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01 MNIST
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'3.5.4 |Anaconda custom (64-bit)| (default, Sep 19 2017, 08:15:17) [MSC v.1900 64 bit (AMD64)]'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import sys; sys.version"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\envs\\py35\\lib\\site-packages\\h5py\\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n",
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from keras.datasets import mnist"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# images는 손글씨 숫자 이미지, labvel은 이미지의 번호\n",
"(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### MNIST 데이터 "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train_images type: <class 'numpy.ndarray'> / len: 60000\n",
"train_labels type: <class 'numpy.ndarray'> / len: 60000\n"
]
}
],
"source": [
"# train: 훈련시킹 이미지들 6만개 \n",
"print('train_images type: {} / len: {}'.format(type(train_images), len(train_images)))\n",
"print('train_labels type: {} / len: {}'.format(type(train_labels), len(train_labels)))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test_images type: <class 'numpy.ndarray'> / len: 10000\n",
"test_labels type: <class 'numpy.ndarray'> / len: 10000\n"
]
}
],
"source": [
"# train_label : 평가 테스트용 이미지들 1만개 \n",
"print('test_images type: {} / len: {}'.format(type(test_images), len(test_images)))\n",
"print('test_labels type: {} / len: {}'.format(type(test_labels), len(test_labels)))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(60000, 28, 28) (10000, 28, 28)\n"
]
}
],
"source": [
"# 차원 확인:shape\n",
"# (60000, 28, 28) -> 28x28 배열이 6만개 있다는 의미\n",
"print(train_images.shape, test_images.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- image 파일은 28x28의 배열로 이루어져 있으며 요소는 01부터 255까지 명암값으로 이루어짐.\n",
"- 이런 이미지 파일이 6만개가 있음\n",
"\n",
"<img src=\"https://tensorflowkorea.gitbooks.io/tensorflow-kr/content/g3doc/images/MNIST-Matrix.png\" width=\"40%\" align=\"left\">\n",
"<img src=\"https://tensorflowkorea.gitbooks.io/tensorflow-kr/content/g3doc/images/mnist-train-xs.png\" width=\"30%\" align=\"left\">\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Matplotlib: 배열을 이미지로 출력 "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAMgAAADFCAYAAAARxr1AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIoP+nAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x18f7b160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(3,3))\n",
"plt.imshow(train_images[0])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 신경망 모델을 keras로 만들기"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from keras import models, layers\n",
"\n",
"network = models.Sequential()\n",
"network.add(layers.Dense(512, activation='relu', input_shape=(28*28,)))\n",
"network.add(layers.Dense(10, activation='softmax', input_shape=(512,)))\n",
"network.compile(\n",
" optimizer = 'adam',\n",
" loss = 'categorical_crossentropy',\n",
" metrics = ['accuracy'],\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 데이터 정규화"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"train_X = train_images.reshape(60000, -1)\n",
"train_X = train_X.astype('float')/ 255\n",
"test_X = test_images.reshape(10000, -1)\n",
"test_X = test_X.astype('float')/ 255"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Reshapes** : make output to a certain shape.\n",
" - 0~255까지 있는 명암값을 255로 나누어 0~1까지의 수로 변환"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"from keras.utils import to_categorical\n",
"train_Y = to_categorical(train_labels)\n",
"test_Y = to_categorical(test_labels)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_Y[0] # 5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**One-Hot Encoding** : A vector that 1 in only one demension, 0 in the remaing demesion.\n",
" - 3일 경우 [0,0,0,1,0,0,0,0,0,0,0,0] 으로 변환. \n",
" - 결과적으로 tran_X는 [60000,10] 실수 배열이 된다.\n",
"<img align=\"left\" width=\"40%\" src=\"https://tensorflowkorea.gitbooks.io/tensorflow-kr/content/g3doc/images/mnist-train-ys.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 머신러닝 훈련 과정"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"60000/60000 [==============================] - 10s 172us/step - loss: 0.2695 - acc: 0.9241\n",
"Epoch 2/5\n",
"60000/60000 [==============================] - 9s 157us/step - loss: 0.1077 - acc: 0.9684\n",
"Epoch 3/5\n",
"60000/60000 [==============================] - 9s 153us/step - loss: 0.0703 - acc: 0.9795\n",
"Epoch 4/5\n",
"60000/60000 [==============================] - 9s 152us/step - loss: 0.0487 - acc: 0.9855\n",
"Epoch 5/5\n",
"60000/60000 [==============================] - 9s 151us/step - loss: 0.0360 - acc: 0.9895\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x13f87b00>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"network.fit(train_X, train_Y, epochs=5, batch_size=128)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 테스트셋을 사용해서 평가"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10000/10000 [==============================] - 1s 96us/step\n"
]
}
],
"source": [
"test_loss, test_acc = network.evaluate(test_X, test_Y)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy : 0.981\n"
]
}
],
"source": [
"print('Accuracy : {:.3f}'.format(test_acc))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 로스 데이터 "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10000/10000 [==============================] - 1s 91us/step\n"
]
}
],
"source": [
"# test_pred: 1만개의 test_X의 예측한 값\n",
"test_pred = network.predict_classes(test_X)\n",
"import numpy as np\n",
"# correctly_predicted : 1만개의 예측된 값을 라벨값과 비교하여 참, 거짓 여부를 판단.\n",
"correctly_predicted = np.equal(test_pred, test_labels)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10000, array([7, 2, 1, ..., 4, 5, 6], dtype=int64))"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(test_pred), test_pred"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10000, array([ True, True, True, ..., True, True, True]))"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(correctly_predicted), correctly_predicted"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 149, 247, 321, 340, 381, 445, 449, 582, 613, 659, 684,\n",
" 691, 740, 813, 877, 951, 956, 965, 1014, 1032, 1039, 1044,\n",
" 1112, 1156, 1178, 1182, 1194, 1226, 1232, 1242, 1247, 1260, 1289,\n",
" 1319, 1326, 1328, 1393, 1395, 1464, 1500, 1522, 1530, 1549, 1553,\n",
" 1587, 1609, 1626, 1681, 1709, 1790, 1800, 1878, 1901, 1941, 1987,\n",
" 2004, 2024, 2053, 2070, 2098, 2109, 2118, 2130, 2135, 2182, 2189,\n",
" 2272, 2293, 2387, 2488, 2534, 2597, 2607, 2648, 2654, 2713, 2720,\n",
" 2758, 2810, 2877, 2896, 2939, 2953, 3005, 3030, 3060, 3073, 3117,\n",
" 3342, 3422, 3475, 3503, 3520, 3533, 3558, 3559, 3567, 3597, 3718,\n",
" 3727, 3751, 3776, 3780, 3796, 3808, 3811, 3818, 3838, 3853, 3893,\n",
" 3906, 3926, 3941, 3976, 3985, 4065, 4078, 4116, 4199, 4248, 4289,\n",
" 4294, 4369, 4497, 4536, 4578, 4601, 4761, 4807, 4823, 4860, 4876,\n",
" 4880, 4886, 4956, 4966, 5078, 5331, 5457, 5600, 5642, 5676, 5734,\n",
" 5887, 5936, 5937, 5955, 5973, 6011, 6023, 6045, 6059, 6166, 6173,\n",
" 6555, 6571, 6574, 6576, 6597, 6598, 6625, 6651, 6783, 6847, 7216,\n",
" 7434, 7732, 7915, 8062, 8094, 8311, 8325, 8522, 8527, 9009, 9015,\n",
" 9024, 9280, 9587, 9634, 9664, 9679, 9692, 9698, 9729, 9745, 9768,\n",
" 9770, 9792, 9811, 9839, 9850, 9944], dtype=int64)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# wong_predictions : 거짓(라벨값과 다름)으로 판단한 숫자들의 인덱스\n",
"wong_predictions = np.where(correctly_predicted == False)[0];\n",
"wong_predictions"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'plt' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-1-9e2214b791fd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined"
]
}
],
"source": [
"fig = plt.figure()"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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WfeOJeb3vWAse/tkOhfH3XNf1u3zopcU/hLfyeI1qVgFOVLMKcKKaVYAT1awCnKhmFeBR\n3z7wzKPFF7cYsd0aXWJzP1N8vvB7Ti131HfcB2YVxm/R5l1iW1xaalesgNeoZhXgRDWrACeqWQU4\nUc0qwIlqVgEe9e0Dw+9p8n34ya6hZSVfiWbx4eMK4/+xwbmF8RmXbFVmd6xFXqOaVYAT1awCnKhm\nFeBENauAMm8SdYKk+ZKm5799OtGW2YqozJtEAZwREad2qI3KGnHhbYXxr33lI11iZx52XmHZM3/S\ntSzAsmefa6svS3d/qTA+fvXXC+PDHiuOW98q8yZRZtYhZe+jHiXp3rxp7PujmvVSmYl6DrAZsC2w\nAOh6Ja9M0uGS7pB0x+u8WmKXzKqptESNiIURsSwilgPnAcWXucM3iTLrSWmJWruTW7Y/cH+zsmbW\nvY6M+uabRI0H3i1pHvA9YLykbYEA5gBf7kRblbR8WWH4+it27BI7+8s3F5b96gnF59xufsy0wvhK\nQ4cWxj+3TXH5IfIh9YGszJtEnd+Jus3MZyaZVYIT1awCnKhmFeBENasAX+GhH733xK4jvIfv89HC\nsg9P/GlhfP/tin/rsObKrxXG//rUloXxb494sDBuA4PXqGYV4EQ1qwAnqlkFOFHNKsCDSQPMU/ut\nVRjf+qgjC+Ovjyr+tdFW3326MD73+yOKG96m575Z//Ea1awCnKhmFeBENasAJ6pZBThRzSrAo74D\nzBtPLSyMjz6uON60nibxte4fVTxjQlvVWx/zGtWsApyoZhXgRDWrACeqWQU4Uc0qoFN3cxsl6XpJ\nMyQ9IOkbOT5c0rWSZuWpb2th1gudWqO+AXwrIrYCdgKOlLQ1MAmYEhFjgCn5sZm1qVN3c1sQEXfl\n/5cCM4CNgH2Bi3Oxi4H9OtGe2Yqm4/uokkYD2wHTgPUjYgGkZAZGNnmObxJl1o2OJqqkocDvgaMj\n4vlWn+ebRJl1r2OJKmkVUpJeEhF/yOGFtZtF5emiTrVntiLp1E2iRLrXzIyIOL1u1uXAIcApeTq5\nE+1Z76262+L+7oL1QqdOyt8ZOBi4T9L0HDuWlKCXSvoCMBeY2KH2zFYonbqb298ANZm9RyfaMFuR\n+cwkswpwoppVgBPVrAJ8hYcVzJIn1i2M39rkur5r3Nj15lHLO9kha4nXqGYV4EQ1qwAnqlkFOFHN\nKsCJalYBHvVdwQx9tHiRb7dq8ZWAX9tpyy6xlafc2dE+Wc+8RjWrACeqWQU4Uc0qwIlqVgFOVLMK\n8KivAfBCvF4YX3npa33cEyviNapZBThRzSrAiWpWAU5Uswro1OVCRwG/ADYg/a743Ij4kaQTgC8B\nT+eix0bElZ1o03rn5Q2Lf/Z932vrFD/htvtK7I21qlOjvrWbRN0laW3gTknX5nlnRMSpHWrHbIXU\nqcuFLgBq95hZKql2kygz64CybxIFcJSkeyVd0Oz+qL5JlFn3yr5J1DnAZsC2pDXuaUXP802izLpX\n6k2iImJhRCyLiOXAecAOnWrPbEWiiHjnlaSbRF0MLImIo+viG9bujyrpGGDHiDiwu7rW0fDYUb4L\nhvW/aTGF52NJs1u19KmybxJ1kKRtgQDmAF/uUHtmK5SybxLlY6ZmHeAzk8wqwIlqVgFOVLMKcKKa\nVYAT1awCnKhmFeBENasAJ6pZBThRzSqgI+f6dpKkp4HH88N3A4v7sTt9xa9zYNokItbr707AAEzU\nepLuiIix/d2Psvl1Wk+86WtWAU5UswoY6Il6bn93oI/4dVq3BvQ+qpklA32NamYM0ESVtJekhyTN\nljSpv/vTSflqjIsk3V8XGy7pWkmz8rTwao1VImmUpOslzZD0gKRv5Pige619YcAlqqQhwE+AvYGt\nSZdz2bp/e9VRFwF7NcQmAVMiYgwwJT+uutpF2bcCdgKOzMtxML7W0g24RCVdqXB2RDwaEa8Bvwb2\n7ec+dUxE3AgsaQjvS7o4HHm6X592qgQRsSAi7sr/LwVqF2UfdK+1LwzERN0IeKLu8TwG/1X3169d\nrTFPR/Zzfzqq4aLsg/q1lmUgJmrRRdI8NF1RBRdlt14YiIk6DxhV93hj4Ml+6ktfWShpQ0jXQgYW\n9XN/OqLoouwM0tdatoGYqLcDYyRtKmlV4EDg8n7uU9kuBw7J/x8CTO7HvnREvij7+cCMiDi9btag\ne619YUCe8CBpH+BMYAhwQUT8oJ+71DGSfgWMJ/2SZCHwPeCPwKXAe4G5wMSIaBxwqhRJuwA3AfeR\n7pkL6aLs0xhkr7UvDMhENbO3G4ibvmbWwIlqVgFOVLMKcKKaVYAT1awCnKhmFeBENasAJ6pZBfw/\n9vgK0Qpqp1wAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1ba44c88>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1e566898>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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3A9m4Hzm65dyimlWAE9WsApyoZhXgRDWrACeqWQW413cQaNv842X+esIL+sQ+8dH/y047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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1cc42e80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for i in range(3):\n",
" wong_prediction = wong_predictions[i] # wong_prediction : wong_predictions에서 추출한 하나의 인덱스 번호\n",
" plt.figure(figsize=(3,3))\n",
" # 처음에 정의한 평가용 1만개의 이미지에 잘 못 인식한 숫자의 인덱스를 적용\n",
" plt.imshow(test_images[wong_prediction]) \n",
" # plt.title('Predicted: {}'.format(test_pred[wong_prediction]), fontsize=30)\n",
" plt.title('Predicted: {} / Label: {}'.format(\n",
" test_pred[wong_prediction], test_labels[wong_prediction] ), fontsize=20)\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 신경망 추가"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [],
"source": [
"network_deep = models.Sequential()\n",
"\n",
"network_deep.add(layers.Dense(units=512, activation='relu', input_shape=(28*28,)))\n",
"network_deep.add(layers.Dense(units=256, activation='relu'))\n",
"network_deep.add(layers.Dense(units=256, activation='relu'))\n",
"network_deep.add(layers.Dense(units=10, activation='softmax'))\n",
" \n",
"network_deep.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"60000/60000 [==============================] - 13s 223us/step - loss: 0.2310 - acc: 0.9322\n",
"Epoch 2/5\n",
"60000/60000 [==============================] - 13s 209us/step - loss: 0.0850 - acc: 0.9737\n",
"Epoch 3/5\n",
"60000/60000 [==============================] - 13s 211us/step - loss: 0.0546 - acc: 0.9826\n",
"Epoch 4/5\n",
"60000/60000 [==============================] - 13s 213us/step - loss: 0.0399 - acc: 0.9867\n",
"Epoch 5/5\n",
"60000/60000 [==============================] - 13s 211us/step - loss: 0.0324 - acc: 0.9895\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x1e08da58>"
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"network_deep.fit(train_X, train_Y, epochs=5, batch_size=128)"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10000/10000 [==============================] - 1s 120us/step\n",
"Accuracy of network_deep : 0.977\n"
]
}
],
"source": [
"_ , acc_deep = network_deep.evaluate(test_X, test_Y)\n",
"print('Accuracy of network_deep : {:.3f}'.format(acc_deep))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 클래스화"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {},
"outputs": [],
"source": [
"from keras.models import Sequential\n",
"from keras.layers import Dense\n",
"class DNN(Sequential):\n",
" def __init__(self, input_size, output_size, *num_hidden_nodes):\n",
" super().__init__()\n",
" num_nodes = (*num_hidden_nodes, output_size)\n",
" print(num_nodes)\n",
" \n",
" for idx, num_node in enumerate(num_nodes):\n",
" activation = 'relu'\n",
" if idx == 0:\n",
" self.add(Dense(num_node, activation = activation, input_shape=(input_size,)))\n",
" else:\n",
" if idx == len(num_nodes) - 1:\n",
" activation = 'softmax'\n",
" self.add(Dense(output_size, activation=activation))\n",
" self.compile(loss='categorical_crossentropy', \n",
" optimizer='adam',\n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(512, 10)\n"
]
}
],
"source": [
"model = DNN(train_X.shape[1], train_Y.shape[1], 512)"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((784,), (10,))"
]
},
"execution_count": 161,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_X[0].shape, train_Y[0].shape"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"60000/60000 [==============================] - 10s 164us/step - loss: 0.2692 - acc: 0.9242\n",
"Epoch 2/5\n",
"60000/60000 [==============================] - 9s 151us/step - loss: 0.1088 - acc: 0.9679\n",
"Epoch 3/5\n",
"60000/60000 [==============================] - 9s 150us/step - loss: 0.0702 - acc: 0.9789\n",
"Epoch 4/5\n",
"60000/60000 [==============================] - 9s 153us/step - loss: 0.0500 - acc: 0.9852\n",
"Epoch 5/5\n",
"60000/60000 [==============================] - 9s 158us/step - loss: 0.0378 - acc: 0.9887\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x1e4f5d30>"
]
},
"execution_count": 163,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(train_X, train_Y, epochs=5, batch_size=128)"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10000/10000 [==============================] - 1s 98us/step\n",
"Loss:0.06835155587559566 / Acc:0.9775\n"
]
}
],
"source": [
"test_loss, test_acc = model.evaluate(test_X, test_Y)\n",
"print('Loss:{} / Acc:{}'.format(test_loss, test_acc))"
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10000/10000 [==============================] - 1s 92us/step\n"
]
}
],
"source": [
"# precidt : ML로 예측한 값.\n",
"precidt = model.predict_classes(test_X)"
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(225, array([ 115, 217, 247, 321, 340, 445, 495, 522, 527, 582, 619,\n",
" 684, 691, 720, 726, 740, 813, 846, 877, 947, 951, 956,\n",
" 965, 1014, 1039, 1055, 1112, 1156, 1173, 1182, 1194, 1226, 1232,\n",
" 1242, 1247, 1260, 1319, 1328, 1393, 1464, 1496, 1522, 1527, 1530,\n",
" 1543, 1549, 1581, 1609, 1717, 1754, 1790, 1813, 1878, 1901, 1941,\n",
" 1955, 1982, 1984, 1987, 2004, 2016, 2018, 2024, 2053, 2070, 2098,\n",
" 2109, 2118, 2130, 2135, 2182, 2272, 2293, 2387, 2414, 2447, 2488,\n",
" 2526, 2582, 2597, 2607, 2618, 2648, 2654, 2730, 2758, 2896, 2915,\n",
" 2921, 2927, 2939, 2953, 2995, 3062, 3073, 3172, 3333, 3405, 3422,\n",
" 3503, 3520, 3558, 3559, 3567, 3597, 3718, 3727, 3749, 3751, 3776,\n",
" 3780, 3818, 3853, 3893, 3906, 3941, 3968, 3976, 4065, 4075, 4078,\n",
" 4140, 4176, 4199, 4248, 4289, 4294, 4419, 4497, 4534, 4536, 4578,\n",
" 4601, 4639, 4671, 4690, 4761, 4807, 4823, 4880, 4956, 4966, 5138,\n",
" 5177, 5331, 5457, 5495, 5600, 5620, 5642, 5649, 5654, 5676, 5714,\n",
" 5734, 5749, 5835, 5858, 5887, 5936, 5937, 5955, 5972, 5973, 5982,\n",
" 5997, 6011, 6045, 6059, 6166, 6532, 6555, 6558, 6560, 6574, 6597,\n",
" 6608, 6651, 6662, 6755, 6783, 6847, 7216, 7434, 7800, 7821, 7823,\n",
" 7915, 7921, 7991, 8020, 8062, 8094, 8321, 8325, 8408, 8456, 8519,\n",
" 8522, 8527, 9009, 9015, 9019, 9024, 9071, 9280, 9534, 9587, 9634,\n",
" 9664, 9679, 9692, 9698, 9729, 9745, 9749, 9768, 9770, 9779, 9792,\n",
" 9808, 9839, 9916, 9944, 9975], dtype=int64))"
]
},
"execution_count": 177,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"comapare_precidt = np.equal(test_precidt, test_labels) # 예측값과 라벨값과 비교\n",
"array_false = np.where(comapare_precidt == False)[0] # 비교해서 다른 것을 array_false에 추가.\n",
"len(array_false), array_false"
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x24732a90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x24774358>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x24700588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x1ba6c668>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2471d860>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x24d338d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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MrEt4SrSZJZwYzCzhxGBmCScGM0s4MZhZwh+7boNtdtqpsPzhLxxcWL70tV9uZTgvceFT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\npxYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x208f3ef0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x20894a20>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2050b978>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x246f8d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 잘못 인식한 숫자중 하나를 추출해서 이미지화\n",
"for i in range(20,30):\n",
" index = array_false[i]\n",
" select = precidt[index]\n",
" label = test_labels[index]\n",
"\n",
" plt.imshow(test_images[index])\n",
" plt.title('index: {}, Predict: {}, Label: {}'.format(index, select, label), fontsize=15)\n",
" plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python3.5",
"language": "python",
"name": "python3.5"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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