我试图使用python idle运行我的代码。但是，当我试图运行它时，在一个特定的行之后shell将重新启动
from __future__ import print_function
import numpy as np
from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D
from keras.layers import concatenate
from keras.optimizers import Adam
from keras.optimizers import SGD
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras import backend as K
K.set_image_dim_ordering('th') # Theano dimension ordering in this code
img_rows = 512
img_cols = 512
smooth = 1.
def dice_coef(y_true, y_pred):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
def dice_coef_np(y_true,y_pred):
y_true_f = y_true.flatten()
y_pred_f = y_pred.flatten()
intersection = np.sum(y_true_f * y_pred_f)
return (2. * intersection + smooth) / (np.sum(y_true_f) + np.sum(y_pred_f) + smooth)
def dice_coef_loss(y_true, y_pred):
return -dice_coef(y_true, y_pred)
def get_unet():
inputs = Input((1,img_rows, img_cols))
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)
#up6 = merge([UpSampling2D(size=(2, 2))(conv5), conv4], mode='concat', concat_axis=1)
up6 = concatenate([UpSampling2D(size=(2, 2))(conv5), conv4], axis=1)
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)
#up7 = merge([UpSampling2D(size=(2, 2))(conv6), conv3], mode='concat', concat_axis=1)
up7 = concatenate([UpSampling2D(size=(2, 2))(conv6), conv3], axis=1)
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)
#up8 = merge([UpSampling2D(size=(2, 2))(conv7), conv2], mode='concat', concat_axis=1)
up8 = concatenate([UpSampling2D(size=(2, 2))(conv7), conv2], axis=1)
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)
#up9 = merge([UpSampling2D(size=(2, 2))(conv8), conv1], mode='concat', concat_axis=1)
up9 = concatenate([UpSampling2D(size=(2, 2))(conv8), conv1], axis=1)
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)
conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)
model = Model(inputs=inputs, outputs=conv10)
model.compile(optimizer=Adam(lr=1.0e-5), loss=dice_coef_loss, metrics=[dice_coef])
return model
def train_and_predict(use_existing):
print('-'*30)
print('Loading and preprocessing train data...')
print('-'*30)
imgs_train = np.load("C:/Users/hirplk/Desktop/unet/Luna2016-Lung-Nodule-Detection-master_new/DATA_PROCESS/scratch/cse/dual/cs5130287/Luna2016/output_final/"+"trainImages.npy").astype(np.float32)
imgs_mask_train = np.load("C:/Users/hirplk/Desktop/unet/Luna2016-Lung-Nodule-Detection-master_new/DATA_PROCESS/scratch/cse/dual/cs5130287/Luna2016/output_final/"+"trainMasks.npy").astype(np.float32)
imgs_test = np.load("C:/Users/hirplk/Desktop/unet/Luna2016-Lung-Nodule-Detection-master_new/DATA_PROCESS/scratch/cse/dual/cs5130287/Luna2016/output_final/"+"testImages.npy").astype(np.float32)
imgs_mask_test_true = np.load("C:/Users/hirplk/Desktop/unet/Luna2016-Lung-Nodule-Detection-master_new/DATA_PROCESS/scratch/cse/dual/cs5130287/Luna2016/output_final/"+"testMasks.npy").astype(np.float32)
mean = np.mean(imgs_train) # mean for data centering
std = np.std(imgs_train) # std for data normalization
imgs_train -= mean # images should already be standardized, but just in case
imgs_train /= std
print('-'*30)
print('Creating and compiling model...')
print('-'*30)
model = get_unet()
# Saving weights to unet.hdf5 at checkpoints
model_checkpoint = ModelCheckpoint('unet.hdf5', monitor='loss', save_best_only=True)
#
# Should we load existing weights?
# Set argument for call to train_and_predict to true at end of script
if use_existing:
model.load_weights('./unet.hdf5')
#
# The final results for this tutorial were produced using a multi-GPU
# machine using TitanX's.
# For a home GPU computation benchmark, on my home set up with a GTX970
# I was able to run 20 epochs with a training set size of 320 and
# batch size of 2 in about an hour. I started getting reseasonable masks
# after about 3 hours of training.
#
print('-'*30)
print('Fitting model...')
print('-'*30)
model.fit(imgs_train, imgs_mask_train, batch_size=2, epochs=10, verbose=1, shuffle=True,
callbacks=[model_checkpoint])
print ('bbbbbbbbbbbbbbbbbbbbbbbbbbbbbb')
这是空闲的输出。
RESTART: C:\Users\hirplk\Desktop\unet\DSB3Tutorial-master\tutorial_code\LUNA_train_unet.py
Warning (from warnings module):
File "C:\Research\Python_installation\lib\site-packages\h5py\__init__.py", line 36
from ._conv import register_converters as _register_converters
FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
Using TensorFlow backend.
------------------------------
Loading and preprocessing train data...
------------------------------
------------------------------
Creating and compiling model...
------------------------------
------------------------------
Fitting model...
------------------------------
Epoch 1/10
=============================== RESTART: Shell ===============================
这是否意味着我的python崩溃了？有没有人经历过这个？它工作得很好，但现在我没有任何方法来实现我的代码
我是否需要从头开始重新安装所有内容