Le telecamere si bloccano durante l'uso di ImageDataGenerator

Question

Per qualche motivo quando utilizzo un ImageDataGenerator con keras si blocca quando avvio il fit. Ottengo il seguente risultato. Si blocca proprio sulla linea Epoch 1/5

Using Theano backend. 
Using gpu device 0: GeForce GTX TITAN (CNMeM is disabled, cuDNN not available) 
Loading Data 
Compiling Model 
Fitting Data 
Epoch 1/5 

Essa mostra che uno dei miei core CPU è in esecuzione al 100% quindi qualcosa sta accadendo sulla CPU pari anche se va usando il mio GPU per adattarsi ai dati. Il codice qui sotto funziona se commento il fit_generator e utilizzo la funzione di adattamento.

import os 
os.environ["THEANO_FLAGS"] = "mode=FAST_RUN,device=gpu,floatX=float32" 
import minst_loader 
import matplotlib.pyplot as plt 
import numpy as np 
from scipy.misc import imrotate 
import random 
from keras.datasets import cifar10 

np.set_printoptions(suppress = True) 

print('Loading Data') 
x_train, y_train = (minst_loader.load_images('/home/chase/Desktop/MINST/train-images.idx3-ubyte'), \ 
         minst_loader.load_labels('/home/chase/Desktop/MINST/train-labels.idx1-ubyte')) 

x_test, y_test = (minst_loader.load_images('/home/chase/Desktop/MINST/t10k-images.idx3-ubyte'), \ 
         minst_loader.load_labels('/home/chase/Desktop/MINST/t10k-labels.idx1-ubyte')) 

for i in range(len(y_train)): 
    v = np.zeros(10) 
    v[y_train[i]] = 1 
    y_train[i] = v 

#  for j in range(8): 
#   x = imrotate(x_train[i], random.random() * 20) 
#   x_train.append(x) 
#   y_train.append(y_train[i]) 


for i in range(len(y_test)): 
    v = np.zeros(10) 
    v[y_test[i]] = 1 
    y_test[i] = v 

x_train = np.array(x_train) 
y_train = np.array(y_train) 
x_test = np.array(x_test) 
y_test = np.array(y_test) 



from keras.models import Sequential 
from keras.layers.core import Dense, Dropout, Flatten 
from keras.layers.noise import GaussianNoise 
from keras.preprocessing.image import ImageDataGenerator 
from keras.optimizers import SGD 
from keras.callbacks import ProgbarLogger, Callback 

datagen = ImageDataGenerator(rotation_range = 20, dim_ordering = 'tf') 

model = Sequential() 
model.add(Flatten(input_shape = (28, 28))) 
model.add(Dense(200, activation = 'tanh')) 
model.add(Dropout(0.5)) 
model.add(Dense(20, activation = 'tanh')) 
model.add(Dense(10, activation = 'softmax')) 

print('Compiling Model') 
sgd = SGD(lr = 0.01, decay = 0.1, momentum = 0.9, nesterov = True) 
model.compile(loss = 'categorical_crossentropy', optimizer = sgd) 
print('Fitting Data') 

#model.fit(x_train, y_train, batch_size = 128, nb_epoch = 400, validation_data = (x_test, y_test)) 
model.fit_generator(datagen.flow(x_train, y_train), samples_per_epoch = len(x_train), nb_epoch = 5) 
def max_index(lst): 
    mi = 0 
    for i in range(1, len(lst)): 
     mi = i if lst[i] > lst[mi] else mi 
    return mi 

result = model.predict(x_test) 
correct = 0 
for y, yt in zip(result, y_test): 
    correct += max_index(y) == max_index(yt) 
print(correct/len(y_test)) 

qui è anche il mio MInst caricatore se qualcuno vuole provare a farlo funzionare ...

import struct 
import numpy as np 
import matplotlib.pyplot as plt 

def load_images(images_file): 
    data = None 
    with open(images_file, 'rb') as f: 
     data = f.read() 

    mn, n, h, w = struct.unpack('>4I', data[0:16]) 
    assert(mn == 2051) 
    data = data[16:] 
    images = [] 
    for i in range(n): 
     img = np.array([float(b) for b in data[w * h * i:w * h * (i + 1)]]) 
     img /= 255.0 
     img = np.reshape(img, (w, h)) 
     images.append(img) 
    return images 

def load_labels(labels_file): 
    data = None 
    with open(labels_file, 'rb') as f: 
     data = f.read() 

    mn, n = struct.unpack('>2I', data[0:8]) 
    assert(mn == 2049) 
    return [int(b) for b in data[8:]] 

Answer 1

Prova verbose=2 come parametro in fit_generator()