084286]\n",
+ "[0.08091935 0.89041386 0.05546536]\n",
+ "[0.06752189 0.90577153 0.05644189]\n",
+ "[0.53112158 0.13157921 0.03248088]\n",
+ "[0.16289604 0.6871928 0.90951927]\n",
+ "[0.08357514 0.89175876 0.05754434]\n",
+ "[0.96260687 0.07011863 0.93542975]\n",
+ "[0.89718491 0.05387639 0.0503831 ]\n",
+ "[0.8854336 0.92887639 0.07354609]\n",
+ "[0.93133861 0.04893077 0.06197398]\n",
+ "[0.87336621 0.8985745 0.91089965]\n",
+ "[0.08085847 0.89046071 0.05542703]\n",
+ "[0.06747214 0.90580998 0.05640447]\n",
+ "[0.53105074 0.13149498 0.03245308]\n",
+ "[0.16277857 0.68716374 0.90959454]\n",
+ "[0.08351229 0.89180654 0.05750465]\n",
+ "[0.96264081 0.07006285 0.93547072]\n",
+ "[0.89726796 0.05383788 0.05034517]\n",
+ "[0.88551416 0.92894818 0.07349878]\n",
+ "[0.93141016 0.04890289 0.06193927]\n",
+ "[0.87343089 0.89862645 0.91095634]\n",
+ "[0.08079774 0.89050747 0.05538878]\n",
+ "[0.06742252 0.90584837 0.05636713]\n",
+ "[0.53098012 0.1314109 0.03242535]\n",
+ "[0.16266137 0.68713468 0.90966965]\n",
+ "[0.0834496 0.89185423 0.05746504]\n",
+ "[0.96267466 0.07000719 0.93551162]\n",
+ "[0.89735084 0.05379946 0.05030731]\n",
+ "[0.88559455 0.92901983 0.07345157]\n",
+ "[0.93148153 0.04887508 0.0619046 ]\n",
+ "[0.8734954 0.89867834 0.91101291]\n",
+ "[0.08073717 0.89055415 0.0553506 ]\n",
+ "[0.06737303 0.9058867 0.05632986]\n",
+ "[0.53090971 0.13132695 0.03239767]\n",
+ "[0.16254442 0.68710565 0.9097446 ]\n",
+ "[0.08338707 0.89190185 0.0574255 ]\n",
+ "[0.96270844 0.06995165 0.93555245]\n",
+ "[0.89743354 0.05376111 0.05026951]\n",
+ "[0.88567477 0.92909131 0.07340447]\n",
+ "[0.93155273 0.04884733 0.06186996]\n",
+ "[0.87355973 0.89873016 0.91106938]\n",
+ "[0.08067675 0.89060076 0.05531249]\n",
+ "[0.06732366 0.90592497 0.05629267]\n",
+ "[0.5308395 0.13124315 0.03237004]\n",
+ "[0.16242774 0.68707662 0.90981938]\n",
+ "[0.08332469 0.89194938 0.05738604]\n",
+ "[0.96274213 0.06989623 0.93559323]\n",
+ "[0.89751607 0.05372283 0.05023179]\n",
+ "[0.88575483 0.92916265 0.07335747]\n",
+ "[0.93162374 0.04881964 0.06183537]\n",
+ "[0.8736239 0.89878192 0.91112573]\n",
+ "[0.08061649 0.89064728 0.05527446]\n",
+ "[0.06727441 0.90596317 0.05625555]\n",
+ "[0.53076952 0.1311595 0.03234247]\n",
+ "[0.16231131 0.68704761 0.90989401]\n",
+ "[0.08326247 0.89199683 0.05734666]\n",
+ "[0.96277573 0.06984092 0.93563393]\n",
+ "[0.89759843 0.05368464 0.05019413]\n",
+ "[0.88583472 0.92923384 0.07331057]\n",
+ "[0.93169458 0.04879202 0.06180082]\n",
+ "[0.8736879 0.89883361 0.91118198]\n",
+ "[0.08055637 0.89069373 0.0552365 ]\n",
+ "[0.06722529 0.90600132 0.0562185 ]\n",
+ "[0.53069974 0.13107598 0.03231496]\n",
+ "[0.16219515 0.68701861 0.90996848]\n",
+ "[0.08320041 0.8920442 0.05730735]\n",
+ "[0.96280926 0.06978573 0.93567457]\n",
+ "[0.89768062 0.05364652 0.05015654]\n",
+ "[0.88591444 0.92930487 0.07326378]\n",
+ "[0.93176524 0.04876447 0.06176631]\n",
+ "[0.87375173 0.89888523 0.91123812]\n",
+ "[0.08049641 0.8907401 0.05519861]\n",
+ "[0.0671763 0.9060394 0.05618153]\n",
+ "[0.53063017 0.13099261 0.03228749]\n",
+ "[0.16207924 0.68698963 0.91004279]\n",
+ "[0.08313851 0.89209149 0.05726812]\n",
+ "[0.9628427 0.06973065 0.93571515]\n",
+ "[0.89776263 0.05360848 0.05011901]\n",
+ "[0.885994 0.92937575 0.07321709]\n",
+ "[0.93183573 0.04873697 0.06173184]\n",
+ "[0.87381539 0.89893679 0.91129414]\n",
+ "[0.08043661 0.89078639 0.0551608 ]\n",
+ "[0.06712742 0.90607742 0.05614463]\n",
+ "[0.5305608 0.13090938 0.03226009]\n",
+ "[0.16196359 0.68696066 0.91011694]\n",
+ "[0.08307675 0.89213871 0.05722896]\n",
+ "[0.96287607 0.06967569 0.93575566]\n",
+ "[0.89784448 0.05357052 0.05008156]\n",
+ "[0.8860734 0.92944648 0.07317051]\n",
+ "[0.93190604 0.04870954 0.06169741]\n",
+ "[0.87387888 0.89898828 0.91135006]\n",
+ "[0.08037695 0.8908326 0.05512306]\n",
+ "[0.06707867 0.90611538 0.05610781]\n",
+ "[0.53049165 0.13082629 0.03223273]\n",
+ "[0.16184819 0.6869317 0.91019094]\n",
+ "[0.08301516 0.89218584 0.05718988]\n",
+ "[0.96290935 0.06962085 0.93579611]\n",
+ "[0.89792615 0.05353264 0.05004417]\n",
+ "[0.88615263 0.92951706 0.07312402]\n",
+ "[0.93197617 0.04868217 0.06166302]\n",
+ "[0.8739422 0.89903971 0.91140587]\n",
+ "[0.08031744 0.89087874 0.05508539]\n",
+ "[0.06703004 0.90615328 0.05607106]\n",
+ "[0.5304227 0.13074334 0.03220544]\n",
+ "[0.16173306 0.68690276 0.91026477]\n",
+ "[0.08295371 0.89223289 0.05715087]\n",
+ "[0.96294255 0.06956612 0.93583649]\n",
+ "[0.89800766 0.05349483 0.05000684]\n",
+ "[0.88623169 0.92958749 0.07307764]\n",
+ "[0.93204614 0.04865487 0.06162866]\n",
+ "[0.87400536 0.89909107 0.91146158]\n",
+ "[0.08025808 0.8909248 0.0550478 ]\n",
+ "[0.06698153 0.90619112 0.05603438]\n",
+ "[0.53035396 0.13066053 0.03217819]\n",
+ "[0.16161817 0.68687383 0.91033846]\n",
+ "[0.08289243 0.89227986 0.05711194]\n",
+ "[0.96297567 0.0695115 0.93587682]\n",
+ "[0.898089 0.05345709 0.04996958]\n",
+ "[0.8863106 0.92965777 0.07303137]\n",
+ "[0.93211592 0.04862762 0.06159435]\n",
+ "[0.87406835 0.89914236 0.91151717]\n",
+ "[0.08019888 0.89097078 0.05501027]\n",
+ "[0.06693315 0.9062289 0.05599777]\n",
+ "[0.53028543 0.13057786 0.032151 ]\n",
+ "[0.16150354 0.68684491 0.91041198]\n",
+ "[0.08283129 0.89232676 0.05707308]\n",
+ "[0.96300871 0.069457 0.93591707]\n",
+ "[0.89817016 0.05341943 0.04993239]\n",
+ "[0.88638934 0.92972791 0.07298519]\n",
+ "[0.93218554 0.04860044 0.06156008]\n",
+ "[0.87413118 0.89919359 0.91157266]\n",
+ "[0.08013982 0.89101668 0.05497282]\n",
+ "[0.06688488 0.90626662 0.05596124]\n",
+ "[0.5302171 0.13049534 0.03212386]\n",
+ "[0.16138917 0.68681601 0.91048535]\n",
+ "[0.0827703 0.89237357 0.05703429]\n",
+ "[0.96304167 0.06940261 0.93595727]\n",
+ "[0.89825116 0.05338185 0.04989527]\n",
+ "[0.88646791 0.92979789 0.07293912]\n",
+ "[0.93225498 0.04857332 0.06152585]\n",
+ "[0.87419385 0.89924476 0.91162805]\n",
+ "[0.08008091 0.89106251 0.05493544]\n",
+ "[0.06683674 0.90630429 0.05592477]\n",
+ "[0.53014897 0.13041295 0.03209678]\n",
+ "[0.16127504 0.68678713 0.91055857]\n",
+ "[0.08270947 0.89242031 0.05699558]\n",
+ "[0.96307455 0.06934833 0.9359974 ]\n",
+ "[0.898332 0.05334434 0.04985821]\n",
+ "[0.88654633 0.92986773 0.07289314]\n",
+ "[0.93232425 0.04854626 0.06149165]\n",
+ "[0.87425635 0.89929586 0.91168332]\n",
+ "[0.08002214 0.89110826 0.05489813]\n",
+ "[0.06678872 0.90634189 0.05588838]\n",
+ "[0.53008105 0.1303307 0.03206975]\n",
+ "[0.16116117 0.68675826 0.91063163]\n",
+ "[0.08264879 0.89246697 0.05695694]\n",
+ "[0.96310735 0.06929417 0.93603747]\n",
+ "[0.89841266 0.05330691 0.04982122]\n",
+ "[0.88662459 0.92993742 0.07284727]\n",
+ "[0.93239335 0.04851926 0.0614575 ]\n",
+ "[0.87431868 0.8993469 0.91173849]\n",
+ "[0.07996352 0.89115394 0.0548609 ]\n",
+ "[0.06674081 0.90637943 0.05585207]\n",
+ "[0.53001333 0.13024859 0.03204277]\n",
+ "[0.16104754 0.6867294 0.91070454]\n"
+ ]
+ }
+ ],
"source": [
"layer_model = model()\n",
"layer_model.train(x, y, 900, 0.05)"
@@ -1085066,24 +1094075,36 @@
},
{
"cell_type": "code",
- "execution_count": 58,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "failure at index: 34: [0.86137119 0.98033212 0.17835986] vs [0.88296932 0.98651723 0.14647191]\n",
- "failure at index: 64: [0.86098567 0.97443643 0.46118326] vs [0.87126406 0.98221484 0.41380405]\n",
- "failure at index: 69: [0.73191424 0.98963623 0.99958056] vs [0.70060927 0.98923262 0.99959592]\n",
- "failure at index: 204: [0.33874672 0.96572138 0.98408441] vs [0.30853544 0.96270177 0.98415545]\n",
- "failure at index: 315: [0.65114339 0.99094295 0.99937111] vs [0.61956672 0.99460339 0.99936677]\n",
- "failure at index: 344: [0.59121444 0.97866749 0.08373359] vs [0.55817934 0.97621698 0.09353375]\n",
- "failure at index: 682: [0.76310881 0.98720785 0.23981463] vs [0.72551516 0.98551648 0.21900081]\n",
- "failure at index: 972: [0.69745141 0.99155537 0.99934323] vs [0.666537 0.99518072 0.99934831]\n"
+ "y: [0.94531382 0.99999432 0.99999352] vs calculated: [0.93749625 0.99241787 0.99450946]\n",
+ "y: [1.76669989e-01 1.40947288e-03 1.93882129e-12] vs calculated: [0.16631415 0.01474774 0.0053406 ]\n",
+ "y: [2.34739214e-05 5.88152065e-01 1.92932525e-10] vs calculated: [0.00646448 0.60113908 0.0052746 ]\n",
+ "y: [2.95491068e-05 8.36812628e-01 3.78530976e-03] vs calculated: [0.00658996 0.84228089 0.01328734]\n",
+ "y: [0.00221489 0.99999956 0.01497311] vs calculated: [0.01286053 0.99331561 0.03011881]\n",
+ "y: [2.15726852e-01 1.99271118e-02 4.66271761e-07] vs calculated: [0.2019411 0.03421361 0.00551063]\n",
+ "y: [0.99451797 0.99999243 1. ] vs calculated: [0.98428141 0.99148869 0.99502296]\n",
+ "y: [7.14238687e-07 9.99998044e-01 9.03924525e-01] vs calculated: [0.00597712 0.99339668 0.90494503]\n",
+ "y: [0.98700232 0.99955495 0.99116532] vs calculated: [0.97646911 0.99010785 0.98198618]\n",
+ "y: [0.94448469 0.514743 0.90184849] vs calculated: [0.94100646 0.50947251 0.90905395]\n"
]
}
],
+ "source": [
+ "for i in range(10):\n",
+ " print(f\"y: {y[i]} vs calculated: {layer_model.call(x[i])}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
"source": [
"treshold = 0.03\n",
"for i in range(len(x)):\n",
@@ -1085127,6 +1094148,20 @@
"\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Start:\n",
+ "700 EP: Die Validation Accuracy ist etwa 0.75, der Loss ist etwa bei 0.85\n",
+ " \n",
+ "800 EP: Die Validation Accuracy ist im max bei etwa 90%, der loss bei 0.85\n",
+ "\n",
+ "Der Verlauf der \n",
+ "### Austausch durch Softmax\n",
+ "700 Ep: Die Accurracy steigt auf vielleicht 90%, der Loss ist auf 0.8"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {
@@ -1085147,7 +1094182,7 @@
},
{
"cell_type": "code",
- "execution_count": 59,
+ "execution_count": 62,
"metadata": {
"id": "Lflgq-Kaux4E"
},
@@ -1085183,7 +1094218,7 @@
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": 68,
"metadata": {
"id": "XeGT-a2Hu4b9"
},
@@ -1085202,12 +1094237,12 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 700/700 [00:00<00:00, 834.98it/s]\n"
+ "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 700/700 [00:00<00:00, 1627.36it/s]\n"
]
},
{
"data": {
- "image/png": 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",
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",
"text/plain": [
"<Figure size 1200x600 with 2 Axes>"
]
@@ -1085269,6 +1094304,13 @@
"ax2.set_xlabel(\"epochs\");"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": null,