Question

我正在运行一个python程序，该程序使用VGG16神经网络，通过keras包，从 Kaggle 数据库中分类猫和狗的图像。为此，我使用标准终端命令：python program.py > output.txt。我还尝试过其他变体python program.py &> output.txt或tee命令python program.py |& tee output.txt，但它似乎没有用。对于第一个命令，我的文本文件只包含：

Using TensorFlow backend.
2017-05-31 13:39:34.218034: W tensorflow/core/platform/cpu_feature_guard.cc:45] 
The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are
 available on your machine and could speed up CPU computations.
2017-05-31 13:39:34.226941: W tensorflow/core/platform/cpu_feature_guard.cc:45] 
The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are
 available on your machine and could speed up CPU computations.

但代码中有很多print个语句！ output.txt文件的预期内容是（仅显示终端输出的前4-5行）：

Using TensorFlow backend.
Defining all the path!

All paths defined!

Getting mean RGB and creating labels!

我输入python program.py时显示的

。部分：

2017-05-31 13:39:34.218034: W tensorflow/core/platform/cpu_feature_guard.cc:45] 
The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are
 available on your machine and could speed up CPU computations.
2017-05-31 13:39:34.226941: W tensorflow/core/platform/cpu_feature_guard.cc:45] 
The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are
 available on your machine and could speed up CPU computations.

部分在终端输出中出现得晚得多。我将我的代码放在这里作为参考，但它长达204行：

import keras
from keras.models import Sequential, Model
from keras.layers import Flatten, Dense, Dropout, Input, Activation
from keras.layers.convolutional import Conv2D, MaxPooling2D, ZeroPadding2D
from keras.layers.merge import Add
from keras.optimizers import SGD, Adam
import cv2, numpy as np
import glob
import csv

####################
## VGG16 Function ##
####################

def VGG_16(weights_path=None, classes=2):

    ######################################
    ## Input: 3x224x224 sized RGB Input ##
    ######################################

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

    layer = 0
    #############
    ## Block 1 ##
    #############
    x = Conv2D(64, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block1_conv1')(inputs)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = Conv2D(64, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block1_conv2')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)

    #############
    ## Block 2 ##
    #############
    x = Conv2D(128, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block2_conv1')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = Conv2D(128, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block2_conv2')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)

    #############
    ## Block 3 ##
    #############
    x = Conv2D(256, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block3_conv1')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = Conv2D(256, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block3_conv2')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = Conv2D(256, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block3_conv3')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)

    #############
    ## Block 4 ##
    #############
    x = Conv2D(512, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block4_conv1')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = Conv2D(512, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block4_conv2')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = Conv2D(512, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block4_conv3')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)

    #############
    ## Block 5 ##
    #############
    x = Conv2D(512, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block5_conv1')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = Conv2D(512, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block5_conv2')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = Conv2D(512, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block5_conv3')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    out = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)

    ###############
    ## Top layer ##
    ###############

    out = Flatten(name='flatten')(out)
    out = Dense(4096, activation='relu', name='fc1')(out)
    out = Dropout(0.5)(out)
    out = Dense(4096, activation='relu', name='fc2')(out)
    out = Dropout(0.5)(out)
    out = Dense(classes, activation='softmax', name='predictions')(out)

    if weights_path:
        model.load_weights(weights_path)

    model = Model(inputs, out, name='vgg-16')

    return model

###################
## Main Function ##
###################

if __name__ == "__main__":

    ################################################
    ## Get all the training and the testing paths ##
    ################################################

    print('Defining all the path!\n')
    cat_path = "./train/cat.*.jpg"
    dog_path = "./train/dog.*.jpg"
    train_path = "./train/*.jpg"
    test_path = "./test1/*.jpg"
    Mean_RGB = []
    x_train = []
    y_train = []
    x_test = []
    print('All paths defined!\n')

    ########################################################################
    ## Get training and testng data sizes, to find the average RGB values ##
    ########################################################################

    print('Getting mean RGB and creating labels!\n')
    for file in glob.glob(cat_path): # To get the sizes of all the cat images
        im = cv2.resize(cv2.imread(file), (224, 224)).astype(np.float32)
        im = np.mean(im, axis=(0,1))
        Mean_RGB.append(tuple(im))
        y_train.append(0)
    for file in glob.glob(dog_path): # To get the sizes of all the dog images
        im = cv2.resize(cv2.imread(file), (224, 224)).astype(np.float32)
        im = np.mean(im, axis=(0,1))
        Mean_RGB.append(tuple(im))
        y_train.append(1)
    y_train = np.array(y_train)
    Mean_RGB = tuple(np.mean(Mean_RGB, axis=0))
    print('Got mean RGB and created labels!\n')

    #########################################################################
    ## Load the training and testing images, after subtracting average RGB ##
    #########################################################################

    print('Loading images as numpy arrays!\n')
    for file in glob.glob(train_path):
        im = cv2.resize(cv2.imread(file), (224, 224)).astype(np.float32)
        im_r = im-Mean_RGB
        im_r = im_r.transpose((2,0,1))
        #im_r = np.expand_dims(im_r, axis=0)
        x_train.append(im_r)
    y_train = y_train.reshape((-1,1))
    y_train = keras.utils.to_categorical(y_train, num_classes=2)
    x_train = np.array(x_train)
    for file in glob.glob(test_path):
        im = cv2.resize(cv2.imread(file), (224, 224)).astype(np.float32)
        im_r = im-Mean_RGB
        im_r = im_r.transpose((2,0,1))
        #im_r = np.expand_dims(im_r, axis=0)
        x_test.append(im_r)
    x_test = np.array(x_test)
    print('All images loaded!\n')

    ##############################
    ## Train and test the model ##
    ##############################

    print('Creating Neural Net!\n')
    model = VGG_16()
    print('\nNeural Net created!\n')
    adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
    model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])

    print('Training Neural Net!\n')
    ### Generating validation data split in training sample
    model.fit(x_train, y_train, batch_size=500, epochs=25, validation_split=0.2, shuffle=True)
    print('Neural Net trained!\n')
    print('Evaluating model on the training images!\n')
    score = model.evaluate(x_train, y_train, batch_size=500, verbose=1)
    print('Model score on training data: ' +str(score)+ '\n')
    print('Predicting class of test images!\n')
    pred = model.predict(x_test, batch_size=1, verbose=1)
    prediction = np.argmax(pred, axis = 1)
    print('Predictions done!\n')
    result = []
    print('Creating output CSV file!\n')
    result.append(['id', 'label'])
    for i in range(0,len(prediction)):
        result.append([i+1,prediction[i]])
    with open("cat-dog-output.csv","wb") as f:
        writer = csv.writer(f)
        writer.writerows(result)
    print('Created output CSV file!\n')

    print('Saving model parameters!\n')
    model.save('vgg16-sim-conn.h5')
    model.save_weights('vgg16-sim-conn-weights.h5')
    print('Model saved!\n')

我不知道究竟发生了什么事情，我们将非常感谢您对此事的任何帮助！

Answer 1

在对python命令和帮助python -h进行了一些修补之后，我发现有一个选项-u用于无缓冲输出。我试了一下，python -u program.py > tee output.txt它完美无缺我也发布了my question in Ask Ubuntu，Steven D.提出了相同的解决方案。他的回答也将我改为similar question in StackOverflow。

Linux终端输出导出错误

1 个答案: