def load_all_data():
X_train, y_train = load_mnist('training')
X_test, y_test = load_mnist('testing')
# Reshape the image data into rows
X_train = np.reshape(X_train, (X_train.shape[0], -1))
X_test = np.reshape(X_test, (X_test.shape[0], -1))
return X_train, y_train, X_test, y_test
import numpy as np
import tensorflow as tf
from load_mnist import *
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# Load Mnist Data
u_bound = 10
mnist_input_size = 784 ## 28 * 28
X_train, y_train = load_mnist('training', range(0, u_bound))
X_test, y_test = load_mnist('testing', range(0, u_bound))
# Get and Scramble training set, with n items per category
training_set = []
training_labels = []
n = 5
for j in range(0, u_bound):
hld = np.array(list(np.where(y_train == np.float64(j)))).flatten()
training_set = training_set + list(
np.random.choice(hld, size=n, replace=False))
training_labels = training_labels + [np.float64(j)] * n
training_set = np.array(training_set)
training_labels = np.array(training_labels)
p = np.random.permutation(
len(training_set)
) ## This scrambles the training set/labels, which would otherwise be in order
training_set = training_set[p] ## this is a list of indices
training_labels = training_labels[p] ## this is a list of generated labels
history = best_network.evaluate(X_test,Y_test)
fig = plt.figure()
plt.plot(loss_log)
plt.title('Final Training Loss over interation')
plt.xlabel('Iternation')
plt.ylabel('Loss')
plt.savefig('./training_loss_figure.png')
plt.show()
print("Final Testing accuracy: "+ str(history[1]))
if __name__ == '__main__':
# load dataset
X_train, Y_train = load_mnist("training")
X_test, Y_test = load_mnist("testing")
# normalize the input data
X_train = X_train / 255.
X_test = X_test / 255.
# reshape image
img_rows = 28
img_cols = 28
X_train = np.reshape(X_train, [np.shape(X_train)[0],img_rows, img_cols,1 ])
X_test = np.reshape(X_test, [np.shape(X_test)[0],img_rows, img_cols,1 ])
# one-hot encoding for labels
Y_train = tf.keras.utils.to_categorical(Y_train, 10)
Y_test = tf.keras.utils.to_categorical(Y_test, 10)
# -*- coding: utf-8 -*-
"""
Created on
@author: fame
"""
from load_mnist import *
import hw1_linear as mlBasics
import numpy as np
# Read in training and test data
X_train, y_train = load_mnist('training', [0, 1])
X_train = np.reshape(X_train, (X_train.shape[0], -1))
X_train = np.divide(X_train, 256)
X_test, y_test = load_mnist('training', [0, 1])
X_test = np.reshape(X_test, (X_test.shape[0], -1))
X_test = np.divide(X_test, 256)
# Starting values for weights W and bias b
W0 = np.zeros(X_train.shape[1])
b0 = 0
# Optimization
num_iters = 100
eta = 0.001
pdb.set_trace()
W, b = mlBasics.train(X_train, y_train, W0, b0, num_iters, eta)
# Test on test data
yhat = mlBasics.predict(X_test, W, b) >= .5
import numpy as np
import chainer
#from chainer import serializers
import chainer.functions as F
from chainer import FunctionSet, Variable, optimizers, cuda
from load_mnist import *
cuda.get_device(0).use()
xp = cuda.cupy
N_train_labeled = 100
N_train_unlabeled = 60000
N_test = 10000
train_l, train_ul, test_set = load_mnist(scale=1.0/256.0, shift=0.0, N_train_labeled=N_train_labeled, N_train_unlabeled=N_train_unlabeled, N_test=N_test)
class VanillaComb(chainer.Chain):
def __init__(self, n):
super(VanillaComb, self).__init__(
b0 = F.Parameter(xp.zeros((1, n)).astype(np.float32)),
w0z = F.Parameter(xp.ones((1, n)).astype(np.float32)),
w0u = F.Parameter(xp.zeros((1, n)).astype(np.float32)),
w0zu = F.Parameter(xp.zeros((1, n)).astype(np.float32)),
ws = F.Parameter(xp.ones((1, n)).astype(np.float32)),
b1 = F.Parameter(xp.zeros((1, n)).astype(np.float32)),
w1z = F.Parameter(xp.ones((1, n)).astype(np.float32)),
w1u = F.Parameter(xp.zeros((1, n)).astype(np.float32)),
w1zu = F.Parameter(xp.zeros((1, n)).astype(np.float32)),
)
fh.setLevel(logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
logger.addHandler(fh)
logger.addHandler(ch)
dim_in = 28 * 28
dim_hidden = 1024
dim_out = 10
dims = [dim_in, dim_hidden, dim_out]
train_x_total, train_y_total = load_mnist('training')
train_x_total = train_x_total.reshape([-1, dim_in])
train_y_total = train_y_total.reshape([-1, 10])
labels = ['CP-ALT', 'CP-ALT-lambda', 'BP', 'BP-adam', 'BP-rms', 'BP-mom']
def draw(learning_rate, n_examples, repeats):
gate = Tanh()
runs = 1
cp_alt = CrossPropAlt(dim_in,
dim_hidden,
dim_out,
learning_rate,
gate,
output_layer='CE',
# -*- coding: utf-8 -*-
import sys, os
sys.path.append(os.pardir) # 親ディレクトリのファイルをインポートするための設定
import numpy as np
import matplotlib.pyplot as plt
from 3-2_data_mnist import load_mnist
from 4-3_two_layer_net import TwoLayerNet
# MNISTの訓練データとテストデータ読み込み
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)
# 2層のニューラルワーク生成
network = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)
# ハイパーパラメーター設定
iters_num = 10000 # 繰り返しの回数を適宜設定する
train_size = x_train.shape[0] # 訓練データのサイズ設定
batch_size = 100 # バッチサイズ
learning_rate = 0.1 # 学習率
# 結果の記録リスト
train_loss_list = []
train_acc_list = []
test_acc_list = []
iter_per_epoch = max(train_size / batch_size, 1) # 1エポック当たりの繰り返し数
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]
# -*- coding: utf-8 -*-
import sys
sys.path.append("C:/Study/Python/Deep-learning from scratch")
import numpy as np
from 3-2_data_mnist import load_mnist
# データの読み込み
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, flatten=True, one_hot_label=False)
#要素数を表示
print(x_train.shape)
print(t_train.shape)
#1行目の要素数をsizeに設定
train_size = x_train.shape[0]
print("train_size: " +str(train_size))
batch_size = 10
batch_mask = np.random.choice(train_size, batch_size)
print("batch_mask: ")
print(batch_mask)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
from keras.utils import to_categorical
import numpy as np
import time
from tf_nn import NeuralNetwork
from load_mnist import *
import pandas as pd
mnist_path = 'Data\\MNIST'
mnist_tr = load_mnist(dataset='training', path = mnist_path)
mnist_test = load_mnist(dataset='testing', path = mnist_path)
n_feature = 784
n_class = 10
# shrink_order = 2
# lambda_weight = .01
# n_hlayer = 2
# n_hidden = [100, 100]
# lr = 0.001
# activation = 'sigmoid'
# summary = pd.DataFrame(columns=['shrink', 'lambda', 'n_layer', 'n_hidden', 'learning rate', 'decay', 'activation'])
summary = pd.DataFrame()
start_time = time.time()
for i in range(20):
shrink_order = np.random.choice([1])
tag = 'online_MNIST'
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
fh = logging.FileHandler('log/%s.txt' % tag)
fh.setLevel(logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
logger.addHandler(fh)
logger.addHandler(ch)
datapath = './dataset/mnist'
train_x, train_y = load_mnist('training', path=datapath)
dim_in = 28 * 28
dim_hidden = 1024
dim_out = 10
train_examples = 5000
train_x = train_x[:train_examples, :].reshape([-1, dim_in])
train_y = np.argmax(train_y[:train_examples, :], axis=1)
def bp_network(lr):
return NaiveNet(dim_in, dim_hidden, dim_out, lr, gpu=True)
def smd_network(lr):
# -*- coding: utf-8 -*-
import numpy as np
#data mnistをインポート
from 3-2_data_mnist import load_mnist
#リサイズやトリミング、回転、反転など基本的な処理はPillow
from PIL import Image
def img_show(img):
pil_img = Image.fromarray(np.uint8(img))
pil_img.show()
#「(訓練画像、訓練ラベル)、（テスト画像、テストラベル）」という形式、引数は「一次配列にして、正規化はせず
(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)
print(img.shape) # (28, 28)
img_show(img)
print(x_train.shape) #(60000,784)
print(t_train.shape) #(60000,)
print(x_test.shape) #(10000, 784)
print(t_test.shape) #(10000,)
def get_data():
(x_train, t_train), (x_test, t_test)= load_mnist(normalize=True, flatten=True, one_hot_label=False)
return x_test, t_test
from datetime import datetime
import os.path
import time
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import math
#from nets import *
from random import *
from dl_feed import *
from load_mnist import *
import spams
if __name__ == "__main__":
images, labels = load_mnist()
psize = 6
X = (get_all_patches_as_matrix(images, psize)[0:2000] - 177.5) / 177.5
param = {
'K': 100, # learns a dictionary with 100 elements
'lambda1': 0.15,
'numThreads': 4,
'batchsize': 400,
'iter': 1000
}
########## FIRST EXPERIMENT ###########
tic = time.time()
D = spams.trainDL(np.transpose(X), **param)
tac = time.time()
t = tac - tic
import numpy as np
import tensorflow as tf
from load_mnist import *
import matplotlib.pyplot as pl
# Load Mnist Data
u_bound = 10
#mnist_input_size = 784 ## 28 * 28
Train_X, Train_Y = load_mnist('training', range(0, u_bound))
Test_X, Test_Y = load_mnist('testing', range(0, u_bound))
print(Train_X.shape, Train_Y.shape)
print(Test_X.shape, Test_Y.shape)
def getDataSubset(X_train,
y_train,
X_test,
y_test,
n=100,
u_bound=10,
mnist_input_size=784):
# Get and Scramble training set, with n items per category
training_set, test_set = [], []
training_labels, test_labels = [], []
for j in range(0, u_bound):
hld = np.array(list(np.where(y_train == np.float64(j)))).flatten()
training_set = training_set + list(
np.random.choice(hld, size=n, replace=False))
training_labels = training_labels + [np.float64(j)] * n
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
fh = logging.FileHandler('log/%s_%s_multi_gpu.txt' % (DATA_SET, NN_ARCH))
fh.setLevel(logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
logger.addHandler(fh)
logger.addHandler(ch)
if DATA_SET == 'MNIST':
train_x, train_y = load_mnist('training')
test_x, test_y = load_mnist('testing')
elif DATA_SET == 'CIFAR':
train_x, train_y, test_x, test_y = load_cifar10('cifar10')
else:
logger.error('No Dataset!')
train_examples = train_x.shape[0]
test_examples = test_x.shape[0]
# train_examples = 1000
# test_examples = 200
train_x = train_x[:train_examples, :, :, :]
train_y = train_y[:train_examples, :]
test_x = test_x[:test_examples, :, :, :]
test_y = test_y[:test_examples, :]
def main():
# Load data - ALL CLASSES
X_train, y_train = load_mnist('training')
X_test, y_test = load_mnist('testing')
# collect all the sample indices
samind = np.zeros(0)
for i in range(0, 10): # get the 100 training samples from the 10 classes
# filter out the indices, where label is i, gives an array with boolean values false and true
val = np.isin(y_train, i)
# filters the corresponding indices
ind = np.array(np.where(val))
# make it 1 dimensional
ind = np.reshape(ind, (ind.shape[1]))
# randomly sample 100 indices
s = np.random.choice(ind, 100, replace=False)
# concatenate new sample results to existing ones
samind = np.concatenate((samind, s), axis=0)
samind = samind.astype(int)
# Reshape the image data into rows
X_train = np.reshape(X_train, (X_train.shape[0], -1))
X_test = np.reshape(X_test, (X_test.shape[0], -1))
# get the 1000 samples and corresponding labels
samples_X = X_train[samind]
samples_y = y_train[samind]
# calculate predictions/accuracy tuples for k=1 and k=5
pred_acc_one = pred_accuracy(samples_X, samples_y, X_test, y_test, 1)
pred_acc_five = pred_accuracy(samples_X, samples_y, X_test, y_test, 5)
# Report results
print("Accuracy for k=1: ", '{0:0.02f}'.format(pred_acc_one[1]),
"of test examples classified correctly.")
print("Accuracy for k=5: ", '{0:0.02f}'.format(pred_acc_five[1]),
"of test examples classified correctly.")
# calculate the confusion matrix
cmat_k1 = confusion_matrix(y_test, pred_acc_one[0])
cmat_k5 = confusion_matrix(y_test, pred_acc_five[0])
print("Confusion matrix for k=1: ")
print(cmat_k1)
print("Confusion matrix for k=5: ")
print(cmat_k5)
# c)
# Dict to receive the averages for the different k's
avg_dict = {}
# randomize the order in the sample set (currently it is sorted by label)
#permutation = np.random.permutation(samples_X.shape[0])
#samples_X = samples_X[permutation]
#samples_y = samples_y[permutation]
# commented because the result stays the same for the 5 fold cv.
# Do five fold cross validation
for k in range(1, 16):
# calculate the average accuracy of knn using five fold cross validation for current k
avg_dict[k] = five_fold_cv(samples_X, samples_y, k)
print("Accuracies for k from 1 to 15 using 5 fold CV:")
pprint(avg_dict)
best = max(avg_dict, key=avg_dict.get)
print(
"As can be seen k={} performs the best with an average accuracy of {}%."
.format(best, avg_dict[best]))
from load_mnist import *
import hw1_knn as mlBasics
import numpy as np
import pdb
# Load data - ALL CLASSES
#2.a
X_train, y_train = load_mnist('training')
X_test, y_test = load_mnist('testing')
# 2.b
label_sample_idx = {}
for idx, label in enumerate(y_train):
if label in label_sample_idx:
label_sample_idx[label].append(idx)
else:
label_sample_idx[label] = [idx]
sample_idx = np.empty(1000, dtype='int')
i, j = 0, 100
for label in label_sample_idx:
sample_idx[i:j] = np.random.choice(label_sample_idx[label], size=100)
i, j = j, j + 100
x_train_sample, y_train_sample = X_train[sample_idx], y_train[sample_idx]
# Reshape images
x_train_sample = np.reshape(x_train_sample, (x_train_sample.shape[0], -1))
X_test = np.reshape(X_test, (X_test.shape[0], -1))
print('Compute Distances')
],
}
model = NNetVAT(options_dict, njobs=1)
model.set_optimizer(optimizers.Adam())
model.initOptimizer()
alpha_plan = [0.002] * 100
for i in range(1,100):
alpha_plan[i] = alpha_plan[i-1]*0.9
batchsize_l = 100
batchsize_ul = 250
N_train_labeled = 100
N_train_unlabeled = 60000
N_test = 10000
train_l, train_ul, test_set = \
load_mnist(scale=1.0/128.0, shift=-1.0, N_train_labeled=N_train_labeled, \
N_train_unlabeled=N_train_unlabeled, N_test=N_test)
for epoch in range(len(alpha_plan)):
# print epoch
sum_loss_l = 0
sum_loss_ul = 0
for it in range(60000/batchsize_ul):
x,t = train_l.get(batchsize_l)
loss_l = model.loss_labeled(x, t)
model.zero_grads()
loss_l.backward()
model.update()
x,_ = train_ul.get(batchsize_ul)
loss_ul = model.loss_unlabeled(x)