def digit_recognize_test():
#载入数据
train = pd.read_csv('../../input/train.csv').sample(20000)
print('train: ' + str(train.shape))
test = pd.read_csv('../../input/test.csv')
print('test: ' + str(test.shape))
train.head()
#train: (2000, 785)
#test: (28000, 784)
# feature matrix
X = train.ix[:, 1:]
# response vector
X = X.values
Y = train['label']
Y = Y.values
#训练数据
training_inputs = [x for x in X]
training_results = [y for y in Y]
training_data = [training_inputs[:10000], training_results[:10000]]
validation_data = [training_inputs[10000:], training_results[10000:]]
print(np.shape(training_results))
#测试数据
test_data = test.values
#predit_inputs = [np.reshape(x, (784, 1)) for x in test_data]
training_data = shared(training_data)
validation_data = shared(validation_data)
test_data = theano.shared(np.asarray(test_data,
dtype=theano.config.floatX),
borrow=True)
mini_batch_size = 10
net = Network([
ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28),
filter_shape=(20, 1, 5, 5),
poolsize=(2, 2)),
ConvPoolLayer(image_shape=(mini_batch_size, 20, 12, 12),
filter_shape=(40, 20, 5, 5),
poolsize=(2, 2)),
FullyConnectedLayer(n_in=40 * 4 * 4, n_out=100),
SoftmaxLayer(n_in=100, n_out=10)
], mini_batch_size)
predictions = net.SGD_kaggle(training_data, 20, mini_batch_size, 0.1,
validation_data, test_data)
####尚未解决
#输出数据
digit_preds = pd.Series(predictions)
image_ids = pd.Series(np.arange(1, len(digit_preds) + 1))
submission = pd.DataFrame([image_ids, digit_preds]).T
submission.columns = ['ImageId', 'Label']
submission.to_csv('../../output/dr_result.csv', index=False, header=True)
print("finished!")
#!python35
# -*- coding: utf-8 -*-
import network3
from network3 import Network, ConvPoolLayer, FullyConnectedLayer, SoftmaxLayer # softmax plus log-likelihood cost is more common in modern image classification networks.
# read data:
training_data, validation_data, test_data = network3.load_data_shared(
'../data/mnist.pkl.gz')
# mini-batch size:
mini_batch_size = 10
net = Network([
ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28),
filter_shape=(20, 1, 5, 5),
poolsize=(2, 2)),
ConvPoolLayer(image_shape=(mini_batch_size, 20, 12, 12),
filter_shape=(40, 20, 5, 5),
poolsize=(2, 2)),
FullyConnectedLayer(n_in=40 * 4 * 4, n_out=100),
SoftmaxLayer(n_in=100, n_out=10)
], mini_batch_size)
net.SGD_kaggle(training_data, 60, mini_batch_size, 0.1, validation_data,
test_data)
