Question

您好我正在为我自己的数据集培训VGG16网络。以下给出了我使用的代码。

from keras.models import Sequential
from scipy.misc import imread
#get_ipython().magic('matplotlib inline')
import matplotlib.pyplot as plt
import numpy as np
import keras
from keras.layers import Dense
import pandas as pd

from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
import numpy as np
from keras.applications.vgg16 import decode_predictions
from keras.utils.np_utils import to_categorical

from sklearn.preprocessing import LabelEncoder
from keras.models import Sequential
from keras.optimizers import SGD
from keras.layers import Input, Dense, Convolution2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, merge, Reshape, Activation
import os
from sklearn.metrics import log_loss
import cv2
from keras.models import Model
from sklearn import cross_validation
from imagenet_utils import preprocess_input
from imagenet_utils import preprocess_input,decode_predictions
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
from skimage import measure, morphology
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from os import walk
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import confusion_matrix

os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
def load_images():



def train_test_separation():


    df = pd.read_csv('C:/CT_SCAN_IMAGE_SET/resnet_50/dbs2017/data/stage1_labels.csv')
    print(df.head())

    #print (filenames,' ',pathname,' ',BASE_PATH )
    #images=load_images()
    labeling = df['cancer'].as_matrix()

    names=['Not_Cancer','Cancer']

    Y_category=keras.utils.to_categorical(labeling, num_classes=2)
    x = np.array([np.mean(np.load('E:/224x224/%s.npy' % str(id)), axis=0) for id in df['id'].tolist()])

    print ('.......................................',Y_category.shape)

    img_rows, img_cols = 224, 224 # Resolution of inputs
    channel = 3
    num_classes = 2
    batch_size = 50
    nb_epoch = 1

    # Load Cifar10 data. Please implement your own load_data() module for your own dataset
    X_train, Y_train, X_valid, Y_valid = cross_validation.train_test_split(x, Y_category, random_state=42, test_size=0.20)

    print (X_train.shape) #(1107, 3, 224, 224)
    print (Y_train.shape) #(277, 3, 224, 224)
    print (X_valid.shape) #(1107, 2)
    print (Y_valid.shape) #(277, 2)

    X_train = X_train.transpose(0,2,3,1)
    Y_train = Y_train.transpose(0,2,3,1)
    print (X_train.shape) # (1107, 224, 224, 3)
    # Load our model


    # Load our model
    model = vgg16_model(img_rows, img_cols, channel, num_classes)
    print ('.........................................',Y_valid.shape) #(277, 2)
    model.summary()
    # Start Fine-tuning and training

    #config = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True)
    #config.gpu_options.allow_growth=True
    #config.gpu_options.per_process_gpu_memory_fraction = 0.95
    #set_session(tf.Session(config=config))


    hist=model.fit(X_train, X_valid,batch_size=batch_size,epochs=nb_epoch,shuffle=True,verbose=1,validation_data=(Y_train, Y_valid))

    #validation_data: tuple (x_val, y_val) or tuple (x_val, y_val, val_sample_weights) on which to evaluate the loss and any model
    #metrics at the end of each epoch. The model will not be trained on this data. This will override validation_split

    (loss,accuracy)=model.evaluate(Y_train,Y_valid,batch_size=batch_size,verbose=1)

    print ("[INFO] loss={:.4f},accuracy: {:.4f}%".format(loss,accuracy*100))

    df = pd.read_csv('C:/CT_SCAN_IMAGE_SET/resnet_50/dbs2017/data/stage1_sample_submission.csv')
    df2 = pd.read_csv('C:/CT_SCAN_IMAGE_SET/resnet_50/dbs2017/data/stage1_solution.csv')
    x = np.array([np.mean(np.load('E:/224x224/%s.npy' % str(id)), axis=0) for id in df['id'].tolist()])

    x = x.transpose(0,2,3,1)
    # Make predictions
    pred = model.predict(x, batch_size=batch_size, verbose=1) #predict(self, x, batch_size=None, verbose=0, steps=None)

    print (pred)


    # Cross-entropy loss score
    score = log_loss(x, predictions_valid)


def vgg16_model(img_rows, img_cols, channel=1, num_classes=None):
    print ('aaaaaaaaaaaaaaaaaa')

    image_input=Input(shape=(224,224,3))

    model = VGG16(weights='imagenet', include_top=True, input_tensor=image_input)
    print ('bbbbbbbbbbbbbbbbbbbbb')
    model.summary()

    last_layer=model.get_layer('fc2').output # last layer which will be 4096

    # Will add one layer over it.
    out=Dense(num_classes, activation='softmax',name='output')(last_layer)

    custom_vgg_model=Model(image_input,out) #Creating the custom model using the Keras, Modelfunction.

    custom_vgg_model.summary()

    #Now you can train the model. Before that you need to compile it.

    custom_vgg_model.compile(loss='categorical_crossentropy',optimizer='adadelta',metrics=['accuracy'])


    #We do not need to train the etire network. Instead we need to train the last layer.
    #All the layers except thelast layer will be freezed

    for layer in custom_vgg_model.layers[:-1]:
        layer.trainable=False

    custom_vgg_model.layers[3].trainable


    return custom_vgg_model




if __name__ == '__main__':
    #calc_features()
    train_test_separation()

但在训练期间，我看到了一些与众不同的东西。我看到我的数据集没有接受过培训。我只改变了softmax分类器层并冻结了上面的所有其他层。在此之后显示我的模型摘要时，我收到了标题中显示的用户警告。

_________________________________________________________________
block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
_________________________________________________________________
flatten (Flatten)            (None, 25088)             0         
_________________________________________________________________
fc1 (Dense)                  (None, 4096)              102764544 
_________________________________________________________________
fc2 (Dense)                  (None, 4096)              16781312  
_________________________________________________________________
output (Dense)               (None, 2)                 8194      
=================================================================

Warning (from warnings module):
  File "C:\Research\Python_installation\lib\site-packages\keras\engine\training.py", line 973
    'Discrepancy between trainable weights and collected trainable'
UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?
Total params: 268,529,282
Trainable params: 134,268,738
Non-trainable params: 134,260,544
_________________________________________________________________
Train on 1107 samples, validate on 277 samples
Epoch 1/1

  50/1107 [>.............................] - ETA: 31:43 - loss: 0.7271 - acc: 0.6800������������������������������������������������������������������������������������
 100/1107 [=>............................] - ETA: 28:14 - loss: 5.3602 - acc: 0.5300������������������������������������������������������������������������������������
 150/1107 [===>..........................] - ETA: 26:16 - loss: 7.7642 - acc: 0.4267������������������������������������������������������������������������������������
 200/1107 [====>.........................] - ETA: 24:44 - loss: 8.8050 - acc: 0.3850������������������������������������������������������������������������������������
 250/1107 [=====>........................] - ETA: 23:21 - loss: 9.1716 - acc: 0.3760������������������������������������������������������������������������������������
 300/1107 [=======>......................] - ETA: 22:06 - loss: 9.8458 - acc: 0.3433������������������������������������������������������������������������������������
 350/1107 [========>.....................] - ETA: 20:37 - loss: 10.2353 - acc: 0.3257�������������������������������������������������������������������������������������
 400/1107 [=========>....................] - ETA: 19:04 - loss: 10.4468 - acc: 0.3175�������������������������������������������������������������������������������������
 450/1107 [===========>..................] - ETA: 17:38 - loss: 10.6829 - acc: 0.3067�������������������������������������������������������������������������������������
 500/1107 [============>.................] - ETA: 16:12 - loss: 10.7429 - acc: 0.3060������������������������������������������

如果模型工作正常，那么

Non-trainable params: 134,260,544

计数不应该是134,260,544，而是8194.但是我在用户警告的情况下将其设为134,260,544而不是8194。

显示时，我验证的数据集的预测输出，如下所示。

[[0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]

这意味着数据集根本没有正确分类。

有人可以帮助我找出我所犯的错误。

我收到的用户警告

Warning (from warnings module):
  File "C:\Research\Python_installation\lib\site-packages\keras\engine\training.py", line 973
    'Discrepancy between trainable weights and collected trainable'
UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?

UserWarning：可训练的权重与收集的可训练权重误差之间的差异

0 个答案: