提高Keras Sound分类CNN的准确性

时间：2018-11-30 23:58:58

标签： python keras conv-neural-network theano

我正在尝试使用keras（Theano Backend）构建一个卷积网络，但是在进行三课训练时，我无法获得高于33％的准确性。如果有人可以看一下代码并帮助我提高准确性，我将不胜感激。

import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
import numpy as np
import os
from matplotlib.image import imread
import pandas as pd
print("Imports Successful")
train_size = 3000
test_size = 750

batch_size = 10
num_classes = 3
epochs = 5

img_rows, img_cols = 1170, 580

count = 0
TrainArray = []
for file in os.listdir("C:/Users/aeshon/Desktop/Data/DataAndLabels/CroppedTrainData"):
    if len(TrainArray) >= 2000:
      count = 0
      while count < 1000:
        img = imread("C:/Users/aeshon/Desktop/Data/DataAndLabels/CroppedTrainData/Road " + str(count) + ".jpg")
        #print("Hi"+str(count))
        new_img = img[:,:,0]
        TrainArray.append(new_img)
        count = count + 1
      break

    if len(TrainArray) >= 1000:
      count = 0
      while count < 1000:
        img = imread("C:/Users/aeshon/Desktop/Data/DataAndLabels/CroppedTrainData/Water " + str(count) + ".jpg")
        #print("Hi"+str(count))
        new_img = img[:,:,0]
        TrainArray.append(new_img)
        count = count + 1
      continue

    img = imread("C:/Users/aeshon/Desktop/Data/DataAndLabels/CroppedTrainData/Gravel " + str(count) + ".jpg")
    #print("Hi"+str(count))
    new_img = img[:,:,0]
    TrainArray.append(new_img)
    count = count + 1
print("Train Array Synthesis Complete")
x_train = np.asarray(TrainArray)
del TrainArray
print("Array Deleted!")
count = 0
TestArray = []
for file in os.listdir("C:/Users/aeshon/Desktop/Data/DataAndLabels/CroppedTestData"):
    if len(TestArray) >= 500:
      count = 0
      while count < 250:
        img = imread("C:/Users/aeshon/Desktop/Data/DataAndLabels/CroppedTestData/Road " + str(count) + ".jpg")
        #print("Hi"+str(count))
        new_img = img[:,:,0]
        TestArray.append(new_img)
        count = count + 1
      break

    if len(TestArray) >= 250:
      count = 0
      while count < 250:
        img = imread("C:/Users/aeshon/Desktop/Data/DataAndLabels/CroppedTestData/Water " + str(count) + ".jpg")
        #print("Hi"+str(count))
        new_img = img[:,:,0]
        TestArray.append(new_img)
        count = count + 1
      continue

    img = imread("C:/Users/aeshon/Desktop/Data/DataAndLabels/CroppedTestData/Gravel " + str(count) + ".jpg")
    #print("Hi"+str(count))
    new_img = img[:,:,0]
    TestArray.append(new_img)
    count = count + 1
print("Test Array Synthesis Complete")
x_test = np.asarray(TestArray)
del TestArray
print("Array Deleted!")

x_train = x_train.reshape(train_size, 1170, 580, 1)
x_test = x_test.reshape(test_size, 1170, 580, 1)

print("x_train shape:", x_train.shape)
#print(x_train.shape[0], 'train samples')
#print(x_test.shape[0], 'test samples')

TrainLabels = np.asarray(pd.read_csv('C:/Users/aeshon/Desktop/Data/DataAndLabels/TrainingLabelsCompressed.csv'))

for i in range(len(TrainLabels)):
    TrainLabels[i] = int(TrainLabels[i])

y_train = TrainLabels

TestLabels = np.asarray(pd.read_csv('C:/Users/aeshon/Desktop/Data/DataAndLabels/TestingLabelsCompressed.csv'))

for i in range(len(TestLabels)):
    TestLabels[i] = int(TestLabels[i])

y_test = TestLabels

y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
print("Label formatting complete!")

print('Data Configuration Successful! Moving on to model compilation')

model = Sequential()
model.add(Conv2D(16, kernel_size=(3,3), activation = 'relu', input_shape = (1170, 580, 1)))
model.add(Conv2D(32, (3,3), activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(64, activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation = 'softmax'))

model.compile(loss = keras.losses.categorical_crossentropy,
              optimizer = keras.optimizers.Adadelta(),
              metrics = ['accuracy'])

print("Model Compiled!")

model.fit(x_train, y_train, batch_size = batch_size, epochs = epochs,
          verbose = 1, validation_data = (x_test, y_test))

score = model.evaluate(x_test, y_test, verbose = 1)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

model_json = model.to_json()
with open("model.json", "w") as json_file:
    json_file.write(model_json)
model.save_weights("C:/Users/aeshon/Desktop/model.h5")
print("Saved model to disk")

在这里您可以看到训练循环的输出（5个历元）

Train on 3000 samples, validate on 750 samples
Epoch 1/5
3000/3000 [==============================] - 10321s 3s/step - loss: 10.5418 - acc: 0.3457 - val_loss: 10.7454 - val_acc: 0.3333
Epoch 2/5
3000/3000 [==============================] - 10165s 3s/step - loss: 10.7615 - acc: 0.3323 - val_loss: 10.7454 - val_acc: 0.3333
Epoch 3/5
3000/3000 [==============================] - 10256s 3s/step - loss: 10.5681 - acc: 0.3443 - val_loss: 10.7454 - val_acc: 0.3333
Epoch 4/5
3000/3000 [==============================] - 10591s 4s/step - loss: 10.8213 - acc: 0.3283 - val_loss: 10.7454 - val_acc: 0.3333
Epoch 5/5
3000/3000 [==============================] - 10750s 4s/step - loss: 10.7400 - acc: 0.3337 - val_loss: 10.7454 - val_acc: 0.3333
750/750 [==============================] - 367s 489ms/step
Test loss: 10.745396969795227
Test accuracy: 0.3333333333333333

我知道这篇文章主要是代码，但是我的问题有些开放。我看过其他文章，包括this one，但我已经使用了分类交叉熵和softmax激活。任何评论都有帮助！

更新：

我已经进行了一些修复，但现在神经网络在66.67％处存在相同的问题。这是现在训练输出的快照：

 Train on 3000 samples, validate on 750 samples
Epoch 1/10
3000/3000 [==============================] - 1438s 479ms/step - loss: 5.4465 - acc: 0.6500 - val_loss: 5.3739 - val_acc: 0.6667
Epoch 2/10
3000/3000 [==============================] - 1432s 477ms/step - loss: 5.3735 - acc: 0.6667 - val_loss: 5.3730 - val_acc: 0.6667
Epoch 3/10
3000/3000 [==============================] - 1439s 480ms/step - loss: 5.3728 - acc: 0.6667 - val_loss: 5.3730 - val_acc: 0.6667
Epoch 4/10
3000/3000 [==============================] - 1470s 490ms/step - loss: 5.3728 - acc: 0.6667 - val_loss: 5.3729 - val_acc: 0.6667
Epoch 5/10
3000/3000 [==============================] - 1440s 480ms/step - loss: 5.3728 - acc: 0.6667 - val_loss: 5.3730 - val_acc: 0.6667
Epoch 6/10
3000/3000 [==============================] - 1435s 478ms/step - loss: 5.3727 - acc: 0.6667 - val_loss: 5.3728 - val_acc: 0.6667

还有其他需要修复的东西吗？

更新：

我尝试在model.fit（）函数之后添加一个混淆矩阵。我正确实施了吗？

Y_pred = model.predict_generator(x_test, test_size // batch_size+1)
y_pred = np.argmax(Y_pred, axis=1)
print('Confusion Matrix')
print(confusion_matrix(labels = [0,1,2], y_pred))
print('Classification Report')
target_names = ['Gravel', 'Road', 'Water']
print(classification_report(labels = [0,1,2], y_pred, target_names=target_names))

0 个答案:

没有答案
相关问题
最新问题