Machine learning 为什么我的有线电视新闻网（CNN）过度安装，我如何修复它？
我正在微调一个名为C3D的3D-CNN，它最初被训练用于从视频剪辑中对运动进行分类 我正在冻结卷积（特征提取）层，并使用GIPHY中的GIF对完全连接的层进行训练，以对GIF进行分类，以便进行情绪分析（正面或负面） 除最终完全连接的层外，所有层的权重都已预加载 我使用5000张图像（2500张正片，2500张负片）进行训练，使用Keras进行70/30训练/测试分割。我使用的Adam优化器的学习率为0.0001 在训练期间，训练精度增加，训练损失减少，但在验证的早期，随着模型开始过度拟合，训练精度和损失并没有改善 我相信我有足够的训练数据，并且在两个完全连接的层上都使用了0.5的落差，那么我该如何应对这种过度拟合呢 Keras的模型架构、培训代码和培训绩效可视化可在下面找到 列车c3d.pyMachine learning 为什么我的有线电视新闻网（CNN）过度安装，我如何修复它？,machine-learning,keras,computer-vision,conv-neural-network,Machine Learning,Keras,Computer Vision,Conv Neural Network,我正在微调一个名为C3D的3D-CNN，它最初被训练用于从视频剪辑中对运动进行分类 我正在冻结卷积（特征提取）层，并使用GIPHY中的GIF对完全连接的层进行训练，以对GIF进行分类，以便进行情绪分析（正面或负面） 除最终完全连接的层外，所有层的权重都已预加载 我使用5000张图像（2500张正片，2500张负片）进行训练，使用Keras进行70/30训练/测试分割。我使用的Adam优化器的学习率为0.0001 在训练期间，训练精度增加，训练损失减少，但在验证的早期，随着模型开始过度拟合，训练精
from training.c3d_model import create_c3d_sentiment_model
from ImageSentiment import load_gif_data
import numpy as np
import pathlib
from keras.callbacks import ModelCheckpoint
from keras.optimizers import Adam
def image_generator(files, batch_size):
"""
Generate batches of images for training instead of loading all images into memory
:param files:
:param batch_size:
:return:
"""
while True:
# Select files (paths/indices) for the batch
batch_paths = np.random.choice(a=files,
size=batch_size)
batch_input = []
batch_output = []
# Read in each input, perform preprocessing and get labels
for input_path in batch_paths:
input = load_gif_data(input_path)
if "pos" in input_path: # if file name contains pos
output = np.array([1, 0]) # label
elif "neg" in input_path: # if file name contains neg
output = np.array([0, 1]) # label
batch_input += [input]
batch_output += [output]
# Return a tuple of (input,output) to feed the network
batch_x = np.array(batch_input)
batch_y = np.array(batch_output)
yield (batch_x, batch_y)
model = create_c3d_sentiment_model()
print(model.summary())
model.load_weights('models/C3D_Sport1M_weights_keras_2.2.4.h5', by_name=True)
for layer in model.layers[:14]: # freeze top layers as feature extractor
layer.trainable = False
for layer in model.layers[14:]: # fine tune final layers
layer.trainable = True
train_files = [str(filepath.absolute()) for filepath in pathlib.Path('data/sample_train').glob('**/*')]
val_files = [str(filepath.absolute()) for filepath in pathlib.Path('data/sample_validation').glob('**/*')]
batch_size = 8
train_generator = image_generator(train_files, batch_size)
validation_generator = image_generator(val_files, batch_size)
model.compile(optimizer=Adam(lr=0.0001),
loss='binary_crossentropy',
metrics=['accuracy'])
mc = ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', verbose=1)
history = model.fit_generator(train_generator, validation_data=validation_generator,
steps_per_epoch=int(np.ceil(len(train_files) / batch_size)),
validation_steps=int(np.ceil(len(val_files) / batch_size)), epochs=5, shuffle=True,
callbacks=[mc])
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv1 (Conv3D) (None, 16, 112, 112, 64) 5248
_________________________________________________________________
pool1 (MaxPooling3D) (None, 16, 56, 56, 64) 0
_________________________________________________________________
conv2 (Conv3D) (None, 16, 56, 56, 128) 221312
_________________________________________________________________
pool2 (MaxPooling3D) (None, 8, 28, 28, 128) 0
_________________________________________________________________
conv3a (Conv3D) (None, 8, 28, 28, 256) 884992
_________________________________________________________________
conv3b (Conv3D) (None, 8, 28, 28, 256) 1769728
_________________________________________________________________
pool3 (MaxPooling3D) (None, 4, 14, 14, 256) 0
_________________________________________________________________
conv4a (Conv3D) (None, 4, 14, 14, 512) 3539456
_________________________________________________________________
conv4b (Conv3D) (None, 4, 14, 14, 512) 7078400
_________________________________________________________________
pool4 (MaxPooling3D) (None, 2, 7, 7, 512) 0
_________________________________________________________________
conv5a (Conv3D) (None, 2, 7, 7, 512) 7078400
_________________________________________________________________
conv5b (Conv3D) (None, 2, 7, 7, 512) 7078400
_________________________________________________________________
zeropad5 (ZeroPadding3D) (None, 2, 8, 8, 512) 0
_________________________________________________________________
pool5 (MaxPooling3D) (None, 1, 4, 4, 512) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 8192) 0
_________________________________________________________________
fc6 (Dense) (None, 4096) 33558528
_________________________________________________________________
dropout_1 (Dropout) (None, 4096) 0
_________________________________________________________________
fc7 (Dense) (None, 4096) 16781312
_________________________________________________________________
dropout_2 (Dropout) (None, 4096) 0
_________________________________________________________________
nfc8 (Dense) (None, 2) 8194
=================================================================
Total params: 78,003,970
Trainable params: 78,003,970
Non-trainable params: 0
_________________________________________________________________
None
我认为错误在于损失函数和最后一个密集层。如模型摘要所示，最后一个致密层为
nfc8 (Dense) (None, 2)
binary\u crossentropy希望这有帮助。我认为错误在损失函数和最后一个密集层中。如模型摘要所示，最后一个致密层为
nfc8 (Dense) (None, 2)
binary\u crossentropy希望这有帮助。可以尝试不同的学习速度，顺便说一句，至少跑8-10个历元。可以尝试不同的学习速度，顺便说一句，至少跑8-10个历元