diff --git "a/MXnet/MXnet\345\237\272\347\241\200\347\237\245\350\257\206.txt~" "b/MXnet/MXnet\345\237\272\347\241\200\347\237\245\350\257\206.txt~" new file mode 100644 index 00000000..4fb7d675 --- /dev/null +++ "b/MXnet/MXnet\345\237\272\347\241\200\347\237\245\350\257\206.txt~" @@ -0,0 +1,341 @@ +http://lucianlv.blog.51cto.com/9871307/1812733 +MXnet基本概念和操作 + +【1】NDArray + + 多维的数据结构，提供在 cpu 或者 gpu 上进行矩阵运算和张量计算， + 能够自动并行计算 + +NDArray 是 MXnet 中最底层的计算单元，与 numpy.ndarray 非常相似， + 但是也有 2 点不同的特性： + + 1）支持多设备 +所有的操作可以在不同的设备上运行，包括 cpu 和 gpu。 + + ####### 测试例子##### + >>> import mxnet as mx + ## 在cpu0上创建一个2X3的矩阵 + >>> a = mx.nd.empty((2, 3)) + ## 在gpu上创建一个2X3的矩阵 + >>> b = mx.nd.empty((2, 3), mx.gpu()) # 在gpu0上创建一个2X3的矩阵 + >>> c = mx.nd.empty((2, 3), mx.gpu(2)) # 在gpu2上创建一个2X3的矩阵 + >>> c.shape # 维度(2L, 3L) + >>> c.context # 设备信息gpu(2) + + ############################################## + ### 其他的初始化方式 + >>> a = mx.nd.zeros((2, 3)) # 创建2X3的全0矩阵 + >>> b = mx.nd.ones((2, 3)) # 创建2X3的全1矩阵 + >>> b[:] = 2 # 所有元素赋值为2 + + >>> b = mx.nd.zeros((2, 3), mx.gpu()) + >>> a = mx.nd.ones((2, 3)) # 创建2X3的全1矩阵 + ## 不同的设备之间进行数据拷贝 + >>> a.copyto(b) # 从cpu拷贝数据到gpu + + ########################################### + ## NDArray转换为numpy.ndarray ## + >>> a = mx.nd.ones((2, 3)) + >>> b = a.asnumpy() + >>> type(b) + + >>> print b[[ 1. 1. 1.] + [ 1. 1. 1.]] + + ########################################## + ## numpy.ndarray转换为NDArray ## + >>> import numpy as np + >>> a = mx.nd.empty((2, 3)) + >>> a[:] = np.random.uniform(-0.1, 0.1, a.shape) + >>> print a.asnumpy()[[-0.06821112 -0.03704893 0.06688045] + [ 0.09947646 -0.07700162 0.07681718]] + + ########################################### + ### NDArray基本运算 ############# + >>> a = mx.nd.ones((2, 3)) * 2 + >>> b = mx.nd.ones((2, 3)) * 4 + >>> print b.asnumpy()[[ 4. 4. 4.] + [ 4. 4. 4.]] + >>> c = a + b # 对应元素求和 + >>> print c.asnumpy() + [[ 6. 6. 6.] + [ 6. 6. 6.]] + >>> d = a * b # 对应元素求积 + >>> print d.asnumpy() + [[ 8. 8. 8.] + [ 8. 8. 8.]] + + + ###################################################### + ### 不同设备上的NDArray需要移动到一起才能计算 ### + >>> a = mx.nd.ones((2, 3)) * 2 ## 默认在CPU上 + >>> b = mx.nd.ones((2, 3), mx.gpu()) * 3 + >>> c = a.copyto(mx.gpu()) * b + >>> print c.asnumpy() + [[ 6. 6. 6.] + [ 6. 6. 6.]] + + ############################################# + ### 数据的导出与载入 ### + ## 1. 通过pickle导出与载入数据 + >>> import mxnet as mx + >>> import pickle as pkl + >>> a = mx.nd.ones((2, 3)) * 2 + >>> data = pkl.dumps(a) ##导出 存储数据 + >>> b = pkl.loads(data) ##载入 获取数据 + >>> print b.asnumpy() + [[ 2. 2. 2.] + [ 2. 2. 2.]] + + ################################## + ## 2. 直接保存为二进制文件 + >>> a = mx.nd.ones((2,3))*2 + >>> b = mx.nd.ones((2,3))*3 + >>> mx.nd.save('mydata.bin', [a, b]) ##保存成二进制文件 + >>> c = mx.nd.load('mydata.bin') ##载入二进制文件数据 + >>> print c[0].asnumpy() ## c[0] 为a + [[ 2. 2. 2.] + [ 2. 2. 2.]] + >>> print c[1].asnumpy() ## c[1] 为b + [[ 3. 3. 3.] + [ 3. 3. 3.]] + + ####################################### + ## 直接保存到分布式文件系统上（s3或hdfs） + >>> mx.nd.save('s3://mybucket/mydata.bin', [a,b]) + >>> mx.nd.save('hdfs///users/myname/mydata.bin', [a,b]) + + ############################################################### + ################# + + + 2）自动并行计算 +不同的操作自动进行并行计算。 + a = mx.nd.ones((2,3)) ## 默认cpu上 + b = a ## cpu上 + c = a.copyto(mx.gpu())## gpu上 + a += 1 + b *= 3 + c *= 3 + + a += 1可与c *= 3并行计算，因为在不同的设备上， + 但是a += 1和b *= 3只能相继执行。 + + +【2】符号式运算 Symbol + Symbol使得非常容易定义神经网络，并且能自动求导 +以下的范例创建了一个 2 层的感知器网络: + +>>> import mxnet as mx +>>> net = mx.symbol.Variable('data') +>>> net = mx.symbol.FullyConnected(data=net, name='fc1', num_hidden=128)##全连接层 +>>> net = mx.symbol.Activation(data=net, name='relu1', act_type="relu") ##激活 +>>> net = mx.symbol.FullyConnected(data=net, name='fc2', num_hidden=64) ##全连接 +>>> net = mx.symbol.SoftmaxOutput(data=net, name='out') ##softmax回归输出层 +>>> type(net) + + + +每一个 Symbol 可以绑定一个名字，Variable 通常用来定义输入， +其他的 Symbol 有一个参数data以一个Symbol 类型作为输入数据， +另外还有其他的超参数num_hidden（隐藏层的神经元数目)，act_type(激活函数的类型)。 + +Symbol 的作用可以被简单的看成是实现了一个函数，函数的参数名称自动生成，可以通过以下的方式查看: + +>>> net.list_arguments() +['data', 'fc1_weight', 'fc1_bias', 'fc2_weight', 'fc2_bias', 'out_label'] + + + +2）我们也可以明确指定这些自动生成的参数的名字: + +>>> net2 = mx.symbol.Variable('data') +>>> w = mx.symbol.Variable('myweight') +>>> net2 = mx.symbol.FullyConnected(data=net2, weight=w, name='fc1', num_hidden=128) +>>> net2.list_arguments() +['data', 'myweight', 'fc1_bias'] + +3）Symbol 可以组合之后，在传入全连接中: +>>> lhs = mx.symbol.Variable('data1') +>>> rhs = mx.symbol.Variable('data2') +>>> net = mx.symbol.FullyConnected(data=lhs + rhs, name='fc1', num_hidden=128) +>>> net.list_arguments() +['data1', 'data2', 'fc1_weight', 'fc1_bias'] + + +3）Symbol 也可以被随后的操作替换: +>>> net = mx.symbol.Variable('data') +>>> net = mx.symbol.FullyConnected(data=net, name='fc1', num_hidden=128) +>>> net2 = mx.symbol.Variable('data2') +>>> net2 = mx.symbol.FullyConnected(data=net2, name='net2', num_hidden=128) +>>> composed_net = net(data=net2, name='compose') +>>> composed_net.list_arguments() +['data2', 'net2_weight', 'net2_bias', 'compose_fc1_weight', 'compose_fc1_bias'] + + + + +4）一旦定义好了 Symbol，只需要指定输入数据的维度，就可以推算出各级中间参数的维度: +>>> net = mx.symbol.Variable('data') +>>> net = mx.symbol.FullyConnected(data=net, name='fc1', num_hidden=10) +>>> arg_shape, out_shape, aux_shape = net.infer_shape(data=(100, 100)) +>>> dict(zip(net.list_arguments(), arg_shape)) +{'data': (100, 100), 'fc1_weight': (10, 100), 'fc1_bias': (10,)} +>>> out_shape +[(100, 10)] + + +5）接下来通过绑定变量，就可以执行实际的运算了: +>>> # 定义计算图 +>>> A = mx.symbol.Variable('A') +>>> B = mx.symbol.Variable('B') +>>> C = A * B + +>>> a = mx.nd.ones(3) * 4 +>>> b = mx.nd.ones(3) * 2 +>>> # 绑定变量到Symbol +>>> c_exec = C.bind(ctx=mx.cpu(), args={'A' : a, 'B': b}) +>>> # 进行前向计算 +>>> c_exec.forward() +>>> c_exec.outputs[0].asnumpy() +[ 8. 8. 8.] + + + +【3】数据同步 KVStore + + KVStore 实现了在多个运算器之间，或者在多台计算机之间的数据同步 +MXNet提供一个分布式的key-value存储来进行数据交换。它主要有两个函数， + +push： 将key-value对从一个设备push进存储 +pull：将某个key上的值从存储中pull出来此外， + KVStore还接受自定义的更新函数来控制收到的值如何写入到存储中。 +最后KVStore提供数种包含最终一致性模型和顺序一致性模型在内的数据一致性模型。 + + +1）通过create可以对 kvstore 进行简单的初始化: +>>> kv = mx.kv.create('local') # 创建一个本地的kvstore +>>> shape = (2,3) +>>> kv.init(3, mx.nd.ones(shape)*2) +>>> a = mx.nd.zeros(shape) +>>> kv.pull(3, out = a) +>>> print a.asnumpy() +[[ 2. 2. 2.] + [ 2. 2. 2.]] + +2）初始化后，可以通过相同的 key 进行数值更新: +>>> kv.push(3, mx.nd.ones(shape)*8) +>>> kv.pull(3, out = a) # 取出值 +>>> print a.asnumpy() +[[ 8. 8. 8.] + [ 8. 8. 8.]] + +3）push的数据可以在任何设备上，此外，可以在同一个 key 上传递多个值，KVStore 会对多个值求和，push聚合后的值: + +>>> gpus = [mx.gpu(i) for i in range(4)] +>>> b = [mx.nd.ones(shape, gpu) for gpu in gpus] +>>> kv.push(3, b) +>>> kv.pull(3, out = a) +>>> print a.asnumpy() +[[ 4. 4. 4.] + [ 4. 4. 4.]] + + + +4）KVStore 对每次push进来的值做的默认行为是ASSIGN，这个行为可以被自定义的行为替换: + +>>> def update(key, input, stored): +>>> print "update on key: %d" % key +>>> stored += input * 2 +>>> kv._set_updater(update) +>>> kv.pull(3, out=a) +>>> print a.asnumpy() +[[ 4. 4. 4.] + [ 4. 4. 4.]] +>>> kv.push(3, mx.nd.ones(shape)) +update on key: 3 +>>> kv.pull(3, out=a) +>>> print a.asnumpy() +[[ 6. 6. 6.] + [ 6. 6. 6.]] + + +5）同push类似，通过一次调用，我们也可以将值同时pull到多个设备上: +>>> b = [mx.nd.ones(shape, gpu) for gpu in gpus] +>>> kv.pull(3, out = b) +>>> print b[1].asnumpy() +[[ 6. 6. 6.] + [ 6. 6. 6.]] + + + +6） 除了单个 key-value 的存储，KVStrore 还提供了批量的接口: + +# 针对单个设备 +>>> keys = [5, 7, 9] +>>> kv.init(keys, [mx.nd.ones(shape)]*len(keys)) +>>> kv.push(keys, [mx.nd.ones(shape)]*len(keys)) +update on key: 5 +update on key: 7 +update on key: 9 +>>> b = [mx.nd.zeros(shape)]*len(keys) +>>> kv.pull(keys, out = b) +>>> print b[1].asnumpy() +[[ 3. 3. 3.] + [ 3. 3. 3.]] + +# 针对多个设备 +>>> b = [[mx.nd.ones(shape, gpu) for gpu in gpus]] * len(keys) +>>> kv.push(keys, b) +update on key: 5 +update on key: 7 +update on key: 9 +>>> kv.pull(keys, out = b) +>>> print b[1][1].asnumpy() +[[ 11. 11. 11.] + [ 11. 11. 11.]] + +##################################### +###################################### +MXnet体验 +以下借助 MXnet 实现了一个简单的单变量线性回归程序： + +import mxnet as mx +import numpy as np +import matplotlib.pyplot as plt + +# 定义输入数据 +X_data = np.linspace(-1, 1, 100) +noise = np.random.normal(0, 0.5, 100) +y_data = 5 * X_data + noise + +# Plot 输入数据 +fig = plt.figure() +ax = fig.add_subplot(1, 1, 1) +ax.scatter(X_data, y_data) + +# 定义mxnet变量 +X = mx.symbol.Variable('data') +Y = mx.symbol.Variable('softmax_label') + +# 定义网络 +Y_ = mx.symbol.FullyConnected(data=X, num_hidden=1, name='pre') +loss = mx.symbol.LinearRegressionOutput(data=Y_, label=Y, name='loss') + +# 定义模型 +model = mx.model.FeedForward( + ctx=mx.cpu(), + symbol=loss, + num_epoch=100, + learning_rate=0.001, + numpy_batch_size=1 + ) + +# 训练模型 +model.fit(X=X_data, y=y_data) + +# 预测 +prediction = model.predict(X_data) +lines = ax.plot(X_data, prediction, 'r-', lw=5) +plt.show() + diff --git a/MXnet/mxnet b/MXnet/mxnet new file mode 160000 index 00000000..87068e6d --- /dev/null +++ b/MXnet/mxnet @@ -0,0 +1 @@ +Subproject commit 87068e6df91ed9b2fdbcffa4d6a83e318c002247 diff --git a/MXnet/self_lear_mxnet/0_linear_regression_dis1.py b/MXnet/self_lear_mxnet/0_linear_regression_dis1.py new file mode 100755 index 00000000..53c03b82 --- /dev/null +++ b/MXnet/self_lear_mxnet/0_linear_regression_dis1.py @@ -0,0 +1,42 @@ +#!/usr/bin/env python +#-*- coding:utf-8 -*- +# https://mxnet.incubator.apache.org/tutorials/python/linear-regression.html +import mxnet as mx +import numpy as np +import matplotlib.pyplot as plt##画图 + +# 定义输入数据 +X_data = np.linspace(-1, 1, 100)## 范围 -1，1，数量100 线性变化 +noise = np.random.normal(0, 0.5, 100)## 噪声 范围 0，0.5 数量100 +y_data = 5 * X_data + noise## y= a*X 插入噪声 + +# Plot 输入数据 +fig = plt.figure() +ax = fig.add_subplot(1, 1, 1)##子图 +ax.scatter(X_data, y_data)## 范围 + +# 定义mxnet变量 +X = mx.symbol.Variable('data') +Y = mx.symbol.Variable('softmax_label') + +# 定义网络 +Y_ = mx.symbol.FullyConnected(data=X, num_hidden=1, name='pre') +loss = mx.symbol.LinearRegressionOutput(data=Y_, label=Y, name='loss') + +# 定义优化模型 +model = mx.model.FeedForward( + ctx=mx.cpu(), + symbol=loss, + num_epoch=100, + learning_rate=0.001,#学习率 + numpy_batch_size=1 + ) + +# 训练模型 +model.fit(X=X_data, y=y_data) + +# 预测 +prediction = model.predict(X_data) +lines = ax.plot(X_data, prediction, 'r-', lw=5) +plt.show() + diff --git a/MXnet/self_lear_mxnet/1_linear_regression_dis2.py b/MXnet/self_lear_mxnet/1_linear_regression_dis2.py new file mode 100755 index 00000000..b0c6b616 --- /dev/null +++ b/MXnet/self_lear_mxnet/1_linear_regression_dis2.py @@ -0,0 +1,64 @@ +#!/usr/bin/env python +#-*- coding:utf-8 -*- +## y=a*x2+1*x1 +# https://mxnet.incubator.apache.org/tutorials/python/linear-regression.html +## 测试 +import mxnet as mx +import numpy as np +import logging +logging.getLogger().setLevel(logging.DEBUG) +import matplotlib.pyplot as plt##画图 + +# 准备输入数据 +#Training data 训练数据 +train_data = np.random.uniform(0, 1, [100, 2])##100行2列 +train_label = np.array([train_data[i][0] + 2 * train_data[i][1] for i in range(100)])## y=y=a*x2+1*x1 +batch_size = 1 + +#Evaluation Data 评估数据 +eval_data = np.array([[7,2],[6,10],[12,2]]) +eval_label = np.array([11,26,16]) + +## 定义 训练方式 batch_size每次训练输入的样例数量 shuffle为重新排列样例输入训练的序列 +## 注意 label_name 与Y = mx.symbol.Variable('lin_reg_label')一致 默认为 softmax_label +train_iter = mx.io.NDArrayIter(train_data,train_label, batch_size, shuffle=True,label_name='lin_reg_label') +eval_iter = mx.io.NDArrayIter(eval_data, eval_label, batch_size, shuffle=False) + +## 定义模型 +X = mx.sym.Variable('data') +Y = mx.symbol.Variable('lin_reg_label') +fully_connected_layer = mx.sym.FullyConnected(data=X, name='fc1', num_hidden = 1)##线性回归 相当于 全连接层 +lro = mx.sym.LinearRegressionOutput(data=fully_connected_layer, label=Y, name="lro")##计算损失函数 loss +model = mx.mod.Module( + symbol = lro ,#损失函数 + data_names=['data'], + label_names = ['lin_reg_label']# 线性回归 网络结构 network structure +) +#可视化模型 +mx.viz.plot_network(symbol=lro) + +##优化训练模型 +model.fit(train_iter, eval_iter, + optimizer_params={'learning_rate':0.005, 'momentum': 0.9},## 学习率 动量 + num_epoch=50, + eval_metric='mse', + batch_end_callback = mx.callback.Speedometer(batch_size, 2)) + +## 验证模型 +print model.predict(eval_iter).asnumpy()##输出预测结果 +## mean squared error (MSE) 平均平方差 +metric = mx.metric.MSE() +model.score(eval_iter, metric) +## 加入噪声 +eval_data = np.array([[7,2],[6,10],[12,2]]) +eval_label = np.array([11.1,26.1,16.1]) #Adding 0.1 to each of the values +eval_iter = mx.io.NDArrayIter(eval_data, eval_label, batch_size, shuffle=False) +model.score(eval_iter, metric) + + + + + + + + diff --git a/MXnet/self_lear_mxnet/1_mnist_test/mnist.py b/MXnet/self_lear_mxnet/1_mnist_test/mnist.py new file mode 100644 index 00000000..1f1ed2a7 --- /dev/null +++ b/MXnet/self_lear_mxnet/1_mnist_test/mnist.py @@ -0,0 +1,28 @@ +#!/usr/bin/env python +#-*- coding:utf-8 -*- +import numpy as np +import os +import urllib +import gzip +import struct +##下载数据 +def download_data(url, force_download=True): + fname = url.split("/")[-1] + if force_download or not os.path.exists(fname): + urllib.urlretrieve(url,fname) + return fname +##读取数据 +def read_data(label_url, image_url): + with gzip.open(download_data(label_url))as flbl: + magic, num = struct.unpack(">II", flbl.read(8)) + label = np.fromstring(flbl.read(), dtype=np.int8) + with gzip.open(download_data(image_url),'rb')as fimg: + magic, num, rows, cols = struct.unpack(">IIII", fimg.read(16)) + image = np.fromstring(fimg.read(), dtype=np.uint8).reshape(len(label), rows, cols) + return (label, image) + +path='http://yann.lecun.com/exdb/mnist/' +(train_lbl, train_img)= read_data( + path+'train-labels-idx1-ubyte.gz', path+'train-images-idx3-ubyte.gz') +(val_lbl, val_img) = read_data( + path+'t10k-labels-idx1-ubyte.gz', path+'t10k-images-idx3-ubyte.gz') diff --git a/MXnet/self_lear_mxnet/1_mnist_test/mnist.py~ b/MXnet/self_lear_mxnet/1_mnist_test/mnist.py~ new file mode 100644 index 00000000..aedd28ef --- /dev/null +++ b/MXnet/self_lear_mxnet/1_mnist_test/mnist.py~ @@ -0,0 +1,28 @@ +#!/usr/bin/env python +#-*- coding:utf-8 -*- +import numpy as np +import os +import turllib +import gzip +import struct +##下载数据 +def download_data(url, force_download=True): + fname = url.split("/")[-1] + if force_download or not os.path.exists(fname): + urllib.urlretrieve(url,fname) + return fname +##读取数据 +def read_data(label_url, image_url): + with gzip.open(download_data(label_url))as flbl: + magic, num = struct.unpack(">II", flbl.read(8)) + label = np.fromstring(flbl.read(), dtype=np.int8) + with gzip.open(download_data(image_url),'rb')as fimg: + magic, num, rows, cols = struct.unpack(">IIII", fimg.read(16)) + image = np.fromstring(fimg.read(), dtype=np.uint8).reshape(len(label), rows, cols) + return (label, image) + +path='http://yann.lecun.com/exdb/mnist/' +(train_lbl, train_img)= read_data( + path+'train-labels-idx1-ubyte.gz', path+'train-images-idx3-ubyte.gz') +(val_lbl, val_img) = read_data( + path+'t10k-labels-idx1-ubyte.gz', path+'t10k-images-idx3-ubyte.gz') diff --git a/MXnet/self_lear_mxnet/2_linear_regression_dis2_with_bis.py b/MXnet/self_lear_mxnet/2_linear_regression_dis2_with_bis.py new file mode 100755 index 00000000..4be417ee --- /dev/null +++ b/MXnet/self_lear_mxnet/2_linear_regression_dis2_with_bis.py @@ -0,0 +1,86 @@ +#!/usr/bin/env python +#-*- coding:utf-8 -*- +## y[i] = 2 * X[i][0] - 3.4 * X[i][1] + 4.2 + noise +# https://zh.gluon.ai/chapter_supervised-learning/linear-regression-scratch.html +## 测试 +##所以我们的第一个教程是如何只利用ndarray和autograd来实现一个线性回归的训练。 +from mxnet import ndarray as nd +from mxnet import autograd +import random + + +#### 准备输入数据 ### +num_inputs = 2##数据维度 +num_examples = 1000##样例大小 + +## 真实的需要估计的参数 +true_w = [2, -3.4]##权重 +true_b = 4.2##偏置 + +X = nd.random_normal(shape=(num_examples, num_inputs))## 1000*2 +y = true_w[0] * X[:, 0] + true_w[1] * X[:, 1] + true_b## 1000*1 +y += .01 * nd.random_normal(shape=y.shape)##加入 噪声 服从均值0和标准差为0.01的正态分布 + +## 读取数据 +batch_size = 10 +def data_iter(): + # 产生一个随机索引 + idx = list(range(num_examples)) + random.shuffle(idx)##打乱 + for i in range(0, num_examples, batch_size):##0 10 20 ... + j = nd.array(idx[i:min(i+batch_size,num_examples)])##随机抽取10个样例 + yield nd.take(X, j), nd.take(y, j)##样例和标签 我们通过python的yield来构造一个迭代器。 +##读取第一个随机数据块 +for data, label in data_iter(): + print(data, label) + break + + +###初始化模型参数 +w = nd.random_normal(shape=(num_inputs, 1))## 2*1权重 +b = nd.zeros((1,))##偏置 +params = [w, b] +#之后训练时我们需要对这些参数求导来更新它们的值，所以我们需要创建它们的梯度。 +for param in params: + param.attach_grad() + +### 定义模型 +# 线性模型就是将输入和模型做乘法再加上偏移 +def net(X): + return nd.dot(X, w) + b + +### 损失函数 +# 使用常见的平方误差来衡量预测目标和真实目标之间的差距 +def square_loss(yhat, y): + # 注意这里我们把y变形成yhat的形状来避免自动广播 + return (yhat - y.reshape(yhat.shape)) ** 2 + +### 优化 +# 这里通过随机梯度下降来求解 +# 模型参数沿着梯度的反方向走特定距离，这个距离一般叫学习率 +def SGD(params, lr): + for param in params: + param[:] = param - lr * param.grad + +### 训练 +epochs = 5##训练迭代数据次数 +learning_rate = .001##学习率 +for e in range(epochs): + total_loss = 0 + for data, label in data_iter(): + with autograd.record():##自动微分 + output = net(data) + loss = square_loss(output, label) + loss.backward()## 反向传播 + SGD(params, learning_rate)##求解梯度 + total_loss += nd.sum(loss).asscalar() + print("Epoch %d, average loss: %f" % (e, total_loss/num_examples)) + +## 查看训练结果 +print true_w, w +print true_b, b + + + + + diff --git a/MXnet/self_lear_mxnet/3_linear_regression_dis2_with_bis_use_Gluon.py b/MXnet/self_lear_mxnet/3_linear_regression_dis2_with_bis_use_Gluon.py new file mode 100755 index 00000000..d700e2c8 --- /dev/null +++ b/MXnet/self_lear_mxnet/3_linear_regression_dis2_with_bis_use_Gluon.py @@ -0,0 +1,125 @@ +#!/usr/bin/env python +#-*- coding:utf-8 -*- +## y[i] = 2 * X[i][0] - 3.4 * X[i][1] + 4.2 + noise +# https://zh.gluon.ai/chapter_supervised-learning/linear-regression-scratch.html +## 测试 +##所以我们的第一个教程是如何只利用ndarray和autograd来实现一个线性回归的训练。 +from mxnet import ndarray as nd +from mxnet import autograd +import random +from mxnet import gluon + +#### 准备输入数据 ### +num_inputs = 2##数据维度 +num_examples = 1000##样例大小 + +## 真实的需要估计的参数 +true_w = [2, -3.4]##权重 +true_b = 4.2##偏置 + +X = nd.random_normal(shape=(num_examples, num_inputs))## 1000*2 +y = true_w[0] * X[:, 0] + true_w[1] * X[:, 1] + true_b## 1000*1 +y += .01 * nd.random_normal(shape=y.shape)##加入 噪声 服从均值0和标准差为0.01的正态分布 + +## 读取数据 +''' +batch_size = 10 +def data_iter(): + # 产生一个随机索引 + idx = list(range(num_examples)) + random.shuffle(idx)##打乱 + for i in range(0, num_examples, batch_size):##0 10 20 ... + j = nd.array(idx[i:min(i+batch_size,num_examples)])##随机抽取10个样例 + yield nd.take(X, j), nd.take(y, j)##样例和标签 我们通过python的yield来构造一个迭代器。 +''' +batch_size = 10 +dataset = gluon.data.ArrayDataset(X, y) +data_iter = gluon.data.DataLoader(dataset, batch_size, shuffle=True) + +##读取第一个随机数据块 +for data, label in data_iter: + print(data, label) + break + +### 定义模型 +''' +###初始化模型参数 +w = nd.random_normal(shape=(num_inputs, 1))## 2*1权重 +b = nd.zeros((1,))##偏置 +params = [w, b] +#之后训练时我们需要对这些参数求导来更新它们的值，所以我们需要创建它们的梯度。 +for param in params: + param.attach_grad() +### 定义模型 +# 线性模型就是将输入和模型做乘法再加上偏移 +def net(X): + return nd.dot(X, w) + b +''' +''' +当我们手写模型的时候，我们需要先声明模型参数，然后再使用它们来构建模型。 +但gluon提供大量提前定制好的层，使得我们只需要主要关注使用哪些层来构建模型。 +例如线性模型就是使用对应的Dense层。 +构建模型最简单的办法是利用Sequential来所有层串起来。 +''' +## 首先我们定义一个空的模型 +net = gluon.nn.Sequential() +#然后我们加入一个Dense层，它唯一必须要定义的参数就是输出节点的个数，在线性模型里面是1. +net.add(gluon.nn.Dense(1)) +#注意这里我们并没有定义说这个层的输入节点是多少，这个在之后真正给数据的时候系统会自动赋值。 + +##初始化模型参数 +net.initialize() + + +### 损失函数 +# 使用常见的平方误差来衡量预测目标和真实目标之间的差距 +''' +def square_loss(yhat, y): + # 注意这里我们把y变形成yhat的形状来避免自动广播 + return (yhat - y.reshape(yhat.shape)) ** 2 +''' +square_loss = gluon.loss.L2Loss()#gluon提供了平方误差函数 + + + +### 优化 +# 这里通过随机梯度下降来求解 +# 模型参数沿着梯度的反方向走特定距离，这个距离一般叫学习率 +''' +def SGD(params, lr): + for param in params: + param[:] = param - lr * param.grad +''' +''' +同样我们无需手动实现随机梯度下降，我们可以用创建一个Trainer的实例，并且将模型参数传递给它就行。 +''' +trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1}) + + +### 训练 +epochs = 5##训练迭代数据次数 +batch_size = 10##每次训练输入的样例个数 +#learning_rate = .001##学习率 +for e in range(epochs): + total_loss = 0 + for data, label in data_iter: + with autograd.record():##自动微分 + output = net(data) + loss = square_loss(output, label) + loss.backward()## 反向传播 + #SGD(params, learning_rate)##求解梯度 + trainer.step(batch_size) + total_loss += nd.sum(loss).asscalar() + print("Epoch %d, average loss: %f" % (e, total_loss/num_examples)) + +## 查看训练结果 +dense = net[0]#我们先从net拿到需要的层，然后访问其权重和位移 +print true_w, dense.weight.data() +print true_b, dense.bias.data() + + + + + + + diff --git a/MXnet/self_lear_mxnet/3_linear_regression_dis2_with_bis_use_Gluon.py~ b/MXnet/self_lear_mxnet/3_linear_regression_dis2_with_bis_use_Gluon.py~ new file mode 100755 index 00000000..df2b7ff2 --- /dev/null +++ b/MXnet/self_lear_mxnet/3_linear_regression_dis2_with_bis_use_Gluon.py~ @@ -0,0 +1,125 @@ +#!/usr/bin/env python +#-*- coding:utf-8 -*- +## y[i] = 2 * X[i][0] - 3.4 * X[i][1] + 4.2 + noise +# https://zh.gluon.ai/chapter_supervised-learning/linear-regression-scratch.html +## 测试 +##所以我们的第一个教程是如何只利用ndarray和autograd来实现一个线性回归的训练。 +from mxnet import ndarray as nd +from mxnet import autograd +import random +from mxnet import gluon + +#### 准备输入数据 ### +num_inputs = 2##数据维度 +num_examples = 1000##样例大小 + +## 真实的需要估计的参数 +true_w = [2, -3.4]##权重 +true_b = 4.2##偏置 + +X = nd.random_normal(shape=(num_examples, num_inputs))## 1000*2 +y = true_w[0] * X[:, 0] + true_w[1] * X[:, 1] + true_b## 1000*1 +y += .01 * nd.random_normal(shape=y.shape)##加入 噪声 服从均值0和标准差为0.01的正态分布 + +## 读取数据 +''' +batch_size = 10 +def data_iter(): + # 产生一个随机索引 + idx = list(range(num_examples)) + random.shuffle(idx)##打乱 + for i in range(0, num_examples, batch_size):##0 10 20 ... + j = nd.array(idx[i:min(i+batch_size,num_examples)])##随机抽取10个样例 + yield nd.take(X, j), nd.take(y, j)##样例和标签 我们通过python的yield来构造一个迭代器。 +''' +batch_size = 10 +dataset = gluon.data.ArrayDataset(X, y) +data_iter = gluon.data.DataLoader(dataset, batch_size, shuffle=True) + +##读取第一个随机数据块 +for data, label in data_iter: + print(data, label) + break + +### 定义模型 +''' +###初始化模型参数 +w = nd.random_normal(shape=(num_inputs, 1))## 2*1权重 +b = nd.zeros((1,))##偏置 +params = [w, b] +#之后训练时我们需要对这些参数求导来更新它们的值，所以我们需要创建它们的梯度。 +for param in params: + param.attach_grad() +### 定义模型 +# 线性模型就是将输入和模型做乘法再加上偏移 +def net(X): + return nd.dot(X, w) + b +''' +''' +当我们手写模型的时候，我们需要先声明模型参数，然后再使用它们来构建模型。 +但gluon提供大量提前定制好的层，使得我们只需要主要关注使用哪些层来构建模型。 +例如线性模型就是使用对应的Dense层。 +构建模型最简单的办法是利用Sequential来所有层串起来。 +''' +## 首先我们定义一个空的模型 +net = gluon.nn.Sequential() +#然后我们加入一个Dense层，它唯一必须要定义的参数就是输出节点的个数，在线性模型里面是1. +net.add(gluon.nn.Dense(1)) +#注意这里我们并没有定义说这个层的输入节点是多少，这个在之后真正给数据的时候系统会自动赋值。 + +##初始化模型参数 +net.initialize() + + +### 损失函数 +# 使用常见的平方误差来衡量预测目标和真实目标之间的差距 +''' +def square_loss(yhat, y): + # 注意这里我们把y变形成yhat的形状来避免自动广播 + return (yhat - y.reshape(yhat.shape)) ** 2 +''' +square_loss = gluon.loss.L2Loss()#gluon提供了平方误差函数 + + + +### 优化 +# 这里通过随机梯度下降来求解 +# 模型参数沿着梯度的反方向走特定距离，这个距离一般叫学习率 +''' +def SGD(params, lr): + for param in params: + param[:] = param - lr * param.grad +''' +''' +同样我们无需手动实现随机梯度下降，我们可以用创建一个Trainer的实例，并且将模型参数传递给它就行。 +''' +trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1}) + + +### 训练 +epochs = 5##训练迭代数据次数 +batch_size = 10##每次训练输入的样例个数 +#learning_rate = .001##学习率 +for e in range(epochs): + total_loss = 0 + for data, label in data_iter(): + with autograd.record():##自动微分 + output = net(data) + loss = square_loss(output, label) + loss.backward()## 反向传播 + #SGD(params, learning_rate)##求解梯度 + trainer.step(batch_size) + total_loss += nd.sum(loss).asscalar() + print("Epoch %d, average loss: %f" % (e, total_loss/num_examples)) + +## 查看训练结果 +dense = net[0]#我们先从net拿到需要的层，然后访问其权重和位移 +print true_w, dense.weight.data() +print true_b, dense.bias.data() + + + + + + + diff --git a/MXnet/self_lear_mxnet/MXnet_forward_backward.png b/MXnet/self_lear_mxnet/MXnet_forward_backward.png new file mode 100644 index 00000000..813c5532 Binary files /dev/null and b/MXnet/self_lear_mxnet/MXnet_forward_backward.png differ diff --git "a/MXnet/self_lear_mxnet/MXnet\345\237\272\347\241\200\347\237\245\350\257\206.txt" "b/MXnet/self_lear_mxnet/MXnet\345\237\272\347\241\200\347\237\245\350\257\206.txt" new file mode 100644 index 00000000..ba1dadee --- /dev/null +++ "b/MXnet/self_lear_mxnet/MXnet\345\237\272\347\241\200\347\237\245\350\257\206.txt" @@ -0,0 +1,417 @@ +http://lucianlv.blog.51cto.com/9871307/1812733 +MXnet基本概念和操作 + +【1】NDArray + + 多维的数据结构，提供在 cpu 或者 gpu 上进行矩阵运算和张量计算， + 能够自动并行计算 + +NDArray 是 MXnet 中最底层的计算单元，与 numpy.ndarray 非常相似， + 但是也有 2 点不同的特性： + + 1）支持多设备 +所有的操作可以在不同的设备上运行，包括 cpu 和 gpu。 + + ####### 测试例子##### + >>> import mxnet as mx + ## 在cpu0上创建一个2X3的矩阵 + >>> a = mx.nd.empty((2, 3)) + ## 在gpu上创建一个2X3的矩阵 + >>> b = mx.nd.empty((2, 3), mx.gpu()) # 在gpu0上创建一个2X3的矩阵 + >>> c = mx.nd.empty((2, 3), mx.gpu(2)) # 在gpu2上创建一个2X3的矩阵 + >>> c.shape # 维度(2L, 3L) + >>> c.context # 设备信息gpu(2) + + ############################################## + ### 其他的初始化方式 + >>> a = mx.nd.zeros((2, 3)) # 创建2X3的全0矩阵 + >>> b = mx.nd.ones((2, 3)) # 创建2X3的全1矩阵 + >>> b[:] = 2 # 所有元素赋值为2 + + >>> b = mx.nd.zeros((2, 3), mx.gpu()) + >>> a = mx.nd.ones((2, 3)) # 创建2X3的全1矩阵 + ## 不同的设备之间进行数据拷贝 + >>> a.copyto(b) # 从cpu拷贝数据到gpu + + ########################################### + ## NDArray转换为numpy.ndarray ## + >>> a = mx.nd.ones((2, 3)) + >>> b = a.asnumpy() + >>> type(b) + + >>> print b[[ 1. 1. 1.] + [ 1. 1. 1.]] + + ########################################## + ## numpy.ndarray转换为NDArray ## + >>> import numpy as np + >>> a = mx.nd.empty((2, 3)) + >>> a[:] = np.random.uniform(-0.1, 0.1, a.shape) + >>> print a.asnumpy()[[-0.06821112 -0.03704893 0.06688045] + [ 0.09947646 -0.07700162 0.07681718]] + + ########################################### + ### NDArray基本运算 ############# + >>> a = mx.nd.ones((2, 3)) * 2 + >>> b = mx.nd.ones((2, 3)) * 4 + >>> print b.asnumpy() + [[ 4. 4. 4.] + [ 4. 4. 4.]] + >>> c = a + b # 对应元素求和 + >>> print c.asnumpy() + [[ 6. 6. 6.] + [ 6. 6. 6.]] + >>> d = a * b # 对应元素求积 + >>> print d.asnumpy() + [[ 8. 8. 8.] + [ 8. 8. 8.]] + + + ###################################################### + ### 不同设备上的NDArray需要移动到一起才能计算 ### + >>> a = mx.nd.ones((2, 3)) * 2 ## 默认在CPU上 + >>> b = mx.nd.ones((2, 3), mx.gpu()) * 3 + >>> c = a.copyto(mx.gpu()) * b + >>> print c.asnumpy() + [[ 6. 6. 6.] + [ 6. 6. 6.]] + + ############################################# + ### 数据的导出与载入 ### + 有两种方法使加载和保存数据变得简单。第一种是使用pickle，NDArray兼容pickle + ## 1. 通过pickle导出与载入数据 + >>> import mxnet as mx + >>> import pickle as pkl + >>> a = mx.nd.ones((2, 3)) * 2 + >>> data = pkl.dumps(a) ##导出 存储数据 + >>> b = pkl.loads(data) ##载入 获取数据 + >>> print b.asnumpy() + [[ 2. 2. 2.] + [ 2. 2. 2.]] + + ################################## + ## 2. 直接保存为二进制文件 + >>> a = mx.nd.ones((2,3))*2 + >>> b = mx.nd.ones((2,3))*3 + >>> mx.nd.save('mydata.bin', [a, b]) ##保存成二进制文件 + >>> c = mx.nd.load('mydata.bin') ##载入二进制文件数据 + >>> print c[0].asnumpy() ## c[0] 为a + [[ 2. 2. 2.] + [ 2. 2. 2.]] + >>> print c[1].asnumpy() ## c[1] 为b + [[ 3. 3. 3.] + [ 3. 3. 3.]] + 可以导入字典: + >>> mx.nd.save('mydata.bin', {'a':a, 'b':b}) + >>> c = mx.nd.load('mydata.bin') + >>> print c['a'].asnumpy() + [[ 2. 2. 2.] + [ 2. 2. 2.]] + >>> print c['b'].asnumpy() + [[ 3. 3. 3.] + [ 3. 3. 3.]] + + ####################################### + ## 直接保存到分布式文件系统上（s3或hdfs） + >>> mx.nd.save('s3://mybucket/mydata.bin', [a,b]) + >>> mx.nd.save('hdfs///users/myname/mydata.bin', [a,b]) + + ############################################################### + ################# + + + 2）自动并行计算 +不同的操作自动进行并行计算。 + a = mx.nd.ones((2,3)) ## 默认cpu上 + b = a ## cpu上 + c = a.copyto(mx.gpu())## gpu上 + a += 1 + b *= 3 + c *= 3 + + a += 1可与c *= 3并行计算，因为在不同的设备上， + 但是a += 1和b *= 3只能相继执行。 + + +【2】符号式运算 Symbol + Symbol使得非常容易定义神经网络，并且能自动求导 +以下的范例创建了一个 2 层的感知器网络: + +>>> import mxnet as mx +>>> net = mx.symbol.Variable('data') +>>> net = mx.symbol.FullyConnected(data=net, name='fc1', num_hidden=128)##全连接层 +>>> net = mx.symbol.Activation(data=net, name='relu1', act_type="relu") ##激活 +>>> net = mx.symbol.FullyConnected(data=net, name='fc2', num_hidden=64) ##全连接 +>>> net = mx.symbol.SoftmaxOutput(data=net, name='out') ##softmax回归输出层 +>>> type(net) + + + +每一个 Symbol 可以绑定一个名字，Variable 通常用来定义输入， +其他的 Symbol 有一个参数data以一个Symbol 类型作为输入数据， +另外还有其他的超参数num_hidden（隐藏层的神经元数目)，act_type(激活函数的类型)。 + +Symbol 的作用可以被简单的看成是实现了一个函数，函数的参数名称自动生成，可以通过以下的方式查看: + +>>> net.list_arguments() +['data', 'fc1_weight', 'fc1_bias', 'fc2_weight', 'fc2_bias', 'out_label'] + +可以看到，上面的这些参数是每个symbol所需要的输入参数 + + data：variable data所需要的输入数据 + fc1_weight和fc1_bias：是第一全连层fc1所需要的权值和偏执 + fc2_weight和fc2_bias：是第二全连层fc2所需要的权值和偏执 + out_lable：损失函数的计算出来的标签 + + + +2）我们也可以明确指定这些自动生成的参数的名字: + +>>> net2 = mx.symbol.Variable('data') +>>> w = mx.symbol.Variable('myweight') +>>> net2 = mx.symbol.FullyConnected(data=net2, weight=w, name='fc1', num_hidden=128) +>>> net2.list_arguments() +['data', 'myweight', 'fc1_bias'] + +3）Symbol 可以组合之后，在传入全连接中: +先在两个symbol之间执行了元素级别的加法，然后将加的结果反馈到了fully connected operator +>>> lhs = mx.symbol.Variable('data1') +>>> rhs = mx.symbol.Variable('data2') +>>> net = mx.symbol.FullyConnected(data=lhs + rhs, name='fc1', num_hidden=128) +>>> net.list_arguments() +['data1', 'data2', 'fc1_weight', 'fc1_bias'] + + +3）Symbol 也可以被随后的操作替换: +>>> net = mx.symbol.Variable('data') +>>> net = mx.symbol.FullyConnected(data=net, name='fc1', num_hidden=128) +>>> net2 = mx.symbol.Variable('data2') +>>> net2 = mx.symbol.FullyConnected(data=net2, name='net2', num_hidden=128) +>>> composed_net = net(data=net2, name='compose') +>>> composed_net.list_arguments() +['data2', 'net2_weight', 'net2_bias', 'compose_fc1_weight', 'compose_fc1_bias'] + +在上面的例子中，net作为一个函数应用于一个已经存在的symbol net， +他们的结果composed_net将会替换原始的参数data net2. + + + +4）一旦定义好了 Symbol，只需要指定输入数据的维度，就可以推算出各级中间参数的维度: +>>> net = mx.symbol.Variable('data') +>>> net = mx.symbol.FullyConnected(data=net, name='fc1', num_hidden=10) +>>> arg_shape, out_shape, aux_shape = net.infer_shape(data=(100, 100)) +>>> dict(zip(net.list_arguments(), arg_shape)) +{'data': (100, 100), 'fc1_weight': (10, 100), 'fc1_bias': (10,)} +>>> out_shape +[(100, 10)] + + +5）接下来通过绑定变量，就可以执行实际的运算了: +>>> # 定义计算图 +>>> A = mx.symbol.Variable('A') +>>> B = mx.symbol.Variable('B') +>>> C = A * B + +>>> a = mx.nd.ones(3) * 4 +>>> b = mx.nd.ones(3) * 2 +>>> # 绑定变量到Symbol +>>> c_exec = C.bind(ctx=mx.cpu(), args={'A' : a, 'B': b}) +>>> # 进行前向计算 +>>> c_exec.forward() +>>> c_exec.outputs[0].asnumpy() +[ 8. 8. 8.] + +对于神经网络而言，一个更加常用的形式是simple_bind，将会为你创建所有的参数数组。 +然后你调用forward和backward来获取梯度。 +>>> # 定义计算流图 +>>> net = some symbol +>>> texec = net.simple_bind(data=input_shape) +>>> texec.forward() +>>> texec.backward() + + +【3】数据同步 KVStore + + KVStore 实现了在多个运算器之间，或者在多台计算机之间的数据同步 +MXNet提供一个分布式的key-value存储来进行数据交换。它主要有两个函数， + +push： 将key-value对从一个设备push进存储 +pull：将某个key上的值从存储中pull出来此外， + KVStore还接受自定义的更新函数来控制收到的值如何写入到存储中。 +最后KVStore提供数种包含最终一致性模型和顺序一致性模型在内的数据一致性模型。 + +分布式梯度下降算法： +KVStore kvstore("dist_async"); +kvstore.set_updater([](NDArray weight, NDArray gradient) { + weight -= eta * gradient; + }); +for (int i = 0; i < max_iter; ++i) { + kvstore.pull(network.weight); + network.forward(); + network.backward(); + kvstore.push(network.gradient); +} +在这里先使用最终一致性模型创建一个kvstore，然后将更新函数注册进去。 +在每轮迭代前，每个计算节点先将最新的权重pull回来，之后将计算的得到的梯度push出去。 +kvstore将会利用更新函数来使用收到的梯度更新其所存储的权重。 + + +1）通过create可以对 kvstore 进行简单的初始化: +在store里面初始化一个（int， NDArray）对，然后将这个值pull拉出去。 +>>> kv = mx.kv.create('local') # 创建一个本地的kvstore +>>> shape = (2,3) +>>> kv.init(3, mx.nd.ones(shape)*2) +>>> a = mx.nd.zeros(shape) +>>> kv.pull(3, out = a) +>>> print a.asnumpy() +[[ 2. 2. 2.] + [ 2. 2. 2.]] + +2）初始化后，可以通过相同的 key 进行数值更新: +对于任何已经被初始化的key值，我们可以push一个新的相同大小的值。 +>>> kv.push(3, mx.nd.ones(shape)*8) ##推入 +>>> kv.pull(3, out = a) # 取出值 +>>> print a.asnumpy() +[[ 8. 8. 8.] + [ 8. 8. 8.]] + +3）push的数据可以在任何设备上，此外，可以在同一个 key 上传递多个值，KVStore 会对多个值求和，push聚合后的值: + +>>> gpus = [mx.gpu(i) for i in range(4)] +>>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]## 四个gpu上分别存储[[ 1. 1. 1.] [ 1. 1. 1.]] +>>> kv.push(3, b) +>>> kv.pull(3, out = a)##push聚合求后的值 +>>> print a.asnumpy() +[[ 4. 4. 4.] + [ 4. 4. 4.]] + + +对每次push，KVStore使用updater将push的值和存储的值加起来。 +默认的updater是ASSIGN，我们可以替换默认的来控制怎样融合数据 +4）KVStore 对每次push进来的值做的默认行为是ASSIGN，这个行为可以被自定义的行为替换: + +>>> def update(key, input, stored): +>>> print "update on key: %d" % key +>>> stored += input * 2 +>>> kv._set_updater(update) ##更新KVStore的updater +>>> kv.pull(3, out=a) +>>> print a.asnumpy() +[[ 4. 4. 4.] + [ 4. 4. 4.]] +>>> kv.push(3, mx.nd.ones(shape)) +update on key: 3 +>>> kv.pull(3, out=a) +>>> print a.asnumpy() +[[ 6. 6. 6.] + [ 6. 6. 6.]] + + +5）同push类似，通过一次调用，我们也可以将值同时pull到多个设备上: +>>> b = [mx.nd.ones(shape, gpu) for gpu in gpus] +>>> kv.pull(3, out = b) +>>> print b[1].asnumpy() +[[ 6. 6. 6.] + [ 6. 6. 6.]] + + + +6） 除了单个 key-value 的存储，KVStrore 还提供了批量的接口: + +# 针对单个设备 +>>> keys = [5, 7, 9] +>>> kv.init(keys, [mx.nd.ones(shape)]*len(keys)) +>>> kv.push(keys, [mx.nd.ones(shape)]*len(keys)) +update on key: 5 +update on key: 7 +update on key: 9 +>>> b = [mx.nd.zeros(shape)]*len(keys) +>>> kv.pull(keys, out = b) +>>> print b[1].asnumpy() +[[ 3. 3. 3.] + [ 3. 3. 3.]] + +# 针对多个设备 +>>> b = [[mx.nd.ones(shape, gpu) for gpu in gpus]] * len(keys) +>>> kv.push(keys, b) +update on key: 5 +update on key: 7 +update on key: 9 +>>> kv.pull(keys, out = b) +>>> print b[1][1].asnumpy() +[[ 11. 11. 11.] + [ 11. 11. 11.]] + +################################## +#分布式训练 +如何在MXNet上写一个分布式程序 + +MXNet提供了一个分布式key-value store名为kvstore减少了数据同步操作的复杂性。 +它提供了以下的功能： + push：将本地数据push到分布式存储，如梯度等 + pull：将数据从分布式存储中pull过来，如新的权值 + set_updater：为分布式存储设置一个updater，明确store是如何将接收到的数据进行融合的， + 例如：怎样使用接收到的梯度来更新权值。 + +如果你的程序按照下面的结构写的，事情会更加简单： +data = mx.io.ImageRecordIter(...) +net = mx.symbol.SoftmaxOutput(...) +model = mx.model.FeedForward.create(symbol = net, X = data, ...) + +上面的数据是取自于一个image record iterator，并使用一个符号化网络进行模型训练。 + +为了将它扩展成一个分布式的程序，首先要建立一个kvstore， +然后将它传入到create函数中。 +下面的程序将上面的SGD修改成了分布式异步的SGD： + +data = mx.io.ImageRecordIter(...) +net = mx.symbol.SoftmaxOutput(...) +kv = mx.kvstore.create('dist_sync') +model = mx.model.FeedForward.create(symbol = net, X = data, kvstore = kv, ...) + + + + +##################################### +###################################### +MXnet体验 +以下借助 MXnet 实现了一个简单的单变量线性回归程序： + +import mxnet as mx +import numpy as np +import matplotlib.pyplot as plt + +# 定义输入数据 +X_data = np.linspace(-1, 1, 100) +noise = np.random.normal(0, 0.5, 100) +y_data = 5 * X_data + noise + +# Plot 输入数据 +fig = plt.figure() +ax = fig.add_subplot(1, 1, 1) +ax.scatter(X_data, y_data) + +# 定义mxnet变量 +X = mx.symbol.Variable('data') +Y = mx.symbol.Variable('softmax_label') + +# 定义网络 +Y_ = mx.symbol.FullyConnected(data=X, num_hidden=1, name='pre') +loss = mx.symbol.LinearRegressionOutput(data=Y_, label=Y, name='loss') + +# 定义模型 +model = mx.model.FeedForward( + ctx=mx.cpu(), + symbol=loss, + num_epoch=100, + learning_rate=0.001, + numpy_batch_size=1 + ) + +# 训练模型 +model.fit(X=X_data, y=y_data) + +# 预测 +prediction = model.predict(X_data) +lines = ax.plot(X_data, prediction, 'r-', lw=5) +plt.show() + diff --git "a/MXnet/self_lear_mxnet/MXnet\345\237\272\347\241\200\347\237\245\350\257\206.txt~" "b/MXnet/self_lear_mxnet/MXnet\345\237\272\347\241\200\347\237\245\350\257\206.txt~" new file mode 100644 index 00000000..54170b44 --- /dev/null +++ "b/MXnet/self_lear_mxnet/MXnet\345\237\272\347\241\200\347\237\245\350\257\206.txt~" @@ -0,0 +1,403 @@ +http://lucianlv.blog.51cto.com/9871307/1812733 +MXnet基本概念和操作 + +【1】NDArray + + 多维的数据结构，提供在 cpu 或者 gpu 上进行矩阵运算和张量计算， + 能够自动并行计算 + +NDArray 是 MXnet 中最底层的计算单元，与 numpy.ndarray 非常相似， + 但是也有 2 点不同的特性： + + 1）支持多设备 +所有的操作可以在不同的设备上运行，包括 cpu 和 gpu。 + + ####### 测试例子##### + >>> import mxnet as mx + ## 在cpu0上创建一个2X3的矩阵 + >>> a = mx.nd.empty((2, 3)) + ## 在gpu上创建一个2X3的矩阵 + >>> b = mx.nd.empty((2, 3), mx.gpu()) # 在gpu0上创建一个2X3的矩阵 + >>> c = mx.nd.empty((2, 3), mx.gpu(2)) # 在gpu2上创建一个2X3的矩阵 + >>> c.shape # 维度(2L, 3L) + >>> c.context # 设备信息gpu(2) + + ############################################## + ### 其他的初始化方式 + >>> a = mx.nd.zeros((2, 3)) # 创建2X3的全0矩阵 + >>> b = mx.nd.ones((2, 3)) # 创建2X3的全1矩阵 + >>> b[:] = 2 # 所有元素赋值为2 + + >>> b = mx.nd.zeros((2, 3), mx.gpu()) + >>> a = mx.nd.ones((2, 3)) # 创建2X3的全1矩阵 + ## 不同的设备之间进行数据拷贝 + >>> a.copyto(b) # 从cpu拷贝数据到gpu + + ########################################### + ## NDArray转换为numpy.ndarray ## + >>> a = mx.nd.ones((2, 3)) + >>> b = a.asnumpy() + >>> type(b) + + >>> print b[[ 1. 1. 1.] + [ 1. 1. 1.]] + + ########################################## + ## numpy.ndarray转换为NDArray ## + >>> import numpy as np + >>> a = mx.nd.empty((2, 3)) + >>> a[:] = np.random.uniform(-0.1, 0.1, a.shape) + >>> print a.asnumpy()[[-0.06821112 -0.03704893 0.06688045] + [ 0.09947646 -0.07700162 0.07681718]] + + ########################################### + ### NDArray基本运算 ############# + >>> a = mx.nd.ones((2, 3)) * 2 + >>> b = mx.nd.ones((2, 3)) * 4 + >>> print b.asnumpy() + [[ 4. 4. 4.] + [ 4. 4. 4.]] + >>> c = a + b # 对应元素求和 + >>> print c.asnumpy() + [[ 6. 6. 6.] + [ 6. 6. 6.]] + >>> d = a * b # 对应元素求积 + >>> print d.asnumpy() + [[ 8. 8. 8.] + [ 8. 8. 8.]] + + + ###################################################### + ### 不同设备上的NDArray需要移动到一起才能计算 ### + >>> a = mx.nd.ones((2, 3)) * 2 ## 默认在CPU上 + >>> b = mx.nd.ones((2, 3), mx.gpu()) * 3 + >>> c = a.copyto(mx.gpu()) * b + >>> print c.asnumpy() + [[ 6. 6. 6.] + [ 6. 6. 6.]] + + ############################################# + ### 数据的导出与载入 ### + 有两种方法使加载和保存数据变得简单。第一种是使用pickle，NDArray兼容pickle + ## 1. 通过pickle导出与载入数据 + >>> import mxnet as mx + >>> import pickle as pkl + >>> a = mx.nd.ones((2, 3)) * 2 + >>> data = pkl.dumps(a) ##导出 存储数据 + >>> b = pkl.loads(data) ##载入 获取数据 + >>> print b.asnumpy() + [[ 2. 2. 2.] + [ 2. 2. 2.]] + + ################################## + ## 2. 直接保存为二进制文件 + >>> a = mx.nd.ones((2,3))*2 + >>> b = mx.nd.ones((2,3))*3 + >>> mx.nd.save('mydata.bin', [a, b]) ##保存成二进制文件 + >>> c = mx.nd.load('mydata.bin') ##载入二进制文件数据 + >>> print c[0].asnumpy() ## c[0] 为a + [[ 2. 2. 2.] + [ 2. 2. 2.]] + >>> print c[1].asnumpy() ## c[1] 为b + [[ 3. 3. 3.] + [ 3. 3. 3.]] + 可以导入字典: + >>> mx.nd.save('mydata.bin', {'a':a, 'b':b}) + >>> c = mx.nd.load('mydata.bin') + >>> print c['a'].asnumpy() + [[ 2. 2. 2.] + [ 2. 2. 2.]] + >>> print c['b'].asnumpy() + [[ 3. 3. 3.] + [ 3. 3. 3.]] + + ####################################### + ## 直接保存到分布式文件系统上（s3或hdfs） + >>> mx.nd.save('s3://mybucket/mydata.bin', [a,b]) + >>> mx.nd.save('hdfs///users/myname/mydata.bin', [a,b]) + + ############################################################### + ################# + + + 2）自动并行计算 +不同的操作自动进行并行计算。 + a = mx.nd.ones((2,3)) ## 默认cpu上 + b = a ## cpu上 + c = a.copyto(mx.gpu())## gpu上 + a += 1 + b *= 3 + c *= 3 + + a += 1可与c *= 3并行计算，因为在不同的设备上， + 但是a += 1和b *= 3只能相继执行。 + + +【2】符号式运算 Symbol + Symbol使得非常容易定义神经网络，并且能自动求导 +以下的范例创建了一个 2 层的感知器网络: + +>>> import mxnet as mx +>>> net = mx.symbol.Variable('data') +>>> net = mx.symbol.FullyConnected(data=net, name='fc1', num_hidden=128)##全连接层 +>>> net = mx.symbol.Activation(data=net, name='relu1', act_type="relu") ##激活 +>>> net = mx.symbol.FullyConnected(data=net, name='fc2', num_hidden=64) ##全连接 +>>> net = mx.symbol.SoftmaxOutput(data=net, name='out') ##softmax回归输出层 +>>> type(net) + + + +每一个 Symbol 可以绑定一个名字，Variable 通常用来定义输入， +其他的 Symbol 有一个参数data以一个Symbol 类型作为输入数据， +另外还有其他的超参数num_hidden（隐藏层的神经元数目)，act_type(激活函数的类型)。 + +Symbol 的作用可以被简单的看成是实现了一个函数，函数的参数名称自动生成，可以通过以下的方式查看: + +>>> net.list_arguments() +['data', 'fc1_weight', 'fc1_bias', 'fc2_weight', 'fc2_bias', 'out_label'] + +可以看到，上面的这些参数是每个symbol所需要的输入参数 + + data：variable data所需要的输入数据 + fc1_weight和fc1_bias：是第一全连层fc1所需要的权值和偏执 + fc2_weight和fc2_bias：是第二全连层fc2所需要的权值和偏执 + out_lable：损失函数的计算出来的标签 + + + +2）我们也可以明确指定这些自动生成的参数的名字: + +>>> net2 = mx.symbol.Variable('data') +>>> w = mx.symbol.Variable('myweight') +>>> net2 = mx.symbol.FullyConnected(data=net2, weight=w, name='fc1', num_hidden=128) +>>> net2.list_arguments() +['data', 'myweight', 'fc1_bias'] + +3）Symbol 可以组合之后，在传入全连接中: +先在两个symbol之间执行了元素级别的加法，然后将加的结果反馈到了fully connected operator +>>> lhs = mx.symbol.Variable('data1') +>>> rhs = mx.symbol.Variable('data2') +>>> net = mx.symbol.FullyConnected(data=lhs + rhs, name='fc1', num_hidden=128) +>>> net.list_arguments() +['data1', 'data2', 'fc1_weight', 'fc1_bias'] + + +3）Symbol 也可以被随后的操作替换: +>>> net = mx.symbol.Variable('data') +>>> net = mx.symbol.FullyConnected(data=net, name='fc1', num_hidden=128) +>>> net2 = mx.symbol.Variable('data2') +>>> net2 = mx.symbol.FullyConnected(data=net2, name='net2', num_hidden=128) +>>> composed_net = net(data=net2, name='compose') +>>> composed_net.list_arguments() +['data2', 'net2_weight', 'net2_bias', 'compose_fc1_weight', 'compose_fc1_bias'] + +在上面的例子中，net作为一个函数应用于一个已经存在的symbol net， +他们的结果composed_net将会替换原始的参数data net2. + + + +4）一旦定义好了 Symbol，只需要指定输入数据的维度，就可以推算出各级中间参数的维度: +>>> net = mx.symbol.Variable('data') +>>> net = mx.symbol.FullyConnected(data=net, name='fc1', num_hidden=10) +>>> arg_shape, out_shape, aux_shape = net.infer_shape(data=(100, 100)) +>>> dict(zip(net.list_arguments(), arg_shape)) +{'data': (100, 100), 'fc1_weight': (10, 100), 'fc1_bias': (10,)} +>>> out_shape +[(100, 10)] + + +5）接下来通过绑定变量，就可以执行实际的运算了: +>>> # 定义计算图 +>>> A = mx.symbol.Variable('A') +>>> B = mx.symbol.Variable('B') +>>> C = A * B + +>>> a = mx.nd.ones(3) * 4 +>>> b = mx.nd.ones(3) * 2 +>>> # 绑定变量到Symbol +>>> c_exec = C.bind(ctx=mx.cpu(), args={'A' : a, 'B': b}) +>>> # 进行前向计算 +>>> c_exec.forward() +>>> c_exec.outputs[0].asnumpy() +[ 8. 8. 8.] + +对于神经网络而言，一个更加常用的形式是simple_bind，将会为你创建所有的参数数组。 +然后你调用forward和backward来获取梯度。 +>>> # 定义计算流图 +>>> net = some symbol +>>> texec = net.simple_bind(data=input_shape) +>>> texec.forward() +>>> texec.backward() + + +【3】数据同步 KVStore + + KVStore 实现了在多个运算器之间，或者在多台计算机之间的数据同步 +MXNet提供一个分布式的key-value存储来进行数据交换。它主要有两个函数， + +push： 将key-value对从一个设备push进存储 +pull：将某个key上的值从存储中pull出来此外， + KVStore还接受自定义的更新函数来控制收到的值如何写入到存储中。 +最后KVStore提供数种包含最终一致性模型和顺序一致性模型在内的数据一致性模型。 + +分布式梯度下降算法： +KVStore kvstore("dist_async"); +kvstore.set_updater([](NDArray weight, NDArray gradient) { + weight -= eta * gradient; + }); +for (int i = 0; i < max_iter; ++i) { + kvstore.pull(network.weight); + network.forward(); + network.backward(); + kvstore.push(network.gradient); +} +在这里先使用最终一致性模型创建一个kvstore，然后将更新函数注册进去。 +在每轮迭代前，每个计算节点先将最新的权重pull回来，之后将计算的得到的梯度push出去。 +kvstore将会利用更新函数来使用收到的梯度更新其所存储的权重。 + + +1）通过create可以对 kvstore 进行简单的初始化: +在store里面初始化一个（int， NDArray）对，然后将这个值pull拉出去。 +>>> kv = mx.kv.create('local') # 创建一个本地的kvstore +>>> shape = (2,3) +>>> kv.init(3, mx.nd.ones(shape)*2) +>>> a = mx.nd.zeros(shape) +>>> kv.pull(3, out = a) +>>> print a.asnumpy() +[[ 2. 2. 2.] + [ 2. 2. 2.]] + +2）初始化后，可以通过相同的 key 进行数值更新: +对于任何已经被初始化的key值，我们可以push一个新的相同大小的值。 +>>> kv.push(3, mx.nd.ones(shape)*8) ##推入 +>>> kv.pull(3, out = a) # 取出值 +>>> print a.asnumpy() +[[ 8. 8. 8.] + [ 8. 8. 8.]] + +3）push的数据可以在任何设备上，此外，可以在同一个 key 上传递多个值，KVStore 会对多个值求和，push聚合后的值: + +>>> gpus = [mx.gpu(i) for i in range(4)] +>>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]## 四个gpu上分别存储[[ 1. 1. 1.] [ 1. 1. 1.]] +>>> kv.push(3, b) +>>> kv.pull(3, out = a)##push聚合求后的值 +>>> print a.asnumpy() +[[ 4. 4. 4.] + [ 4. 4. 4.]] + + +对每次push，KVStore使用updater将push的值和存储的值加起来。 +默认的updater是ASSIGN，我们可以替换默认的来控制怎样融合数据 +4）KVStore 对每次push进来的值做的默认行为是ASSIGN，这个行为可以被自定义的行为替换: + +>>> def update(key, input, stored): +>>> print "update on key: %d" % key +>>> stored += input * 2 +>>> kv._set_updater(update) ##更新KVStore的updater +>>> kv.pull(3, out=a) +>>> print a.asnumpy() +[[ 4. 4. 4.] + [ 4. 4. 4.]] +>>> kv.push(3, mx.nd.ones(shape)) +update on key: 3 +>>> kv.pull(3, out=a) +>>> print a.asnumpy() +[[ 6. 6. 6.] + [ 6. 6. 6.]] + + +5）同push类似，通过一次调用，我们也可以将值同时pull到多个设备上: +>>> b = [mx.nd.ones(shape, gpu) for gpu in gpus] +>>> kv.pull(3, out = b) +>>> print b[1].asnumpy() +[[ 6. 6. 6.] + [ 6. 6. 6.]] + + + +6） 除了单个 key-value 的存储，KVStrore 还提供了批量的接口: + +# 针对单个设备 +>>> keys = [5, 7, 9] +>>> kv.init(keys, [mx.nd.ones(shape)]*len(keys)) +>>> kv.push(keys, [mx.nd.ones(shape)]*len(keys)) +update on key: 5 +update on key: 7 +update on key: 9 +>>> b = [mx.nd.zeros(shape)]*len(keys) +>>> kv.pull(keys, out = b) +>>> print b[1].asnumpy() +[[ 3. 3. 3.] + [ 3. 3. 3.]] + +# 针对多个设备 +>>> b = [[mx.nd.ones(shape, gpu) for gpu in gpus]] * len(keys) +>>> kv.push(keys, b) +update on key: 5 +update on key: 7 +update on key: 9 +>>> kv.pull(keys, out = b) +>>> print b[1][1].asnumpy() +[[ 11. 11. 11.] + [ 11. 11. 11.]] + +################################## +#分布式训练 +如何在MXNet上写一个分布式程序 + +MXNet提供了一个分布式key-value store名为kvstore减少了数据同步操作的复杂性。它提供了以下的功能： + + push：将本地数据push到分布式存储，如梯度等 + pull：将数据从分布式存储中pull过来，如新的权值 + set_updater：为分布式存储设置一个updater，明确store是如何将接收到的数据进行融合的，例如：怎样使用接收到的梯度来更新权值。 + +如果你的程序按照下面的结构写的，事情会更加简单： + + + + + +##################################### +###################################### +MXnet体验 +以下借助 MXnet 实现了一个简单的单变量线性回归程序： + +import mxnet as mx +import numpy as np +import matplotlib.pyplot as plt + +# 定义输入数据 +X_data = np.linspace(-1, 1, 100) +noise = np.random.normal(0, 0.5, 100) +y_data = 5 * X_data + noise + +# Plot 输入数据 +fig = plt.figure() +ax = fig.add_subplot(1, 1, 1) +ax.scatter(X_data, y_data) + +# 定义mxnet变量 +X = mx.symbol.Variable('data') +Y = mx.symbol.Variable('softmax_label') + +# 定义网络 +Y_ = mx.symbol.FullyConnected(data=X, num_hidden=1, name='pre') +loss = mx.symbol.LinearRegressionOutput(data=Y_, label=Y, name='loss') + +# 定义模型 +model = mx.model.FeedForward( + ctx=mx.cpu(), + symbol=loss, + num_epoch=100, + learning_rate=0.001, + numpy_batch_size=1 + ) + +# 训练模型 +model.fit(X=X_data, y=y_data) + +# 预测 +prediction = model.predict(X_data) +lines = ax.plot(X_data, prediction, 'r-', lw=5) +plt.show() + diff --git a/MXnet/self_lear_mxnet/SSD/0_default_anchor_boxes.py b/MXnet/self_lear_mxnet/SSD/0_default_anchor_boxes.py new file mode 100755 index 00000000..37f29a82 --- /dev/null +++ b/MXnet/self_lear_mxnet/SSD/0_default_anchor_boxes.py @@ -0,0 +1,31 @@ +#!/usr/bin/env python +#-*- coding:utf-8 -*- + +import mxnet as mx +from mxnet import nd +from mxnet.contrib.ndarray import MultiBoxPrior##MultiBoxPrior产生预设框 + +n = 40 +# 输入形状: batch × channel × height × weight +x = nd.random_uniform(shape=(1, 3, n, n)) +## 图像 n 个预设尺寸 m 个预设的长宽比 输出为 n+m-1 个方框 +y = MultiBoxPrior(x, sizes=[.5, .25, .1], ratios=[1, 2, .5]) + +## 取位于 (20,20) 像素点的第一个预设框 +# box的格式为 (x_min, y_min, x_max, y_max) 且为比例 +boxes = y.reshape((n, n, -1, 4)) +print('The first anchor box at row 21, column 21:', boxes[20, 20, 0, :]) + +import matplotlib.pyplot as plt +#"""convert an anchor box to a matplotlib rectangle""" +def box_to_rect(box, color, linewidth=3): + box = box.asnumpy() + return plt.Rectangle( + (box[0], box[1]), (box[2]-box[0]), (box[3]-box[1]), + fill=False, edgecolor=color, linewidth=linewidth) +colors = ['blue', 'green', 'red', 'black', 'magenta']# 3+3-1=5个 +plt.imshow(nd.ones((n, n, 3)).asnumpy()) +anchors = boxes[20, 20, :, :] +for i in range(anchors.shape[0]): + plt.gca().add_patch(box_to_rect(anchors[i,:]*n, colors[i])) +plt.show() diff --git a/MXnet/self_lear_mxnet/SSD/1_download_dataset.py b/MXnet/self_lear_mxnet/SSD/1_download_dataset.py new file mode 100644 index 00000000..20f2d080 --- /dev/null +++ b/MXnet/self_lear_mxnet/SSD/1_download_dataset.py @@ -0,0 +1,29 @@ +#!/usr/bin/env python +#-*- coding:utf-8 -*- +from mxnet.test_utils import download +import os.path as osp +def verified(file_path, sha1hash): + import hashlib + sha1 = hashlib.sha1() + with open(file_path, 'rb') as f: + while True: + data = f.read(1048576) + if not data: + break + sha1.update(data) + matched = sha1.hexdigest() == sha1hash + if not matched: + print('Found hash mismatch in file {}, possibly due to incomplete download.'.format(file_path)) + return matched + +url_format = 'https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/datasets/pikachu/{}' +hashes = {'train.rec': 'e6bcb6ffba1ac04ff8a9b1115e650af56ee969c8', + 'train.idx': 'dcf7318b2602c06428b9988470c731621716c393', + 'val.rec': 'd6c33f799b4d058e82f2cb5bd9a976f69d72d520'} +for k, v in hashes.items():##键 值 + fname = 'pikachu_' + k + target = osp.join('data', fname)#新建数据集文件夹 + url = url_format.format(k) + if not osp.exists(target) or not verified(target, v): + print('Downloading', target, url) + download(url, fname=fname, dirname='data', overwrite=True) diff --git a/MXnet/self_lear_mxnet/SSD/1_download_dataset.py~ b/MXnet/self_lear_mxnet/SSD/1_download_dataset.py~ new file mode 100644 index 00000000..b65d2944 --- /dev/null +++ b/MXnet/self_lear_mxnet/SSD/1_download_dataset.py~ @@ -0,0 +1,29 @@ +#!/usr/bin/env python +#-*- coding:utf-8 -*- +from mxnet.test_utils import download +import os.path as osp +def verified(file_path, sha1hash): + import hashlib + sha1 = hashlib.sha1() + with open(file_path, 'rb') as f: + while True: + data = f.read(1048576) + if not data: + break + sha1.update(data) + matched = sha1.hexdigest() == sha1hash + if not matched: + print('Found hash mismatch in file {}, possibly due to incomplete download.'.format(file_path)) + return matched + +url_format = 'https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/datasets/pikachu/{}' +hashes = {'train.rec': 'e6bcb6ffba1ac04ff8a9b1115e650af56ee969c8', + 'train.idx': 'dcf7318b2602c06428b9988470c731621716c393', + 'val.rec': 'd6c33f799b4d058e82f2cb5bd9a976f69d72d520'} +for k, v in hashes.items():##键 值 + fname = 'pikachu_' + k + target = osp.join('data', fname) + url = url_format.format(k) + if not osp.exists(target) or not verified(target, v): + print('Downloading', target, url) + download(url, fname=fname, dirname='data', overwrite=True) diff --git a/MXnet/self_lear_mxnet/SSD/2_load_dataset.py b/MXnet/self_lear_mxnet/SSD/2_load_dataset.py new file mode 100644 index 00000000..c97bb94d --- /dev/null +++ b/MXnet/self_lear_mxnet/SSD/2_load_dataset.py @@ -0,0 +1,31 @@ +#!/usr/bin/env python +#-*- coding:utf-8 -*- +import mxnet.image as image +#加载数据可以用mxnet.image.ImageDetIter， +#同时还提供了大量数据增强的选项，比如翻转，随机截取等等。 +data_shape = 256 +batch_size = 32 +def get_iterators(data_shape, batch_size): + class_names = ['pikachu'] + num_class = len(class_names) + train_iter = image.ImageDetIter( + batch_size=batch_size, + data_shape=(3, data_shape, data_shape), + path_imgrec='./data/pikachu_train.rec', + path_imgidx='./data/pikachu_train.idx', + shuffle=True, + mean=True, + rand_crop=1, + min_object_covered=0.95, + max_attempts=200) + val_iter = image.ImageDetIter( + batch_size=batch_size, + data_shape=(3, data_shape, data_shape), + path_imgrec='./data/pikachu_val.rec', + shuffle=False, + mean=True) + return train_iter, val_iter, class_names, num_class + +train_data, test_data, class_names, num_class = get_iterators(data_shape, batch_size) +batch = train_data.next() +print(batch) diff --git a/MXnet/self_lear_mxnet/SSD/2_load_dataset.py~ b/MXnet/self_lear_mxnet/SSD/2_load_dataset.py~ new file mode 100644 index 00000000..c97bb94d --- /dev/null +++ b/MXnet/self_lear_mxnet/SSD/2_load_dataset.py~ @@ -0,0 +1,31 @@ +#!/usr/bin/env python +#-*- coding:utf-8 -*- +import mxnet.image as image +#加载数据可以用mxnet.image.ImageDetIter， +#同时还提供了大量数据增强的选项，比如翻转，随机截取等等。 +data_shape = 256 +batch_size = 32 +def get_iterators(data_shape, batch_size): + class_names = ['pikachu'] + num_class = len(class_names) + train_iter = image.ImageDetIter( + batch_size=batch_size, + data_shape=(3, data_shape, data_shape), + path_imgrec='./data/pikachu_train.rec', + path_imgidx='./data/pikachu_train.idx', + shuffle=True, + mean=True, + rand_crop=1, + min_object_covered=0.95, + max_attempts=200) + val_iter = image.ImageDetIter( + batch_size=batch_size, + data_shape=(3, data_shape, data_shape), + path_imgrec='./data/pikachu_val.rec', + shuffle=False, + mean=True) + return train_iter, val_iter, class_names, num_class + +train_data, test_data, class_names, num_class = get_iterators(data_shape, batch_size) +batch = train_data.next() +print(batch) diff --git a/MXnet/self_lear_mxnet/SSD/Default_anchor_boxes.py~ b/MXnet/self_lear_mxnet/SSD/Default_anchor_boxes.py~ new file mode 100755 index 00000000..37f29a82 --- /dev/null +++ b/MXnet/self_lear_mxnet/SSD/Default_anchor_boxes.py~ @@ -0,0 +1,31 @@ +#!/usr/bin/env python +#-*- coding:utf-8 -*- + +import mxnet as mx +from mxnet import nd +from mxnet.contrib.ndarray import MultiBoxPrior##MultiBoxPrior产生预设框 + +n = 40 +# 输入形状: batch × channel × height × weight +x = nd.random_uniform(shape=(1, 3, n, n)) +## 图像 n 个预设尺寸 m 个预设的长宽比 输出为 n+m-1 个方框 +y = MultiBoxPrior(x, sizes=[.5, .25, .1], ratios=[1, 2, .5]) + +## 取位于 (20,20) 像素点的第一个预设框 +# box的格式为 (x_min, y_min, x_max, y_max) 且为比例 +boxes = y.reshape((n, n, -1, 4)) +print('The first anchor box at row 21, column 21:', boxes[20, 20, 0, :]) + +import matplotlib.pyplot as plt +#"""convert an anchor box to a matplotlib rectangle""" +def box_to_rect(box, color, linewidth=3): + box = box.asnumpy() + return plt.Rectangle( + (box[0], box[1]), (box[2]-box[0]), (box[3]-box[1]), + fill=False, edgecolor=color, linewidth=linewidth) +colors = ['blue', 'green', 'red', 'black', 'magenta']# 3+3-1=5个 +plt.imshow(nd.ones((n, n, 3)).asnumpy()) +anchors = boxes[20, 20, :, :] +for i in range(anchors.shape[0]): + plt.gca().add_patch(box_to_rect(anchors[i,:]*n, colors[i])) +plt.show() diff --git a/MXnet/self_lear_mxnet/get-pip.py b/MXnet/self_lear_mxnet/get-pip.py new file mode 100644 index 00000000..2bd2a162 --- /dev/null +++ b/MXnet/self_lear_mxnet/get-pip.py @@ -0,0 +1,20061 @@ +#!/usr/bin/env python +# +# Hi There! +# You may be wondering what this giant blob of binary data here is, you might +# even be worried that we're up to something nefarious (good for you for being +# paranoid!). This is a base85 encoding of a zip file, this zip file contains +# an entire copy of pip. +# +# Pip is a thing that installs packages, pip itself is a package that someone +# might want to install, especially if they're looking to run this get-pip.py +# script. Pip has a lot of code to deal with the security of installing +# packages, various edge cases on various platforms, and other such sort of +# "tribal knowledge" that has been encoded in its code base. Because of this +# we basically include an entire copy of pip inside this blob. We do this +# because the alternatives are attempt to implement a "minipip" that probably +# doesn't do things correctly and has weird edge cases, or compress pip itself +# down into a single file. +# +# If you're wondering how this is created, it is using an invoke task located +# in tasks/generate.py called "installer". It can be invoked by using +# ``invoke generate.installer``. + +import os.path +import pkgutil +import shutil +import sys +import struct +import tempfile + +# Useful for very coarse version differentiation. +PY2 = sys.version_info[0] == 2 +PY3 = sys.version_info[0] == 3 + +if PY3: + iterbytes = iter +else: + def iterbytes(buf): + return (ord(byte) for byte in buf) + +try: + from base64 import b85decode +except ImportError: + _b85alphabet = (b"0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ" + b"abcdefghijklmnopqrstuvwxyz!#$%&()*+-;<=>?@^_`{|}~") + + def b85decode(b): + _b85dec = [None] * 256 + for i, c in enumerate(iterbytes(_b85alphabet)): + _b85dec[c] = i + + padding = (-len(b)) % 5 + b = b + b'~' * padding + out = [] + packI = struct.Struct('!I').pack + for i in range(0, len(b), 5): + chunk = b[i:i + 5] + acc = 0 + try: + for c in iterbytes(chunk): + acc = acc * 85 + _b85dec[c] + except TypeError: + for j, c in enumerate(iterbytes(chunk)): + if _b85dec[c] is None: + raise ValueError( + 'bad base85 character at position %d' % (i + j) + ) + raise + try: + out.append(packI(acc)) + except struct.error: + raise ValueError('base85 overflow in hunk starting at byte %d' + % i) + + result = b''.join(out) + if padding: + result = result[:-padding] + return result + + +def bootstrap(tmpdir=None): + # Import pip so we can use it to install pip and maybe setuptools too + import pip + from pip.commands.install import InstallCommand + from pip.req import InstallRequirement + + # Wrapper to provide default certificate with the lowest priority + class CertInstallCommand(InstallCommand): + def parse_args(self, args): + # If cert isn't specified in config or environment, we provide our + # own certificate through defaults. + # This allows user to specify custom cert anywhere one likes: + # config, environment variable or argv. + if not self.parser.get_default_values().cert: + self.parser.defaults["cert"] = cert_path # calculated below + return super(CertInstallCommand, self).parse_args(args) + + pip.commands_dict["install"] = CertInstallCommand + + implicit_pip = True + implicit_setuptools = True + implicit_wheel = True + + # Check if the user has requested us not to install setuptools + if "--no-setuptools" in sys.argv or os.environ.get("PIP_NO_SETUPTOOLS"): + args = [x for x in sys.argv[1:] if x != "--no-setuptools"] + implicit_setuptools = False + else: + args = sys.argv[1:] + + # Check if the user has requested us not to install wheel + if "--no-wheel" in args or os.environ.get("PIP_NO_WHEEL"): + args = [x for x in args if x != "--no-wheel"] + implicit_wheel = False + + # We only want to implicitly install setuptools and wheel if they don't + # already exist on the target platform. + if implicit_setuptools: + try: + import setuptools # noqa + implicit_setuptools = False + except ImportError: + pass + if implicit_wheel: + try: + import wheel # noqa + implicit_wheel = False + except ImportError: + pass + + # We want to support people passing things like 'pip<8' to get-pip.py which + # will let them install a specific version. However because of the dreaded + # DoubleRequirement error if any of the args look like they might be a + # specific for one of our packages, then we'll turn off the implicit + # install of them. + for arg in args: + try: + req = InstallRequirement.from_line(arg) + except: + continue + + if implicit_pip and req.name == "pip": + implicit_pip = False + elif implicit_setuptools and req.name == "setuptools": + implicit_setuptools = False + elif implicit_wheel and req.name == "wheel": + implicit_wheel = False + + # Add any implicit installations to the end of our args + if implicit_pip: + args += ["pip"] + if implicit_setuptools: + args += ["setuptools"] + if implicit_wheel: + args += ["wheel"] + + delete_tmpdir = False + try: + # Create a temporary directory to act as a working directory if we were + # not given one. + if tmpdir is None: + tmpdir = tempfile.mkdtemp() + delete_tmpdir = True + + # We need to extract the SSL certificates from requests so that they + # can be passed to --cert + cert_path = os.path.join(tmpdir, "cacert.pem") + with open(cert_path, "wb") as cert: + cert.write(pkgutil.get_data("pip._vendor.requests", "cacert.pem")) + + # Execute the included pip and use it to install the latest pip and + # setuptools from PyPI + sys.exit(pip.main(["install", "--upgrade"] + args)) + finally: + # Remove our temporary directory + if delete_tmpdir and tmpdir: + shutil.rmtree(tmpdir, ignore_errors=True) + + +def main(): + tmpdir = None + try: + # Create a temporary working directory + tmpdir = tempfile.mkdtemp() + + # Unpack the zipfile into the temporary directory + pip_zip = os.path.join(tmpdir, "pip.zip") + with open(pip_zip, "wb") as fp: + fp.write(b85decode(DATA.replace(b"\n", b""))) + + # Add the zipfile to sys.path so that we can import it + sys.path.insert(0, pip_zip) + + # Run the bootstrap + bootstrap(tmpdir=tmpdir) + finally: + # Clean up our temporary working directory + if tmpdir: + shutil.rmtree(tmpdir, ignore_errors=True) + + +DATA = b""" 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+PHp0u%!j0000806LatN$y5^m0m3X0LEPa02%-Q00000000000HlEirV;>fX>ct$E-)@JE@WwQbS-IaW +^XT2MMF> README.md # 添加文件 +git init #初始化 +git config --global user.email "you@example.com" # 邮箱 +git config --global user.name "Your Name" # 用户名 +git add README.md # 添加文件 +git commit -m "first commit" # 提交 +git remote add origin https://github.com/Ewenwan/MVision.git #增加一个远程服务器端版本库 +git push -u origin master # 将本地文件提交到Github。 + +提交所有文件: +git clone https://github.com/Ewenwan/PyML.git #复制本地 +将其他文件全部 放入 PyML 文件夹 +git add . +git commit -m "add all files" +git push -u origin master # 将本地文件提交到Github + + +git status 查看 文件修改情况 +git add 对应文件 +git commit -m "add files" +git push -u origin master # 将本地文件提交到Github diff --git "a/MXnet/self_lear_mxnet/gluon_\344\270\255\346\226\207\346\225\231\347\250\213.pdf" "b/MXnet/self_lear_mxnet/gluon_\344\270\255\346\226\207\346\225\231\347\250\213.pdf" new file mode 100644 index 00000000..858bc2c1 Binary files /dev/null and "b/MXnet/self_lear_mxnet/gluon_\344\270\255\346\226\207\346\225\231\347\250\213.pdf" differ diff --git a/MXnet/self_lear_mxnet/mxnet-notebooks b/MXnet/self_lear_mxnet/mxnet-notebooks new file mode 160000 index 00000000..5872e3d8 --- /dev/null +++ b/MXnet/self_lear_mxnet/mxnet-notebooks @@ -0,0 +1 @@ +Subproject commit 5872e3d8d590e4472375f36380d9cb140a926e91 diff --git "a/MXnet/self_lear_mxnet/\345\255\246\344\271\240\350\256\260\345\275\225.txt" "b/MXnet/self_lear_mxnet/\345\255\246\344\271\240\350\256\260\345\275\225.txt" new file mode 100644 index 00000000..9ce09d01 --- /dev/null +++ "b/MXnet/self_lear_mxnet/\345\255\246\344\271\240\350\256\260\345\275\225.txt" @@ -0,0 +1,690 @@ + +中文教程 +https://zh.gluon.ai/ + +mxnet的gluon + +GAN生成模型 +http://www.jianshu.com/u/a24acc5449c7 + + +官方安装方法 +http://mxnet.incubator.apache.org/get_started/install.html + + +http://www.cnblogs.com/daihengchen/p/5924768.html + +视频教程 +http://space.bilibili.com/209599371#!/ + + +1、安装依赖 最新版本的pip +sudo apt-get update +sudo apt-get install -y wget python gcc +wget https://bootstrap.pypa.io/get-pip.py && sudo python get-pip.py + +2、安装MXnet +sudo pip install mxnet==0.11.0 +一般在/usr/local/lib/python2.7/dist-packages/mxnet + +GPU版本 +sudo pip install mxnet-cu75 --pre --user # for CUDA 7.5 +sudo pip install mxnet-cu80 --pre --user # for CUDA 8.0 + +3、安装可视化工具（可选） mxnet.viz +sudo apt-get install graphviz +sudo pip install graphviz + +4、安装 Intel MKL +sudo pip install mxnet-mkl==0.11.0 + +5、验证安装 +python + +>>> import mxnet as mx +>>> a = mx.nd.ones((2, 3)) +>>> b = a * 2 + 1 +>>> b.asnumpy() +array([[ 3., 3., 3.], + [ 3., 3., 3.]], dtype=float32) + +>>> exit() + +6、源码安装 +sudo apt-get update +sudo apt-get install -y build-essential git libatlas-base-dev libopencv-dev +git clone --recursive https://github.com/dmlc/mxnet +cd mxnet; make -j4 + +出现错误 +/usr/bin/ld: cannot find -lcblas +安装 +sudo apt-get install libatlas-base-dev + + +添加搜索路径 +// ~表示我的mxnet安装在home下面 +export PYTHONPATH=~/mxnet/python +cp -r ~/mxnet/python/mxnet . +cp ~/mxnet/lib/libmxnet.so mxnet/ + +//我的工作目录 +export PYTHONPATH=/home/ewenwan/ewenwan/learn/MXnet/mxnet/python +cp -r /home/ewenwan/ewenwan/learn/MXnet/mxnet/python/mxnet . +cp -r /home/ewenwan/ewenwan/learn/MXnet/mxnet/lib/libmxnet.so mxnet/ + +本次对话生效 +sudo vi /etc/profile +在最后添加 + +export PYTHONPATH=/home/ewenwan/ewenwan/learn/MXnet/mxnet/python + +7、github教程 +https://github.com/dmlc/mxnet-notebooks + + +http://blog.csdn.net/shuzfan/article/details/50037273 + +8、转换图片格式 +就好像在caffe中训练模型之前要现将训练图片转化为leveldb或者LMDB格式数据一样， +mxnet在训练之前也要先将图片转化为REC格式的数据。 + +linux下转换使用的工具是mxnet/bin下名为“im2rec”的可执行文件， +该文件的源码可以查看mxnet/tools/im2rec.cc or mxnet/tools/im2rec.py + +首先，来说明以下im2rec这个转换工具有哪些输入参数： + +【1】首先是必须输入的3个参数，依次是 +image_lst: //保存图片和标签信息的文本的名字 +image_root_dir：//图片的存放路径 +output_rec： //转换后的.rec格式文件的存放路径 + + +【2】可选的输入参数有： +label_width: //标签个数，默认为1，即每个图片只有一个标签 +resize: //默认为-1，即不调整图片尺寸。否则将图片的较短边的长度调整为resize大小，另一边按比例调整 +%%例如图片尺寸为300*200，resize=100,那么调整之后图片尺寸为150*100. +quality： //JPEG编码质量，默认为80。输入范围为1-100.（mxnet转换数据时是编码存储的） +center_crop：//默认为0，if(center_crop)，则将图片以中心裁剪为正方形。注意：只有if(resize>0)成立时，该参数才有效。 +nsplit： //默认为1，数据集分块 用于将部分图片进行转换，按照位置将image_lst中的图片分成nsplit个part + %%注意，这里只是大概平分，例如12个训练图片，nsplit=3，实际分块为5+4+3，而不是预想中的4+4+4.（关于这一点我也还没搞明白） +part： //默认为0，用于将部分图片进行转换。和nsplit结合使用，可以只转换nsplit中的第part个分块 + +【3】这里举一个实际应用的例子 +（1）首先是保存图片和标签信息的文本，这里我的文件是train.txt. +编号 标签 图片名 +1 0 FY2D1.bmp +2 1 FY2E1.bmp +3 0 FY2D2.bmp +4 1 FY2E2.bmp + +python im2rec.py train.txt data/ train.rec quality=90 resize=1000 + + +9、迭代器 +迭代器-将转换后的数据送入网络: + +总体来说，开始一项训练，需要3个部分：训练参数、网络模型和训练数据。 +在上一小节，我已经详细介绍了如何将原始图片数据转化为mxnet可以接受的REC格式数据。 +但是，实际训练的时候我们还需要一个工具来将REC数据送入我们的训练网络中。 +这个工具就是“迭代器”-iterator + + +【1】mxnet为我们提供了快速构建迭代器的函数ImageRecordIter，该函数位于python/mxnet/io.py中， +其返回一个迭代器对象。或者可以在python命令窗中键入以下命令来获取io函数的位置以及说明： + +import mxnet as mx +help(mx.io) +#或者输入help(mx.io.ImageRecordIter)来直接查看迭代器的参数说明 + + +【2】接下来说明ImageRecordIter的几个常用参数：（更多的参数可以通过上面的help查看） + +path_imgrec：rec图像数据的存储路径；string型数据；默认为'./data/imgrec.rec' +data_shape：迭代器产生的每一个实例的形状，即图片数据的形状；元组型数据；必选参数 +mean_img：图像均值文件的存放路径；string型数据；默认为空 +batch_size：batch的大小（一次训练的图片数量）；int型数据；必选参数 +shuffle：是否打乱数据；布尔型数据；默认为False; +mirror：是否对图片执行镜像操作；布尔型数据；默认为False; +rand_mirror：是否随机地对图片执行镜像操作；布尔型数据；默认为False; +rand_crop：是否随机地对图片执行剪切操作；布尔型数据；默认为False; +label_width：图片的标签个数；int型数据；默认为1 +mean_r、 mean_g、 mean_b:3个颜色通道的均值；float型数据；默认为0 +num_parts：将训练数据分为几份；int型数据；默认为1 +part_index:第几部分数据将会被读取；int型数据；默认为0 + + +【3】接下来以实例来说明怎样构建迭代器， +该例子位于example/image-classification/train_imagenet.py中 + +#分别为训练数据和验证数据各构建一个迭代器 +def get_iterator(args, kv): + train = mx.io.ImageRecordIter( + path_imgrec = args.data_dir + "train.rec", + mean_img = args.data_dir + "mean.bin", + data_shape = data_shape, + batch_size = args.batch_size, + rand_crop = True, + rand_mirror = True, + num_parts = kv.num_workers, + part_index = kv.rank) + val = mx.io.ImageRecordIter( + path_imgrec = args.data_dir + "val.rec", + mean_img = args.data_dir + "mean.bin", + rand_crop = False, + rand_mirror = False, + data_shape = data_shape, + batch_size = args.batch_size, + num_parts = kv.num_workers, + part_index = kv.rank) + return (train, val) + + +10 构建一个网络模型 +一个网络模型是一层一层连接器来的，因此想要构建一个网络模型， +首先我们得知道mxnet中都有哪些可以用的层。(以下按照使用频率来排序) +下面介绍一些比较常用的层，更多的层大家可以在python命令窗执行以下代码来查询 +import mxnet as mx +help(mx.symbol) +【1】Activation：激活函数 + data: 输入数据，symbol型变量 + act_type: {'relu', 'sigmoid', 'tanh'}, 可选的3种激活函数 + +【2】Convolution:卷积层 +data:输入数据，symbol型变量 +weight：权重矩阵，symbol型变量 +bias:偏置，symbol型变量 +kernal:卷积核的尺寸（y,x）,二维tuple型变量 +stride：卷积步长（y,x）,二维tuple型变量，默认为（1,1） +pad：卷积的填补量（y,x）,二维tuple型变量，默认为（0,0） +num_filter ：卷积滤波器(通道)个数，int型变量 +no_bias ：是否取消偏置，布尔型变量，默认为False +num_group：groups划分的个数，int型变量，默认为1. + 该选项不被CUDNN所支持， you can use SliceChannel to num_group, + apply convolution and concat instead to achieve the same need. +workspace：卷积的Tmp工作空间的大小MB。long型变量，默认为512 + + +【3】Deconvolution：反卷积层 + + 参数同卷积层一致 + +【4】Dropout： 神经元失效层 + data:输入数据，symbol型变量 + p : 训练时对输入数据进行dropped out的比例，float型变量，默认为0.5 + +【5】Flatten:将N维数据变为1维 + +data:输入数据，symbol型变量 +name : string, optional. Name of the resulting symbol. + +【6】FullyConnected：全连接层 +data:输入数据，symbol型变量 +weight：权重矩阵，symbol型变量 +bias:偏置，symbol型变量 +no_bias ：是否取消偏置，布尔型变量，默认为False +num_hidden：输出隐藏节点的个数，int型变量 + + +【7】LRN：Local Response Normalization，即局部响应归一化层 +data:输入数据，symbol型变量 +alpha : float, optional, default=0.0001.value of the alpha variance scaling parameter in the normalization formula +beta : float, optional, default=0.75.value of the beta power parameter in the normalization formula +knorm : float, optional, default=2.value of the k parameter in normalization formula +nsize : int (non-negative), required.normalization window width in elements. + +【8】Pooling 池化层 + +data:输入数据，symbol型变量 +kernal:pooling核的尺寸（y,x）,二维tuple型变量 +stride：pooling步长（y,x）,二维tuple型变量，默认为（1,1） +pad：pooling的填补量（y,x）,二维tuple型变量，默认为（0,0） +pool_type : pooling的类型，可选的3种{'avg', 'max', 'sum'} + + +【9】SoftmaxOutput：使用logloss进行BP 最后回归输出 + +data:输入数据，symbol型变量 +grad_scale : 为梯度乘以一个比例系数，float型变量，默认为1 +multi_output : 布尔型变量，默认为False + # If set to true, for a (n,k,x_1,..,x_n) dimensionalinput tensor, softmax will generate n*x_1*...*x_n output, eachhas k classes + +【10】Variable：定义一个symbol型变量 + +name : 变量的名字，str型变量 +attr : dict of string -> string.Additional attributes to set on the variable. + +mxnet/example/image-classification文件夹下以symbol开头的几个.py文件都是定义好的网络模型。 +我们可以任意打开一个来具体了解一下如何构建一个网络。 +有一点需要注意的是：mxnet中通常要在卷积层和全连接层之间价加一个Flatten层用于衔接 + + +11 开始训练 +首先，来总结一下我们现在都有了什么： +保存网络模型的.py文件、 +REC格式的训练数据、 +将数据送入网络的迭代器。 + +在此基础上，我们缺少的就只剩下网络训练的一些控制参数：如学习率、训练日志、GPU选择等等。 +在初期，我们往往可以先快速的拿来用，而不用在意其细节。那么，最简单的办法就是直接修改套用mxnet中的示例。 +示例主要集中在/example/image-classification和/tests/python当中。 +下面就简单的介绍如何快速开始训练： + +（1）新建一个文件夹，将/example/image-classification文件夹下 + find_mxnet.py、symbol_alexnet.py、train_imagenet.py和train_model.py这4个文件copy进来； +（2）保持find_mxnet.py文件不变，在symbol_alexnet.py中按照里面Alexnet网络的构建方法来建立自己的网络。 + 最后最好再把文件名改为自己的网络名，如mynet.py; +（3）将train_imagenet.py最好也修改一下名字，如改为train_xx.py.将第40行代码中的’symbol’删除， + 否则你的网络文件就必须以‘symbol’开始命名，如symbol_mynet.py. + +net = importlib.import_module('symbol_' + args.network).get_symbol(args.num_classes) +改为： +net = importlib.import_module(args.network).get_symbol(args.num_classes) + + +剩下的就是要在parser.add_argument中修改默认值了，否则你在调用mynet.py就必须准确的指定每一个参数。 +（4）写一个脚本文件，运行脚本文件就可以开始训练了。如 + +python train_xx.py --data-dir=xx --model-prefix==xx --gpus=0 +# 如果想要在每次epoch结束时保存一个模型，那么model-prefix就不能为空 + + + + +12 利用mxnet 从零开始码一个皮卡丘检测器-CNN目标检测入门教程 SSD算法 SSD (Single-Shot MultiBox Object Detection). +https://zhuanlan.zhihu.com/p/28867241 +https://zhuanlan.zhihu.com/gluon + + + +目标检测通俗的来说是为了找到图像或者视频里的所有目标物体。在下面这张图中，两狗一猫的位置，包括它们所属的类（狗／猫），需要被正确的检测到。 + + +所以和图像分类不同的地方在于，目标检测需要找到尽量多的目标物体，而且要准确的定位物体的位置，一般用矩形框来表示。 + +在接下来的章节里，我们先介绍一个流行的目标检测算法，SSD (Single-Shot MultiBox Object Detection). + + +SSD: Single Shot MultiBox Detector + +顾名思义，算法的核心是用卷积神经网络一次前向推导求出大量多尺度（几百到几千） +的方框来表示目标检测的结果。网络的结构用下图表示。 + +跟所有的图像相关的网络一样，我们需要一个主干网络来提取特征，同时也是作为第一个预测特征层。 +网络在当前层产生大量的预设框，和与之对应的每个方框的分类概率（背景，猫，狗等等） +以及真正的物体和预设框的偏移量。在完成当前层的预测后，我们会下采样当前特征层， +作为新的预测层，重新产生新的预设框，分类概率，偏移量。这个过程往往会重复好几次， +直到预测特征层到达全局尺度（ 1 \times 1 )。 + +接下来我们用例子解释每个细节实现。 + + +【1】预设框 Default anchor boxes + +预设框的形状和大小可以由参数控制，我们往往设置一堆预设框， +以期望任意图像上的物体都能有一个预设框能大致重合，由于每个预设框需要对应的预测网络预测值， +所以希望对于每个物体都有100%重合的预设框是不现实的，可能会需要几十万甚至几百万的预设框， +但是采样的预设框越多，重合概率越好，用几千到上万个预设框基本能实现略大于70%的最好重合率， +同时保证了检测的速度。 + +为了保证重合覆盖率，对于每个特征层上的像素点，我们用不同的大小和长宽比来采样预设框。 +假设在某个特定的特征层（ w × h ），每个预设框的中心点就是特征像素点的中心， +然后我们用如下的公式采样预设框： + +预设框的中心为特征像素点的中心 + +x =(i+0.5)/w , y=(j+0.5)/h i：[0：w） j:[0:h) +对于长宽比 r = 1 , 尺寸s: (0,1] , 生成的预设框大小 ws * hs + +这里例子里，我们用事先实现的层MultiBoxPrior产生预设框，输入 n 个预设尺寸， +和 m 个预设的长宽比，输出为 n+m-1 个方框而不是 n \times m 个。 + 当然我们完全可以使用其他的算法产生不同的预设框， +但是实践中我们发现上述的方法覆盖率和相应需要的预设框数量比较合适。 + + +import mxnet as mx +from mxnet import nd +from mxnet.contrib.ndarray import MultiBoxPrior + +n = 40 +# 输入形状: batch x channel x height x weight +x = nd.random_uniform(shape=(1, 3, n, n)) + +y = MultiBoxPrior(x, sizes=[.5, .25, .1], ratios=[1, 2, .5]) + +# 取位于 (20,20) 像素点的第一个预设框 +# 格式为 (x_min, y_min, x_max, y_max) +boxes = y.reshape((n, n, -1, 4)) +print('The first anchor box at row 21, column 21:', boxes[20, 20, 0, :]) + +import matplotlib.pyplot as plt + """convert an anchor box to a matplotlib rectangle""" +def box_to_rect(box, color, linewidth=3): + box = box.asnumpy() + return plt.Rectangle( + (box[0], box[1]), (box[2]-box[0]), (box[3]-box[1]), + fill=False, edgecolor=color, linewidth=linewidth) +colors = ['blue', 'green', 'red', 'black', 'magenta'] +plt.imshow(nd.ones((n, n, 3)).asnumpy()) +anchors = boxes[20, 20, :, :] +for i in range(anchors.shape[0]): + plt.gca().add_patch(box_to_rect(anchors[i,:]*n, colors[i])) +plt.show() + + + + +【2】分类预测 Predict classes + +这个部分的目标很简单，就是预测每个预设框对应的分类。 +我们用 3 × 3 ， Padding (填充) 1 × 1 的卷积来预测分类概率， +这样可以做到卷积后空间上的形状不会变化，而且由于卷积的特点， +每个卷积核会扫过每个预测特征层的所有像素点，得到 c × h × w 个预测值， +h × w 对应了空间上所有的像素点，每个通道对应特定的预设框 c = (N_{class} + 1) × N_{anchors} 。 +N_{class} + 1) 所有类别+1个背景类。 +假设有10个正类，每个像素5个预设框，那么我们就需要 11 × 5 = 55 个通道。 +具体的来说，对于每个像素第 i 个预设框： + + 通道 i × (N_{class} + 1) 的值对应背景（非物体）的得分 + 通道 i × (N_{class} + 1) + 1 + j 对应了第 j 类的得分 + +from mxnet.gluon import nn +def class_predictor(num_anchors, num_classes): + """return a layer to predict classes""" + return nn.Conv2D(num_anchors * (num_classes + 1), 3, padding=1) + +cls_pred = class_predictor(5, 10) +cls_pred.initialize() +x = nd.zeros((2, 3, 20, 20)) +print('Class prediction', cls_pred(x).shape) + + + +【3】预测预设框偏移 Predict anchor boxes +为了找到物体准确的位置，光靠预设框本身是不行的，我们还需要预测偏移量以便把真正的物体框出来。 + +假设 b 是某个预设框， Y 是目标物体的真实矩形框，我们需要预测的偏移为 +\Delta(Y, b) = (t_x, t_y, t_{width}, t_{height}) ，全都是长度为4的向量， 我们求得偏移 + + t_x = (Y_x - b_x) / b_{width} + t_y = (Y_y - b_y) / b_{height} + t_{width} = (Y_{width} - b_{width}) / b_{width} + t_{height} = (Y_{height} - b_{height}) / b_{height} +所有的偏移量都除以预设框的长或宽是为了更好的收敛。 + +类似分类概率，我们同样用 3 × 3 填充 1 × 1 的卷积来预测偏移。 +这次不同的是，对于每个预设框，我们只需要 4 个通道来预测偏移量， +一共需要 N_{anchors} × 4 个通道， +第 i 个预设框对应的偏移量存在通道 i × 4 到通道 i × 4 + 3 之间。 + +def box_predictor(num_anchors): + """return a layer to predict delta locations""" + return nn.Conv2D(num_anchors * 4, 3, padding=1) + +box_pred = box_predictor(10) +box_pred.initialize() +x = nd.zeros((2, 3, 20, 20)) +print('Box prediction', box_pred(x).shape) + +>>>> Box prediction (2, 40, 20, 20) + + +【4】下采样特征层 Down-sample features + +每次我们下采样特征层到一半的长宽，用Pooling(池化)操作就可以轻松的做到， +当然也可以用stride(步长)为2的卷积直接得到。在下采样之前， +我们会希望增加几层卷积层作为缓冲，防止特征值对应多尺度带来的混乱， +同时又能增加网络的深度，得到更好的抽象。 +"""stack two Conv-BatchNorm-Relu blocks and then a pooling layer to halve the feature size""" +def down_sample(num_filters): + out = nn.HybridSequential() + for _ in range(2): + out.add(nn.Conv2D(num_filters, 3, strides=1, padding=1))##卷积 + out.add(nn.BatchNorm(in_channels=num_filters))##正则化 + out.add(nn.Activation('relu'))##激活 + out.add(nn.MaxPool2D(2))##最大值池化 + return out + +blk = down_sample(10) +blk.initialize() +x = nd.zeros((2, 3, 20, 20)) +print('Before', x.shape, 'after', blk(x).shape) + +>>> Before (2, 3, 20, 20) after (2, 10, 10, 10) + + + + +【5】整合多个特征层预测值 Manage predictions from multiple layers + +SSD算法的一个关键点在于它用到了多尺度的特征层来预测不同大小的物体。 +相对来说，浅层的特征层的空间尺度更大，越到网络的深层，空间尺度越小， +最后我们往往下采样直到 1 × 1 ，用来预测全图大小的物体。 +所以每个特征层产生的预设框， 分类概率，框偏移量需要被整合起来统一在全图与真实的物体比较。 +为了做到一一对应，我们统一把所有的预设框， 分类概率，框偏移量 平铺再连接。 +得到的是按顺序排列但是摊平的所有预测值和预设框。 + + +# 随便创建一个大小为 20x20的预测层 +feat1 = nd.zeros((2, 8, 20, 20)) +print('Feature map 1', feat1.shape) +cls_pred1 = class_predictor(5, 10) +cls_pred1.initialize() +y1 = cls_pred1(feat1) +print('Class prediction for feature map 1', y1.shape) +# 下采样 +ds = down_sample(16) +ds.initialize() +feat2 = ds(feat1) +print('Feature map 2', feat2.shape) +cls_pred2 = class_predictor(3, 10) +cls_pred2.initialize() +y2 = cls_pred2(feat2) +print('Class prediction for feature map 2', y2.shape) + + +我们统一把所有的预设框， 分类概率，框偏移量 平铺再连接。 + +def flatten_prediction(pred): + return nd.flatten(nd.transpose(pred, axes=(0, 2, 3, 1))) + +def concat_predictions(preds): + return nd.concat(*preds, dim=1) + +flat_y1 = flatten_prediction(y1) +print('Flatten class prediction 1', flat_y1.shape) +flat_y2 = flatten_prediction(y2) +print('Flatten class prediction 2', flat_y2.shape) +print('Concat class predictions', concat_predictions([flat_y1, flat_y2]).shape) + +我们总是确保在 dim = 1 上连接，以免打乱一一对应的关系。 + + +【6】主干网络 Body network + +主干网络用来从原始图像输入提取特征。 +一般来说我们会用预先训练好的用于分类的高性能网络（VGG, ResNet等）来提取特征。 +在这里我们就简单地堆叠几层卷积和下采样层作为主干网络的演示。 + + +rom mxnet import gluon +def body(): + """return the body network""" + out = nn.HybridSequential() + for nfilters in [16, 32, 64]: + out.add(down_sample(nfilters)) + return out + +bnet = body() +bnet.initialize() +x = nd.zeros((2, 3, 256, 256)) +print('Body network', [y.shape for y in bnet(x)]) + +>>> Body network [(64, 32, 32), (64, 32, 32)] + + +【7】设计一个简单的SSD示意网络 Create a toy SSD model + +我们这里介绍一个示意用的简单SSD网络，出于速度的考量，输入图像尺寸定为 256 \times 256 。 +def toy_ssd_model(num_anchors, num_classes): + """return SSD modules""" + downsamples = nn.Sequential() + class_preds = nn.Sequential() + box_preds = nn.Sequential() + + downsamples.add(down_sample(128)) + downsamples.add(down_sample(128)) + downsamples.add(down_sample(128)) + + for scale in range(5): + class_preds.add(class_predictor(num_anchors, num_classes)) + box_preds.add(box_predictor(num_anchors)) + + return body(), downsamples, class_preds, box_preds + +print(toy_ssd_model(5, 2)) + + +【8】网络前向推导 Forward + +既然我们已经设计完网络结构了，接下来可以定义网络前向推导的步骤。 + +首先得到主干网络的输出，然后对于每一个特征预测层，推导当前层的预设框， +分类概率和偏移量。最后我们把这些输入摊平，连接，作为网络的输出。 + +def toy_ssd_forward(x, body, downsamples, class_preds, box_preds, sizes, ratios): + # 计算主干网络的输出 + x = body(x) + + # 在每个预测层, 计算预设框，分类概率，偏移量 + # 然后在下采样到下一层预测层，重复 + default_anchors = [] + predicted_boxes = [] + predicted_classes = [] + + for i in range(5): + default_anchors.append(MultiBoxPrior(x, sizes=sizes[i], ratios=ratios[i])) + predicted_boxes.append(flatten_prediction(box_preds[i](x))) + predicted_classes.append(flatten_prediction(class_preds[i](x))) + if i < 3: + x = downsamples[i](x) + elif i == 3: + # 最后一层可以简单地用全局Pooling + x = nd.Pooling(x, global_pool=True, pool_type='max', kernel=(4, 4)) + + return default_anchors, predicted_classes, predicted_boxes + + +【9】打包收工 Put all things together + +from mxnet import gluon +class ToySSD(gluon.Block): + def __init__(self, num_classes, **kwargs): + super(ToySSD, self).__init__(**kwargs) + # 5个预测层，每层负责的预设框尺寸不同，由小到大，符合网络的形状 + self.anchor_sizes = [[.2, .272], [.37, .447], [.54, .619], [.71, .79], [.88, .961]] + # 每层的预设框都用 1，2，0.5作为长宽比候选 + self.anchor_ratios = [[1, 2, .5]] * 5 + self.num_classes = num_classes + + with self.name_scope(): + self.body, self.downsamples, self.class_preds, self.box_preds = toy_ssd_model(4, num_classes) + + def forward(self, x): + default_anchors, predicted_classes, predicted_boxes = toy_ssd_forward(x, self.body, self.downsamples, + self.class_preds, self.box_preds, self.anchor_sizes, self.anchor_ratios) + # 把从每个预测层输入的结果摊平并连接，以确保一一对应 + anchors = concat_predictions(default_anchors) + box_preds = concat_predictions(predicted_boxes) + class_preds = concat_predictions(predicted_classes) + # 改变下形状，为了更方便地计算softmax + class_preds = nd.reshape(class_preds, shape=(0, -1, self.num_classes + 1)) + + return anchors, class_preds, box_preds + + +【10】网络输出示意 Outputs of ToySSD +# 新建一个2个正类的SSD网络 +net = ToySSD(2) +net.initialize() +x = nd.zeros((1, 3, 256, 256)) +default_anchors, class_predictions, box_predictions = net(x) +print('Outputs:', 'anchors', default_anchors.shape, 'class prediction', class_predictions.shape, 'box prediction', box_predictions.shape) + + +【11】数据集 Dataset + +聊了半天怎么构建一个虚无的网络，接下来看看真正有意思的东西。 + +我们用3D建模批量生成了一个皮卡丘的数据集，产生了1000张图片作为这个展示用的训练集。 +这个数据集里面，皮神会以各种角度，各种姿势出现在各种背景图中，就像Pokemon Go里增强现实那样炫酷。 +因为是生成的数据集，我们自然可以得到每只皮神的真实坐标和大小，用来作为训练的真实标记。 + + +【11】下载数据集 Download dataset +下载提前准备好的数据集并验证 + +from mxnet.test_utils import download +import os.path as osp +def verified(file_path, sha1hash): + import hashlib + sha1 = hashlib.sha1() + with open(file_path, 'rb') as f: + while True: + data = f.read(1048576) + if not data: + break + sha1.update(data) + matched = sha1.hexdigest() == sha1hash + if not matched: + print('Found hash mismatch in file {}, possibly due to incomplete download.'.format(file_path)) + return matched + +url_format = 'https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/datasets/pikachu/{}' +hashes = {'train.rec': 'e6bcb6ffba1ac04ff8a9b1115e650af56ee969c8', + 'train.idx': 'dcf7318b2602c06428b9988470c731621716c393', + 'val.rec': 'd6c33f799b4d058e82f2cb5bd9a976f69d72d520'} +for k, v in hashes.items(): + fname = 'pikachu_' + k + target = osp.join('data', fname) + url = url_format.format(k) + if not osp.exists(target) or not verified(target, v): + print('Downloading', target, url) + download(url, fname=fname, dirname='data', overwrite=True) + + + +【12】 +加载数据 Load dataset + +加载数据可以用mxnet.image.ImageDetIter， +同时还提供了大量数据增强的选项，比如翻转，随机截取等等。 + +import mxnet.image as image +data_shape = 256 +batch_size = 32 +def get_iterators(data_shape, batch_size): + class_names = ['pikachu'] + num_class = len(class_names) + train_iter = image.ImageDetIter( + batch_size=batch_size, + data_shape=(3, data_shape, data_shape), + path_imgrec='./data/pikachu_train.rec', + path_imgidx='./data/pikachu_train.idx', + shuffle=True, + mean=True, + rand_crop=1, + min_object_covered=0.95, + max_attempts=200) + val_iter = image.ImageDetIter( + batch_size=batch_size, + data_shape=(3, data_shape, data_shape), + path_imgrec='./data/pikachu_val.rec', + shuffle=False, + mean=True) + return train_iter, val_iter, class_names, num_class + +train_data, test_data, class_names, num_class = get_iterators(data_shape, batch_size) +batch = train_data.next() +print(batch + diff --git "a/MXnet/\345\255\246\344\271\240\350\256\260\345\275\225.txt~" "b/MXnet/\345\255\246\344\271\240\350\256\260\345\275\225.txt~" new file mode 100644 index 00000000..efdd4c24 --- /dev/null +++ "b/MXnet/\345\255\246\344\271\240\350\256\260\345\275\225.txt~" @@ -0,0 +1,678 @@ + +中文教程 +https://zh.gluon.ai/ + +mxnet的gluon + +GAN生成模型 +http://www.jianshu.com/u/a24acc5449c7 + + +官方安装方法 +http://mxnet.incubator.apache.org/get_started/install.html + +1、安装依赖 最新版本的pip +sudo apt-get update +sudo apt-get install -y wget python gcc +wget https://bootstrap.pypa.io/get-pip.py && sudo python get-pip.py + +2、安装MXnet +sudo pip install mxnet==0.11.0 +一般在/usr/local/lib/python2.7/dist-packages/mxnet + +GPU版本 +sudo pip install mxnet-cu75 --pre --user # for CUDA 7.5 +sudo pip install mxnet-cu80 --pre --user # for CUDA 8.0 + +3、安装可视化工具（可选） mxnet.viz +sudo apt-get install graphviz +sudo pip install graphviz + +4、安装 Intel MKL +sudo pip install mxnet-mkl==0.11.0 + +5、验证安装 +python + +>>> import mxnet as mx +>>> a = mx.nd.ones((2, 3)) +>>> b = a * 2 + 1 +>>> b.asnumpy() +array([[ 3., 3., 3.], + [ 3., 3., 3.]], dtype=float32) + +>>> exit() + +6、源码安装 +sudo apt-get update +sudo apt-get install -y build-essential git libatlas-base-dev libopencv-dev +git clone --recursive https://github.com/dmlc/mxnet +cd mxnet; make -j4 + +出现错误 +/usr/bin/ld: cannot find -lcblas +安装 +sudo apt-get install libatlas-base-dev + + +添加搜索路径 +// ~表示我的mxnet安装在home下面 +export PYTHONPATH=~/mxnet/python +cp -r ~/mxnet/python/mxnet . +cp ~/mxnet/lib/libmxnet.so mxnet/ + +//我的工作目录 +export PYTHONPATH=/home/ewenwan/ewenwan/learn/MXnet/mxnet/python +cp -r /home/ewenwan/ewenwan/learn/MXnet/mxnet/python/mxnet . +cp -r /home/ewenwan/ewenwan/learn/MXnet/mxnet/lib/libmxnet.so mxnet/ + + +7、github教程 +https://github.com/dmlc/mxnet-notebooks + + +http://blog.csdn.net/shuzfan/article/details/50037273 + +8、转换图片格式 +就好像在caffe中训练模型之前要现将训练图片转化为leveldb或者LMDB格式数据一样， +mxnet在训练之前也要先将图片转化为REC格式的数据。 + +linux下转换使用的工具是mxnet/bin下名为“im2rec”的可执行文件， +该文件的源码可以查看mxnet/tools/im2rec.cc or mxnet/tools/im2rec.py + +首先，来说明以下im2rec这个转换工具有哪些输入参数： + +【1】首先是必须输入的3个参数，依次是 +image_lst: //保存图片和标签信息的文本的名字 +image_root_dir：//图片的存放路径 +output_rec： //转换后的.rec格式文件的存放路径 + + +【2】可选的输入参数有： +label_width: //标签个数，默认为1，即每个图片只有一个标签 +resize: //默认为-1，即不调整图片尺寸。否则将图片的较短边的长度调整为resize大小，另一边按比例调整 +%%例如图片尺寸为300*200，resize=100,那么调整之后图片尺寸为150*100. +quality： //JPEG编码质量，默认为80。输入范围为1-100.（mxnet转换数据时是编码存储的） +center_crop：//默认为0，if(center_crop)，则将图片以中心裁剪为正方形。注意：只有if(resize>0)成立时，该参数才有效。 +nsplit： //默认为1，数据集分块 用于将部分图片进行转换，按照位置将image_lst中的图片分成nsplit个part + %%注意，这里只是大概平分，例如12个训练图片，nsplit=3，实际分块为5+4+3，而不是预想中的4+4+4.（关于这一点我也还没搞明白） +part： //默认为0，用于将部分图片进行转换。和nsplit结合使用，可以只转换nsplit中的第part个分块 + +【3】这里举一个实际应用的例子 +（1）首先是保存图片和标签信息的文本，这里我的文件是train.txt. +编号 标签 图片名 +1 0 FY2D1.bmp +2 1 FY2E1.bmp +3 0 FY2D2.bmp +4 1 FY2E2.bmp + +python im2rec.py train.txt data/ train.rec quality=90 resize=1000 + + +9、迭代器 +迭代器-将转换后的数据送入网络: + +总体来说，开始一项训练，需要3个部分：训练参数、网络模型和训练数据。 +在上一小节，我已经详细介绍了如何将原始图片数据转化为mxnet可以接受的REC格式数据。 +但是，实际训练的时候我们还需要一个工具来将REC数据送入我们的训练网络中。 +这个工具就是“迭代器”-iterator + + +【1】mxnet为我们提供了快速构建迭代器的函数ImageRecordIter，该函数位于python/mxnet/io.py中， +其返回一个迭代器对象。或者可以在python命令窗中键入以下命令来获取io函数的位置以及说明： + +import mxnet as mx +help(mx.io) +#或者输入help(mx.io.ImageRecordIter)来直接查看迭代器的参数说明 + + +【2】接下来说明ImageRecordIter的几个常用参数：（更多的参数可以通过上面的help查看） + +path_imgrec：rec图像数据的存储路径；string型数据；默认为'./data/imgrec.rec' +data_shape：迭代器产生的每一个实例的形状，即图片数据的形状；元组型数据；必选参数 +mean_img：图像均值文件的存放路径；string型数据；默认为空 +batch_size：batch的大小（一次训练的图片数量）；int型数据；必选参数 +shuffle：是否打乱数据；布尔型数据；默认为False; +mirror：是否对图片执行镜像操作；布尔型数据；默认为False; +rand_mirror：是否随机地对图片执行镜像操作；布尔型数据；默认为False; +rand_crop：是否随机地对图片执行剪切操作；布尔型数据；默认为False; +label_width：图片的标签个数；int型数据；默认为1 +mean_r、 mean_g、 mean_b:3个颜色通道的均值；float型数据；默认为0 +num_parts：将训练数据分为几份；int型数据；默认为1 +part_index:第几部分数据将会被读取；int型数据；默认为0 + + +【3】接下来以实例来说明怎样构建迭代器， +该例子位于example/image-classification/train_imagenet.py中 + +#分别为训练数据和验证数据各构建一个迭代器 +def get_iterator(args, kv): + train = mx.io.ImageRecordIter( + path_imgrec = args.data_dir + "train.rec", + mean_img = args.data_dir + "mean.bin", + data_shape = data_shape, + batch_size = args.batch_size, + rand_crop = True, + rand_mirror = True, + num_parts = kv.num_workers, + part_index = kv.rank) + val = mx.io.ImageRecordIter( + path_imgrec = args.data_dir + "val.rec", + mean_img = args.data_dir + "mean.bin", + rand_crop = False, + rand_mirror = False, + data_shape = data_shape, + batch_size = args.batch_size, + num_parts = kv.num_workers, + part_index = kv.rank) + return (train, val) + + +10 构建一个网络模型 +一个网络模型是一层一层连接器来的，因此想要构建一个网络模型， +首先我们得知道mxnet中都有哪些可以用的层。(以下按照使用频率来排序) +下面介绍一些比较常用的层，更多的层大家可以在python命令窗执行以下代码来查询 +import mxnet as mx +help(mx.symbol) +【1】Activation：激活函数 + data: 输入数据，symbol型变量 + act_type: {'relu', 'sigmoid', 'tanh'}, 可选的3种激活函数 + +【2】Convolution:卷积层 +data:输入数据，symbol型变量 +weight：权重矩阵，symbol型变量 +bias:偏置，symbol型变量 +kernal:卷积核的尺寸（y,x）,二维tuple型变量 +stride：卷积步长（y,x）,二维tuple型变量，默认为（1,1） +pad：卷积的填补量（y,x）,二维tuple型变量，默认为（0,0） +num_filter ：卷积滤波器(通道)个数，int型变量 +no_bias ：是否取消偏置，布尔型变量，默认为False +num_group：groups划分的个数，int型变量，默认为1. + 该选项不被CUDNN所支持， you can use SliceChannel to num_group, + apply convolution and concat instead to achieve the same need. +workspace：卷积的Tmp工作空间的大小MB。long型变量，默认为512 + + +【3】Deconvolution：反卷积层 + + 参数同卷积层一致 + +【4】Dropout： 神经元失效层 + data:输入数据，symbol型变量 + p : 训练时对输入数据进行dropped out的比例，float型变量，默认为0.5 + +【5】Flatten:将N维数据变为1维 + +data:输入数据，symbol型变量 +name : string, optional. Name of the resulting symbol. + +【6】FullyConnected：全连接层 +data:输入数据，symbol型变量 +weight：权重矩阵，symbol型变量 +bias:偏置，symbol型变量 +no_bias ：是否取消偏置，布尔型变量，默认为False +num_hidden：输出隐藏节点的个数，int型变量 + + +【7】LRN：Local Response Normalization，即局部响应归一化层 +data:输入数据，symbol型变量 +alpha : float, optional, default=0.0001.value of the alpha variance scaling parameter in the normalization formula +beta : float, optional, default=0.75.value of the beta power parameter in the normalization formula +knorm : float, optional, default=2.value of the k parameter in normalization formula +nsize : int (non-negative), required.normalization window width in elements. + +【8】Pooling 池化层 + +data:输入数据，symbol型变量 +kernal:pooling核的尺寸（y,x）,二维tuple型变量 +stride：pooling步长（y,x）,二维tuple型变量，默认为（1,1） +pad：pooling的填补量（y,x）,二维tuple型变量，默认为（0,0） +pool_type : pooling的类型，可选的3种{'avg', 'max', 'sum'} + + +【9】SoftmaxOutput：使用logloss进行BP 最后回归输出 + +data:输入数据，symbol型变量 +grad_scale : 为梯度乘以一个比例系数，float型变量，默认为1 +multi_output : 布尔型变量，默认为False + # If set to true, for a (n,k,x_1,..,x_n) dimensionalinput tensor, softmax will generate n*x_1*...*x_n output, eachhas k classes + +【10】Variable：定义一个symbol型变量 + +name : 变量的名字，str型变量 +attr : dict of string -> string.Additional attributes to set on the variable. + +mxnet/example/image-classification文件夹下以symbol开头的几个.py文件都是定义好的网络模型。 +我们可以任意打开一个来具体了解一下如何构建一个网络。 +有一点需要注意的是：mxnet中通常要在卷积层和全连接层之间价加一个Flatten层用于衔接 + + +11 开始训练 +首先，来总结一下我们现在都有了什么： +保存网络模型的.py文件、 +REC格式的训练数据、 +将数据送入网络的迭代器。 + +在此基础上，我们缺少的就只剩下网络训练的一些控制参数：如学习率、训练日志、GPU选择等等。 +在初期，我们往往可以先快速的拿来用，而不用在意其细节。那么，最简单的办法就是直接修改套用mxnet中的示例。 +示例主要集中在/example/image-classification和/tests/python当中。 +下面就简单的介绍如何快速开始训练： + +（1）新建一个文件夹，将/example/image-classification文件夹下 + find_mxnet.py、symbol_alexnet.py、train_imagenet.py和train_model.py这4个文件copy进来； +（2）保持find_mxnet.py文件不变，在symbol_alexnet.py中按照里面Alexnet网络的构建方法来建立自己的网络。 + 最后最好再把文件名改为自己的网络名，如mynet.py; +（3）将train_imagenet.py最好也修改一下名字，如改为train_xx.py.将第40行代码中的’symbol’删除， + 否则你的网络文件就必须以‘symbol’开始命名，如symbol_mynet.py. + +net = importlib.import_module('symbol_' + args.network).get_symbol(args.num_classes) +改为： +net = importlib.import_module(args.network).get_symbol(args.num_classes) + + +剩下的就是要在parser.add_argument中修改默认值了，否则你在调用mynet.py就必须准确的指定每一个参数。 +（4）写一个脚本文件，运行脚本文件就可以开始训练了。如 + +python train_xx.py --data-dir=xx --model-prefix==xx --gpus=0 +# 如果想要在每次epoch结束时保存一个模型，那么model-prefix就不能为空 + + + + +12 利用mxnet 从零开始码一个皮卡丘检测器-CNN目标检测入门教程 SSD算法 SSD (Single-Shot MultiBox Object Detection). +https://zhuanlan.zhihu.com/p/28867241 +https://zhuanlan.zhihu.com/gluon + + + +目标检测通俗的来说是为了找到图像或者视频里的所有目标物体。在下面这张图中，两狗一猫的位置，包括它们所属的类（狗／猫），需要被正确的检测到。 + + +所以和图像分类不同的地方在于，目标检测需要找到尽量多的目标物体，而且要准确的定位物体的位置，一般用矩形框来表示。 + +在接下来的章节里，我们先介绍一个流行的目标检测算法，SSD (Single-Shot MultiBox Object Detection). + + +SSD: Single Shot MultiBox Detector + +顾名思义，算法的核心是用卷积神经网络一次前向推导求出大量多尺度（几百到几千） +的方框来表示目标检测的结果。网络的结构用下图表示。 + +跟所有的图像相关的网络一样，我们需要一个主干网络来提取特征，同时也是作为第一个预测特征层。 +网络在当前层产生大量的预设框，和与之对应的每个方框的分类概率（背景，猫，狗等等） +以及真正的物体和预设框的偏移量。在完成当前层的预测后，我们会下采样当前特征层， +作为新的预测层，重新产生新的预设框，分类概率，偏移量。这个过程往往会重复好几次， +直到预测特征层到达全局尺度（ 1 \times 1 )。 + +接下来我们用例子解释每个细节实现。 + + +【1】预设框 Default anchor boxes + +预设框的形状和大小可以由参数控制，我们往往设置一堆预设框， +以期望任意图像上的物体都能有一个预设框能大致重合，由于每个预设框需要对应的预测网络预测值， +所以希望对于每个物体都有100%重合的预设框是不现实的，可能会需要几十万甚至几百万的预设框， +但是采样的预设框越多，重合概率越好，用几千到上万个预设框基本能实现略大于70%的最好重合率， +同时保证了检测的速度。 + +为了保证重合覆盖率，对于每个特征层上的像素点，我们用不同的大小和长宽比来采样预设框。 +假设在某个特定的特征层（ w × h ），每个预设框的中心点就是特征像素点的中心， +然后我们用如下的公式采样预设框： + +预设框的中心为特征像素点的中心 + +x =(i+0.5)/w , y=(j+0.5)/h i：[0：w） j:[0:h) +对于长宽比 r = 1 , 尺寸s: (0,1] , 生成的预设框大小 ws * hs + +这里例子里，我们用事先实现的层MultiBoxPrior产生预设框，输入 n 个预设尺寸， +和 m 个预设的长宽比，输出为 n+m-1 个方框而不是 n \times m 个。 + 当然我们完全可以使用其他的算法产生不同的预设框， +但是实践中我们发现上述的方法覆盖率和相应需要的预设框数量比较合适。 + + +import mxnet as mx +from mxnet import nd +from mxnet.contrib.ndarray import MultiBoxPrior + +n = 40 +# 输入形状: batch x channel x height x weight +x = nd.random_uniform(shape=(1, 3, n, n)) + +y = MultiBoxPrior(x, sizes=[.5, .25, .1], ratios=[1, 2, .5]) + +# 取位于 (20,20) 像素点的第一个预设框 +# 格式为 (x_min, y_min, x_max, y_max) +boxes = y.reshape((n, n, -1, 4)) +print('The first anchor box at row 21, column 21:', boxes[20, 20, 0, :]) + +import matplotlib.pyplot as plt + """convert an anchor box to a matplotlib rectangle""" +def box_to_rect(box, color, linewidth=3): + box = box.asnumpy() + return plt.Rectangle( + (box[0], box[1]), (box[2]-box[0]), (box[3]-box[1]), + fill=False, edgecolor=color, linewidth=linewidth) +colors = ['blue', 'green', 'red', 'black', 'magenta'] +plt.imshow(nd.ones((n, n, 3)).asnumpy()) +anchors = boxes[20, 20, :, :] +for i in range(anchors.shape[0]): + plt.gca().add_patch(box_to_rect(anchors[i,:]*n, colors[i])) +plt.show() + + + + +【2】分类预测 Predict classes + +这个部分的目标很简单，就是预测每个预设框对应的分类。 +我们用 3 × 3 ， Padding (填充) 1 × 1 的卷积来预测分类概率， +这样可以做到卷积后空间上的形状不会变化，而且由于卷积的特点， +每个卷积核会扫过每个预测特征层的所有像素点，得到 c × h × w 个预测值， +h × w 对应了空间上所有的像素点，每个通道对应特定的预设框 c = (N_{class} + 1) × N_{anchors} 。 +N_{class} + 1) 所有类别+1个背景类。 +假设有10个正类，每个像素5个预设框，那么我们就需要 11 × 5 = 55 个通道。 +具体的来说，对于每个像素第 i 个预设框： + + 通道 i × (N_{class} + 1) 的值对应背景（非物体）的得分 + 通道 i × (N_{class} + 1) + 1 + j 对应了第 j 类的得分 + +from mxnet.gluon import nn +def class_predictor(num_anchors, num_classes): + """return a layer to predict classes""" + return nn.Conv2D(num_anchors * (num_classes + 1), 3, padding=1) + +cls_pred = class_predictor(5, 10) +cls_pred.initialize() +x = nd.zeros((2, 3, 20, 20)) +print('Class prediction', cls_pred(x).shape) + + + +【3】预测预设框偏移 Predict anchor boxes +为了找到物体准确的位置，光靠预设框本身是不行的，我们还需要预测偏移量以便把真正的物体框出来。 + +假设 b 是某个预设框， Y 是目标物体的真实矩形框，我们需要预测的偏移为 +\Delta(Y, b) = (t_x, t_y, t_{width}, t_{height}) ，全都是长度为4的向量， 我们求得偏移 + + t_x = (Y_x - b_x) / b_{width} + t_y = (Y_y - b_y) / b_{height} + t_{width} = (Y_{width} - b_{width}) / b_{width} + t_{height} = (Y_{height} - b_{height}) / b_{height} +所有的偏移量都除以预设框的长或宽是为了更好的收敛。 + +类似分类概率，我们同样用 3 × 3 填充 1 × 1 的卷积来预测偏移。 +这次不同的是，对于每个预设框，我们只需要 4 个通道来预测偏移量， +一共需要 N_{anchors} × 4 个通道， +第 i 个预设框对应的偏移量存在通道 i × 4 到通道 i × 4 + 3 之间。 + +def box_predictor(num_anchors): + """return a layer to predict delta locations""" + return nn.Conv2D(num_anchors * 4, 3, padding=1) + +box_pred = box_predictor(10) +box_pred.initialize() +x = nd.zeros((2, 3, 20, 20)) +print('Box prediction', box_pred(x).shape) + +>>>> Box prediction (2, 40, 20, 20) + + +【4】下采样特征层 Down-sample features + +每次我们下采样特征层到一半的长宽，用Pooling(池化)操作就可以轻松的做到， +当然也可以用stride(步长)为2的卷积直接得到。在下采样之前， +我们会希望增加几层卷积层作为缓冲，防止特征值对应多尺度带来的混乱， +同时又能增加网络的深度，得到更好的抽象。 +"""stack two Conv-BatchNorm-Relu blocks and then a pooling layer to halve the feature size""" +def down_sample(num_filters): + out = nn.HybridSequential() + for _ in range(2): + out.add(nn.Conv2D(num_filters, 3, strides=1, padding=1))##卷积 + out.add(nn.BatchNorm(in_channels=num_filters))##正则化 + out.add(nn.Activation('relu'))##激活 + out.add(nn.MaxPool2D(2))##最大值池化 + return out + +blk = down_sample(10) +blk.initialize() +x = nd.zeros((2, 3, 20, 20)) +print('Before', x.shape, 'after', blk(x).shape) + +>>> Before (2, 3, 20, 20) after (2, 10, 10, 10) + + + + +【5】整合多个特征层预测值 Manage predictions from multiple layers + +SSD算法的一个关键点在于它用到了多尺度的特征层来预测不同大小的物体。 +相对来说，浅层的特征层的空间尺度更大，越到网络的深层，空间尺度越小， +最后我们往往下采样直到 1 × 1 ，用来预测全图大小的物体。 +所以每个特征层产生的预设框， 分类概率，框偏移量需要被整合起来统一在全图与真实的物体比较。 +为了做到一一对应，我们统一把所有的预设框， 分类概率，框偏移量 平铺再连接。 +得到的是按顺序排列但是摊平的所有预测值和预设框。 + + +# 随便创建一个大小为 20x20的预测层 +feat1 = nd.zeros((2, 8, 20, 20)) +print('Feature map 1', feat1.shape) +cls_pred1 = class_predictor(5, 10) +cls_pred1.initialize() +y1 = cls_pred1(feat1) +print('Class prediction for feature map 1', y1.shape) +# 下采样 +ds = down_sample(16) +ds.initialize() +feat2 = ds(feat1) +print('Feature map 2', feat2.shape) +cls_pred2 = class_predictor(3, 10) +cls_pred2.initialize() +y2 = cls_pred2(feat2) +print('Class prediction for feature map 2', y2.shape) + + +我们统一把所有的预设框， 分类概率，框偏移量 平铺再连接。 + +def flatten_prediction(pred): + return nd.flatten(nd.transpose(pred, axes=(0, 2, 3, 1))) + +def concat_predictions(preds): + return nd.concat(*preds, dim=1) + +flat_y1 = flatten_prediction(y1) +print('Flatten class prediction 1', flat_y1.shape) +flat_y2 = flatten_prediction(y2) +print('Flatten class prediction 2', flat_y2.shape) +print('Concat class predictions', concat_predictions([flat_y1, flat_y2]).shape) + +我们总是确保在 dim = 1 上连接，以免打乱一一对应的关系。 + + +【6】主干网络 Body network + +主干网络用来从原始图像输入提取特征。 +一般来说我们会用预先训练好的用于分类的高性能网络（VGG, ResNet等）来提取特征。 +在这里我们就简单地堆叠几层卷积和下采样层作为主干网络的演示。 + + +rom mxnet import gluon +def body(): + """return the body network""" + out = nn.HybridSequential() + for nfilters in [16, 32, 64]: + out.add(down_sample(nfilters)) + return out + +bnet = body() +bnet.initialize() +x = nd.zeros((2, 3, 256, 256)) +print('Body network', [y.shape for y in bnet(x)]) + +>>> Body network [(64, 32, 32), (64, 32, 32)] + + +【7】设计一个简单的SSD示意网络 Create a toy SSD model + +我们这里介绍一个示意用的简单SSD网络，出于速度的考量，输入图像尺寸定为 256 \times 256 。 +def toy_ssd_model(num_anchors, num_classes): + """return SSD modules""" + downsamples = nn.Sequential() + class_preds = nn.Sequential() + box_preds = nn.Sequential() + + downsamples.add(down_sample(128)) + downsamples.add(down_sample(128)) + downsamples.add(down_sample(128)) + + for scale in range(5): + class_preds.add(class_predictor(num_anchors, num_classes)) + box_preds.add(box_predictor(num_anchors)) + + return body(), downsamples, class_preds, box_preds + +print(toy_ssd_model(5, 2)) + + +【8】网络前向推导 Forward + +既然我们已经设计完网络结构了，接下来可以定义网络前向推导的步骤。 + +首先得到主干网络的输出，然后对于每一个特征预测层，推导当前层的预设框， +分类概率和偏移量。最后我们把这些输入摊平，连接，作为网络的输出。 + +def toy_ssd_forward(x, body, downsamples, class_preds, box_preds, sizes, ratios): + # 计算主干网络的输出 + x = body(x) + + # 在每个预测层, 计算预设框，分类概率，偏移量 + # 然后在下采样到下一层预测层，重复 + default_anchors = [] + predicted_boxes = [] + predicted_classes = [] + + for i in range(5): + default_anchors.append(MultiBoxPrior(x, sizes=sizes[i], ratios=ratios[i])) + predicted_boxes.append(flatten_prediction(box_preds[i](x))) + predicted_classes.append(flatten_prediction(class_preds[i](x))) + if i < 3: + x = downsamples[i](x) + elif i == 3: + # 最后一层可以简单地用全局Pooling + x = nd.Pooling(x, global_pool=True, pool_type='max', kernel=(4, 4)) + + return default_anchors, predicted_classes, predicted_boxes + + +【9】打包收工 Put all things together + +from mxnet import gluon +class ToySSD(gluon.Block): + def __init__(self, num_classes, **kwargs): + super(ToySSD, self).__init__(**kwargs) + # 5个预测层，每层负责的预设框尺寸不同，由小到大，符合网络的形状 + self.anchor_sizes = [[.2, .272], [.37, .447], [.54, .619], [.71, .79], [.88, .961]] + # 每层的预设框都用 1，2，0.5作为长宽比候选 + self.anchor_ratios = [[1, 2, .5]] * 5 + self.num_classes = num_classes + + with self.name_scope(): + self.body, self.downsamples, self.class_preds, self.box_preds = toy_ssd_model(4, num_classes) + + def forward(self, x): + default_anchors, predicted_classes, predicted_boxes = toy_ssd_forward(x, self.body, self.downsamples, + self.class_preds, self.box_preds, self.anchor_sizes, self.anchor_ratios) + # 把从每个预测层输入的结果摊平并连接，以确保一一对应 + anchors = concat_predictions(default_anchors) + box_preds = concat_predictions(predicted_boxes) + class_preds = concat_predictions(predicted_classes) + # 改变下形状，为了更方便地计算softmax + class_preds = nd.reshape(class_preds, shape=(0, -1, self.num_classes + 1)) + + return anchors, class_preds, box_preds + + +【10】网络输出示意 Outputs of ToySSD +# 新建一个2个正类的SSD网络 +net = ToySSD(2) +net.initialize() +x = nd.zeros((1, 3, 256, 256)) +default_anchors, class_predictions, box_predictions = net(x) +print('Outputs:', 'anchors', default_anchors.shape, 'class prediction', class_predictions.shape, 'box prediction', box_predictions.shape) + + +【11】数据集 Dataset + +聊了半天怎么构建一个虚无的网络，接下来看看真正有意思的东西。 + +我们用3D建模批量生成了一个皮卡丘的数据集，产生了1000张图片作为这个展示用的训练集。 +这个数据集里面，皮神会以各种角度，各种姿势出现在各种背景图中，就像Pokemon Go里增强现实那样炫酷。 +因为是生成的数据集，我们自然可以得到每只皮神的真实坐标和大小，用来作为训练的真实标记。 + + +【11】下载数据集 Download dataset +下载提前准备好的数据集并验证 + +from mxnet.test_utils import download +import os.path as osp +def verified(file_path, sha1hash): + import hashlib + sha1 = hashlib.sha1() + with open(file_path, 'rb') as f: + while True: + data = f.read(1048576) + if not data: + break + sha1.update(data) + matched = sha1.hexdigest() == sha1hash + if not matched: + print('Found hash mismatch in file {}, possibly due to incomplete download.'.format(file_path)) + return matched + +url_format = 'https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/datasets/pikachu/{}' +hashes = {'train.rec': 'e6bcb6ffba1ac04ff8a9b1115e650af56ee969c8', + 'train.idx': 'dcf7318b2602c06428b9988470c731621716c393', + 'val.rec': 'd6c33f799b4d058e82f2cb5bd9a976f69d72d520'} +for k, v in hashes.items(): + fname = 'pikachu_' + k + target = osp.join('data', fname) + url = url_format.format(k) + if not osp.exists(target) or not verified(target, v): + print('Downloading', target, url) + download(url, fname=fname, dirname='data', overwrite=True) + + + +【12】 +加载数据 Load dataset + +加载数据可以用mxnet.image.ImageDetIter， +同时还提供了大量数据增强的选项，比如翻转，随机截取等等。 + +import mxnet.image as image +data_shape = 256 +batch_size = 32 +def get_iterators(data_shape, batch_size): + class_names = ['pikachu'] + num_class = len(class_names) + train_iter = image.ImageDetIter( + batch_size=batch_size, + data_shape=(3, data_shape, data_shape), + path_imgrec='./data/pikachu_train.rec', + path_imgidx='./data/pikachu_train.idx', + shuffle=True, + mean=True, + rand_crop=1, + min_object_covered=0.95, + max_attempts=200) + val_iter = image.ImageDetIter( + batch_size=batch_size, + data_shape=(3, data_shape, data_shape), + path_imgrec='./data/pikachu_val.rec', + shuffle=False, + mean=True) + return train_iter, val_iter, class_names, num_class + +train_data, test_data, class_names, num_class = get_iterators(data_shape, batch_size) +batch = train_data.next() +print(batch + diff --git "a/github\346\217\220\344\272\244\350\256\260\345\275\225.txt" "b/github\346\217\220\344\272\244\350\256\260\345\275\225.txt" index ec20e5f8..4fa98aa4 100644 --- "a/github\346\217\220\344\272\244\350\256\260\345\275\225.txt" +++ "b/github\346\217\220\344\272\244\350\256\260\345\275\225.txt" @@ -1,11 +1,11 @@ cd 目标文件夹 -echo "# PyML" >> README.md # 添加文件 +echo "# MVsion" >> README.md # 添加文件 git init #初始化 git config --global user.email "you@example.com" # 邮箱 git config --global user.name "Your Name" # 用户名 git add README.md # 添加文件 git commit -m "first commit" # 提交 -git remote add origin https://github.com/Ewenwan/PyML.git #增加一个远程服务器端版本库 +git remote add origin https://github.com/Ewenwan/MVision.git #增加一个远程服务器端版本库 git push -u origin master # 将本地文件提交到Github。 提交所有文件: