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#7 Bug 수정
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‎lab-06-1-softmax_classifier-eager.ipynb

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@@ -12,10 +12,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 56,
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"metadata": {
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"scrolled": true
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},
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
@@ -27,7 +25,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 57,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -55,7 +53,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
@@ -80,7 +78,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
@@ -106,7 +104,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 60,
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
@@ -136,14 +134,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tf.Tensor([[8.78809988e-01 1.21101834e-01 8.81008382e-05]], shape=(1, 3), dtype=float32)\n"
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"tf.Tensor([[0.9302204 0.06200533 0.00777428]], shape=(1, 3), dtype=float32)\n"
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]
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}
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],
@@ -152,12 +150,12 @@
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"sample_db = [[8,2,1,4]]\n",
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"sample_db = np.asarray(sample_db, dtype=np.float32)\n",
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"\n",
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"print(softmax_regression(sample_db))"
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"print(hypothesis(sample_db))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 62,
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
@@ -181,7 +179,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 63,
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
@@ -203,18 +201,18 @@
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},
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{
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"cell_type": "code",
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"execution_count": 65,
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[<tf.Tensor: id=107174, shape=(4, 3), dtype=float32, numpy=\n",
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"[<tf.Tensor: id=118, shape=(4, 3), dtype=float32, numpy=\n",
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"array([[ 0.06914616, -0.6509784 , 0.58183223],\n",
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" [-1.5221257 , -1.214863 , 2.7369888 ],\n",
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" [-1.2473826 , -1.7611003 , 3.008483 ],\n",
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" [-1.2014605 , -1.8659232 , 3.0673838 ]], dtype=float32)>, <tf.Tensor: id=107172, shape=(3,), dtype=float32, numpy=array([-0.15212911, -0.342192 , 0.49432108], dtype=float32)>]\n"
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" [-1.2014605 , -1.8659232 , 3.0673838 ]], dtype=float32)>, <tf.Tensor: id=116, shape=(3,), dtype=float32, numpy=array([-0.15212911, -0.342192 , 0.49432108], dtype=float32)>]\n"
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]
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}
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],
@@ -231,34 +229,34 @@
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},
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{
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"cell_type": "code",
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"execution_count": 89,
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Loss at epoch 1: 0.120537\n",
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"Loss at epoch 100: 0.118783\n",
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"Loss at epoch 200: 0.117060\n",
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"Loss at epoch 300: 0.115383\n",
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"Loss at epoch 400: 0.113752\n",
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"Loss at epoch 500: 0.112164\n",
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"Loss at epoch 600: 0.110618\n",
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"Loss at epoch 700: 0.109112\n",
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"Loss at epoch 800: 0.107645\n",
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"Loss at epoch 900: 0.106214\n",
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"Loss at epoch 1000: 0.104820\n",
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"Loss at epoch 1100: 0.103461\n",
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"Loss at epoch 1200: 0.102135\n",
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"Loss at epoch 1300: 0.100841\n",
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"Loss at epoch 1400: 0.099578\n",
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"Loss at epoch 1500: 0.098345\n",
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"Loss at epoch 1600: 0.097141\n",
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"Loss at epoch 1700: 0.095966\n",
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"Loss at epoch 1800: 0.094817\n",
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"Loss at epoch 1900: 0.093694\n",
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"Loss at epoch 2000: 0.092597\n"
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"Loss at epoch 1: 0.126001\n",
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"Loss at epoch 100: 0.124082\n",
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"Loss at epoch 200: 0.122203\n",
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"Loss at epoch 300: 0.120380\n",
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"Loss at epoch 400: 0.118610\n",
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"Loss at epoch 500: 0.116891\n",
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"Loss at epoch 600: 0.115221\n",
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"Loss at epoch 700: 0.113598\n",
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"Loss at epoch 800: 0.112019\n",
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"Loss at epoch 900: 0.110482\n",
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"Loss at epoch 1000: 0.108987\n",
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"Loss at epoch 1100: 0.107531\n",
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"Loss at epoch 1200: 0.106112\n",
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"Loss at epoch 1300: 0.104730\n",
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"Loss at epoch 1400: 0.103382\n",
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"Loss at epoch 1500: 0.102067\n",
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"Loss at epoch 1600: 0.100785\n",
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"Loss at epoch 1700: 0.099534\n",
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"Loss at epoch 1800: 0.098313\n",
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"Loss at epoch 1900: 0.097120\n",
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"Loss at epoch 2000: 0.095956\n"
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]
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}
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],
@@ -284,22 +282,22 @@
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},
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{
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"cell_type": "code",
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"execution_count": 90,
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tf.Tensor(\n",
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"[[0.00353154 0.0319833 0.96448517]\n",
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" [0.00868691 0.10975333 0.8815598 ]\n",
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" [0.00679857 0.13375703 0.8594444 ]\n",
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" [0.00680363 0.13399178 0.8592046 ]\n",
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" [0.01918449 0.05515586 0.9256596 ]\n",
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" [0.01832496 0.05755147 0.9241236 ]\n",
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" [0.01991962 0.05325432 0.92682606]\n",
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" [0.01994611 0.05318815 0.92686576]], shape=(8, 3), dtype=float32)\n",
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"[[0.00783868 0.08934675 0.90281457]\n",
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" [0.00837632 0.10334261 0.88828105]\n",
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" [0.00890698 0.11949147 0.8716016 ]\n",
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" [0.01300187 0.34645918 0.64053893]\n",
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" [0.01916472 0.3473155 0.6335198 ]\n",
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" [0.01627325 0.38224185 0.6014849 ]\n",
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" [0.03545809 0.24856271 0.7159792 ]\n",
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" [0.04392094 0.21500538 0.74107367]], shape=(8, 3), dtype=float32)\n",
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"tf.Tensor([2 2 2 2 2 2 2 2], shape=(8,), dtype=int64)\n"
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]
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}
@@ -316,22 +314,22 @@
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},
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{
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"cell_type": "code",
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"execution_count": 95,
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tf.Tensor(\n",
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"[[8.8750041e-10 2.7697413e-06 9.9999726e-01]\n",
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" [2.9207354e-03 2.1559663e-02 9.7551960e-01]\n",
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" [3.2986515e-13 2.1821767e-02 9.7817820e-01]\n",
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" [6.4432254e-10 7.9563278e-01 2.0436718e-01]\n",
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" [1.7372852e-02 9.7724211e-01 5.3850519e-03]\n",
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" [8.0847805e-03 9.9191523e-01 3.9386168e-17]\n",
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" [8.6395258e-01 1.3604739e-01 1.9627089e-10]\n",
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" [9.7442448e-01 2.5575478e-02 1.9792436e-13]], shape=(8, 3), dtype=float32)\n",
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"[[1.17311458e-06 4.64814686e-04 9.99534011e-01]\n",
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" [8.37631337e-03 1.03342585e-01 8.88281107e-01]\n",
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" [1.04251918e-09 1.33687243e-01 8.66312802e-01]\n",
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" [5.01113000e-08 8.58640432e-01 1.41359538e-01]\n",
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" [1.16531663e-01 8.71398807e-01 1.20694786e-02]\n",
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" [5.55007122e-02 9.44499254e-01 7.49097243e-11]\n",
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" [8.76283109e-01 1.23716690e-01 2.13872141e-07]\n",
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" [9.74358737e-01 2.56412514e-02 1.90751481e-09]], shape=(8, 3), dtype=float32)\n",
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"tf.Tensor([2 2 2 1 1 1 0 0], shape=(8,), dtype=int64)\n",
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"tf.Tensor([2 2 2 1 1 1 0 0], shape=(8,), dtype=int64)\n"
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]
@@ -353,7 +351,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -386,25 +384,9 @@
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" grads = self.grad_fn(X, Y)\n",
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" optimizer.apply_gradients(zip(grads, self.variables))\n",
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" if (i==0) | ((i+1)%verbose==0):\n",
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" print('Loss at epoch %d: %f' %(i+1, self.loss_fn(X, Y).numpy()))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)\n",
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"model = softmax_classifer(nb_classes)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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" print('Loss at epoch %d: %f' %(i+1, self.loss_fn(X, Y).numpy()))\n",
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" \n",
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"model = softmax_classifer(nb_classes)\n",
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"model.fit(x_data, y_data)"
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]
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}

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