Linux终端输出导出错误

Question

我是运行一个 python 程序，它使用 VGG16 神经网络，通过 keras 包，对来自 Kaggle 数据库。为此，我使用标准终端命令：python program.py > output.txt。我还尝试了其他变体，python program.py &> output.txt，或 tee 命令，python program.py |& tee output.txt，但它似乎不起作用。对于第一个命令，我的文本文件仅包含：

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.

但是代码有很多print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!

当我输入 python program.py 时显示。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.

终端输出中的部分要晚得多。我把我的代码放在这里供参考，但它有 204 行长：

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')

我不知道到底发生了什么，在此问题上的任何帮助将不胜感激！

Answer 1

在对 python 命令和帮助进行一些修改后，python -h 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 效果很好。
我发布了 my question in Ask Ubuntu as well and Steven D. suggested the same solution. His answer also redirected me to a similar question in Whosebug.

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