from __future__ import absolute_import
from __future__ import print_function
import numpy as np
np.random.seed(1337) # for reproducibility
import cPickle as pickle
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD, Adam, RMSprop
from keras.utils import np_utils
'''
Train a simple deep NN on the MNIST dataset.
Get to 98.30% test accuracy after 20 epochs (there is *a lot* of margin for parameter tuning).
2 seconds per epoch on a GRID K520 GPU.
'''
batch_size = 128
nb_classes = 10
nb_epoch = 10
def read_data(data_file):
import gzip
f = gzip.open(data_file, "rb")
train, val, test = pickle.load(f)
f.close()
train_x = train[0]
train_y = train[1]
test_x = test[0]
test_y = test[1]
return train_x, train_y, test_x, test_y
# the data, shuffled and split between tran and test sets
#(X_train, y_train), (X_test, y_test) = mnist.load_data()
train_x, train_y, test_x, test_y = read_data("C:\Users\PC\.spyder2\mnist.pkl.gz")
X_train = train_x
X_test = test_x
X_train = X_train.astype("float32")
X_test = X_test.astype("float32")
X_train /= 255
X_test /= 255
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(train_y, nb_classes)
Y_test = np_utils.to_categorical(test_y, nb_classes)
model = Sequential()
model.add(Dense(input_dim=784, output_dim=128))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(output_dim=128))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(output_dim=10))
model.add(Activation('softmax'))
rms = RMSprop()
model.compile(loss='categorical_crossentropy', optimizer=rms,metrics=['accuracy'])
model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch)
score = model.evaluate(X_test, Y_test, batch_size=batch_size)
print('Test score:', score[0])
print('Test accuracy:', score[1])
亲测可以运行…………
前提是anaconda+theano+keras配置成功……。
MNIST数据集（mnist.pkl.gz）
参考：http://www.cnblogs.com/8335IT/p/5701061.html
