小编use*_*200的帖子

您好,我正在尝试使用Resnet神经网络通过使用微调方法来训练癌症数据集

这是我以前用来微调的方法.

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

model = ResNet50(input_tensor=image_input, include_top=True,weights='imagenet')
model.summary()
last_layer = model.get_layer('avg_pool').output
x= Flatten(name='flatten')(last_layer)
out = Dense(num_classes, activation='softmax', name='output_layer')(x)
custom_resnet_model = Model(inputs=image_input,outputs= out)
custom_resnet_model.summary()

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

custom_resnet_model.layers[-1].trainable

custom_resnet_model.compile(Adam(lr=0.001),loss='categorical_crossentropy',metrics=['accuracy'])

custom_resnet_model.summary()

tensorboard = TensorBoard(log_dir='./logs', histogram_freq=0,
                      write_graph=True, write_images=False)

hist = custom_resnet_model.fit(X_train, X_valid, batch_size=32, epochs=nb_epoch, verbose=1, validation_data=(Y_train, Y_valid),callbacks=[tensorboard])

(loss, accuracy) = custom_resnet_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 = …

我正在尝试安装支持GPU的tensorflow.

我尝试了以下链接中的信息

https://www.tensorflow.org/install/install_windows

1. CUDA®Toolkit8.0
2. cuDNN v6.0
3. 具有CUDA Compute Capability 3.0的GPU卡 - GeForce 940MX

然后用来pip3 install --upgrade tensorflow-gpu安装tensorflow.

但是在尝试导入tensorflow时我收到以下错误.

    Traceback (most recent call last):
      File "C:\Research\Python_installation\lib\site-packages\tensorflow\python\platform\self_check.py", line 75, in preload_check
        ctypes.WinDLL(build_info.cudart_dll_name)
      File "C:\Research\Python_installation\lib\ctypes\__init__.py", line 347, in __init__
        self._handle = _dlopen(self._name, mode)
    OSError: [WinError 126] The specified module could not be found

    During handling of the above exception, another exception occurred:

    Traceback (most recent call last):
      File "<pyshell#6>", line 1, in <module>
        import tensorflow as tf
      File "C:\Research\Python_installation\lib\site-packages\tensorflow\__init__.py", line 24, …

I'm trying to detect lung cancer nodules using DICOM files. The main steps in cancer detection included following steps.

1) Preprocessing
  * Converting the pixel values to Hounsfield Units (HU)
  * Resampling to an isomorphic resolution to remove variance in scanner resolution
  *Lung segmentation
2) Training the data set using preprocessed images in Tensorflow CNN
3) Testing and validation

I followed few online tutorials to do this.

I need to combine the given solutions in

1) https://www.kaggle.com/gzuidhof/full-preprocessing-tutorial
2) https://www.kaggle.com/sentdex/first-pass-through-data-w-3d-convnet. …

嗨,我正在为我自己的数据集训练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 …

我正在尝试使用随机森林方法找到最佳特征集，我需要将数据集拆分为测试和训练。这是我的代码

from sklearn.model_selection import train_test_split

def train_test_split(x,y):
    # split data train 70 % and test 30 %
    x_train, x_test, y_train, y_test = train_test_split(x, y,train_size=0.3,random_state=42)
    #normalization
    x_train_N = (x_train-x_train.mean())/(x_train.max()-x_train.min())
    x_test_N = (x_test-x_test.mean())/(x_test.max()-x_test.min())

train_test_split(data,data_y)

参数 data,data_y 解析正确。但我收到以下错误。我想不通这是为什么。