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302 lines
12 KiB
302 lines
12 KiB
# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import itertools
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import random
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import numpy
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import cPickle
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import sys,os,gc
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from PIL import Image
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from paddle.trainer.config_parser import parse_config
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from paddle.trainer.config_parser import logger
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import py_paddle.swig_paddle as api
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def CHECK_EQ(a, b):
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assert a == b, "a=%s, b=%s" % (a, b)
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def copy_shared_parameters(src, dst):
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src_params = [src.getParameter(i)
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for i in xrange(src.getParameterSize())]
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src_params = dict([(p.getName(), p) for p in src_params])
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for i in xrange(dst.getParameterSize()):
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dst_param = dst.getParameter(i)
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src_param = src_params.get(dst_param.getName(), None)
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if src_param is None:
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continue
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src_value = src_param.getBuf(api.PARAMETER_VALUE)
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dst_value = dst_param.getBuf(api.PARAMETER_VALUE)
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CHECK_EQ(len(src_value), len(dst_value))
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dst_value.copyFrom(src_value)
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dst_param.setValueUpdated()
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def print_parameters(src):
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src_params = [src.getParameter(i)
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for i in xrange(src.getParameterSize())]
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print "***************"
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for p in src_params:
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print "Name is %s" % p.getName()
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print "value is %s \n" % p.getBuf(api.PARAMETER_VALUE).copyToNumpyArray()
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def load_mnist_data(imageFile):
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f = open(imageFile, "rb")
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f.read(16)
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# Define number of samples for train/test
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if "train" in imageFile:
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#n = 60000
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n = 60000
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else:
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n = 10000
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data = numpy.zeros((n, 28*28), dtype = "float32")
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for i in range(n):
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pixels = []
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for j in range(28 * 28):
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pixels.append(float(ord(f.read(1))) / 255.0 * 2.0 - 1.0)
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data[i, :] = pixels
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f.close()
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return data
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def load_cifar_data(cifar_path):
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batch_size = 10000
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data = numpy.zeros((5*batch_size, 32*32*3), dtype = "float32")
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for i in range(1, 6):
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file = cifar_path + "/data_batch_" + str(i)
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fo = open(file, 'rb')
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dict = cPickle.load(fo)
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fo.close()
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data[(i - 1)*batch_size:(i*batch_size), :] = dict["data"]
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data = data / 255.0 * 2.0 - 1.0
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return data
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def merge(images, size):
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if images.shape[1] == 28*28:
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h, w, c = 28, 28, 1
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else:
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h, w, c = 32, 32, 3
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img = numpy.zeros((h * size[0], w * size[1], c))
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for idx in xrange(size[0] * size[1]):
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i = idx % size[1]
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j = idx // size[1]
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#img[j*h:j*h+h, i*w:i*w+w, :] = (images[idx, :].reshape((h, w, c), order="F") + 1.0) / 2.0 * 255.0
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img[j*h:j*h+h, i*w:i*w+w, :] = \
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((images[idx, :].reshape((h, w, c), order="F").transpose(1, 0, 2) + 1.0) / 2.0 * 255.0)
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return img.astype('uint8')
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def saveImages(images, path):
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merged_img = merge(images, [8, 8])
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if merged_img.shape[2] == 1:
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im = Image.fromarray(numpy.squeeze(merged_img)).convert('RGB')
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else:
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im = Image.fromarray(merged_img, mode="RGB")
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im.save(path)
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def get_real_samples(batch_size, data_np):
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return data_np[numpy.random.choice(data_np.shape[0], batch_size,
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replace=False),:]
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def get_noise(batch_size, noise_dim):
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return numpy.random.normal(size=(batch_size, noise_dim)).astype('float32')
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def get_sample_noise(batch_size, sample_dim):
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return numpy.random.normal(size=(batch_size, sample_dim),
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scale=0.01).astype('float32')
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def get_fake_samples(generator_machine, batch_size, noise):
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gen_inputs = api.Arguments.createArguments(1)
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gen_inputs.setSlotValue(0, api.Matrix.createGpuDenseFromNumpy(noise))
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gen_outputs = api.Arguments.createArguments(0)
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generator_machine.forward(gen_inputs, gen_outputs, api.PASS_TEST)
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fake_samples = gen_outputs.getSlotValue(0).copyToNumpyMat()
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return fake_samples
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def get_training_loss(training_machine, inputs):
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outputs = api.Arguments.createArguments(0)
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training_machine.forward(inputs, outputs, api.PASS_TEST)
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loss = outputs.getSlotValue(0).copyToNumpyMat()
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return numpy.mean(loss)
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def prepare_discriminator_data_batch_pos(batch_size, data_np, sample_noise):
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real_samples = get_real_samples(batch_size, data_np)
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labels = numpy.ones(batch_size, dtype='int32')
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inputs = api.Arguments.createArguments(3)
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inputs.setSlotValue(0, api.Matrix.createGpuDenseFromNumpy(real_samples))
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inputs.setSlotValue(1, api.Matrix.createGpuDenseFromNumpy(sample_noise))
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inputs.setSlotIds(2, api.IVector.createGpuVectorFromNumpy(labels))
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return inputs
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def prepare_discriminator_data_batch_neg(generator_machine, batch_size, noise,
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sample_noise):
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fake_samples = get_fake_samples(generator_machine, batch_size, noise)
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#print fake_samples.shape
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labels = numpy.zeros(batch_size, dtype='int32')
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inputs = api.Arguments.createArguments(3)
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inputs.setSlotValue(0, api.Matrix.createGpuDenseFromNumpy(fake_samples))
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inputs.setSlotValue(1, api.Matrix.createGpuDenseFromNumpy(sample_noise))
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inputs.setSlotIds(2, api.IVector.createGpuVectorFromNumpy(labels))
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return inputs
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def prepare_generator_data_batch(batch_size, noise, sample_noise):
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label = numpy.ones(batch_size, dtype='int32')
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#label = numpy.zeros(batch_size, dtype='int32')
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inputs = api.Arguments.createArguments(3)
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inputs.setSlotValue(0, api.Matrix.createGpuDenseFromNumpy(noise))
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inputs.setSlotValue(1, api.Matrix.createGpuDenseFromNumpy(sample_noise))
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inputs.setSlotIds(2, api.IVector.createGpuVectorFromNumpy(label))
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return inputs
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def find(iterable, cond):
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for item in iterable:
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if cond(item):
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return item
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return None
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def get_layer_size(model_conf, layer_name):
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layer_conf = find(model_conf.layers, lambda x: x.name == layer_name)
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assert layer_conf is not None, "Cannot find '%s' layer" % layer_name
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return layer_conf.size
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("-d", "--dataSource", help="mnist or cifar")
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parser.add_argument("--useGpu", default="1",
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help="1 means use gpu for training")
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parser.add_argument("--gpuId", default="0",
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help="the gpu_id parameter")
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args = parser.parse_args()
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dataSource = args.dataSource
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useGpu = args.useGpu
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assert dataSource in ["mnist", "cifar"]
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assert useGpu in ["0", "1"]
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api.initPaddle('--use_gpu=' + useGpu, '--dot_period=10', '--log_period=100',
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'--gpu_id=' + args.gpuId)
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gen_conf = parse_config("gan_conf_image.py", "mode=generator_training,data=" + dataSource)
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dis_conf = parse_config("gan_conf_image.py", "mode=discriminator_training,data=" + dataSource)
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generator_conf = parse_config("gan_conf_image.py", "mode=generator,data=" + dataSource)
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batch_size = dis_conf.opt_config.batch_size
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noise_dim = get_layer_size(gen_conf.model_config, "noise")
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sample_dim = get_layer_size(dis_conf.model_config, "sample")
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if dataSource == "mnist":
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data_np = load_mnist_data("./data/raw_data/train-images-idx3-ubyte")
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else:
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data_np = load_cifar_data("./data/cifar-10-batches-py/")
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if not os.path.exists("./%s_samples/" % dataSource):
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os.makedirs("./%s_samples/" % dataSource)
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# this create a gradient machine for discriminator
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dis_training_machine = api.GradientMachine.createFromConfigProto(
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dis_conf.model_config)
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gen_training_machine = api.GradientMachine.createFromConfigProto(
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gen_conf.model_config)
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# generator_machine is used to generate data only, which is used for
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# training discrinator
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logger.info(str(generator_conf.model_config))
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generator_machine = api.GradientMachine.createFromConfigProto(
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generator_conf.model_config)
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dis_trainer = api.Trainer.create(
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dis_conf, dis_training_machine)
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gen_trainer = api.Trainer.create(
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gen_conf, gen_training_machine)
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dis_trainer.startTrain()
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gen_trainer.startTrain()
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copy_shared_parameters(gen_training_machine, dis_training_machine)
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copy_shared_parameters(gen_training_machine, generator_machine)
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curr_train = "dis"
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curr_strike = 0
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MAX_strike = 10
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for train_pass in xrange(100):
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dis_trainer.startTrainPass()
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gen_trainer.startTrainPass()
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for i in xrange(1000):
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# data_batch_dis = prepare_discriminator_data_batch(
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# generator_machine, batch_size, noise_dim, sample_dim)
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# dis_loss = get_training_loss(dis_training_machine, data_batch_dis)
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noise = get_noise(batch_size, noise_dim)
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sample_noise = get_sample_noise(batch_size, sample_dim)
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data_batch_dis_pos = prepare_discriminator_data_batch_pos(
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batch_size, data_np, sample_noise)
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dis_loss_pos = get_training_loss(dis_training_machine, data_batch_dis_pos)
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sample_noise = get_sample_noise(batch_size, sample_dim)
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data_batch_dis_neg = prepare_discriminator_data_batch_neg(
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generator_machine, batch_size, noise, sample_noise)
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dis_loss_neg = get_training_loss(dis_training_machine, data_batch_dis_neg)
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dis_loss = (dis_loss_pos + dis_loss_neg) / 2.0
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data_batch_gen = prepare_generator_data_batch(
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batch_size, noise, sample_noise)
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gen_loss = get_training_loss(gen_training_machine, data_batch_gen)
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if i % 100 == 0:
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print "d_pos_loss is %s d_neg_loss is %s" % (dis_loss_pos, dis_loss_neg)
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print "d_loss is %s g_loss is %s" % (dis_loss, gen_loss)
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if (not (curr_train == "dis" and curr_strike == MAX_strike)) and ((curr_train == "gen" and curr_strike == MAX_strike) or dis_loss_neg > gen_loss):
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if curr_train == "dis":
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curr_strike += 1
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else:
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curr_train = "dis"
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curr_strike = 1
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dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_neg)
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dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_pos)
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# dis_loss = numpy.mean(dis_trainer.getForwardOutput()[0]["value"])
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# print "getForwardOutput loss is %s" % dis_loss
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copy_shared_parameters(dis_training_machine, gen_training_machine)
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else:
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if curr_train == "gen":
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curr_strike += 1
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else:
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curr_train = "gen"
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curr_strike = 1
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gen_trainer.trainOneDataBatch(batch_size, data_batch_gen)
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copy_shared_parameters(gen_training_machine, dis_training_machine)
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copy_shared_parameters(gen_training_machine, generator_machine)
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dis_trainer.finishTrainPass()
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gen_trainer.finishTrainPass()
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fake_samples = get_fake_samples(generator_machine, batch_size, noise)
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saveImages(fake_samples, "./%s_samples/train_pass%s.png" % (dataSource, train_pass))
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dis_trainer.finishTrain()
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gen_trainer.finishTrain()
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if __name__ == '__main__':
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main()
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