我正在尝试将 CNN 的验证准确率从 76%(当前)提高到 90% 以上。我将在下面展示有关我的 CNN 性能和配置的所有信息。
本质上，我希望我的 CNN 区分两类梅尔谱图:
第 1 类
第 2 类
这是准确率与纪元的关系图:
这是损失与纪元的关系图
最后，这里是模型架构配置
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),activation='relu',input_shape=(3, 640, 480)))
model.add(Conv2D(64, (3, 3), activation='relu', dim_ordering="th"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
这是我对 model.compile() 和 model.fit() 的调用
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.SGD(lr=0.001),
metrics=['accuracy'])
print("Compiled model")
history = model.fit(X_train, Y_train,
batch_size=8,
epochs=50,
verbose=1,
validation_data=(X_test, Y_test))
如何更改我的 CNN 配置以提高验证准确度分数？
我尝试过的事情:
任何帮助将不胜感激!
更新 #1
我将 epoch 数增加到 200，在让程序运行一夜之后，我得到了大约 76.31% 的验证准确度分数
我在下面发布了一张准确性与时代和损失与时代的图片
我还可以更改关于我的模型架构的其他哪些方面以获得更好的准确性？
最佳答案
首先你必须得到music_tagger_cnn.py 并将其放入项目路径中。之后，您可以构建模型:
from music_tagger_cnn import *
input_tensor = Input(shape=(1, 18, 119))
model =MusicTaggerCNN(input_tensor=input_tensor, include_top=False, weights='msd')
您可以通过所需的维度更改输入张量...
我通常使用 Theano 昏暗排序，但使用 Tensorflow 作为后端，这就是为什么:
from keras import backend as K
K.set_image_dim_ordering('th')
使用 Theano dim ordering，您必须考虑到必须更改样本尺寸的顺序
X_train = X_train.transpose(0, 3, 2, 1)
X_val = X_val.transpose(0, 3, 2, 1)
之后，您必须卡住这些不想更新的图层
for layer in model.layers:
layer.trainable = False
现在您可以设置自己的输出，例如:
last_layer = model.get_layer('pool3').output
out = Flatten()(last_layer)
out = Dense(128, activation='relu', name='fc2')(out)
out = Dropout(0.5)(out)
out = Dense(n_classes, activation='softmax', name='fc3')(out)
model = Model(input=model.input, output=out)
在那之后，你必须能够训练它只做:
sgd = SGD(lr=0.01, momentum=0, decay=0.002, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
history = model.fit(X_train, labels_train,
validation_data=(X_val, labels_val), nb_epoch=100, batch_size=5)
请注意，标签应采用单热编码
我希望它会有所帮助!!
更新:发布代码以便我可以帮助调试这些行并防止崩溃。
input_tensor = Input(shape=(3, 640, 480))
model = MusicTaggerCNN(input_tensor=input_tensor, include_top=False, weights='msd')
for layer in model.layers:
layer.trainable = False
last_layer = model.get_layer('pool3').output
out = Flatten()(last_layer)
out = Dense(128, activation='relu', name='fc2')(out)
out = Dropout(0.5)(out)
out = Dense(n_classes, activation='softmax', name='fc3')(out)
model = Model(input=model.input, output=out)
sgd = SGD(lr=0.01, momentum=0, decay=0.002, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
history = model.fit(X_train, labels_train,
validation_data=(X_test, Y_test), nb_epoch=100, batch_size=5)
编辑＃2
# -*- coding: utf-8 -*-
'''MusicTaggerCNN model for Keras.
# Reference:
- [Automatic tagging using deep convolutional neural networks](https://arxiv.org/abs/1606.00298)
- [Music-auto_tagging-keras](https://github.com/keunwoochoi/music-auto_tagging-keras)
'''
from __future__ import print_function
from __future__ import absolute_import
from keras import backend as K
from keras.layers import Input, Dense
from keras.models import Model
from keras.layers import Dense, Dropout, Flatten
from keras.layers.convolutional import Convolution2D
from keras.layers.convolutional import MaxPooling2D, ZeroPadding2D
from keras.layers.normalization import BatchNormalization
from keras.layers.advanced_activations import ELU
from keras.utils.data_utils import get_file
from keras.layers import Input, Dense
TH_WEIGHTS_PATH = 'https://github.com/keunwoochoi/music-auto_tagging-keras/blob/master/data/music_tagger_cnn_weights_theano.h5'
TF_WEIGHTS_PATH = 'https://github.com/keunwoochoi/music-auto_tagging-keras/blob/master/data/music_tagger_cnn_weights_tensorflow.h5'
def MusicTaggerCNN(weights='msd', input_tensor=None,
include_top=True):
'''Instantiate the MusicTaggerCNN architecture,
optionally loading weights pre-trained
on Million Song Dataset. Note that when using TensorFlow,
for best performance you should set
`image_dim_ordering="tf"` in your Keras config
at ~/.keras/keras.json.
The model and the weights are compatible with both
TensorFlow and Theano. The dimension ordering
convention used by the model is the one
specified in your Keras config file.
For preparing mel-spectrogram input, see
`audio_conv_utils.py` in [applications](https://github.com/fchollet/keras/tree/master/keras/applications).
You will need to install [Librosa](http://librosa.github.io/librosa/)
to use it.
# Arguments
weights: one of `None` (random initialization)
or "msd" (pre-training on ImageNet).
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
to use as image input for the model.
include_top: whether to include the 1 fully-connected
layer (output layer) at the top of the network.
If False, the network outputs 256-dim features.
# Returns
A Keras model instance.
'''
if weights not in {'msd', None}:
raise ValueError('The `weights` argument should be either '
'`None` (random initialization) or `msd` '
'(pre-training on Million Song Dataset).')
# Determine proper input shape
if K.image_dim_ordering() == 'th':
input_shape = (3, 640, 480)
else:
input_shape = (3, 640, 480)
if input_tensor is None:
melgram_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
melgram_input = Input(tensor=input_tensor, shape=input_shape)
else:
melgram_input = input_tensor
# Determine input axis
if K.image_dim_ordering() == 'th':
channel_axis = 1
freq_axis = 2
time_axis = 3
else:
channel_axis = 3
freq_axis = 1
time_axis = 2
# Input block
x = BatchNormalization(axis=freq_axis, name='bn_0_freq')(melgram_input)
# Conv block 1
x = Convolution2D(64, 3, 3, border_mode='same', name='conv1')(x)
x = BatchNormalization(axis=channel_axis, mode=0, name='bn1')(x)
x = ELU()(x)
x = MaxPooling2D(pool_size=(2, 4), name='pool1')(x)
# Conv block 2
x = Convolution2D(128, 3, 3, border_mode='same', name='conv2')(x)
x = BatchNormalization(axis=channel_axis, mode=0, name='bn2')(x)
x = ELU()(x)
x = MaxPooling2D(pool_size=(2, 4), name='pool2')(x)
# Conv block 3
x = Convolution2D(128, 3, 3, border_mode='same', name='conv3')(x)
x = BatchNormalization(axis=channel_axis, mode=0, name='bn3')(x)
x = ELU()(x)
x = MaxPooling2D(pool_size=(2, 4), name='pool3')(x)
# Output
x = Flatten()(x)
if include_top:
x = Dense(50, activation='sigmoid', name='output')(x)
# Create model
model = Model(melgram_input, x)
if weights is None:
return model
else:
# Load input
if K.image_dim_ordering() == 'tf':
raise RuntimeError("Please set image_dim_ordering == 'th'."
"You can set it at ~/.keras/keras.json")
model.load_weights('data/music_tagger_cnn_weights_%s.h5' % K._BACKEND,
by_name=True)
return model
编辑 #3
我尝试了使用 MusicTaggerCRNN 作为 melgram 的特征提取器的 keras 示例。然后我训练了一个带有 2 个密集层和一个二进制输出的简单神经网络。我的例子中的样本不适用于你的情况，但它也是一个二元分类器
我用过
keras==1.2.2和 tensorflow-gpu==1.0.0并为我工作。这是代码:
from keras.applications.music_tagger_crnn import MusicTaggerCRNN
from keras.applications.music_tagger_crnn import preprocess_input, decode_predictions
import numpy as np
from keras.layers import Input, Dense
from keras.models import Model
from keras.layers import Dense, Dropout, Flatten
from keras.optimizers import SGD
model = MusicTaggerCRNN(weights='msd', include_top=False)
#Samples simulation
audio_paths_train = ['data/genres/blues/blues.00000.au','data/genres/classical/classical.00000.au','data/genres/classical/classical.00002.au', 'data/genres/blues/blues.00003.au']
audio_paths_test = ['data/genres/blues/blues.00001.au', 'data/genres/classical/classical.00001.au', 'data/genres/blues/blues.00002.au', 'data/genres/classical/classical.00003.au']
labels_train = [0,1,1,0]
labels_test = [0, 1, 0, 1]
melgrams_train = [preprocess_input(audio_path) for audio_path in audio_paths_train]
melgrams_test = [preprocess_input(audio_path) for audio_path in audio_paths_test]
feats_train = [model.predict(np.expand_dims(melgram, axis=0)) for melgram in melgrams_train]
feats_test = [model.predict(np.expand_dims(melgram, axis=0)) for melgram in melgrams_test]
feats_train = np.array(feats_train)
feats_test = np.array(feats_test)
_input = Input(shape=(1,32))
x = Flatten(name='flatten')(_input)
x = Dense(128, activation='relu', name='fc6')(x)
x = Dense(64, activation='relu', name='fc7')(x)
x = Dense(1, activation='softmax', name='fc8')(x)
class_model = Model(_input, x)
sgd = SGD(lr=0.01, momentum=0, decay=0.02, nesterov=True)
class_model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])
history = class_model.fit(feats_train, labels_train, validation_data=(feats_test, labels_test), nb_epoch=100, batch_size=5, class_weight='auto')
print(history.history['acc'])
# Final evaluation of the model
scores = class_model.evaluate(feats_test, labels_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1] * 100))
关于python-3.x - 在 Python3 中使用 Keras 优化 CNN 的架构，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/46315258/