Linux Terminal Output Export Fehler

Question

Ich bin ein Python-Programm ausgeführt wird, das VGG16 neuronales Netz verwendet wird, durch das keras Paket, Bilder von Katzen und Hunden zu klassifizieren, von der Kaggle Datenbank. Um dies zu tun, verwende ich den Standard-Terminal-Befehl: python program.py > output.txt. Ich habe auch die anderen Varianten, python program.py &> output.txt, oder tee Befehl, python program.py |& tee output.txt, ausprobiert, aber es scheint nicht zu funktionieren. Für den ersten Befehl enthält meine Textdatei nur:

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. 

aber der Code eine Menge print Aussagen hat! Der zu erwartende Inhalt der output.txt Datei ist (nur die ersten Zeilen 4-5 des Terminals Ausgangs gezeigt):

Using TensorFlow backend. 
Defining all the path! 

All paths defined! 

Getting mean RGB and creating labels! 

die angezeigt wird, wenn ich nur python program.py geben. Der Teil:

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. 

Teil kommt viel später in der Terminal-Ausgabe. Ich stelle meinen Code hier als Referenz, aber es ist 204 Zeilen lang:

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') 

Ich weiß nicht, was wirklich los ist und jede mögliche Hilfe in dieser Angelegenheit wird sehr geschätzt!

Answer 1

Nach einigem Basteln mit dem python Befehl und der Hilfe, python -h Ich fand, dass es eine Option -u für ungepufferte Ausgabe gibt. Ich habe es ausprobiert, python -u program.py > tee output.txt und es hat perfekt funktioniert.
Ich hatte auch my question in Ask Ubuntu gepostet und Steven D. schlug die gleiche Lösung vor. Seine Antwort leitete mich auch auf eine similar question in StackOverflow.