Я хочу получить матрицу путаницы в соответствии с y_test и pred_test, но поднимаю вопрос "По крайней мере, одна указанная метка должна быть в y_true", я не знаю, почему
metrics.confusion_matrix(np.argmax(y_test,axis=1),pred_test)
y_test = [[0. 1. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 1.]
[0. 0. 0. 0. 1. 0.]
...
[0. 0. 0. 1. 0. 0.]
[0. 0. 1. 0. 0. 0.]
[0. 0. 1. 0. 0. 0.]]
pred_test = [1 4 5 ... 3 2 2]
np.argmax(y_test,axis=1) = [1 5 4 ... 3 2 2]
File "D:\Anaconda\lib\site-packages\sklearn\metrics\classification.py", line 259, in confusion_matrix
raise ValueError("At least one label specified must be in y_true")
ValueError: At least one label specified must be in y_true
Я создаю сверточную нейронную сеть. смоделируйте и используйте перекрестную проверку для оценки, наконец, сгенерируйте матрицу путаницы. В настоящее время возникают проблемы с генерацией путаницы.
Набор данных введите описание ссылки здесь . Полный код выглядит следующим образом:
import matplotlib
#matplotlib.use('Agg')
import timing
from keras.layers import Input,Dense,Conv2D,MaxPooling2D,UpSampling2D,Flatten
from keras.models import Model
from keras import backend as K
from keras.utils.np_utils import to_categorical
import numpy as np
import pandas as pd
import seaborn as sns
from keras.models import Sequential# 导入Sequential
from keras.utils import np_utils, generic_utils
from keras.callbacks import LearningRateScheduler
import os
from keras.layers import Dropout
from keras.backend.tensorflow_backend import set_session
import tensorflow as tf
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.cross_validation import KFold, StratifiedKFold
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn import metrics
import time
from scipy import stats
from keras import optimizers
import matplotlib.pyplot as plt
from keras import regularizers
import keras
from keras.callbacks import TensorBoard
config = tf.ConfigProto(allow_soft_placement=True)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
time1 = time.time()
class LossHistory(keras.callbacks.Callback):
def on_train_begin(self, logs={}):
self.losses = {'batch':[], 'epoch':[]}
self.accuracy = {'batch':[], 'epoch':[]}
self.val_loss = {'batch':[], 'epoch':[]}
self.val_acc = {'batch':[], 'epoch':[]}
def on_batch_end(self, batch, logs={}):
self.losses['batch'].append(logs.get('loss'))
self.accuracy['batch'].append(logs.get('acc'))
self.val_loss['batch'].append(logs.get('val_loss'))
self.val_acc['batch'].append(logs.get('val_acc'))
def on_epoch_end(self, batch, logs={}):
self.losses['epoch'].append(logs.get('loss'))
self.accuracy['epoch'].append(logs.get('acc'))
self.val_loss['epoch'].append(logs.get('val_loss'))
self.val_acc['epoch'].append(logs.get('val_acc'))
def loss_plot(self, loss_type):
iters = range(len(self.losses[loss_type]))
plt.figure()
# acc
plt.plot(iters, self.accuracy[loss_type], 'r', label='train acc')
# loss
plt.plot(iters, self.losses[loss_type], 'g', label='train loss')
if loss_type == 'epoch':
# val_acc
plt.plot(iters, self.val_acc[loss_type], 'b', label='val acc')
# val_loss
plt.plot(iters, self.val_loss[loss_type], 'k', label='val loss')
plt.grid(True)
plt.xlabel(loss_type)
plt.ylabel('acc-loss')
plt.legend(loc="center")
plt.show()
#plt.savefig('common.png')
#dataset
RANDOM_SEED = 42
def read_data(file_path):
column_names = ['user-id', 'activity', 'timestamp', 'x-axis', 'y-axis', 'z-axis']
m = pd.read_csv(file_path,names=column_names, header=None,sep=',')
return m
def feature_normalize(dataset):
mu = np.mean(dataset,axis=0)
sigma = np.std(dataset,axis=0)
return (dataset-mu)/sigma
dataset1 = read_data('ab.txt')
dataset = pd.DataFrame(dataset1)
dataset['x-axis'] = feature_normalize(dataset['x-axis'])
dataset['y-axis'] = feature_normalize(dataset['y-axis'])
dataset['z-axis'] = feature_normalize(dataset['z-axis'])
N_TIME_STEPS = 200
N_FEATURES = 3
step = 200
segments = []
labels = []
for i in range(0, len(dataset) - N_TIME_STEPS, step):
xs = dataset['x-axis'].values[i: i + N_TIME_STEPS]
ys = dataset['y-axis'].values[i: i + N_TIME_STEPS]
zs = dataset['z-axis'].values[i: i + N_TIME_STEPS]
label = stats.mode(dataset['activity'][i: i + N_TIME_STEPS])[0][0]
segments.append([xs, ys, zs])
labels.append(label)
print("reduced size of data", np.array(segments).shape)
reshaped_segments = np.asarray(segments,dtype=np.float32).reshape(-1,1, N_TIME_STEPS, 3)
print("Reshape the segments", np.array(reshaped_segments).shape)
#x_train1, x_val_test, y_train1, y_val_test = train_test_split(reshaped_segments, labels, test_size=0.25, random_state=RANDOM_SEED)
batch_size = 128
num_classes =6
def create_model():
input_shape = Input(shape=(1,200,3))
x = Conv2D(5, kernel_size=(1, 1), padding='valid')(input_shape)
x1 = keras.layers.concatenate([input_shape, x], axis=-1)
x = Conv2D(50, kernel_size=(1, 7),padding='valid',
kernel_initializer='glorot_uniform',
kernel_regularizer = keras.regularizers.l2(0.0015))(x1)
x = keras.layers.core.Activation('relu')(x)
x = MaxPooling2D(pool_size=(1, 2))(x)
x = Conv2D(50, kernel_size=(1, 7),padding='valid',kernel_initializer='glorot_uniform',
kernel_regularizer=keras.regularizers.l2(0.0015))(x)
x = keras.layers.core.Activation('relu')(x)
x = MaxPooling2D(pool_size=(1, 2))(x)
x = Flatten()(x)
x = Dropout(0.9)(x)
output = Dense(num_classes, activation='softmax',kernel_initializer='glorot_uniform',)(x)
model = Model(inputs=input_shape,outputs=output)
model.summary()
sgd = optimizers.SGD(lr=0.005,decay=1e-6,momentum=0.9,nesterov=True)
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=sgd,
metrics=['accuracy'])
return model
history = LossHistory()
epochs = 4000
#setting learning rate
def scheduler(epoch):
if epoch > 0.75 * epochs:
lr = 0.0005
elif epoch > 0.25 * epochs:
lr = 0.001
else:
lr = 0.005
return lr
scheduler = LearningRateScheduler(scheduler)
estimator = KerasClassifier(build_fn=create_model)
#divide dataset
scores = []
confusions = []
sign = ['DOWNSTAIRS','JOGGING','SITTING','STANDING','UPSTAIRS','WALKING']
encoder = LabelEncoder()
encoder_y = encoder.fit_transform(labels)
train_labels = to_categorical(encoder_y,num_classes=None)
#kfold = StratifiedKFold(reshaped_segments.shape[0],n_folds=10,shuffle=True,random_state=42)
kfold = StratifiedKFold(labels,n_folds=3,shuffle=True,random_state=42)
for train_index,test_index in kfold:
print(test_index)
x_train, x_test = reshaped_segments[train_index], reshaped_segments[test_index]
y_train, y_test = train_labels[train_index], train_labels[test_index]
estimator.fit(x_train,y_train,callbacks=[scheduler,history],epochs=10,batch_size=128,verbose=0)
scores.append(estimator.score(x_test,y_test))
print(y_test)
print(type(y_test))
pred_test = estimator.predict(x_test)
print(pred_test)
print(np.argmax(y_test,axis=1))
confusions.append(metrics.confusion_matrix(np.argmax(y_test,axis=1),pred_test,sign))
matrix = [[0,0,0,0,0,0],[0,0,0,0,0,0],[0,0,0,0,0,0],[0,0,0,0,0,0],[0,0,0,0,0,0],[0,0,0,0,0,0]]
for i in np.arange(n_folds-1):
for j in len(confusions[0]):
for k in len(confusions[0][0]):
matrix[j][k] = matrix[j][k] + confusions[i][j][k] + confusions[i+1][j][k]
model.save('model.h5')
model.save_weights('my_model_weights.h5')
print('score:',scores)
scores = np.mean(scores)
print('mean:',scores)
plt.figure(figsize=(16,14))
sns.heatmap(matrix, xticklabels=sign, yticklabels=sign, annot=True, fmt="d");
plt.title("CONFUSION MATRIX : ")
plt.ylabel('True Label')
plt.xlabel('Predicted label')
plt.savefig('cmatrix.png')
plt.show();