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Add examples of Boston housing experiment
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yanjun committed Mar 10, 2020
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211 changes: 211 additions & 0 deletions Chap1_CNN_Classification/cnn_keras_cpu.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import tensorflow.keras as keras\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense, Dropout, Flatten\n",
"from tensorflow.keras.layers import Conv2D, MaxPooling2D"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"#生成虚拟数据, 标准数据集需要GPU，100张100*100RGB数据"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"# 生成虚拟数据\n",
"x_train = np.random.random((100, 100, 100, 3))\n",
"y_train = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)\n",
"x_test = np.random.random((20, 100, 100, 3))\n",
"y_test = keras.utils.to_categorical(np.random.randint(10, size=(20, 1)), num_classes=10)\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"#采用VGG结构，论文见https://arxiv.org/abs/1409.1556\" "
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()\n",
"# 输入: 3 通道 100x100 像素图像 -> (100, 100, 3) 张量。\n",
"# 使用 32 个大小为 3x3 的卷积滤波器。\n",
"model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)))\n",
"model.add(Conv2D(32, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"model.add(Dropout(0.25))\n",
"\n",
"model.add(Conv2D(64, (3, 3), activation='relu'))\n",
"model.add(Conv2D(64, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"model.add(Dropout(0.25))\n",
"\n",
"model.add(Flatten())\n",
"model.add(Dense(256, activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(10, activation='softmax'))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.optimizers import SGD"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"#带动量的随机梯度下降法进行优化，需要Compile，是因为Keras是静态图，在实际运行的时候进行编译，keras对优化器的说明见https://keras.io/zh/optimizers/"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
"model.compile(loss='categorical_crossentropy', optimizer=sgd)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"#训练10个epocch，只看loss"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 100 samples\n",
"Epoch 1/10\n",
"100/100 [==============================] - 5s 54ms/sample - loss: 2.3074\n",
"Epoch 2/10\n",
"100/100 [==============================] - 2s 20ms/sample - loss: 2.3743\n",
"Epoch 3/10\n",
"100/100 [==============================] - 2s 19ms/sample - loss: 2.2947\n",
"Epoch 4/10\n",
"100/100 [==============================] - 2s 20ms/sample - loss: 2.2786\n",
"Epoch 5/10\n",
"100/100 [==============================] - 2s 20ms/sample - loss: 2.2792\n",
"Epoch 6/10\n",
"100/100 [==============================] - 2s 19ms/sample - loss: 2.3000\n",
"Epoch 7/10\n",
"100/100 [==============================] - 2s 19ms/sample - loss: 2.2852\n",
"Epoch 8/10\n",
"100/100 [==============================] - 2s 20ms/sample - loss: 2.2815\n",
"Epoch 9/10\n",
"100/100 [==============================] - 2s 20ms/sample - loss: 2.2767\n",
"Epoch 10/10\n",
"100/100 [==============================] - 2s 20ms/sample - loss: 2.2814\n"
]
},
{
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x69233c8d0>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(x_train, y_train, batch_size=32, epochs=10)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"#测试集验证一下，另一个MNIST数据集需要同学自己在百度或谷歌的深度学习框架上训练"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20/1 [========================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 19ms/sample - loss: 2.3132\n"
]
}
],
"source": [
"score = model.evaluate(x_test, y_test, batch_size=32)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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