add week6 homework

李宏毅机器学习-作业/week6/CNN.py

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+''Trains a simple convnet on the MNIST dataset.
+Gets to 99.25% test accuracy after 12 epochs
+(there is still a lot of margin for parameter tuning).
+16 seconds per epoch on a GRID K520 GPU.
+''
+
+from __future__ import print_function
+import keras
+from keras.datasets import mnist
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Flatten
+from keras.layers import Conv2D, MaxPooling2D
+from keras import backend as K
+
+batch_size = 128
+num_classes = 10
+epochs = 12
+
+# input image dimensions
+img_rows, img_cols = 28, 28
+
+# the data, split between train and test sets
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+if K.image_data_format() == 'channels_first':
+    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
+    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
+    input_shape = (1, img_rows, img_cols)
+else:
+    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
+    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
+    input_shape = (img_rows, img_cols, 1)
+
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255
+x_test /= 255
+print('x_train shape:', x_train.shape)
+print(x_train.shape[0], 'train samples')
+print(x_test.shape[0], 'test samples')
+
+# convert class vectors to binary class matrices
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
+
+model = Sequential()
+model.add(Conv2D(32, kernel_size=(3, 3),
+                 activation='relu',
+                 input_shape=input_shape))
+model.add(Conv2D(64, (3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+model.add(Flatten())
+model.add(Dense(128, activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(num_classes, activation='softmax'))
+
+model.compile(loss=keras.losses.categorical_crossentropy,
+              optimizer=keras.optimizers.Adadelta(),
+              metrics=['accuracy'])
+
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=epochs,
+          verbose=1,
+          validation_data=(x_test, y_test))
+score = model.evaluate(x_test, y_test, verbose=0)
+print('Test loss:', score[0])
+print('Test accuracy:', score[1])

李宏毅机器学习-作业/week6/Week6-CNN实现手写数字识别.md

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+
+### 卷积神经网络CNN实现手写数字识别
+
+在学习机器学习的时候，首当其冲的就是准备一份通用的数据集，方便与其他的算法进行比较。
+
+### MNIST简介
+
+MNIST数据集原网址：http://yann.lecun.com/exdb/mnist/
+
+![](assets/markdown-img-paste-20190216133336752.png)
+
+数据集是这样的一些手写数字
+
+**问题：通过某个算法将0-9的数字进行分类**
+
+### 下载
+Github源码下载：数据集（源文件+解压文件+字体图像jpg格式），py源码文件
+文件目录
+```python
+/utils/data_util.py 用于加载MNIST数据集方法文件
+/utils/test.py 用于测试的文件，一个简单的KNN测试MNIST数据集
+/data/train-images.idx3-ubyte 训练集X
+/dataset/train-labels.idx1-ubyte 训练集y
+/dataset/data/t10k-images.idx3-ubyte 测试集X
+/dataset/data/t10k-labels.idx1-ubyte 测试集y
+```
+
+### 结构解释
+MNIST数据集解释
+将MNIST文件解压后，发现这些文件并不是标准的图像格式。这些图像数据都保存在二进制文件中。每个样本图像的宽高为28*28。
+
+mnist的结构如下，选取train-images
+```python
+[code]TRAINING SET IMAGE FILE (train-images-idx3-ubyte):
+
+[offset] [type]          [value]          [description]
+0000     32 bit integer  0x00000803(2051) magic number
+0004     32 bit integer  60000            number of images
+0008     32 bit integer  28               number of rows
+0012     32 bit integer  28               number of columns
+0016     unsigned byte   ??               pixel
+0017     unsigned byte   ??               pixel
+........
+xxxx     unsigned byte   ??               pixel
+```
+
+首先该数据是以二进制存储的，我们读取的时候要以’rb’方式读取；其次，真正的数据只有[value]这一项，其他的[type]等只是来描述的，并不真正在数据文件里面。也就是说，在读取真实数据之前，我们要读取4个
+
+32 bit integer
+.由[offset]我们可以看出真正的pixel是从0016开始的，一个int 32位，所以在读取pixel之前我们要读取4个 32 bit integer，也就是magic number, number of images, number of rows, number of columns. 当然，在这里使用struct.unpack_from()会比较方便.
+
+### 算法实现