def show_mnist():
def img_show(img):
pil_img = Image.fromarray(np.uint8(img))
pil_img.show()
(x_train, t_train), (x_test, t_test) = load_mnist(flatten=True,
normalize=False)
img = x_train[0]
label = t_train[0]
print(label)
print(img.shape)
img = img.reshape(28, 28)
print(img.shape)
img_show(img)
# 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 dplearning.dataset.mnist import load_mnist
from dplearning.common.multi_layer_net_extend import MultiLayerNetExtend
# 读入数据
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)
network = MultiLayerNetExtend(input_size=784, hidden_size_list=[100, 100], output_size=10,
use_batchnorm=True)
x_batch = x_train[:1]
t_batch = t_train[:1]
grad_backprop = network.gradient(x_batch, t_batch)
grad_numerical = network.numerical_gradient(x_batch, t_batch)
for key in grad_numerical.keys():
diff = np.average( np.abs(grad_backprop[key] - grad_numerical[key]) )
print(key + ":" + str(diff))
# coding: utf-8
import sys, os
sys.path.append(os.pardir) # 为了导入父目录而进行的设定
import numpy as np
import matplotlib.pyplot as plt
from dplearning.ch08.deep_convnet import DeepConvNet
from dplearning.dataset.mnist import load_mnist
(x_train, t_train), (x_test, t_test) = load_mnist(flatten=False)
network = DeepConvNet()
network.load_params("deep_convnet_params.pkl")
print("calculating test accuracy ... ")
#sampled = 1000
#x_test = x_test[:sampled]
#t_test = t_test[:sampled]
classified_ids = []
acc = 0.0
batch_size = 100
for i in range(int(x_test.shape[0] / batch_size)):
tx = x_test[i * batch_size:(i + 1) * batch_size]
tt = t_test[i * batch_size:(i + 1) * batch_size]
y = network.predict(tx, train_flg=False)
y = np.argmax(y, axis=1)
classified_ids.append(y)
acc += np.sum(y == tt)
# 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 dplearning.dataset.mnist import load_mnist
from dplearning.common.multi_layer_net_extend import MultiLayerNetExtend
from dplearning.common.optimizer import SGD, Adam
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True)
# 减少学习数据
x_train = x_train[:1000]
t_train = t_train[:1000]
max_epochs = 20
train_size = x_train.shape[0]
batch_size = 100
learning_rate = 0.01
def __train(weight_init_std):
bn_network = MultiLayerNetExtend(input_size=784, hidden_size_list=[100, 100, 100, 100, 100], output_size=10,
weight_init_std=weight_init_std, use_batchnorm=True)
network = MultiLayerNetExtend(input_size=784, hidden_size_list=[100, 100, 100, 100, 100], output_size=10,
weight_init_std=weight_init_std)
optimizer = SGD(lr=learning_rate)
train_acc_list = []
bn_train_acc_list = []
# 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 dplearning.dataset.mnist import load_mnist
from PIL import Image
def img_show(img):
pil_img = Image.fromarray(np.uint8(img))
pil_img.show()
(x_train, t_train), (x_test, t_test) = load_mnist(flatten=True,
normalize=False)
img = x_train[0]
label = t_train[0]
print(label) # 5
print(img.shape) # (784,)
img = img.reshape(28, 28) # 把图像的形状变为原来的尺寸
print(img.shape) # (28, 28)
img_show(img)
def get_data():
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True,
flatten=True,
one_hot_label=True)
return x_test, t_test
def get_data():
(train_img, train_label), (test_img, test_label) = load_mnist()
return test_img, test_label
