def train(num_gpus, batch_size, lr):
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
ctx = [mx.gpu(i) for i in range(num_gpus)]
print('running on:', ctx)
net.initialize(init=init.Normal(sigma=0.01), ctx=ctx, force_reinit=True)
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr})
loss = gloss.SoftmaxCrossEntropyLoss()
for epoch in range(4):
start = time.time()
for X, y in train_iter:
gpu_Xs = gutils.split_and_load(X, ctx)
gpu_ys = gutils.spilt_and_load(y, ctx)
with autograd.record():
ls = [
loss(net(gpu_X), gpu_y)
for gpu_X, gpu_y in zip(gpu_Xs, gpu_ys)
]
for l in ls:
l.backward()
trainer.step(batch_size)
nd.waitall()
train_time = time.time() - start
test_acc = d2l.evaluate_accuracy(test_iter, net, ctx[0])
print('epoch %d, time %.1f sec, test_acc %.2f' %
(epoch + 1, train_time, test_acc))
def lenet():
"""
:return:
"""
net = nn.Sequential()
net.add(
nn.Conv2D(channels=6, kernel_size=5, activation='sigmoid'),
nn.MaxPool2D(pool_size=2, strides=2),
nn.Conv2D(channels=16, kernel_size=5, activation='sigmoid'),
nn.MaxPool2D(pool_size=2, strides=2),
# Dense会默认将(批量大小, 通道, 高, 宽)形状的输入转换成
# (批量大小, 通道 * 高 * 宽)形状的输入
nn.Dense(120, activation='sigmoid'),
nn.Dense(84, activation='sigmoid'),
nn.Dense(10))
X = nd.random.uniform(shape=(1, 1, 28, 28))
net.initialize()
for layer in net:
X = layer(X)
print(layer.name, 'output shape:\t', X.shape)
lr, num_epochs, batch_size = 0.9, 5, 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size=batch_size)
net.initialize(force_reinit=True, init=init.Xavier())
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr})
d2l.train_ch5(net, train_iter, test_iter, batch_size, trainer, mxnet.cpu(),
num_epochs)
def basic_multilayer():
"""
基本的多层感知机实现
:return:
"""
def relu(X):
return nd.maximum(X, 0)
def net(X):
X = X.reshape((-1, num_inputs))
H = relu(nd.dot(X, W1) + b1)
return nd.dot(H, W2) + b2
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
num_inputs, num_outputs, num_hiddens = 784, 10, 256
W1 = nd.random.normal(scale=0.01, shape=(num_inputs, num_hiddens))
b1 = nd.zeros(num_hiddens)
W2 = nd.random.normal(scale=0.01, shape=(num_hiddens, num_outputs))
b2 = nd.zeros(num_outputs)
params = [W1, b1, W2, b2]
for param in params:
param.attach_grad()
loss = gloss.SoftmaxCrossEntropyLoss()
num_epochs, lr = 5, 0.5
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, params, lr)
def main():
batch_size = 256
# 下载、读取数据集
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
num_inputs = 784
num_outputs = 10
W = nd.random.normal(scale=0.01, shape=(num_inputs, num_outputs))
b = nd.zeros(num_outputs)
# 附上梯度，开辟梯度缓冲区
W.attach_grad()
b.attach_grad()
# 迭代周期数 学习率
num_epochs, lr = 5, 0.1
train_ch3(net, train_iter, test_iter, cross_entropy, num_epochs,
batch_size, num_inputs, W, b, [W, b], lr)
for X, y in test_iter:
print(X, y)
break
true_labels = d2l.get_fashion_mnist_labels(y.asnumpy())
pred_labels = d2l.get_fashion_mnist_labels(
net(X, num_inputs, W, b).argmax(axis=1).asnumpy())
titles = [
true + '\n' + pred for true, pred in zip(true_labels, pred_labels)
]
show_fashion_mnist(X[0:9], titles[0:9])
def densenet():
"""
DenseNet的主要构建模块是稠密块（dense block）和过渡层（transition layer）。前者定义了输入和输出是如何连结的，后者则用来控制
通道数，使之不过大。
:return:
"""
class DenseBlock(nn.Block):
def __init__(self, num_convs, num_channels, **kwargs):
super(DenseBlock, self).__init__(**kwargs)
self.net = nn.Sequential()
for _ in range(num_convs):
self.net.add(conv_block(num_channels))
def forward(self, X):
for blk in self.net:
Y = blk(X)
X = nd.concat(X, Y, dim=1) # 在通道维上将输入和输出连结
return X
def conv_block(num_channels):
blk = nn.Sequential()
blk.add(nn.BatchNorm(), nn.Activation('relu'),
nn.Conv2D(num_channels, kernel_size=3, padding=1))
return blk
def transition_block(num_channels):
blk = nn.Sequential()
blk.add(nn.BatchNorm(), nn.Activation('relu'),
nn.Conv2D(num_channels, kernel_size=1),
nn.AvgPool2D(pool_size=2, strides=2))
return blk
net = nn.Sequential()
net.add(nn.Conv2D(64, kernel_size=7, strides=2, padding=3), nn.BatchNorm(),
nn.Activation('relu'),
nn.MaxPool2D(pool_size=3, strides=2, padding=1))
num_channels, growth_rate = 64, 32 # num_channels为当前的通道数
num_convs_in_dense_blocks = [4, 4, 4, 4]
for i, num_convs in enumerate(num_convs_in_dense_blocks):
net.add(DenseBlock(num_convs, growth_rate))
# 上一个稠密块的输出通道数
num_channels += num_convs * growth_rate
# 在稠密块之间加入通道数减半的过渡层
if i != len(num_convs_in_dense_blocks) - 1:
num_channels //= 2
net.add(transition_block(num_channels))
net.add(nn.BatchNorm(), nn.Activation('relu'), nn.GlobalAvgPool2D(),
nn.Dense(10))
lr, num_epochs, batch_size, ctx = 0.1, 5, 256, d2l.try_gpu()
net.initialize(ctx=ctx, init=init.Xavier())
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr})
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=96)
d2l.train_ch5(net, train_iter, test_iter, batch_size, trainer, ctx,
num_epochs)
def generate(self):
net = nn.Sequential()
net.add(nn.Dense(256, activation='relu'), nn.Dense(10))
net.initialize(init.Normal(sigma=0.01))
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
loss = gloss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'sgd',
{'learning_rate': 0.5})
num_epochs = 5
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size,
None, None, trainer)
def method1():
num_epochs, lr, batch_size = 5, 0.5, 256
loss = gloss.SoftmaxCrossEntropyLoss()
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
net = nn.Sequential()
net.add(nn.Dense(num_hiddens1, activation="relu"),
nn.Dropout(drop_prob1),
nn.Dense(num_hiddens2, activation="relu"),
nn.Dropout(drop_prob2),
nn.Dense(num_outputs))
net.initialize(init.Normal(sigma=0.01))
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr})
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None,
None, trainer)
def main():
batch_size = 256
# 下载、读取数据集
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
# 定义初始化模型
net = nn.Sequential()
net.add(nn.Dense(10))
net.initialize(init.Normal(sigma=0.01))
# 定义损失函数
loss = gloss.SoftmaxCrossEntropyLoss()
# 定义优化算法
trainer = gluon.Trainer(net.collect_params(), 'sgd',
{'learning_rate': 0.01})
num_epochs = 5
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size,
None, None, trainer)
def vgg():
"""
:return:
"""
def vgg_block(num_convs, num_channels):
blk = nn.Sequential()
for _ in range(num_convs):
blk.add(
nn.Conv2D(num_channels,
kernel_size=3,
padding=1,
activation='relu'))
blk.add(nn.MaxPool2D(pool_size=2, strides=2))
return blk
def vgg(conv_arch):
net = nn.Sequential()
# 卷积层部分
for (num_convs, num_channels) in conv_arch:
net.add(vgg_block(num_convs, num_channels))
# 全连接层部分
net.add(nn.Dense(4096, activation='relu'), nn.Dropout(0.5),
nn.Dense(4096, activation='relu'), nn.Dropout(0.5),
nn.Dense(10))
return net
conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))
net = vgg(conv_arch)
net.initialize()
X = nd.random.uniform(shape=(1, 1, 224, 224))
for blk in net:
X = blk(X)
print(blk.name, 'output shape:\t', X.shape)
ratio = 4
small_conv_arch = [(pair[0], pair[1] // ratio) for pair in conv_arch]
net = vgg(small_conv_arch)
lr, num_epochs, batch_size, ctx = 0.05, 5, 128, d2l.try_gpu()
net.initialize(ctx=ctx, init=init.Xavier())
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr})
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)
d2l.train_ch5(net, train_iter, test_iter, batch_size, trainer, ctx,
num_epochs)
def dropout_gluon():
drop_prob1, drop_prob2, lr, batch_size, num_epochs = 0.2, 0.5, 0.1, 64, 50
net = nn.Sequential()
net.add(
nn.Dense(256, activation="relu"),
nn.Dropout(drop_prob1), # 在第一个全连接层后添加丢弃层
nn.Dense(256, activation="relu"),
nn.Dropout(drop_prob2), # 在第二个全连接层后添加丢弃层
nn.Dense(10))
net.initialize(init.Normal(sigma=0.01))
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
loss = gloss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr})
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size,
None, None, trainer)
def simple_multilayer():
"""
多层感知机简洁实现
:return:
"""
net = nn.Sequential()
net.add(nn.Dense(256, activation='relu'), nn.Dense(10))
net.add(gluon.nn.Dropout(0.2))
net.initialize(init.Normal(sigma=0.01))
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
loss = gloss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.5})
num_epochs = 10
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None, None, trainer)
def nin():
"""
:return:
"""
def nin_block(num_channels, kernel_size, strides, padding):
blk = nn.Sequential()
blk.add(
nn.Conv2D(num_channels,
kernel_size,
strides,
padding,
activation='relu'),
nn.Conv2D(num_channels, kernel_size=1, activation='relu'),
nn.Conv2D(num_channels, kernel_size=1, activation='relu'))
return blk
net = nn.Sequential()
net.add(
nin_block(96, kernel_size=11, strides=4, padding=0),
nn.MaxPool2D(pool_size=3, strides=2),
nin_block(256, kernel_size=5, strides=1, padding=2),
nn.MaxPool2D(pool_size=3, strides=2),
nin_block(384, kernel_size=3, strides=1, padding=1),
nn.MaxPool2D(pool_size=3, strides=2),
nn.Dropout(0.5),
# 标签类别数是10
nin_block(10, kernel_size=3, strides=1, padding=1),
# 全局平均池化层将窗口形状自动设置成输入的高和宽
nn.GlobalAvgPool2D(),
# 将四维的输出转成二维的输出，其形状为(批量大小, 10)
nn.Flatten())
X = nd.random.uniform(shape=(1, 1, 224, 224))
net.initialize()
for layer in net:
X = layer(X)
print(layer.name, 'output shape:\t', X.shape)
lr, num_epochs, batch_size, ctx = 0.1, 5, 128, d2l.try_gpu()
net.initialize(force_reinit=True, ctx=ctx, init=init.Xavier())
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr})
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)
d2l.train_ch5(net, train_iter, test_iter, batch_size, trainer, ctx,
num_epochs)
def train(num_gpus, batch_size, lr):
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
ctx = [mx.gpu(i) for i in range(num_gpus)]
print('runing on:', ctx)
#将模型参数复制到num_gpus块显卡的显存上
gpu_params = [get_params(params, c) for c in ctx]
for epoch in range(4):
start = time.time()
for X, y in train_iter:
#对单个小批量进行多GPU训练
train_batch(X, y, gpu_params, ctx, lr)
nd.waitall()
train_time = time.time() - start
def net(x): #在gpu(0)上验证模型
return lenet(x, gpu_params[0])
test_acc = d2l.evaluate_accuracy(test_iter, net, ctx[0])
print('epoch %d, time %.1f sec, test_acc %.2f' %
(epoch + 1, train_time, test_acc))
def batchnormalization_simple():
"""
Gluon中nn模块定义的BatchNorm类使用起来更加简单。它不需要指定自己定义的BatchNorm类中所需的num_features和num_dims参数值。
在Gluon中，这些参数值都将通过延后初始化而自动获取。下面我们用Gluon实现使用批量归一化的LeNet。
:return:
"""
net = nn.Sequential()
net.add(nn.Conv2D(6, kernel_size=5), nn.BatchNorm(),
nn.Activation('sigmoid'), nn.MaxPool2D(pool_size=2, strides=2),
nn.Conv2D(16, kernel_size=5), nn.BatchNorm(),
nn.Activation('sigmoid'), nn.MaxPool2D(pool_size=2, strides=2),
nn.Dense(120), nn.BatchNorm(), nn.Activation('sigmoid'),
nn.Dense(84), nn.BatchNorm(), nn.Activation('sigmoid'),
nn.Dense(10))
lr, num_epochs, batch_size, ctx = 1.0, 5, 256, d2l.try_gpu()
net.initialize(ctx=ctx, init=init.Xavier())
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr})
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
d2l.train_ch5(net, train_iter, test_iter, batch_size, trainer, ctx,
num_epochs)
def train(num_gpus, batch_size, lr):
comm = MPI.COMM_WORLD
comm_rank = comm.Get_rank()
comm_size = comm.Get_size()
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
#ctx = [mx.gpu(i) for i in range(num_gpus)]
if comm_rank == 0:
ctx = mx.gpu(0)
else:
ctx = mx.gpu(1)
print('running on:', ctx)
net.initialize(init=init.Normal(sigma=0.01), ctx=ctx, force_reinit=True)
trainer = gluon.Trainer(net.collect_params(),
'sgd', {'learning_rate': lr},
SSP_FLAG=True,
thre=2)
loss = gloss.SoftmaxCrossEntropyLoss()
for epoch in range(400000):
start = time.time()
for X, y in train_iter:
gpu_Xs = gutils.split_and_load(X, ctx)
gpu_ys = gutils.split_and_load(y, ctx)
with autograd.record():
ls = [
loss(net(gpu_X), gpu_y)
for gpu_X, gpu_y in zip(gpu_Xs, gpu_ys)
]
for l in ls:
l.backward()
trainer.step(epoch, batch_size)
train_time = time.time() - start
test_acc = d2l.evaluate_accuracy(test_iter, net, ctx[comm_rank])
print('epoch %d, time %.1f sec, test acc %.2f, process %d' %
(epoch + 1, train_time, test_acc, comm_rank))
print(len(self.parameters))
for i, k in enumerate(keys):
if i not in [30, 31]:
sess.run(self.parameters[i].assign(weights[k]))
print('-------------all done------------------')
x_imgs = tf.placeholder(tf.float32, [None, 96, 96, 1])
vgg = vgg16(x_imgs, 10)
y_imgs = tf.placeholder(tf.int32, [None, 10])
fc3_cat_and_dog = vgg.probs
batch_size = 500
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=fc3_cat_and_dog,
labels=y_imgs))
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=0.01).minimize(loss)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
train_iter, test_iter = d2l.load_data_fashion_mnist(200, resize=96)
epoch = 5
for i in range(epoch):
j = 0
for features, labels in train_iter:
X_train = nd.transpose(features, axes=(0, 2, 3, 1)).asnumpy()
y_train = labels.asnumpy()
Y_train = np_utils.to_categorical(y_train, 10)
sess.run(optimizer, feed_dict={x_imgs: X_train, y_imgs: Y_train})
j = j + 1
if j % 10 == 0:
print(sess.run(loss, feed_dict={x_imgs: X_train, y_imgs: Y_train}))
@Author : 剑怜情
'''
from mxnet.gluon import data as gdata
from mxnet.gluon import nn
import d2lzh as d2l
from mxnet import nd,autograd,gluon
from mxnet import initializer as init
mnist_train = gdata.vision.FashionMNIST(train=True)
mnist_test = gdata.vision.FashionMNIST(train=False)
print("mnist_train.shape:",len(mnist_train))
print("mnist_test.shape:",len(mnist_test))
# 处理成iter
batch_size=64
train_iter,test_iter = d2l.load_data_fashion_mnist(64)
# 定义W,b
num_input = 784
num_outputs = 10
# W = nd.random.normal(scale=1,shape=(num_input,num_outputs))
# b = nd.zeros(shape=num_outputs)
# W.attach_grad()
# b.attach_grad()
# 定义模型、分类模型、损失函数
def softmax(X):
x_exp = X.exp()
partition = x_exp.sum(axis=1,keepdims = True)
def method0():
num_epochs, lr, batch_size = 5, 0.5, 256
loss = gloss.SoftmaxCrossEntropyLoss()
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size,
params, lr)
def googlenet():
"""
GoogLeNet吸收了NiN中网络串联网络的思想，并在此基础上做了很大改进。在随后的几年里，研究人员对GoogLeNet进行了数次改进，本节将介绍这个模型系列的第一个版本。
:return:
"""
class Inception(nn.Block):
# c1 - c4为每条线路里的层的输出通道数
def __init__(self, c1, c2, c3, c4, **kwargs):
super(Inception, self).__init__(**kwargs)
# 线路1，单1 x 1卷积层
self.p1_1 = nn.Conv2D(c1, kernel_size=1, activation='relu')
# 线路2，1 x 1卷积层后接3 x 3卷积层
self.p2_1 = nn.Conv2D(c2[0], kernel_size=1, activation='relu')
self.p2_2 = nn.Conv2D(c2[1],
kernel_size=3,
padding=1,
activation='relu')
# 线路3，1 x 1卷积层后接5 x 5卷积层
self.p3_1 = nn.Conv2D(c3[0], kernel_size=1, activation='relu')
self.p3_2 = nn.Conv2D(c3[1],
kernel_size=5,
padding=2,
activation='relu')
# 线路4，3 x 3最大池化层后接1 x 1卷积层
self.p4_1 = nn.MaxPool2D(pool_size=3, strides=1, padding=1)
self.p4_2 = nn.Conv2D(c4, kernel_size=1, activation='relu')
def forward(self, x):
p1 = self.p1_1(x)
p2 = self.p2_2(self.p2_1(x))
p3 = self.p3_2(self.p3_1(x))
p4 = self.p4_2(self.p4_1(x))
return nd.concat(p1, p2, p3, p4, dim=1) # 在通道维上连结输出
b1 = nn.Sequential()
b1.add(
nn.Conv2D(64, kernel_size=7, strides=2, padding=3, activation='relu'),
nn.MaxPool2D(pool_size=3, strides=2, padding=1))
b2 = nn.Sequential()
b2.add(nn.Conv2D(64, kernel_size=1, activation='relu'),
nn.Conv2D(192, kernel_size=3, padding=1, activation='relu'),
nn.MaxPool2D(pool_size=3, strides=2, padding=1))
b3 = nn.Sequential()
b3.add(Inception(64, (96, 128), (16, 32), 32),
Inception(128, (128, 192), (32, 96), 64),
nn.MaxPool2D(pool_size=3, strides=2, padding=1))
b4 = nn.Sequential()
b4.add(Inception(192, (96, 208), (16, 48), 64),
Inception(160, (112, 224), (24, 64), 64),
Inception(128, (128, 256), (24, 64), 64),
Inception(112, (144, 288), (32, 64), 64),
Inception(256, (160, 320), (32, 128), 128),
nn.MaxPool2D(pool_size=3, strides=2, padding=1))
b5 = nn.Sequential()
b5.add(Inception(256, (160, 320), (32, 128), 128),
Inception(384, (192, 384), (48, 128), 128), nn.GlobalAvgPool2D())
net = nn.Sequential()
net.add(b1, b2, b3, b4, b5, nn.Dense(10))
X = nd.random.uniform(shape=(1, 1, 96, 96))
net.initialize()
for layer in net:
X = layer(X)
print(layer.name, 'output shape:\t', X.shape)
lr, num_epochs, batch_size, ctx = 0.1, 5, 128, d2l.try_gpu()
net.initialize(force_reinit=True, ctx=ctx, init=init.Xavier())
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr})
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=96)
d2l.train_ch5(net, train_iter, test_iter, batch_size, trainer, ctx,
num_epochs)
# DLN is free software; you can redistribute it and/or modify it. # # DLN is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # # Contributors: # Xiao Wang - initial implementation import d2lzh as d2l from mxnet import nd from mxnet.gluon import loss as gloss # load data batch_size = 256 iter_trainning, iter_testing = d2l.load_data_fashion_mnist(batch_size) # initialize parameters number_inputs = 784 number_outputs = 10 number_hiddens = 256 W1 = nd.random.normal(scale=0.01, shape=(number_inputs, number_hiddens)) b1 = nd.zeros(number_hiddens) W2 = nd.random.normal(scale=0.01, shape=(number_hiddens, number_outputs)) b2 = nd.zeros(number_outputs) parameters = [W1, b1, W2, b2] for parameter in parameters:
class Test5:
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size=batch_size)
def try_gpu(self): # 本函数已保存在d2lzh包中方便以后使用
try:
ctx = mx.gpu()
_ = nd.zeros((1, ), ctx=ctx)
except mx.base.MXNetError:
ctx = mx.cpu()
return ctx
# 描述
def evaluate_accuracy(data_iter, net, ctx):
acc_sum, n = nd.array([0], ctx=ctx), 0
for X, y in data_iter:
# 如果ctx代表GPU及相应的显存，将数据复制到显存上
X, y = X.as_in_context(ctx), y.as_in_context(ctx).astype('float32')
acc_sum += (net(X).argmax(axis=1) == y).sum()
n += y.size
return acc_sum.asscalar() / n
def generate(self):
net = nn.Sequential()
net.add(
nn.Conv2D(channels=6, kernel_size=5, activation='sigmoid'),
nn.MaxPool2D(pool_size=2, strides=2),
nn.Conv2D(channels=16, kernel_size=5, activation='sigmoid'),
nn.MaxPool2D(pool_size=2, strides=2),
# Dense会默认将(批量大小, 通道, 高, 宽)形状的输入转换成
# (批量大小, 通道 * 高 * 宽)形状的输入
nn.Dense(120, activation='sigmoid'),
nn.Dense(84, activation='sigmoid'),
nn.Dense(10))
X = nd.random.uniform(shape=(1, 1, 28, 28))
net.initialize()
for layer in net:
X = layer(X)
print(layer.name, 'output shape:\t', X.shape)
def train_ch5(net, train_iter, test_iter, batch_size, trainer, ctx,
num_epochs):
print('training on', ctx)
loss = gloss.SoftmaxCrossEntropyLoss()
for epoch in range(num_epochs):
train_l_sum, train_acc_sum, n, start = 0.0, 0.0, 0, time.time()
for X, y in train_iter:
X, y = X.as_in_context(ctx), y.as_in_context(ctx)
with autograd.record():
y_hat = net(X)
l = loss(y_hat, y).sum()
l.backward()
trainer.step(batch_size)
y = y.astype('float32')
train_l_sum += l.asscalar()
train_acc_sum += (y_hat.argmax(axis=1) == y).sum().asscalar()
n += y.size
test_acc = evaluate_accuracy(test_iter, net, ctx)
print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, '
'time %.1f sec' %
(epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc,
time.time() - start))
def main(self):
lr, num_epochs = 0.9, 5
net.initialize(force_reinit=True, ctx=ctx, init=init.Xavier())
trainer = gluon.Trainer(net.collect_params(), 'sgd',
{'learning_rate': lr})
train_ch5(net, train_iter, test_iter, batch_size, trainer, ctx,
num_epochs)
def batchnormalization():
"""
批量归一化利用小批量上的均值和标准差，不断调整神经网络的中间输出，从而使整个神经网络在各层的中间输出的数值更稳定
:return:
"""
def batch_norm(X, gamma, beta, moving_mean, moving_var, eps, momentum):
# 通过autograd来判断当前模式是训练模式还是预测模式
if not autograd.is_training():
# 如果是在预测模式下，直接使用传入的移动平均所得的均值和方差
X_hat = (X - moving_mean) / nd.sqrt(moving_var + eps)
else:
assert len(X.shape) in (2, 4)
if len(X.shape) == 2:
# 使用全连接层的情况，计算特征维上的均值和方差
mean = X.mean(axis=0)
var = ((X - mean)**2).mean(axis=0)
else:
# 使用二维卷积层的情况，计算通道维上（axis=1）的均值和方差。这里我们需要保持
# X的形状以便后面可以做广播运算
mean = X.mean(axis=(0, 2, 3), keepdims=True)
var = ((X - mean)**2).mean(axis=(0, 2, 3), keepdims=True)
# 训练模式下用当前的均值和方差做标准化
X_hat = (X - mean) / nd.sqrt(var + eps)
# 更新移动平均的均值和方差
moving_mean = momentum * moving_mean + (1.0 - momentum) * mean
moving_var = momentum * moving_var + (1.0 - momentum) * var
Y = gamma * X_hat + beta # 拉伸和偏移
return Y, moving_mean, moving_var
class BatchNorm(nn.Block):
def __init__(self, num_features, num_dims, **kwargs):
super(BatchNorm, self).__init__(**kwargs)
if num_dims == 2:
shape = (1, num_features)
else:
shape = (1, num_features, 1, 1)
# 参与求梯度和迭代的拉伸和偏移参数，分别初始化成1和0
self.gamma = self.params.get('gamma', shape=shape, init=init.One())
self.beta = self.params.get('beta', shape=shape, init=init.Zero())
# 不参与求梯度和迭代的变量，全在内存上初始化成0
self.moving_mean = nd.zeros(shape)
self.moving_var = nd.zeros(shape)
def forward(self, X):
# 如果X不在内存上，将moving_mean和moving_var复制到X所在显存上
if self.moving_mean.context != X.context:
self.moving_mean = self.moving_mean.copyto(X.context)
self.moving_var = self.moving_var.copyto(X.context)
# 保存更新过的moving_mean和moving_var
Y, self.moving_mean, self.moving_var = batch_norm(
X,
self.gamma.data(),
self.beta.data(),
self.moving_mean,
self.moving_var,
eps=1e-5,
momentum=0.9)
return Y
net = nn.Sequential()
net.add(nn.Conv2D(6, kernel_size=5), BatchNorm(6, num_dims=4),
nn.Activation('sigmoid'), nn.MaxPool2D(pool_size=2, strides=2),
nn.Conv2D(16, kernel_size=5), BatchNorm(16, num_dims=4),
nn.Activation('sigmoid'), nn.MaxPool2D(pool_size=2, strides=2),
nn.Dense(120), BatchNorm(120, num_dims=2),
nn.Activation('sigmoid'), nn.Dense(84), BatchNorm(84, num_dims=2),
nn.Activation('sigmoid'), nn.Dense(10))
lr, num_epochs, batch_size, ctx = 1.0, 5, 256, d2l.try_gpu()
net.initialize(ctx=ctx, init=init.Xavier())
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr})
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
d2l.train_ch5(net, train_iter, test_iter, batch_size, trainer, ctx,
num_epochs)
net[1].gamma.data().reshape((-1, )), net[1].beta.data().reshape((-1, ))
import d2lzh as d2l
from mxnet import gluon, init
from mxnet.gluon import loss as gloss, nn
bathsize = 256
trainer_iter, test_iter = d2l.load_data_fashion_mnist(bathsize)
net = nn.Sequential()
net.add(nn.Dense(10))
net.initialize(init.Normal(sigma=0.01))
loss = gloss.SoftmaxCELoss()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})
num = 5
d2l.train_ch3(net, trainer_iter, test_iter, loss, num, bathsize, None, None,
trainer)
for x, y in test_iter:
break
truelabes = d2l.get_fashion_mnist_labels(y.asnumpy())
falselabes = d2l.get_fashion_mnist_labels(net(x).argmax(axis=1).asnumpy())
title = [true + '\n' + pred for true, pred in zip(truelabes, falselabes)]
d2l.show_fashion_mnist(x[0:9], title[0:9])
def resnet():
"""
残差块通过跨层的数据通道从而能够训练出有效的深度神经网络。
:return:
"""
class Residual(nn.Block): # 本类已保存在d2lzh包中方便以后使用
def __init__(self,
num_channels,
use_1x1conv=False,
strides=1,
**kwargs):
super(Residual, self).__init__(**kwargs)
self.conv1 = nn.Conv2D(num_channels,
kernel_size=3,
padding=1,
strides=strides)
self.conv2 = nn.Conv2D(num_channels, kernel_size=3, padding=1)
if use_1x1conv:
self.conv3 = nn.Conv2D(num_channels,
kernel_size=1,
strides=strides)
else:
self.conv3 = None
self.bn1 = nn.BatchNorm()
self.bn2 = nn.BatchNorm()
def forward(self, X):
Y = nd.relu(self.bn1(self.conv1(X)))
Y = self.bn2(self.conv2(Y))
if self.conv3:
X = self.conv3(X)
return nd.relu(Y + X)
def resnet_block(num_channels, num_residuals, first_block=False):
blk = nn.Sequential()
for i in range(num_residuals):
if i == 0 and not first_block:
blk.add(Residual(num_channels, use_1x1conv=True, strides=2))
else:
blk.add(Residual(num_channels))
return blk
net = nn.Sequential()
net.add(nn.Conv2D(64, kernel_size=7, strides=2, padding=3), nn.BatchNorm(),
nn.Activation('relu'),
nn.MaxPool2D(pool_size=3, strides=2, padding=1))
net.add(resnet_block(64, 2, first_block=True), resnet_block(128, 2),
resnet_block(256, 2), resnet_block(512, 2))
net.add(nn.GlobalAvgPool2D(), nn.Dense(10))
X = nd.random.uniform(shape=(1, 1, 224, 224))
net.initialize()
for layer in net:
X = layer(X)
print(layer.name, 'output shape:\t', X.shape)
lr, num_epochs, batch_size, ctx = 0.05, 5, 256, d2l.try_gpu()
net.initialize(force_reinit=True, ctx=ctx, init=init.Xavier())
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr})
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=96)
d2l.train_ch5(net, train_iter, test_iter, batch_size, trainer, ctx,
num_epochs)
import d2lzh
from mxnet import gluon, init, nd
from mxnet.gluon import loss as gloss, nn
batch_size = 256
train_iter, test_iter = d2lzh.load_data_fashion_mnist(batch_size)
net = nn.Sequential()
net.add(nn.Dense(256, activation='relu'), nn.Dense(10))
net.initialize(init.Normal(sigma=0.01))
loss = gloss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.5})
num_epoch = 5
d2lzh.train_ch3(net, train_iter, test_iter, loss, num_epoch, batch_size, None,
None, trainer)
nd.concat(X, nd.power)
class MLP:
batch_size = 256
num_inputs, num_outputs, num_hiddens = 784, 10, 256
W1 = torch.tensor(np.random.normal(0, 0.01, (num_inputs, num_hiddens)),
dtype=torch.float)
b1 = torch.zeros(num_hiddens, dtype=torch.float)
W2 = torch.tensor(np.random.normal(0, 0.01, (num_hiddens, num_outputs)),
dtype=torch.float)
b2 = torch.zeros(num_outputs, dtype=torch.float)
params = [W1, b1, W2, b2]
for param in params:
param.requires_grad_(requires_grad=True)
loss = torch.nn.CrossEntropyLoss()
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
@staticmethod
def relu(X):
'''
激活函数，自己定义的，现在用relu更常用
'''
return torch.max(input=X, other=torch.tensor(0.0))
def net(self, X):
X = X.view((-1, self.num_inputs))
H = self.relu(torch.matmul(X, self.W1) + self.b1)
return torch.matmul(H, self.W2) + self.b2
def train(self):
num_epochs, lr = 5, 100.0
d2l.train_ch3(self.net, self.train_iter, self.test_iter, self.loss,
num_epochs, self.batch_size, self.params, lr)
pass
@staticmethod
def xyplot(x_vals, y_vals, name):
d2l.set_figsize(figsize=(5, 2.5))
d2l.plt.plot(x_vals.detach().numpy(), y_vals.detach().numpy())
d2l.plt.xlabel('x')
d2l.plt.ylabel(name + '(x)')
plt.show()
plt.close()
@staticmethod
def testxplot(mtd=0):
"""
H=ϕ(XWh+bh),
O=HWo+bo,
其中ϕ就是激活函数{rele|sigmoid|tanh等}
"""
x = torch.arange(-8.0, 8.0, 0.1, requires_grad=True)
switcher = {
0: x.relu, # 分段函数
1: x.sigmoid, # 0-1之间
2: x.tanh, # -1到1之间
}
if switcher.get(mtd):
y = switcher.get(mtd)()
MLP.xyplot(x, y, 'relu')
y.sum().backward()
MLP.xyplot(x, x.grad, 'grad of relu')
def resnet_block(num_channels, num_residuals, first_block=False):
blk = nn.Sequential()
for i in range(num_residuals):
if i == 0 and not first_block:
blk.add(Residual(num_channels, use_1x1conv=True, strides=2))
else:
blk.add(Residual(num_channels))
return blk
net.add(resnet_block(64, 2, first_block=True),
resnet_block(128, 2),
resnet_block(256, 2),
resnet_block(512, 2))
net.add(nn.GlobalAvgPool2D(), nn.Dense(10))
X = nd.random.uniform(shape=(1, 1, 224, 224))
net.initialize()
for layer in net:
X = layer(X)
print(layer.name, 'output shape:\t', X.shape)
lr, num_epochs, batch_size, ctx = 0.05, 5, 256, d2l.try_gpu()
net.initialize(force_reinit=True, ctx=ctx, init=init.Xavier())
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr})
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=96)
d2l.train_ch5(net, train_iter, test_iter, batch_size, trainer, ctx,
num_epochs)
class EasyMLP:
num_inputs, num_outputs, num_hiddens = 784, 10, 256
net = nn.Sequential(
d2l.FlattenLayer(),
nn.Linear(num_inputs, num_hiddens),
nn.ReLU(),
nn.Linear(num_hiddens, num_outputs),
)
for params in net.parameters():
init.normal_(params, mean=0, std=0.01)
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
loss = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.5)
num_epochs = 5
@staticmethod
def sgd(params, lr, batch_size):
# 为了和原书保持一致，这里除以了batch_size，但是应该是不用除的，因为一般用PyTorch计算loss时就默认已经
# 沿batch维求了平均了。
for param in params:
param.data -= lr * param.grad / batch_size # 注意这里更改param时用的param.data
@staticmethod
def evaluate_accuracy(data_iter, net, device=None):
if device is None and isinstance(net, torch.nn.Module):
# 如果没指定device就使用net的device
device = list(net.parameters())[0].device
acc_sum, n = 0.0, 0
with torch.no_grad():
for X, y in data_iter:
if isinstance(net, torch.nn.Module):
net.eval() # 评估模式, 这会关闭dropout
acc_sum += (net(X.to(device)).argmax(
dim=1) == y.to(device)).float().sum().cpu().item()
net.train() # 改回训练模式
else: # 自定义的模型, 3.13节之后不会用到, 不考虑GPU
if ('is_training'
in net.__code__.co_varnames): # 如果有is_training这个参数
# 将is_training设置成False
acc_sum += (net(X, is_training=False).argmax(
dim=1) == y).float().sum().item()
else:
acc_sum += (net(X).argmax(
dim=1) == y).float().sum().item()
n += y.shape[0]
return acc_sum / n
def train(self):
# d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None, None, optimizer)
for epoch in range(self.num_epochs):
train_l_sum, train_acc_sum, n = 0.0, 0.0, 0
for X, y in self.train_iter:
y_hat = self.net(X)
l = self.loss(y_hat, y).sum()
# 梯度清零
if self.optimizer is not None:
self.optimizer.zero_grad()
elif self.params is not None and self.params[
0].grad is not None:
for param in self.params:
param.grad.data.zero_()
l.backward()
if self.optimizer is None:
self.sgd(self.params, self.lr, self.batch_size)
else:
self.optimizer.step() # “softmax回归的简洁实现”一节将用到
train_l_sum += l.item()
train_acc_sum += (y_hat.argmax(dim=1) == y).sum().item()
n += y.shape[0]
test_acc = self.evaluate_accuracy(self.test_iter, self.net)
print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f' %
(epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc))
