diff --git a/Code/Day 42.ipynb b/Code/Day 42.ipynb new file mode 100644 index 0000000..74a6a68 --- /dev/null +++ b/Code/Day 42.ipynb @@ -0,0 +1,288 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "这是Python，TensorFlow和Keras教程系列的深度学习基础知识的第4部分。\n", + "\n", + "在这一部分，我们将讨论的是TensorBoard。TensorBoard是一个方便的应用程序，允许您在浏览器中查看模型或模型的各个方面。我们将TensorBoard与Keras一起使用的方式是通过Keras回调。实际上有很多Keras回调，你可以自己制作。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from keras.callbacks import TensorBoard\n", + "from keras." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#创建TensorBoard回调对象\n", + "NAME = \"Cats-vs-dogs-CNN\"\n", + "\n", + "tensorboard = TensorBoard(log_dir=\"logs/{}\".format(NAME))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "最终，你会希望获得更多的自定义NAME，但现在这样做。因此，这将保存模型的训练数据logs/NAME，然后由TensorBoard读取。\n", + "\n", + "最后，我们可以通过将它添加到.fit方法中来将此回调添加到我们的模型中，例如：\n", + "```python\n", + "model.fit(X, y,\n", + " batch_size=32,\n", + " epochs=3,\n", + " validation_split=0.3,\n", + " callbacks=[tensorboard])\n", + "```\n", + "请注意，这callbacks是一个列表。您也可以将其他回调传递到此列表中。我们的模型还没有定义，所以现在让我们把它们放在一起：" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 17462 samples, validate on 7484 samples\n", + "Epoch 1/3\n", + "17462/17462 [==============================] - 44s 3ms/step - loss: 0.6992 - acc: 0.5480 - val_loss: 0.6900 - val_acc: 0.5274\n", + "Epoch 2/3\n", + "17462/17462 [==============================] - 41s 2ms/step - loss: 0.6754 - acc: 0.5782 - val_loss: 0.6685 - val_acc: 0.5885\n", + "Epoch 3/3\n", + "17462/17462 [==============================] - 41s 2ms/step - loss: 0.6377 - acc: 0.6483 - val_loss: 0.6217 - val_acc: 0.6625\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.datasets import cifar10\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n", + "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n", + "from tensorflow.keras.callbacks import TensorBoard\n", + "# more info on callbakcs: https://keras.io/callbacks/ model saver is cool too.\n", + "import pickle\n", + "import time\n", + "\n", + "NAME = \"Cats-vs-dogs-CNN\"\n", + "\n", + "pickle_in = open(\"../datasets/X.pickle\",\"rb\")\n", + "X = pickle.load(pickle_in)\n", + "\n", + "pickle_in = open(\"../datasets/y.pickle\",\"rb\")\n", + "y = pickle.load(pickle_in)\n", + "\n", + "X = X/255.0\n", + "\n", + "model = Sequential()\n", + "\n", + "model.add(Conv2D(256, (3, 3), input_shape=X.shape[1:]))\n", + "model.add(Activation('relu'))\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "\n", + "model.add(Conv2D(256, (3, 3)))\n", + "model.add(Activation('relu'))\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "\n", + "model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors\n", + "model.add(Dense(64))\n", + "\n", + "model.add(Dense(1))\n", + "model.add(Activation('sigmoid'))\n", + "\n", + "tensorboard = TensorBoard(log_dir=\"logs/{}\".format(NAME))\n", + "\n", + "model.compile(loss='binary_crossentropy',\n", + " optimizer='adam',\n", + " metrics=['accuracy'],\n", + " )\n", + "\n", + "model.fit(X, y,\n", + " batch_size=32,\n", + " epochs=3,\n", + " validation_split=0.3,\n", + " callbacks=[tensorboard])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "运行此之后，您应该有一个名为的新目录logs。我们现在可以使用tensorboard从这个目录中可视化初始结果。打开控制台，切换到工作目录，然后键入：tensorboard --logdir=logs/。您应该看到一个通知：TensorBoard 1.10.0 at http://H-PC:6006 (Press CTRL+C to quit)“h-pc”是您机器的名称。打开浏览器并前往此地址。你应该看到类似的东西：\n", + "\n", + "现在我们可以看到我们的模型随着时间的推移。让我们改变模型中的一些东西。首先，我们从未在密集层中添加激活。另外，让我们尝试整体较小的模型：" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 17462 samples, validate on 7484 samples\n", + "Epoch 1/10\n", + "17462/17462 [==============================] - 11s 604us/step - loss: 0.6033 - acc: 0.6652 - val_loss: 0.5298 - val_acc: 0.7320\n", + "Epoch 2/10\n", + "17462/17462 [==============================] - 11s 646us/step - loss: 0.4859 - acc: 0.7659 - val_loss: 0.4723 - val_acc: 0.7763\n", + "Epoch 3/10\n", + "17462/17462 [==============================] - 11s 641us/step - loss: 0.4270 - acc: 0.8045 - val_loss: 0.4603 - val_acc: 0.7803\n", + "Epoch 4/10\n", + "17462/17462 [==============================] - 12s 699us/step - loss: 0.3675 - acc: 0.8347 - val_loss: 0.4476 - val_acc: 0.7929\n", + "Epoch 5/10\n", + "17462/17462 [==============================] - 12s 707us/step - loss: 0.3012 - acc: 0.8694 - val_loss: 0.4854 - val_acc: 0.7797\n", + "Epoch 6/10\n", + "17462/17462 [==============================] - 12s 705us/step - loss: 0.2165 - acc: 0.9118 - val_loss: 0.5450 - val_acc: 0.7865\n", + "Epoch 7/10\n", + "17462/17462 [==============================] - 12s 712us/step - loss: 0.1332 - acc: 0.9510 - val_loss: 0.6512 - val_acc: 0.7821\n", + "Epoch 8/10\n", + "17462/17462 [==============================] - 12s 705us/step - loss: 0.0764 - acc: 0.9743 - val_loss: 0.7487 - val_acc: 0.7809\n", + "Epoch 9/10\n", + "17462/17462 [==============================] - 12s 713us/step - loss: 0.0389 - acc: 0.9887 - val_loss: 0.9041 - val_acc: 0.7743\n", + "Epoch 10/10\n", + "17462/17462 [==============================] - 12s 708us/step - loss: 0.0287 - acc: 0.9921 - val_loss: 1.0411 - val_acc: 0.7702\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n", + "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n", + "from tensorflow.keras.callbacks import TensorBoard\n", + "# more info on callbakcs: https://keras.io/callbacks/ model saver is cool too.\n", + "import pickle\n", + "import time\n", + "\n", + "NAME = \"Cats-vs-dogs-64x2-CNN\"\n", + "\n", + "pickle_in = open(\"../datasets/X.pickle\",\"rb\")\n", + "X = pickle.load(pickle_in)\n", + "\n", + "pickle_in = open(\"../datasets/y.pickle\",\"rb\")\n", + "y = pickle.load(pickle_in)\n", + "\n", + "X = X/255.0\n", + "\n", + "model = Sequential()\n", + "\n", + "model.add(Conv2D(64, (3, 3), input_shape=X.shape[1:]))\n", + "model.add(Activation('relu'))\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "\n", + "model.add(Conv2D(64, (3, 3)))\n", + "model.add(Activation('relu'))\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "\n", + "model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors\n", + "model.add(Dense(64))\n", + "model.add(Activation('relu'))\n", + "\n", + "model.add(Dense(1))\n", + "model.add(Activation('sigmoid'))\n", + "\n", + "tensorboard = TensorBoard(log_dir=\"logs/{}\".format(NAME))\n", + "\n", + "model.compile(loss='binary_crossentropy',\n", + " optimizer='adam',\n", + " metrics=['accuracy'],\n", + " )\n", + "\n", + "model.fit(X, y,\n", + " batch_size=32,\n", + " epochs=10,\n", + " validation_split=0.3,\n", + " callbacks=[tensorboard])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "除此之外，我还改名为NAME = \"Cats-vs-dogs-64x2-CNN\"。不要忘记这样做，否则你会偶然附加到你以前的型号的日志，它看起来不太好。我们现在检查TensorBoard：\n", + "\n", + "看起来更好！但是，您可能会立即注意到验证丢失的形状。损失是衡量错误的标准，看起来很明显，在我们的第四个时代之后，事情开始变得糟糕。\n", + "\n", + "有趣的是，我们的验证准确性仍然持续，但我想它最终会开始下降。更可能的是，第一件遭受的事情确实是你的验证损失。这应该提醒你，你几乎肯定会开始过度适应。这种情况发生的原因是该模型不断尝试减少样本损失。\n", + "\n", + "在某些时候，模型不是学习关于实际数据的一般事物，而是开始只记忆输入数据。如果你继续这样做，是的，样本中的“准确性”会上升，但你的样本，以及你试图为模型提供的任何新数据可能会表现得很差。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "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": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Info-graphs/Day 42-1.png b/Info-graphs/Day 42-1.png new file mode 100644 index 0000000..5cd698b Binary files /dev/null and b/Info-graphs/Day 42-1.png differ diff --git a/Info-graphs/Day 42-2.png b/Info-graphs/Day 42-2.png new file mode 100644 index 0000000..24c99df Binary files /dev/null and b/Info-graphs/Day 42-2.png differ diff --git a/README.md b/README.md index 566a310..30da9ba 100644 --- a/README.md +++ b/README.md @@ -193,6 +193,7 @@ B站视频在[这里](https://space.bilibili.com/88461692/#/channel/detail?cid=2 ## 第4部分 | 深度学习基础Python，TensorFlow和Keras | 第42天 视频地址在[这里](https://www.youtube.com/watch?v=wQ8BIBpya2k&t=19s&index=2&list=PLQVvvaa0QuDfhTox0AjmQ6tvTgMBZBEXN)。 +
中文文字版[notebook](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2042.ipynb)。 ## K-均值聚类 | 第43天 转到无监督学习，并研究了聚类。可在[作者网站](http://www.avikjain.me/)查询。发现一个奇妙的[动画](http://shabal.in/visuals/kmeans/6.html)有助于理解K-均值聚类。