CNTK - 神經網路二元分類

本章將介紹如何使用 CNTK 進行神經網路二元分類。

使用神經網路進行二元分類類似於多類別分類，唯一的區別是隻有兩個輸出節點而不是三個或更多。在這裡，我們將使用兩種技術（單節點和雙節點技術）來使用神經網路執行二元分類。單節點技術比雙節點技術更常用。

載入資料集

為了使用神經網路實現這兩種技術，我們將使用鈔票資料集。該資料集可以從 UCI 機器學習知識庫下載，網址為 https://archive.ics.uci.edu/ml/datasets/banknote+authentication。

在我們的示例中，我們將使用 50 個具有 forgery = 0 類別的真實資料項和 50 個具有 forgery = 1 類別的假資料項。

準備訓練和測試檔案

完整的資料集包含 1372 個數據項。原始資料集如下所示：

3.6216, 8.6661, -2.8076, -0.44699, 0
4.5459, 8.1674, -2.4586, -1.4621, 0
…
-1.3971, 3.3191, -1.3927, -1.9948, 1
0.39012, -0.14279, -0.031994, 0.35084, 1

現在，首先我們需要將此原始資料轉換為雙節點 CNTK 格式，如下所示：

|stats 3.62160000 8.66610000 -2.80730000 -0.44699000 |forgery 0 1 |# authentic 
|stats 4.54590000 8.16740000 -2.45860000 -1.46210000 |forgery 0 1 |# authentic 
. . .
|stats -1.39710000 3.31910000 -1.39270000 -1.99480000 |forgery 1 0 |# fake 
|stats 0.39012000 -0.14279000 -0.03199400 0.35084000 |forgery 1 0 |# fake

您可以使用以下 Python 程式從原始資料建立 CNTK 格式的資料：

fin = open(".\\...", "r") #provide the location of saved dataset text file.
for line in fin:
   line = line.strip()
   tokens = line.split(",")
   if tokens[4] == "0":
    print("|stats %12.8f %12.8f %12.8f %12.8f |forgery 0 1 |# authentic" % \
(float(tokens[0]), float(tokens[1]), float(tokens[2]), float(tokens[3])) )
   else:
    print("|stats %12.8f %12.8f %12.8f %12.8f |forgery 1 0 |# fake" % \
(float(tokens[0]), float(tokens[1]), float(tokens[2]), float(tokens[3])) )
fin.close()

雙節點二元分類模型

雙節點分類和多類別分類之間幾乎沒有區別。在這裡，我們首先需要以 CNTK 格式處理資料檔案，為此我們將使用名為 create_reader 的輔助函式，如下所示：

def create_reader(path, input_dim, output_dim, rnd_order, sweeps):
x_strm = C.io.StreamDef(field='stats', shape=input_dim, is_sparse=False)
y_strm = C.io.StreamDef(field='forgery', shape=output_dim, is_sparse=False)
streams = C.io.StreamDefs(x_src=x_strm, y_src=y_strm)
deserial = C.io.CTFDeserializer(path, streams)
mb_src = C.io.MinibatchSource(deserial, randomize=rnd_order, max_sweeps=sweeps)
return mb_src

現在，我們需要為我們的神經網路設定架構引數，並提供資料檔案的位置。這可以透過以下 Python 程式碼完成：

def main():
print("Using CNTK version = " + str(C.__version__) + "\n")
input_dim = 4
hidden_dim = 10
output_dim = 2
train_file = ".\\...\\" #provide the name of the training file
test_file = ".\\...\\" #provide the name of the test file

現在，藉助以下程式碼行，我們的程式將建立未經訓練的神經網路：

X = C.ops.input_variable(input_dim, np.float32)
Y = C.ops.input_variable(output_dim, np.float32)
with C.layers.default_options(init=C.initializer.uniform(scale=0.01, seed=1)):
hLayer = C.layers.Dense(hidden_dim, activation=C.ops.tanh, name='hidLayer')(X)
oLayer = C.layers.Dense(output_dim, activation=None, name='outLayer')(hLayer)
nnet = oLayer
model = C.ops.softmax(nnet)

現在，一旦我們建立了未經訓練的雙節點模型，我們需要設定一個 Learner 演算法物件，然後使用它來建立一個 Trainer 訓練物件。我們將使用 SGD 學習器和 cross_entropy_with_softmax 損失函式：

tr_loss = C.cross_entropy_with_softmax(nnet, Y)
tr_clas = C.classification_error(nnet, Y)
max_iter = 500
batch_size = 10
learn_rate = 0.01
learner = C.sgd(nnet.parameters, learn_rate)
trainer = C.Trainer(nnet, (tr_loss, tr_clas), [learner])

現在，一旦我們完成了 Trainer 物件，我們需要建立一個 reader 函式來讀取訓練資料：

rdr = create_reader(train_file, input_dim, output_dim, rnd_order=True, sweeps=C.io.INFINITELY_REPEAT)
banknote_input_map = { X : rdr.streams.x_src, Y : rdr.streams.y_src }

現在，是時候訓練我們的神經網路模型了：

for i in range(0, max_iter):
curr_batch = rdr.next_minibatch(batch_size, input_map=iris_input_map) trainer.train_minibatch(curr_batch)
if i % 500 == 0:
mcee = trainer.previous_minibatch_loss_average
macc = (1.0 - trainer.previous_minibatch_evaluation_average) * 100
print("batch %4d: mean loss = %0.4f, accuracy = %0.2f%% " \ % (i, mcee, macc))

訓練完成後，讓我們使用測試資料項評估模型：

print("\nEvaluating test data \n")
rdr = create_reader(test_file, input_dim, output_dim, rnd_order=False, sweeps=1)
banknote_input_map = { X : rdr.streams.x_src, Y : rdr.streams.y_src }
num_test = 20
all_test = rdr.next_minibatch(num_test, input_map=iris_input_map) acc = (1.0 - trainer.test_minibatch(all_test)) * 100
print("Classification accuracy = %0.2f%%" % acc)

在評估我們訓練好的神經網路模型的準確性之後，我們將使用它來對未見過的資料進行預測：

np.set_printoptions(precision = 1, suppress=True)
unknown = np.array([[0.6, 1.9, -3.3, -0.3]], dtype=np.float32)
print("\nPredicting Banknote authenticity for input features: ")
print(unknown[0])
pred_prob = model.eval(unknown)
np.set_printoptions(precision = 4, suppress=True)
print("Prediction probabilities are: ")
print(pred_prob[0])
if pred_prob[0,0] < pred_prob[0,1]:
  print(“Prediction: authentic”)
else:
  print(“Prediction: fake”)

完整的雙節點分類模型

def create_reader(path, input_dim, output_dim, rnd_order, sweeps):
x_strm = C.io.StreamDef(field='stats', shape=input_dim, is_sparse=False)
y_strm = C.io.StreamDef(field='forgery', shape=output_dim, is_sparse=False)
streams = C.io.StreamDefs(x_src=x_strm, y_src=y_strm)
deserial = C.io.CTFDeserializer(path, streams)
mb_src = C.io.MinibatchSource(deserial, randomize=rnd_order, max_sweeps=sweeps)
return mb_src
def main():
print("Using CNTK version = " + str(C.__version__) + "\n")
input_dim = 4
hidden_dim = 10
output_dim = 2
train_file = ".\\...\\" #provide the name of the training file
test_file = ".\\...\\" #provide the name of the test file
X = C.ops.input_variable(input_dim, np.float32)
Y = C.ops.input_variable(output_dim, np.float32)
withC.layers.default_options(init=C.initializer.uniform(scale=0.01, seed=1)):
hLayer = C.layers.Dense(hidden_dim, activation=C.ops.tanh, name='hidLayer')(X)
oLayer = C.layers.Dense(output_dim, activation=None, name='outLayer')(hLayer)
nnet = oLayer
model = C.ops.softmax(nnet)
tr_loss = C.cross_entropy_with_softmax(nnet, Y)
tr_clas = C.classification_error(nnet, Y)
max_iter = 500
batch_size = 10
learn_rate = 0.01
learner = C.sgd(nnet.parameters, learn_rate)
trainer = C.Trainer(nnet, (tr_loss, tr_clas), [learner])
rdr = create_reader(train_file, input_dim, output_dim, rnd_order=True, sweeps=C.io.INFINITELY_REPEAT)
banknote_input_map = { X : rdr.streams.x_src, Y : rdr.streams.y_src }
for i in range(0, max_iter):
curr_batch = rdr.next_minibatch(batch_size, input_map=iris_input_map) trainer.train_minibatch(curr_batch)
if i % 500 == 0:
mcee = trainer.previous_minibatch_loss_average
macc = (1.0 - trainer.previous_minibatch_evaluation_average) * 100
print("batch %4d: mean loss = %0.4f, accuracy = %0.2f%% " \ % (i, mcee, macc))
print("\nEvaluating test data \n")
rdr = create_reader(test_file, input_dim, output_dim, rnd_order=False, sweeps=1)
banknote_input_map = { X : rdr.streams.x_src, Y : rdr.streams.y_src }
num_test = 20
all_test = rdr.next_minibatch(num_test, input_map=iris_input_map) acc = (1.0 - trainer.test_minibatch(all_test)) * 100
print("Classification accuracy = %0.2f%%" % acc)
np.set_printoptions(precision = 1, suppress=True)
unknown = np.array([[0.6, 1.9, -3.3, -0.3]], dtype=np.float32)
print("\nPredicting Banknote authenticity for input features: ")
print(unknown[0])
pred_prob = model.eval(unknown)
np.set_printoptions(precision = 4, suppress=True)
print("Prediction probabilities are: ")
print(pred_prob[0])
if pred_prob[0,0] < pred_prob[0,1]:
print(“Prediction: authentic”)
else:
print(“Prediction: fake”)
if __name__== ”__main__”:
main()

輸出

Using CNTK version = 2.7
batch 0: mean loss = 0.6928, accuracy = 80.00%
batch 50: mean loss = 0.6877, accuracy = 70.00%
batch 100: mean loss = 0.6432, accuracy = 80.00%
batch 150: mean loss = 0.4978, accuracy = 80.00%
batch 200: mean loss = 0.4551, accuracy = 90.00%
batch 250: mean loss = 0.3755, accuracy = 90.00%
batch 300: mean loss = 0.2295, accuracy = 100.00%
batch 350: mean loss = 0.1542, accuracy = 100.00%
batch 400: mean loss = 0.1581, accuracy = 100.00%
batch 450: mean loss = 0.1499, accuracy = 100.00%
Evaluating test data
Classification accuracy = 84.58%
Predicting banknote authenticity for input features:
[0.6 1.9 -3.3 -0.3]
Prediction probabilities are:
[0.7847 0.2536]
Prediction: fake

單節點二元分類模型

實現程式與我們上面為雙節點分類所做的幾乎相同。主要變化在於使用雙節點分類技術時。

我們可以使用 CNTK 內建的 classification_error() 函式，但是對於單節點分類，CNTK 不支援 classification_error() 函式。這就是我們需要實現一個程式定義的函式的原因，如下所示：

def class_acc(mb, x_var, y_var, model):
num_correct = 0; num_wrong = 0
x_mat = mb[x_var].asarray()
y_mat = mb[y_var].asarray()
for i in range(mb[x_var].shape[0]):
   p = model.eval(x_mat[i]
   y = y_mat[i]
   if p[0,0] < 0.5 and y[0,0] == 0.0 or p[0,0] >= 0.5 and y[0,0] == 1.0:
num_correct += 1
 else:
  num_wrong += 1
return (num_correct * 100.0)/(num_correct + num_wrong)

有了這個更改，讓我們看看完整的單節點分類示例：

完整的單節點分類模型

import numpy as np
import cntk as C
def create_reader(path, input_dim, output_dim, rnd_order, sweeps):
x_strm = C.io.StreamDef(field='stats', shape=input_dim, is_sparse=False)
y_strm = C.io.StreamDef(field='forgery', shape=output_dim, is_sparse=False)
streams = C.io.StreamDefs(x_src=x_strm, y_src=y_strm)
deserial = C.io.CTFDeserializer(path, streams)
mb_src = C.io.MinibatchSource(deserial, randomize=rnd_order, max_sweeps=sweeps)
return mb_src
def class_acc(mb, x_var, y_var, model):
num_correct = 0; num_wrong = 0
x_mat = mb[x_var].asarray()
y_mat = mb[y_var].asarray()
for i in range(mb[x_var].shape[0]):
  p = model.eval(x_mat[i]
  y = y_mat[i]
  if p[0,0] < 0.5 and y[0,0] == 0.0 or p[0,0] >= 0.5 and y[0,0] == 1.0:
  num_correct += 1
 else:
  num_wrong += 1
return (num_correct * 100.0)/(num_correct + num_wrong)
def main():
print("Using CNTK version = " + str(C.__version__) + "\n")
input_dim = 4
hidden_dim = 10
output_dim = 1
train_file = ".\\...\\" #provide the name of the training file
test_file = ".\\...\\" #provide the name of the test file
X = C.ops.input_variable(input_dim, np.float32)
Y = C.ops.input_variable(output_dim, np.float32)
with C.layers.default_options(init=C.initializer.uniform(scale=0.01, seed=1)):
hLayer = C.layers.Dense(hidden_dim, activation=C.ops.tanh, name='hidLayer')(X)
oLayer = C.layers.Dense(output_dim, activation=None, name='outLayer')(hLayer)
model = oLayer
tr_loss = C.cross_entropy_with_softmax(model, Y)
max_iter = 1000
batch_size = 10
learn_rate = 0.01
learner = C.sgd(model.parameters, learn_rate)
trainer = C.Trainer(model, (tr_loss), [learner])
rdr = create_reader(train_file, input_dim, output_dim, rnd_order=True, sweeps=C.io.INFINITELY_REPEAT)
banknote_input_map = {X : rdr.streams.x_src, Y : rdr.streams.y_src }
for i in range(0, max_iter):
curr_batch = rdr.next_minibatch(batch_size, input_map=iris_input_map) trainer.train_minibatch(curr_batch)
if i % 100 == 0:
mcee=trainer.previous_minibatch_loss_average
ca = class_acc(curr_batch, X,Y, model)
print("batch %4d: mean loss = %0.4f, accuracy = %0.2f%% " \ % (i, mcee, ca))
print("\nEvaluating test data \n")
rdr = create_reader(test_file, input_dim, output_dim, rnd_order=False, sweeps=1)
banknote_input_map = { X : rdr.streams.x_src, Y : rdr.streams.y_src }
num_test = 20
all_test = rdr.next_minibatch(num_test, input_map=iris_input_map)
acc = class_acc(all_test, X,Y, model)
print("Classification accuracy = %0.2f%%" % acc)
np.set_printoptions(precision = 1, suppress=True)
unknown = np.array([[0.6, 1.9, -3.3, -0.3]], dtype=np.float32)
print("\nPredicting Banknote authenticity for input features: ")
print(unknown[0])
pred_prob = model.eval({X:unknown})
print("Prediction probability: ")
print(“%0.4f” % pred_prob[0,0])
if pred_prob[0,0] < 0.5:
  print(“Prediction: authentic”)
else:
  print(“Prediction: fake”)
if __name__== ”__main__”:
   main()

輸出

Using CNTK version = 2.7
batch 0: mean loss = 0.6936, accuracy = 10.00%
batch 100: mean loss = 0.6882, accuracy = 70.00%
batch 200: mean loss = 0.6597, accuracy = 50.00%
batch 300: mean loss = 0.5298, accuracy = 70.00%
batch 400: mean loss = 0.4090, accuracy = 100.00%
batch 500: mean loss = 0.3790, accuracy = 90.00%
batch 600: mean loss = 0.1852, accuracy = 100.00%
batch 700: mean loss = 0.1135, accuracy = 100.00%
batch 800: mean loss = 0.1285, accuracy = 100.00%
batch 900: mean loss = 0.1054, accuracy = 100.00%
Evaluating test data
Classification accuracy = 84.00%
Predicting banknote authenticity for input features:
[0.6 1.9 -3.3 -0.3]
Prediction probability:
0.8846
Prediction: fake

列印頁面