python3.4 appcarsh:keras在使用model.fit（）时崩溃了

我试图运行keras examples（mnist_cnn.py）给出的代码，但当程序运行到model.fit（）行中的代码时，它崩溃了：

以下是问题签名：

Problem signature:
  Problem Event Name:   APPCRASH
  Application Name: python.exe
  Application Version:  0.0.0.0
  Application Timestamp:    53787196
  Fault Module Name:    meb999ccf2462e9645a4b300d52c9e009.pyd
  Fault Module Version: 0.0.0.0
  Fault Module Timestamp:   593a257a
  Exception Code:   c0000005
  Exception Offset: 00002c1f
  OS Version:   6.1.7601.2.1.0.256.1
  Locale ID:    2052
  Additional Information 1: 0a9e
  Additional Information 2: 0a9e372d3b4ad19135b953a78882e789
  Additional Information 3: 0a9e
  Additional Information 4: 0a9e372d3b4ad19135b953a78882e789

以下是我使用的代码：

#!/usr/bin/env python3.4

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K

batch_size = 128
num_classes = 10
epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28

# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

此外，我使用theano后端，32位窗口环境。如果有人能回答我的问题，非常感谢

这是命令行，在print（'ecoph 1/12'）之后，它会崩溃：

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您是否尝试过通过减小批量大小来减少内存消耗？是的，我将批量大小从128个减少到8个，并仅在128个样本上而不是60000个样本上训练模型，但同样的问题也发生了。。。