《深度学习入门》学习代码
资源下载: 《深度学习入门》pdf+代码 提取码：4gai
《深度学习入门》学习笔记
文章目录
-
- 第三章 nn
- 第四章 2_layer_nn
- 第五章 2_layer_nn
- 第六章 optimizer
- 第七章 简单的ConvNet
- 第八章 DeepConvNet
第三章 nn
调用训练好的sample_weight.pkl
one_hot 标签：[0 0 1 0 0 0 0 0 0 0]
# 神经网络识别 MNIST手写数字集
# coding: utf-8
import sys, os
sys.path.append(os.pardir) # 为了导入父目录的文件而进行的设定
import numpy as np
import pickle
from dataset.mnist import load_mnist
from common.functions import sigmoid, softmax
def get_data():
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, flatten=True, one_hot_label=False)
# normalize=True, 数据的值限定在0~1，正则化
return x_test, t_test
def init_network():
with open("sample_weight.pkl", 'rb') as f:
network = pickle.load(f)
return network
def predict(network, x):
W1, W2, W3 = network['W1'], network['W2'], network['W3']
b1, b2, b3 = network['b1'], network['b2'], network['b3']
a1 = np.dot(x, W1) + b1
z1 = sigmoid(a1)
a2 = np.dot(z1, W2) + b2
z2 = sigmoid(a2)
a3 = np.dot(z2, W3) + b3
y = softmax(a3)
return y
# x, t = get_data()
# network = init_network()
# accuracy_cnt = 0
# for i in range(len(x)):
# y = predict(network, x[i])
# p= np.argmax(y) # 获取概率最高的元素的索引
# if p == t[i]:
# accuracy_cnt += 1
# print("label = ", str(t[i]), "\tpredict = ", str(p))
#
# print("Accuracy:" + str(float(accuracy_cnt) / len(x)))
## 批处理
x, t = get_data()
network = init_network()
batch_size = 100 # 批数量
accuracy_cnt = 0
for i in range(0, len(x), batch_size):
x_batch = x[i:i+batch_size]
y_batch = predict(network, x_batch)
p = np.argmax(y_batch, axis=1)
accuracy_cnt += np.sum(p == t[i:i+batch_size])
print("Accuracy:" + str(float(accuracy_cnt) / len(x)))
运行结果：
Accuracy:0.9352
第四章 2_layer_nn
# 2_layer_nn
import sys, os
sys.path.append(os.pardir) # 为了导入父目录的文件而进行的设定
import numpy as np
import matplotlib.pyplot as plt
from dataset.mnist import load_mnist
from common.functions import *
from common.gradient import numerical_gradient
class TwoLayerNet:
def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):
# 初始化权重
self.params = {
}
self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)
self.params['b1'] = np.zeros(hidden_size)
self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)
self.params['b2'] = np.zeros(output_size)
def predict(self, x):
# 识别、推理
# x 是图像数据
W1, W2 = self.params['W1'], self.params['W2']
b1, b2 = self.params['b1'], self.params['b2']
a1 = np.dot(x, W1) + b1
z1 = sigmoid(a1)
a2 = np.dot(z1, W2) + b2
y = softmax(a2)
return y
# x:输入数据, t:监督数据
def loss(self, x, t):
y = self.predict(x)
return cross_entropy_error(y, t)
def accuracy(self, x, t):
y = self.predict(x)
y = np.argmax(y, axis=1)
t = np.argmax(t, axis=1)
accuracy = np.sum(y == t) / float(x.shape[0])
return accuracy
# x:输入数据, t:监督数据
def numerical_gradient(self, x, t):
# 计算梯度
loss_W = lambda W: self.loss(x, t)
grads = {
} # 字典变量
grads['W1'] = numerical_gradient(loss_W, self.params['W1'])
grads['b1'] = numerical_gradient(loss_W, self.params['b1'])
grads['W2'] = numerical_gradient(loss_W, self.params['W2'])
grads['b2'] = numerical_gradient(loss_W, self.params['b2'])
return grads
def gradient(self, x, t):
# 计算梯度
# numerical_gradient的高速版
W1, W2 = self.params['W1'], self.params['W2']
b1, b2 = self.params['b1'], self.params['b2']
grads = {
}
batch_num = x.shape[0]
# forward
a1 = np.dot(x, W1) + b1
z1 = sigmoid(a1)
a2 = np.dot(z1, W2) + b2
y = softmax(a2)
# backward
dy = (y - t) / batch_num
grads['W2'] = np.dot(z1.T, dy)
grads['b2'] = np.sum(dy, axis=0)
da1 = np.dot(dy, W2.T)
dz1 = sigmoid_grad(a1) * da1
grads['W1'] = np.dot(x.T, dz1)
grads['b1'] = np.sum(dz1, axis=0)
return grads
def train():
# 读入数据
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)
train_loss_list = []
train_acc_list = []
test_acc_list = []
# 超参数
iters_num = 50000 # 适当设定循环的次数
train_size = x_train.shape[0]
batch_size = 100
learning_rate = 0.1
network = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)
iter_per_epoch = max(train_size / batch_size, 1)
for i in range(iters_num):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
# 计算梯度
# grad = network.numerical_gradient(x_batch, t_batch)
grad = network.gradient(x_batch, t_batch)
# 更新参数
for key in ('W1', 'b1', 'W2', 'b2'):
network.params[key] -= learning_rate * grad[key]
# 记录学习过程
loss = network.loss(x_batch, t_batch)
train_loss_list.append(loss)
if i % iter_per_epoch == 0:
train_acc = network.accuracy(x_train, t_train)
test_acc = network.accuracy(x_test, t_test)
train_acc_list.append(train_acc)
test_acc_list.append(test_acc)
print("train acc, test acc | " + str(train_acc) + ", " + str(test_acc))
# 绘制图形
markers = {
'train': 'o', 'test': 's'}
x = np.arange(len(train_acc_list))
plt.plot(x, train_acc_list, label='train acc')
plt.plot(x, test_acc_list, label='test acc', linestyle='--')
plt.xlabel("epochs")
plt.ylabel("accuracy")
plt.ylim(0, 1.0)
plt.legend(loc='lower right')
plt.show()
train()
结果：
train acc, test acc | 0.10218333333333333, 0.101
train acc, test acc | 0.7928333333333333, 0.7966
train acc, test acc | 0.87415, 0.8808
train acc, test acc | 0.8976833333333334, 0.9031
train acc, test acc | 0.9083666666666667, 0.9113
train acc, test acc | 0.91435, 0.9167
train acc, test acc | 0.9186166666666666, 0.9209
train acc, test acc |0.9231, 0.9253
train acc, test acc | 0.9273666666666667, 0.9291
train acc, test acc | 0.9316166666666666, 0.933
。。。。
train acc, test acc | 0.9474166666666667, 0.9466
第五章 2_layer_nn
与上一个类似
# coding: utf-8
import sys, os
sys.path.append(os.pardir) # 为了导入父目录的文件而进行的设定
import numpy as np
from common.layers import *
from common.gradient import numerical_gradient
from collections import OrderedDict
from dataset.mnist import load_mnist
class TwoLayerNet:
def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):
# 初始化权重
self.params = {
}
self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)
self.params['b1'] = np.zeros(hidden_size)
self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)
self.params['b2'] = np.zeros(output_size)
# 生成层
self.layers = OrderedDict()
self.layers['Affine1'] = Affine(self.params['W1'], self.params['b1'])
self.layers['Relu1'] = Relu()
self.layers['Affine2'] = Affine(self.params['W2'], self.params['b2'])
self.lastLayer = SoftmaxWithLoss()
def predict(self, x):
for layer in self.layers.values():
x = layer.forward(x)
return x
# x:输入数据, t:监督数据
def loss(self, x, t):
y = self.predict(x)
return self.lastLayer.forward(y, t)
def accuracy(self, x, t):
y = self.predict(x)
y = np.argmax(y, axis=1)
if t.ndim != 1: t = np.argmax(t, axis=1)
accuracy = np.sum(y == t) / float(x.shape[0])
return accuracy
# x:输入数据, t:监督数据
def numerical_gradient(self, x, t):
loss_W = lambda W: self.loss(x, t)
grads = {
}
grads['W1'] = numerical_gradient(loss_W, self.params['W1'])
grads['b1'] = numerical_gradient(loss_W, self.params['b1'])
grads['W2'] = numerical_gradient(loss_W, self.params['W2'])
grads['b2'] = numerical_gradient(loss_W, self.params['b2'])
return grads
def gradient(self, x, t):
# forward
self.loss(x, t)
# backward
dout = 1
dout = self.lastLayer.backward(dout)
layers = list(self.layers.values())
layers.reverse()
for layer in layers:
dout = layer.backward(dout)
# 设定
grads = {
}
grads['W1'], grads['b1'] = self.layers['Affine1'].dW, self.layers['Affine1'].db
grads['W2'], grads['b2'] = self.layers['Affine2'].dW, self.layers['Affine2'].db
return grads
def train():
# 读入数据
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)
network = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)
# iters_num = 10000
iters_num = 100
train_size = x_train.shape[0]
batch_size = 100
learning_rate = 0.1
train_loss_list = []
train_acc_list = []
test_acc_list = []
iter_per_epoch = max(train_size / batch_size, 1)
for i in range(iters_num):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
# 梯度
# grad = network.numerical_gradient(x_batch, t_batch)
grad = network.gradient(x_batch, t_batch)
# 更新
for key in ('W1', 'b1', 'W2', 'b2'):
network.params[key] -= learning_rate * grad[key]
loss = network.loss(x_batch, t_batch)
train_loss_list.append(loss)
if i % iter_per_epoch == 0:
train_acc = network.accuracy(x_train, t_train)
test_acc = network.accuracy(x_test, t_test)
train_acc_list.append(train_acc)
test_acc_list.append(test_acc)
print("train acc, test acc | " + str(train_acc) + ", " + str(test_acc))
y = network.predict(x_test[0:9])
y = np.argmax(y, axis=1)
print("预测值： ", y)
print("真实值： ",np.argmax(t_test[0:9], axis=1))
train()
train acc, test acc | 0.9786666666666667, 0.9701
预测值： [7 2 1 0 4 1 4 9 6]
真实值： [7 2 1 0 4 1 4 9 5]
第六章 optimizer
基于MNIST数据集的更新方法的比较,SGD、Momentum、
AdaGrad、Adam
# coding: utf-8
import os
import sys
sys.path.append(os.pardir) # 为了导入父目录的文件而进行的设定
import matplotlib.pyplot as plt
from dataset.mnist import load_mnist
from common.util import smooth_curve
from common.multi_layer_net import MultiLayerNet
from common.optimizer import *
# 0:读入MNIST数据==========
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True)
train_size = x_train.shape[0]
batch_size = 128
max_iterations = 2000
# 1:进行实验的设置==========
optimizers = {
}
optimizers['SGD'] = SGD()
optimizers['Momentum'] = Momentum()
optimizers['AdaGrad'] = AdaGrad()
optimizers['Adam'] = Adam()
# optimizers['RMSprop'] = RMSprop()
networks = {
}
train_loss = {
}
for key in optimizers.keys():
networks[key] = MultiLayerNet(
input_size=784, hidden_size_list=[100, 100, 100, 100],
output_size=10)
train_loss[key] = []
# 2:开始训练==========
for i in range(max_iterations):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
for key in optimizers.keys():
grads = networks[key].gradient(x_batch, t_batch)
optimizers[key].update(networks[key].params, grads)
loss = networks[key].loss(x_batch, t_batch)
train_loss[key].append(loss)
if i % 100 == 0:
print("===========" + "iteration:" + str(i) + "===========")
for key in optimizers.keys():
loss = networks[key].loss(x_batch, t_batch)
print(key + ":" + str(loss))
# 3.绘制图形==========
markers = {
"SGD": "o", "Momentum": "x", "AdaGrad": "s", "Adam": "D"}
x = np.arange(max_iterations)
for key in optimizers.keys():
plt.plot(x, smooth_curve(train_loss[key]), marker=markers[key], markevery=100, label=key)
plt.xlabel("iterations")
plt.ylabel("loss")
plt.ylim(0, 1)
plt.legend()
plt.show()
结果
# coding: utf-8
import sys, os
sys.path.append(os.pardir) # 为了导入父目录的文件而进行的设定
import numpy as np
import matplotlib.pyplot as plt
from collections import OrderedDict
from common.optimizer import *
def f(x, y):
return x ** 2 / 20.0 + y ** 2
def df(x, y):
return x / 10.0, 2.0 * y
init_pos = (-7.0, 2.0)
params = {
}
params['x'], params['y'] = init_pos[0], init_pos[1]
grads = {
}
grads['x'], grads['y'] = 0, 0
optimizers = OrderedDict()
optimizers["SGD"] = SGD(lr=0.95)
optimizers["Momentum"] = Momentum(lr=0.1)
optimizers["AdaGrad"] = AdaGrad(lr=1.5)
optimizers["Adam"] = Adam(lr=0.3)
idx = 1
for key in optimizers:
optimizer = optimizers[key]
x_history = []
y_history = []
params['x'], params['y'] = init_pos[0], init_pos[1]
for i in range(30):
x_history.append(params['x'])
y_history.append(params['y'])
grads['x'], grads['y'] = df(params['x'], params['y'])
optimizer.update(params, grads)
x = np.arange(-10, 10, 0.01)
y = np.arange(-5, 5, 0.01)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
# for simple contour line
mask = Z > 7
Z[mask] = 0
# plot
plt.subplot(2, 2, idx)
idx += 1
plt.plot(x_history, y_history, 'o-', color="red")
plt.contour(X, Y, Z)
plt.ylim(-10, 10)
plt.xlim(-10, 10)
plt.plot(0, 0, '+')
# colorbar()
# spring()
plt.title(key)
plt.xlabel("x")
plt.ylabel("y")
plt.show()
结果
第七章 简单的ConvNet
# coding: utf-8
import sys, os
sys.path.append(os.pardir) # 为了导入父目录的文件而进行的设定
import pickle
import numpy as np
from collections import OrderedDict
from common.layers import *
from common.functions import *
from common.gradient import numerical_gradient
import matplotlib.pyplot as plt
from dataset.mnist import load_mnist
from common.trainer import Trainer
class ConvNet:
"""简单的ConvNet
conv - relu - pool - affine - relu - affine - softmax
Parameters
----------
input_size : 输入大小（MNIST的情况下为784）
hidden_size_list : 隐藏层的神经元数量的列表（e.g. [100, 100, 100]）
output_size : 输出大小（MNIST的情况下为10）
activation : 'relu' or 'sigmoid'
weight_init_std : 指定权重的标准差（e.g. 0.01）
指定'relu'或'he'的情况下设定“He的初始值”
指定'sigmoid'或'xavier'的情况下设定“Xavier的初始值”
"""
def __init__(self, input_dim=(1, 28, 28),
conv_param={
'filter_num': 30, 'filter_size': 5, 'pad': 0, 'stride': 1},
hidden_size=100, output_size=10, weight_init_std=0.01):
# 取出卷积层超参数
filter_num = conv_param['filter_num']
filter_size = conv_param['filter_size']
filter_pad = conv_param['pad']
filter_stride = conv_param['stride']
input_size = input_dim[1]
conv_output_size = (input_size - filter_size + 2 * filter_pad) / filter_stride + 1
pool_output_size = int(filter_num * (conv_output_size / 2) * (conv_output_size / 2))
# 初始化权重
self.params = {
}
self.params['W1'] = weight_init_std * \
np.random.randn(filter_num, input_dim[0], filter_size, filter_size)
self.params['b1'] = np.zeros(filter_num)
self.params['W2'] = weight_init_std * \
np.random.randn(pool_output_size, hidden_size)
self.params['b2'] = np.zeros(hidden_size)
self.params['W3'] = weight_init_std * \
np.random.randn(hidden_size, output_size)
self.params['b3'] = np.zeros(output_size)
# 生成层
self.layers = OrderedDict()
self.layers['Conv1'] = Convolution(self.params['W1'], self.params['b1'],
conv_param['stride'], conv_param['pad'])
self.layers['Relu1'] = Relu()
self.layers['Pool1'] = Pooling(pool_h=2, pool_w=2, stride=2)
self.layers['Affine1'] = Affine(self.params['W2'], self.params['b2'])
self.layers['Relu2'] = Relu()
self.layers['Affine2'] = Affine(self.params['W3'], self.params['b3'])
self.last_layer = SoftmaxWithLoss()
def predict(self, x):
for layer in self.layers.values():
x = layer.forward(x)
return x
def loss(self, x, t):
"""求损失函数
参数x是输入数据、t是教师标签
"""
y = self.predict(x)
return self.last_layer.forward(y, t)
def accuracy(self, x, t, batch_size=100):
if t.ndim != 1: t = np.argmax(t, axis=1)
acc = 0.0
for i in range(int(x.shape[0] / batch_size)):
tx = x[i * batch_size:(i + 1) * batch_size]
tt = t[i * batch_size:(i + 1) * batch_size]
y = self.predict(tx)
y = np.argmax(y, axis=1)
acc += np.sum(y == tt)
return acc / x.shape[0]
def numerical_gradient(self, x, t):
"""求梯度（数值微分）
Parameters
----------
x : 输入数据
t : 教师标签
Returns
-------
具有各层的梯度的字典变量
grads['W1']、grads['W2']、...是各层的权重
grads['b1']、grads['b2']、...是各层的偏置
"""
loss_w = lambda w: self.loss(x, t)
grads = {
}
for idx in (1, 2, 3):
grads['W' + str(idx)] = numerical_gradient(loss_w, self.params['W' + str(idx)])
grads['b' + str(idx)] = numerical_gradient(loss_w, self.params['b' + str(idx)])
return grads
def gradient(self, x, t):
"""求梯度（误差反向传播法）
Parameters
----------
x : 输入数据
t : 教师标签
Returns
-------
具有各层的梯度的字典变量
grads['W1']、grads['W2']、...是各层的权重
grads['b1']、grads['b2']、...是各层的偏置
"""
# forward
self.loss(x, t)
# backward
dout = 1
dout = self.last_layer.backward(dout)
layers = list(self.layers.values())
layers.reverse()
for layer in layers:
dout = layer.backward(dout)
# 设定
grads = {
}
grads['W1'], grads['b1'] = self.layers['Conv1'].dW, self.layers['Conv1'].db
grads['W2'], grads['b2'] = self.layers['Affine1'].dW, self.layers['Affine1'].db
grads['W3'], grads['b3'] = self.layers['Affine2'].dW, self.layers['Affine2'].db
return grads
def save_params(self, file_name="params.pkl"):
params = {
}
for key, val in self.params.items():
params[key] = val
with open(file_name, 'wb') as f:
pickle.dump(params, f)
def load_params(self, file_name="params.pkl"):
with open(file_name, 'rb') as f:
params = pickle.load(f)
for key, val in params.items():
self.params[key] = val
for i, key in enumerate(['Conv1', 'Affine1', 'Affine2']):
self.layers[key].W = self.params['W' + str(i + 1)]
self.layers[key].b = self.params['b' + str(i + 1)]
def train():
# 读入数据
(x_train, t_train), (x_test, t_test) = load_mnist(flatten=False)
# 处理花费时间较长的情况下减少数据
x_train, t_train = x_train[:5000], t_train[:5000]
x_test, t_test = x_test[:1000], t_test[:1000]
max_epochs = 20
network = ConvNet(input_dim=(1, 28, 28),
conv_param={
'filter_num': 30, 'filter_size': 5, 'pad': 0, 'stride': 1},
hidden_size=100, output_size=10, weight_init_std=0.01)
trainer = Trainer(network, x_train, t_train, x_test, t_test,
epochs=max_epochs, mini_batch_size=100,
optimizer='Adam', optimizer_param={
'lr': 0.001},
evaluate_sample_num_per_epoch=1000)
trainer.train()
# 保存参数
network.save_params("params.pkl")
print("Saved Network Parameters!")
# 绘制图形
markers = {
'train': 'o', 'test': 's'}
x = np.arange(max_epochs)
plt.plot(x, trainer.train_acc_list, marker='o', label='train', markevery=2)
plt.plot(x, trainer.test_acc_list, marker='s', label='test', markevery=2)
plt.xlabel("epochs")
plt.ylabel("accuracy")
plt.ylim(0, 1.0)
plt.legend(loc='lower right')
plt.show()
y = network.predict(x_test[0:9])
y = np.argmax(y, axis=1)
print("预测值： ", y)
print("真实值： ", np.argmax(t_test[0:9], axis=1))
train()
结果
train loss:0.07818116457244638
第八章 DeepConvNet
# coding: utf-8 书P236
import sys, os
sys.path.append(os.pardir) # 为了导入父目录的文件而进行的设定
import pickle
import numpy as np
from collections import OrderedDict
from common.layers import *
from dataset.mnist import load_mnist
from common.trainer import Trainer
class DeepConvNet:
"""识别率为99%以上的高精度的ConvNet
网络结构如下所示
conv - relu - conv- relu - pool -
conv - relu - conv- relu - pool -
conv - relu - conv- relu - pool -
affine - relu - dropout - affine - dropout - softmax
"""
def __init__(self, input_dim=(1, 28, 28),
conv_param_1 = {
'filter_num':16, 'filter_size':3, 'pad':1, 'stride':1},
conv_param_2 = {
'filter_num':16, 'filter_size':3, 'pad':1, 'stride':1},
conv_param_3 = {
'filter_num':32, 'filter_size':3, 'pad':1, 'stride':1},
conv_param_4 = {
'filter_num':32, 'filter_size':3, 'pad':2, 'stride':1},
conv_param_5 = {
'filter_num':64, 'filter_size':3, 'pad':1, 'stride':1},
conv_param_6 = {
'filter_num':64, 'filter_size':3, 'pad':1, 'stride':1},
hidden_size=50, output_size=10):
# 初始化权重===========
# 各层的神经元平均与前一层的几个神经元有连接（TODO:自动计算）
pre_node_nums = np.array([1*3*3, 16*3*3, 16*3*3, 32*3*3, 32*3*3, 64*3*3, 64*4*4, hidden_size])
wight_init_scales = np.sqrt(2.0 / pre_node_nums) # 使用ReLU的情况下推荐的初始值
self.params = {
}
pre_channel_num = input_dim[0]
for idx, conv_param in enumerate([conv_param_1, conv_param_2, conv_param_3, conv_param_4, conv_param_5, conv_param_6]):
self.params['W' + str(idx+1)] = wight_init_scales[idx] * np.random.randn(conv_param['filter_num'], pre_channel_num, conv_param['filter_size'], conv_param['filter_size'])
self.params['b' + str(idx+1)] = np.zeros(conv_param['filter_num'])
pre_channel_num = conv_param['filter_num']
self.params['W7'] = wight_init_scales[6] * np.random.randn(64*4*4, hidden_size)
self.params['b7'] = np.zeros(hidden_size)
self.params['W8'] = wight_init_scales[7] * np.random.randn(hidden_size, output_size)
self.params['b8'] = np.zeros(output_size)
# 生成层===========
self.layers = []
self.layers.append(Convolution(self.params['W1'], self.params['b1'],
conv_param_1['stride'], conv_param_1['pad']))
self.layers.append(Relu())
self.layers.append(Convolution(self.params['W2'], self.params['b2'],
conv_param_2['stride'], conv_param_2['pad']))
self.layers.append(Relu())
self.layers.append(Pooling(pool_h=2, pool_w=2, stride=2))
self.layers.append(Convolution(self.params['W3'], self.params['b3'],
conv_param_3['stride'], conv_param_3['pad']))
self.layers.append(Relu())
self.layers.append(Convolution(self.params['W4'], self.params['b4'],
conv_param_4['stride'], conv_param_4['pad']))
self.layers.append(Relu())
self.layers.append(Pooling(pool_h=2, pool_w=2, stride=2))
self.layers.append(Convolution(self.params['W5'], self.params['b5'],
conv_param_5['stride'], conv_param_5['pad']))
self.layers.append(Relu())
self.layers.append(Convolution(self.params['W6'], self.params['b6'],
conv_param_6['stride'], conv_param_6['pad']))
self.layers.append(Relu())
self.layers.append(Pooling(pool_h=2, pool_w=2, stride=2))
self.layers.append(Affine(self.params['W7'], self.params['b7']))
self.layers.append(Relu())
self.layers.append(Dropout(0.5))
self.layers.append(Affine(self.params['W8'], self.params['b8']))
self.layers.append(Dropout(0.5))
self.last_layer = SoftmaxWithLoss()
def predict(self, x, train_flg=False):
for layer in self.layers:
if isinstance(layer, Dropout):
x = layer.forward(x, train_flg)
else:
x = layer.forward(x)
return x
def loss(self, x, t):
y = self.predict(x, train_flg=True)
return self.last_layer.forward(y, t)
def accuracy(self, x, t, batch_size=100):
if t.ndim != 1 : t = np.argmax(t, axis=1)
acc = 0.0
for i in range(int(x.shape[0] / batch_size)):
tx = x[i*batch_size:(i+1)*batch_size]
tt = t[i*batch_size:(i+1)*batch_size]
y = self.predict(tx, train_flg=False)
y = np.argmax(y, axis=1)
acc += np.sum(y == tt)
return acc / x.shape[0]
def gradient(self, x, t):
# forward
self.loss(x, t)
# backward
dout = 1
dout = self.last_layer.backward(dout)
tmp_layers = self.layers.copy()
tmp_layers.reverse()
for layer in tmp_layers:
dout = layer.backward(dout)
# 设定
grads = {
}
for i, layer_idx in enumerate((0, 2, 5, 7, 10, 12, 15, 18)):
grads['W' + str(i+1)] = self.layers[layer_idx].dW
grads['b' + str(i+1)] = self.layers[layer_idx].db
return grads
def save_params(self, file_name="params.pkl"):
params = {
}
for key, val in self.params.items():
params[key] = val
with open(file_name, 'wb') as f:
pickle.dump(params, f)
def load_params(self, file_name="params.pkl"):
with open(file_name, 'rb') as f:
params = pickle.load(f)
for key, val in params.items():
self.params[key] = val
for i, layer_idx in enumerate((0, 2, 5, 7, 10, 12, 15, 18)):
self.layers[layer_idx].W = self.params['W' + str(i+1)]
self.layers[layer_idx].b = self.params['b' + str(i+1)]
def train():
(x_train, t_train), (x_test, t_test) = load_mnist(flatten=False)
# 处理花费时间较长的情况下减少数据
x_train, t_train = x_train[:5000], t_train[:5000]
x_test, t_test = x_test[:1000], t_test[:1000]
network = DeepConvNet()
trainer = Trainer(network, x_train, t_train, x_test, t_test,
epochs=20, mini_batch_size=100,
optimizer='Adam', optimizer_param={
'lr': 0.001},
evaluate_sample_num_per_epoch=1000)
trainer.train()
# 保存参数
network.save_params("deep_convnet_params.pkl")
print("Saved Network Parameters!")
train()