# coding: utf-8
import urllib.request
import os.path
import gzip
import pickle
import os
import numpy as np
url_base = 'http://yann.lecun.com/exdb/mnist/'
key_file = {
'train_img':'train-images-idx3-ubyte.gz',
'train_label':'train-labels-idx1-ubyte.gz',
'test_img':'t10k-images-idx3-ubyte.gz',
'test_label':'t10k-labels-idx1-ubyte.gz'
}
dataset_dir = os.path.dirname("/Users/yhhan/git/deeplink/0.Common/data/MNIST_data/.")
save_file = dataset_dir + "/mnist.pkl"
train_num = 60000
test_num = 10000
img_dim = (1, 28, 28)
img_size = 784
def _download(file_name):
file_path = dataset_dir + "/" + file_name
print(file_path)
if os.path.exists(file_path):
return
print("Downloading " + file_name + " ... ")
urllib.request.urlretrieve(url_base + file_name, file_path)
print("Done")
def download_mnist():
for v in key_file.values():
_download(v)
def _load_label(file_name):
file_path = dataset_dir + "/" + file_name
print("Converting " + file_name + " to NumPy Array ...")
with gzip.open(file_path, 'rb') as f:
labels = np.frombuffer(f.read(), np.uint8, offset=8)
print("Done")
return labels
def _load_img(file_name):
file_path = dataset_dir + "/" + file_name
print("Converting " + file_name + " to NumPy Array ...")
with gzip.open(file_path, 'rb') as f:
data = np.frombuffer(f.read(), np.uint8, offset=16)
data = data.reshape(-1, img_size)
print("Done")
return data
def _convert_numpy():
dataset = {}
dataset['train_img'] = _load_img(key_file['train_img'])
dataset['train_label'] = _load_label(key_file['train_label'])
dataset['test_img'] = _load_img(key_file['test_img'])
dataset['test_label'] = _load_label(key_file['test_label'])
dataset['validation_img'] = dataset['train_img'][55000:]
dataset['validation_label'] = dataset['train_label'][55000:]
dataset['train_img'] = dataset['train_img'][:55000]
dataset['train_label'] = dataset['train_label'][:55000]
return dataset
def init_mnist():
download_mnist()
dataset = _convert_numpy()
print("Creating pickle file ...")
with open(save_file, 'wb') as f:
pickle.dump(dataset, f, -1)
print("Done!")
def _change_one_hot_label(X):
T = np.zeros((X.size, 10))
for idx, row in enumerate(T):
row[X[idx]] = 1
return T
def load_mnist(normalize=True, flatten=True, one_hot_label=False):
if not os.path.exists(save_file):
init_mnist()
with open(save_file, 'rb') as f:
dataset = pickle.load(f)
if normalize:
for key in ('train_img', 'validation_img', 'test_img'):
dataset[key] = dataset[key].astype(np.float32)
dataset[key] /= 255.0
if one_hot_label:
dataset['train_label'] = _change_one_hot_label(dataset['train_label'])
dataset['validation_label'] = _change_one_hot_label(dataset['validation_label'])
dataset['test_label'] = _change_one_hot_label(dataset['test_label'])
if not flatten:
for key in ('train_img', 'validation_img', 'test_img'):
dataset[key] = dataset[key].reshape(-1, 1, 28, 28)
return (dataset['train_img'], dataset['train_label']), (dataset['validation_img'], dataset['validation_label']), (dataset['test_img'], dataset['test_label'])
if __name__ == '__main__':
init_mnist()
/Users/yhhan/git/deeplink/0.Common/data/MNIST_data/train-images-idx3-ubyte.gz /Users/yhhan/git/deeplink/0.Common/data/MNIST_data/train-labels-idx1-ubyte.gz /Users/yhhan/git/deeplink/0.Common/data/MNIST_data/t10k-images-idx3-ubyte.gz /Users/yhhan/git/deeplink/0.Common/data/MNIST_data/t10k-labels-idx1-ubyte.gz Converting train-images-idx3-ubyte.gz to NumPy Array ... Done Converting train-labels-idx1-ubyte.gz to NumPy Array ... Done Converting t10k-images-idx3-ubyte.gz to NumPy Array ... Done Converting t10k-labels-idx1-ubyte.gz to NumPy Array ... Done Creating pickle file ... Done!
import matplotlib.pyplot as plt
%matplotlib inline
fig = plt.figure(figsize=(20, 5))
for i in range(5):
print(label_train[i])
img = img_train[i]
img = img.reshape(28, 28)
img.shape = (28, 28)
plt.subplot(150 + (i+1))
plt.imshow(img, cmap='gray')
[ 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [ 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.] [ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
import numpy as np
def softmax(x):
if x.ndim == 2:
x = x.T
x = x - np.max(x, axis=0)
y = np.exp(x) / np.sum(np.exp(x), axis=0)
return y.T
x = x - np.max(x)
return np.exp(x) / np.sum(np.exp(x))
def cross_entropy_error(y, t):
#print(y.shape, t.shape)
if y.ndim == 1:
y = y.reshape(1, y.size)
t = t.reshape(1, t.size)
if t.size == y.size:
t = t.argmax(axis=1)
batch_size = y.shape[0]
return -np.sum(np.log(y[np.arange(batch_size), t])) / batch_size
class Relu:
def __init__(self):
self.mask = None
def forward(self, x):
self.mask = (x <= 0)
out = x.copy()
out[self.mask] = 0
return out
def backward(self, din):
din[self.mask] = 0
dx = din
return dx
class Sigmoid:
def __init__(self):
self.out = None
def forward(self, x):
out = sigmoid(x)
self.out = out
return out
def backward(self, din):
dx = din * self.out * (1.0 - self.out)
return dx
class Affine:
def __init__(self, W, b):
self.W = W
self.b = b
self.x = None
self.dW = None
self.db = None
def forward(self, x):
self.x = x
out = np.dot(self.x, self.W) + self.b
return out
def backward(self, din):
dx = np.dot(din, self.W.T)
self.dW = np.dot(self.x.T, din)
self.db = np.sum(din, axis=0)
return dx
class SoftmaxWithCrossEntropyLoss:
def __init__(self):
self.loss = None
self.y = None
self.t = None
def forward(self, x, t):
self.t = t
self.y = softmax(x)
self.loss = cross_entropy_error(self.y, self.t)
return self.loss
def backward(self, din=1):
batch_size = self.t.shape[0]
dx = (self.y - self.t) / float(batch_size)
return dx
import sys, os
from collections import OrderedDict
from scipy import stats
from pandas import DataFrame
class TwoLayerNet2:
def __init__(self, input_size, hidden_layer_size, output_size, weight_init_std = 0.01):
self.params = {}
self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_layer_size)
self.params['b1'] = np.zeros(hidden_layer_size)
self.params['W2'] = weight_init_std * np.random.randn(hidden_layer_size, output_size)
self.params['b2'] = np.zeros(output_size)
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 = SoftmaxWithCrossEntropyLoss()
def predict(self, x):
for layer in self.layers.values():
x = layer.forward(x)
return x
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
def backpropagation_gradient(self, x, t):
# forward
self.loss(x, t)
# backward
din = 1
din = self.lastLayer.backward(din)
layers = list(self.layers.values())
layers.reverse()
for layer in layers:
din = layer.backward(din)
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 learning(self, learning_rate, x_batch, t_batch):
grads = self.backpropagation_gradient(x_batch, t_batch)
for key in ('W1', 'b1', 'W2', 'b2'):
self.params[key] -= learning_rate * grads[key]
import math
(img_train, label_train), (img_validation, label_validation), (img_test, label_test) = load_mnist(flatten=True, normalize=True, one_hot_label=True)
network = TwoLayerNet2(input_size=784, hidden_layer_size=128, output_size=10)
num_epochs = 50
train_size = img_train.shape[0]
batch_size = 1000
learning_rate = 0.1
train_error_list = []
validation_error_list = []
test_accuracy_list = []
epoch_list = []
num_batch = math.ceil(train_size / batch_size)
for i in range(num_epochs):
# batch_mask = np.random.choice(train_size, batch_size)
# x_batch = img_train[batch_mask]
# t_batch = label_train[batch_mask]
# network.learning(learning_rate, x_batch, t_batch)
j = 0
for j in range(num_batch):
x_batch = img_train[j * batch_size : j * batch_size + batch_size]
t_batch = label_train[j * batch_size : j * batch_size + batch_size]
network.learning(learning_rate, x_batch, t_batch)
epoch_list.append(i)
train_loss = network.loss(x_batch, t_batch)
train_error_list.append(train_loss)
validation_loss = network.loss(img_validation, label_validation)
validation_error_list.append(validation_loss)
test_accuracy = network.accuracy(img_test, label_test)
test_accuracy_list.append(test_accuracy)
print("Epoch: {0:5d}, Train Error: {1:7.5f}, Validation Error: {2:7.5f} - Test Accuracy: {3:7.5f}".format(
i,
train_loss,
validation_loss,
test_accuracy
))
Epoch: 0, Train Error: 1.82162, Validation Error: 1.81296 - Test Accuracy: 0.66850 Epoch: 1, Train Error: 0.81920, Validation Error: 0.77085 - Test Accuracy: 0.80570 Epoch: 2, Train Error: 0.56291, Validation Error: 0.49662 - Test Accuracy: 0.85830 Epoch: 3, Train Error: 0.46438, Validation Error: 0.39428 - Test Accuracy: 0.88320 Epoch: 4, Train Error: 0.41313, Validation Error: 0.34332 - Test Accuracy: 0.89080 Epoch: 5, Train Error: 0.38163, Validation Error: 0.31348 - Test Accuracy: 0.89730 Epoch: 6, Train Error: 0.35985, Validation Error: 0.29374 - Test Accuracy: 0.90090 Epoch: 7, Train Error: 0.34316, Validation Error: 0.27942 - Test Accuracy: 0.90370 Epoch: 8, Train Error: 0.32947, Validation Error: 0.26836 - Test Accuracy: 0.90800 Epoch: 9, Train Error: 0.31782, Validation Error: 0.25930 - Test Accuracy: 0.91060 Epoch: 10, Train Error: 0.30754, Validation Error: 0.25159 - Test Accuracy: 0.91480 Epoch: 11, Train Error: 0.29838, Validation Error: 0.24484 - Test Accuracy: 0.91650 Epoch: 12, Train Error: 0.29007, Validation Error: 0.23888 - Test Accuracy: 0.91870 Epoch: 13, Train Error: 0.28252, Validation Error: 0.23354 - Test Accuracy: 0.92050 Epoch: 14, Train Error: 0.27556, Validation Error: 0.22861 - Test Accuracy: 0.92240 Epoch: 15, Train Error: 0.26892, Validation Error: 0.22396 - Test Accuracy: 0.92350 Epoch: 16, Train Error: 0.26262, Validation Error: 0.21955 - Test Accuracy: 0.92500 Epoch: 17, Train Error: 0.25659, Validation Error: 0.21535 - Test Accuracy: 0.92650 Epoch: 18, Train Error: 0.25086, Validation Error: 0.21133 - Test Accuracy: 0.92790 Epoch: 19, Train Error: 0.24528, Validation Error: 0.20750 - Test Accuracy: 0.92910 Epoch: 20, Train Error: 0.23987, Validation Error: 0.20377 - Test Accuracy: 0.93010 Epoch: 21, Train Error: 0.23464, Validation Error: 0.20022 - Test Accuracy: 0.93120 Epoch: 22, Train Error: 0.22956, Validation Error: 0.19679 - Test Accuracy: 0.93190 Epoch: 23, Train Error: 0.22462, Validation Error: 0.19345 - Test Accuracy: 0.93280 Epoch: 24, Train Error: 0.21975, Validation Error: 0.19022 - Test Accuracy: 0.93420 Epoch: 25, Train Error: 0.21500, Validation Error: 0.18710 - Test Accuracy: 0.93550 Epoch: 26, Train Error: 0.21043, Validation Error: 0.18406 - Test Accuracy: 0.93640 Epoch: 27, Train Error: 0.20602, Validation Error: 0.18108 - Test Accuracy: 0.93760 Epoch: 28, Train Error: 0.20175, Validation Error: 0.17814 - Test Accuracy: 0.93840 Epoch: 29, Train Error: 0.19756, Validation Error: 0.17531 - Test Accuracy: 0.93930 Epoch: 30, Train Error: 0.19355, Validation Error: 0.17257 - Test Accuracy: 0.94000 Epoch: 31, Train Error: 0.18968, Validation Error: 0.16990 - Test Accuracy: 0.94060 Epoch: 32, Train Error: 0.18591, Validation Error: 0.16736 - Test Accuracy: 0.94150 Epoch: 33, Train Error: 0.18223, Validation Error: 0.16487 - Test Accuracy: 0.94220 Epoch: 34, Train Error: 0.17865, Validation Error: 0.16244 - Test Accuracy: 0.94300 Epoch: 35, Train Error: 0.17518, Validation Error: 0.16012 - Test Accuracy: 0.94410 Epoch: 36, Train Error: 0.17180, Validation Error: 0.15787 - Test Accuracy: 0.94500 Epoch: 37, Train Error: 0.16846, Validation Error: 0.15568 - Test Accuracy: 0.94580 Epoch: 38, Train Error: 0.16523, Validation Error: 0.15355 - Test Accuracy: 0.94640 Epoch: 39, Train Error: 0.16212, Validation Error: 0.15150 - Test Accuracy: 0.94740 Epoch: 40, Train Error: 0.15907, Validation Error: 0.14953 - Test Accuracy: 0.94870 Epoch: 41, Train Error: 0.15614, Validation Error: 0.14761 - Test Accuracy: 0.94890 Epoch: 42, Train Error: 0.15331, Validation Error: 0.14572 - Test Accuracy: 0.94990 Epoch: 43, Train Error: 0.15056, Validation Error: 0.14390 - Test Accuracy: 0.95030 Epoch: 44, Train Error: 0.14793, Validation Error: 0.14215 - Test Accuracy: 0.95070 Epoch: 45, Train Error: 0.14544, Validation Error: 0.14046 - Test Accuracy: 0.95160 Epoch: 46, Train Error: 0.14306, Validation Error: 0.13877 - Test Accuracy: 0.95200 Epoch: 47, Train Error: 0.14067, Validation Error: 0.13715 - Test Accuracy: 0.95220 Epoch: 48, Train Error: 0.13837, Validation Error: 0.13555 - Test Accuracy: 0.95280 Epoch: 49, Train Error: 0.13608, Validation Error: 0.13401 - Test Accuracy: 0.95340
import matplotlib.pyplot as plt
%matplotlib inline
# Draw Graph about Error Values & Accuracy Values
def draw_error_values_and_accuracy(epoch_list, train_error_list, validation_error_list, test_accuracy_list):
# Draw Error Values and Accuracy
fig = plt.figure(figsize=(20, 5))
plt.subplot(121)
plt.plot(epoch_list[1:], train_error_list[1:], 'r', label='Train')
plt.plot(epoch_list[1:], validation_error_list[1:], 'g', label='Validation')
plt.ylabel('Total Error')
plt.xlabel('Epochs')
plt.grid(True)
plt.legend(loc='upper right')
plt.subplot(122)
plt.plot(epoch_list[1:], test_accuracy_list[1:], 'b', label='Test')
plt.ylabel('Accuracy')
plt.xlabel('Epochs')
plt.yticks(np.arange(0.0, 1.0, 0.05))
plt.grid(True)
plt.legend(loc='lower right')
plt.show()
draw_error_values_and_accuracy(epoch_list, train_error_list, validation_error_list, test_accuracy_list)
def draw_false_prediction(diff_index_list):
fig = plt.figure(figsize=(20, 5))
for i in range(5):
j = diff_index_list[i]
print("False Prediction Index: %s, Prediction: %s, Ground Truth: %s" % (j, prediction[j], ground_truth[j]))
img = np.array(img_test[j])
img.shape = (28, 28)
plt.subplot(150 + (i+1))
plt.imshow(img, cmap='gray')
prediction = np.argmax(network.predict(img_test), axis=1)
ground_truth = np.argmax(label_test, axis=1)
print(prediction)
print(ground_truth)
diff_index_list = []
for i in range(len(img_test)):
if (prediction[i] != ground_truth[i]):
diff_index_list.append(i)
print("Total Test Image: {0}, Number of False Prediction: {1}".format(len(img_test), len(diff_index_list)))
print("Test Accuracy:", float(len(img_test) - len(diff_index_list)) / float(len(img_test)))
draw_false_prediction(diff_index_list)
[7 2 1 ..., 4 5 6] [7 2 1 ..., 4 5 6] Total Test Image: 10000, Number of False Prediction: 466 Test Accuracy: 0.9534 False Prediction Index: 8, Prediction: 6, Ground Truth: 5 False Prediction Index: 33, Prediction: 6, Ground Truth: 4 False Prediction Index: 92, Prediction: 4, Ground Truth: 9 False Prediction Index: 124, Prediction: 4, Ground Truth: 7 False Prediction Index: 149, Prediction: 9, Ground Truth: 2