Merge pull request #276 from wanghaoshuang/recognize_digits_demo

Add demo of recognize_digits on paddlecloud

Author: wanghaoshuang

demo/recognize_digits/train.py

@@ -0,0 +1,162 @@
+from PIL import Image
+import numpy as np
+import paddle.v2 as paddle
+import paddle.v2.dataset.common as common
+import os
+import sys
+import glob
+import pickle
+
+
+# NOTE: must change this to your own username on paddlecloud.
+USERNAME = "wanghaoshuang@baidu.com"
+DC = os.getenv("PADDLE_CLOUD_CURRENT_DATACENTER")
+common.DATA_HOME = "/pfs/%s/home/%s" % (DC, USERNAME)
+TRAIN_FILES_PATH = os.path.join(common.DATA_HOME, "mnist")
+TEST_FILES_PATH = os.path.join(common.DATA_HOME, "mnist")
+
+TRAINER_ID = int(os.getenv("PADDLE_INIT_TRAINER_ID", "-1"))
+TRAINER_COUNT = int(os.getenv("PADDLE_INIT_NUM_GRADIENT_SERVERS", "-1"))
+
+def prepare_dataset():
+ # convert will also split the dataset by line-count
+ common.convert(TRAIN_FILES_PATH,
+ paddle.dataset.mnist.train(),
+ 8192, "train")
+ common.convert(TEST_FILES_PATH,
+ paddle.dataset.mnist.test(),
+ 1, "test")
+
+def cluster_reader_recordio(trainer_id, trainer_count, flag):
+ '''
+ read from cloud dataset which is stored as recordio format
+ each trainer will read a subset of files of the whole dataset.
+ '''
+ import recordio
+ def reader():
+ PATTERN_STR = "%s-*" % flag
+ FILES_PATTERN = os.path.join(TRAIN_FILES_PATH, PATTERN_STR)
+ file_list = glob.glob(FILES_PATTERN)
+ file_list.sort()
+ my_file_list = []
+ # read files for current trainer_id
+ for idx, f in enumerate(file_list):
+ if idx % trainer_count == trainer_id:
+ my_file_list.append(f)
+ for f in my_file_list:
+ print "processing ", f
+ reader = recordio.reader(f)
+ record_raw = reader.read()
+ while record_raw:
+ yield pickle.loads(record_raw)
+ record_raw = reader.read()
+ reader.close()
+ return reader
+
+
+
+def softmax_regression(img):
+ predict = paddle.layer.fc(
+ input=img, size=10, act=paddle.activation.Softmax())
+ return predict
+
+
+def multilayer_perceptron(img):
+ # The first fully-connected layer
+ hidden1 = paddle.layer.fc(input=img, size=128, act=paddle.activation.Relu())
+ # The second fully-connected layer and the according activation function
+ hidden2 = paddle.layer.fc(
+ input=hidden1, size=64, act=paddle.activation.Relu())
+ # The thrid fully-connected layer, note that the hidden size should be 10,
+ # which is the number of unique digits
+ predict = paddle.layer.fc(
+ input=hidden2, size=10, act=paddle.activation.Softmax())
+ return predict
+
+
+def convolutional_neural_network(img):
+ # first conv layer
+ conv_pool_1 = paddle.networks.simple_img_conv_pool(
+ input=img,
+ filter_size=5,
+ num_filters=20,
+ num_channel=1,
+ pool_size=2,
+ pool_stride=2,
+ act=paddle.activation.Relu())
+ # second conv layer
+ conv_pool_2 = paddle.networks.simple_img_conv_pool(
+ input=conv_pool_1,
+ filter_size=5,
+ num_filters=50,
+ num_channel=20,
+ pool_size=2,
+ pool_stride=2,
+ act=paddle.activation.Relu())
+ # fully-connected layer
+ predict = paddle.layer.fc(
+ input=conv_pool_2, size=10, act=paddle.activation.Softmax())
+ return predict
+
+
+def main():
+ paddle.init()
+
+ # define network topology
+ images = paddle.layer.data(
+ name='pixel', type=paddle.data_type.dense_vector(784))
+ label = paddle.layer.data(
+ name='label', type=paddle.data_type.integer_value(10))
+
+ # Here we can build the prediction network in different ways. Please
+ # choose one by uncomment corresponding line.
+ # predict = softmax_regression(images)
+ # predict = multilayer_perceptron(images)
+ predict = convolutional_neural_network(images)
+
+ cost = paddle.layer.classification_cost(input=predict, label=label)
+
+ parameters = paddle.parameters.create(cost)
+
+ optimizer = paddle.optimizer.Momentum(
+ learning_rate=0.1 / 128.0,
+ momentum=0.9,
+ regularization=paddle.optimizer.L2Regularization(rate=0.0005 * 128))
+
+ trainer = paddle.trainer.SGD(
+ cost=cost, parameters=parameters, update_equation=optimizer)
+
+ def event_handler(event):
+ if isinstance(event, paddle.event.EndIteration):
+ if event.batch_id % 100 == 0:
+ print "Pass %d, Batch %d, Cost %f, %s" % (
+ event.pass_id, event.batch_id, event.cost, event.metrics)
+ if isinstance(event, paddle.event.EndPass):
+ result = trainer.test(
+ reader=paddle.batch(
+ cluster_reader_recordio(TRAINER_ID, TRAINER_COUNT, "test"),
+ batch_size=2))
+ print "Test with Pass %d, Cost %f, %s\n" % (
+ event.pass_id, result.cost, result.metrics)
+
+ trainer.train(
+ reader=paddle.batch(
+ cluster_reader_recordio(TRAINER_ID, TRAINER_COUNT, "train"),
+ batch_size=128),
+ event_handler=event_handler,
+ num_passes=5)
+
+if __name__ == '__main__':
+ usage = "python train.py [prepare|train]"
+ if len(sys.argv) != 2:
+ print usage
+ exit(1)
+
+ if TRAINER_ID == -1 or TRAINER_COUNT == -1:
+ print "no cloud environ found, must run on cloud"
+ exit(1)
+
+ if sys.argv[1] == "prepare":
+ prepare_dataset()
+ elif sys.argv[1] == "train":
+ main()