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michitakaiida/keras_iris_sample.ipynb

Created September 14, 2016 15:29
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keras_iris_sample.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#matplotlib用の設定\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('ggplot')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#irisデータ読み込み\n",
"from sklearn import datasets\n",
"iris = datasets.load_iris()\n",
"features = iris.data\n",
"targets = iris.target"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" [ 5.7, 2.6, 3.5, 1. ],\n",
" [ 5.5, 2.4, 3.8, 1.1],\n",
" [ 5.5, 2.4, 3.7, 1. ],\n",
" [ 5.8, 2.7, 3.9, 1.2],\n",
" [ 6. , 2.7, 5.1, 1.6],\n",
" [ 5.4, 3. , 4.5, 1.5],\n",
" [ 6. , 3.4, 4.5, 1.6],\n",
" [ 6.7, 3.1, 4.7, 1.5],\n",
" [ 6.3, 2.3, 4.4, 1.3],\n",
" [ 5.6, 3. , 4.1, 1.3],\n",
" [ 5.5, 2.5, 4. , 1.3],\n",
" [ 5.5, 2.6, 4.4, 1.2],\n",
" [ 6.1, 3. , 4.6, 1.4],\n",
" [ 5.8, 2.6, 4. , 1.2],\n",
" [ 5. , 2.3, 3.3, 1. ],\n",
" [ 5.6, 2.7, 4.2, 1.3],\n",
" [ 5.7, 3. , 4.2, 1.2],\n",
" [ 5.7, 2.9, 4.2, 1.3],\n",
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" [ 6.9, 3.2, 5.7, 2.3],\n",
" [ 5.6, 2.8, 4.9, 2. ],\n",
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" [ 6.3, 2.7, 4.9, 1.8],\n",
" [ 6.7, 3.3, 5.7, 2.1],\n",
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" [ 6. , 3. , 4.8, 1.8],\n",
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" [ 5.8, 2.7, 5.1, 1.9],\n",
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]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#特徴ベクトルの確認\n",
"features"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#正解ベクトルの確認\n",
"targets"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(150, 4)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#特徴ベクトルのデータ構造の確認(４次元の特徴が150レコード)\n",
"features.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x115cb5470>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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1HjzIzjfeACDs87H1pZeS0trZ1+Tiq18uivXf2fCrRXhd1Qlt66+s5MvfLGq+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\nzWdrta3v2DHUUAhLnz7ojJ0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ID+V3azuFrrD5bFzBldQYarBgwekrSHckIUQnSNFPM31E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\ngCE/fz/V7fO4U6EAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1140bef28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#特徴の１次元目と２次元目でplotしてみる\n",
"plt.scatter(features[:, 0], features[:, 1], c=targets)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"#kerasに必要なものをインポート\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Activation\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/20\n",
"150/150 [==============================] - 0s - loss: 1.0919 - acc: 0.4467 \n",
"Epoch 2/20\n",
"150/150 [==============================] - 0s - loss: 0.8269 - acc: 0.6333 \n",
"Epoch 3/20\n",
"150/150 [==============================] - 0s - loss: 0.7604 - acc: 0.6667 \n",
"Epoch 4/20\n",
"150/150 [==============================] - 0s - loss: 0.6830 - acc: 0.6933 \n",
"Epoch 5/20\n",
"150/150 [==============================] - 0s - loss: 0.6372 - acc: 0.6600 \n",
"Epoch 6/20\n",
"150/150 [==============================] - 0s - loss: 0.5918 - acc: 0.7200 \n",
"Epoch 7/20\n",
"150/150 [==============================] - 0s - loss: 0.5555 - acc: 0.7200 \n",
"Epoch 8/20\n",
"150/150 [==============================] - 0s - loss: 0.5305 - acc: 0.7733 \n",
"Epoch 9/20\n",
"150/150 [==============================] - 0s - loss: 0.5099 - acc: 0.7867 \n",
"Epoch 10/20\n",
"150/150 [==============================] - 0s - loss: 0.4795 - acc: 0.8867 \n",
"Epoch 11/20\n",
"150/150 [==============================] - 0s - loss: 0.4636 - acc: 0.8400 \n",
"Epoch 12/20\n",
"150/150 [==============================] - 0s - loss: 0.4481 - acc: 0.8133 \n",
"Epoch 13/20\n",
"150/150 [==============================] - 0s - loss: 0.4344 - acc: 0.8133 \n",
"Epoch 14/20\n",
"150/150 [==============================] - 0s - loss: 0.4191 - acc: 0.8867 \n",
"Epoch 15/20\n",
"150/150 [==============================] - 0s - loss: 0.4124 - acc: 0.8733 \n",
"Epoch 16/20\n",
"150/150 [==============================] - 0s - loss: 0.3941 - acc: 0.8733 \n",
"Epoch 17/20\n",
"150/150 [==============================] - 0s - loss: 0.3872 - acc: 0.8667 \n",
"Epoch 18/20\n",
"150/150 [==============================] - 0s - loss: 0.3749 - acc: 0.9000 \n",
"Epoch 19/20\n",
"150/150 [==============================] - 0s - loss: 0.3722 - acc: 0.9200 \n",
"Epoch 20/20\n",
"150/150 [==============================] - 0s - loss: 0.3510 - acc: 0.9400 \n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x115c9c160>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#モデルの設定\n",
"model = Sequential()\n",
"model.add(Dense(12, input_dim=4))\n",
"model.add(Activation('relu'))\n",
"model.add(Dense(3, input_dim=12))\n",
"model.add(Activation('softmax')) \n",
"model.compile(optimizer='SGD',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
"model.fit(features, targets, nb_epoch=20, batch_size=5)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 80/150 [===============>..............] - ETA: 0s"
]
},
{
"data": {
"text/plain": [
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]
},
"execution_count": 9,
"metadata": {},
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}
],
"source": [
"model.predict(features, batch_size=10, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"120/150 [=======================>......] - ETA: 0s"
]
},
{
"data": {
"text/plain": [
"[0.353749048213164, 0.82000000278155005]"
]
},
"execution_count": 10,
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"source": [
"model.evaluate(features, targets, batch_size=10)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
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"outputs": [
{
"data": {
"text/plain": [
"['loss', 'acc']"
]
},
"execution_count": 11,
"metadata": {},
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}
],
"source": [
"model.metrics_names"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
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},
"outputs": [
{
"ename": "Exception",
"evalue": "Error when checking model target: expected activation_4 to have shape (None, 3) but got array with shape (150, 1)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mException\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-809119062291>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mActivation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'softmax'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'SGD'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'categorical_crossentropy'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnb_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/michi/Documents/DEV/keras/kerastest/lib/python3.4/site-packages/keras/models.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, **kwargs)\u001b[0m\n\u001b[1;32m 427\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 428\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 429\u001b[0;31m sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m 430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 431\u001b[0m def evaluate(self, x, y, batch_size=32, verbose=1,\n",
"\u001b[0;32m/Users/michi/Documents/DEV/keras/kerastest/lib/python3.4/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight)\u001b[0m\n\u001b[1;32m 1034\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1035\u001b[0m \u001b[0mcheck_batch_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1036\u001b[0;31m batch_size=batch_size)\n\u001b[0m\u001b[1;32m 1037\u001b[0m \u001b[0;31m# prepare validation data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1038\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/michi/Documents/DEV/keras/kerastest/lib/python3.4/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[0;34m(self, x, y, sample_weight, class_weight, check_batch_dim, batch_size)\u001b[0m\n\u001b[1;32m 965\u001b[0m \u001b[0moutput_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 966\u001b[0m \u001b[0mcheck_batch_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 967\u001b[0;31m exception_prefix='model target')\n\u001b[0m\u001b[1;32m 968\u001b[0m sample_weights = standardize_sample_weights(sample_weight,\n\u001b[1;32m 969\u001b[0m self.output_names)\n",
"\u001b[0;32m/Users/michi/Documents/DEV/keras/kerastest/lib/python3.4/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mstandardize_input_data\u001b[0;34m(data, names, shapes, check_batch_dim, exception_prefix)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;34m' to have shape '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshapes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;34m' but got array with shape '\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m str(array.shape))\n\u001b[0m\u001b[1;32m 109\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mException\u001b[0m: Error when checking model target: expected activation_4 to have shape (None, 3) but got array with shape (150, 1)"
]
}
],
"source": [
"#わざと損失関数の設定をミスってみる\n",
"model = Sequential()\n",
"model.add(Dense(12, input_dim=4))\n",
"model.add(Activation('relu'))\n",
"model.add(Dense(3, input_dim=12))\n",
"model.add(Activation('softmax')) \n",
"model.compile(optimizer='SGD',loss='categorical_crossentropy',metrics=['accuracy'])\n",
"model.fit(features, targets, nb_epoch=20, batch_size=5)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/20\n",
"150/150 [==============================] - 0s - loss: 6.0804 - acc: 0.3333 \n",
"Epoch 2/20\n",
"150/150 [==============================] - 0s - loss: 6.0658 - acc: 0.3333 \n",
"Epoch 3/20\n",
"150/150 [==============================] - 0s - loss: 6.0658 - acc: 0.3333 \n",
"Epoch 4/20\n",
"150/150 [==============================] - 0s - loss: 6.0658 - acc: 0.3333 \n",
"Epoch 5/20\n",
"150/150 [==============================] - 0s - loss: 6.0658 - acc: 0.3333 \n",
"Epoch 6/20\n",
"150/150 [==============================] - 0s - loss: 6.0658 - acc: 0.3333 \n",
"Epoch 7/20\n",
"150/150 [==============================] - 0s - loss: 6.0658 - acc: 0.3333 \n",
"Epoch 8/20\n",
"150/150 [==============================] - 0s - loss: 6.0658 - acc: 0.3333 \n",
"Epoch 9/20\n",
"150/150 [==============================] - 0s - loss: 6.0658 - acc: 0.3333 \n",
"Epoch 10/20\n",
"150/150 [==============================] - 0s - loss: 6.0658 - acc: 0.3333 \n",
"Epoch 11/20\n",
"150/150 [==============================] - 0s - loss: 6.0658 - acc: 0.3333 \n",
"Epoch 12/20\n",
"150/150 [==============================] - 0s - loss: 6.0658 - acc: 0.3333 \n",
"Epoch 13/20\n",
"150/150 [==============================] - 0s - loss: 6.0658 - acc: 0.3333 \n",
"Epoch 14/20\n",
"150/150 [==============================] - 0s - loss: 6.0658 - acc: 0.3333 \n",
"Epoch 15/20\n",
"150/150 [==============================] - 0s - loss: 6.0658 - acc: 0.3333 \n",
"Epoch 16/20\n",
"150/150 [==============================] - 0s - loss: 6.0658 - acc: 0.3333 \n",
"Epoch 17/20\n",
"150/150 [==============================] - 0s - loss: 6.0658 - acc: 0.3333 \n",
"Epoch 18/20\n",
"150/150 [==============================] - 0s - loss: 6.0658 - acc: 0.3333 \n",
"Epoch 19/20\n",
"150/150 [==============================] - 0s - loss: 6.0658 - acc: 0.3333 \n",
"Epoch 20/20\n",
"150/150 [==============================] - 0s - loss: 6.0658 - acc: 0.3333 \n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x11dd52630>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = Sequential()\n",
"model.add(Dense(12, input_dim=4))\n",
"model.add(Activation('relu'))\n",
"model.add(Dense(3, input_dim=12))\n",
"model.add(Activation('relu')) #最後をsoftmaxでなく、reluにすると一気に正答率が落ちる\n",
"model.compile(optimizer='SGD',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
"model.fit(features, targets, nb_epoch=20, batch_size=5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.1"
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"nbformat": 4,
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