Isso pode estar relacionado ao seu problema? link Tente adicionar o sinalizador -u ao iniciar o Python
Estou executando um programa python que usa VGG16 rede neural, através do pacote keras , para classificar imagens de gatos e cachorros, do banco de dados Kaggle . Para fazer isso, estou usando o comando do terminal padrão: python program.py > output.txt . Eu também tentei as outras variantes, python program.py &> output.txt , ou, o comando tee , python program.py |& tee output.txt , mas parece não estar funcionando. Para o primeiro comando, meu arquivo de texto contém apenas:
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.
mas o código tem muitas declarações print ! O conteúdo esperado do arquivo output.txt é (apenas as primeiras 4-5 linhas da saída do terminal mostrada):
Using TensorFlow backend.
Defining all the path!
All paths defined!
Getting mean RGB and creating labels!
que é exibido quando eu digito apenas python program.py . A parte:
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.
parte vem muito mais tarde na saída do terminal. Eu estou colocando meu código aqui para referência, mas ele tem 204 linhas de comprimento:
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')
Eu não sei o que realmente está acontecendo e qualquer ajuda nesse assunto será muito apreciada!
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