Feature/simple gan for api (#6149)

* Expose sigmoid_cross_entropy_with_logits Also, change the `labels` to `label` for api consistency * Very simple GAN based on pure FC layers

Author: reyoung

python/paddle/v2/fluid/tests/demo/fc_gan.py

@@ -0,0 +1,157 @@
+import errno
+import math
+import os
+
+import matplotlib
+import numpy
+
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+NOISE_SIZE = 100
+NUM_PASS = 1000
+NUM_REAL_IMGS_IN_BATCH = 121
+NUM_TRAIN_TIMES_OF_DG = 3
+LEARNING_RATE = 2e-5
+
+
+def D(x):
+ hidden = fluid.layers.fc(input=x,
+ size=200,
+ act='relu',
+ param_attr='D.w1',
+ bias_attr='D.b1')
+ logits = fluid.layers.fc(input=hidden,
+ size=1,
+ act=None,
+ param_attr='D.w2',
+ bias_attr='D.b2')
+ return logits
+
+
+def G(x):
+ hidden = fluid.layers.fc(input=x,
+ size=200,
+ act='relu',
+ param_attr='G.w1',
+ bias_attr='G.b1')
+ img = fluid.layers.fc(input=hidden,
+ size=28 * 28,
+ act='tanh',
+ param_attr='G.w2',
+ bias_attr='G.b2')
+ return img
+
+
+def plot(gen_data):
+ gen_data.resize(gen_data.shape[0], 28, 28)
+ n = int(math.ceil(math.sqrt(gen_data.shape[0])))
+ fig = plt.figure(figsize=(n, n))
+ gs = gridspec.GridSpec(n, n)
+ gs.update(wspace=0.05, hspace=0.05)
+
+ for i, sample in enumerate(gen_data):
+ ax = plt.subplot(gs[i])
+ plt.axis('off')
+ ax.set_xticklabels([])
+ ax.set_yticklabels([])
+ ax.set_aspect('equal')
+ plt.imshow(sample.reshape(28, 28), cmap='Greys_r')
+
+ return fig
+
+
+def main():
+ try:
+ os.makedirs("./out")
+ except OSError as e:
+ if e.errno != errno.EEXIST:
+ raise
+
+ startup_program = fluid.Program()
+ d_program = fluid.Program()
+ dg_program = fluid.Program()
+
+ with fluid.program_guard(d_program, startup_program):
+ img = fluid.layers.data(name='img', shape=[784], dtype='float32')
+ d_loss = fluid.layers.sigmoid_cross_entropy_with_logits(
+ x=D(img),
+ label=fluid.layers.data(
+ name='label', shape=[1], dtype='float32'))
+ d_loss = fluid.layers.mean(x=d_loss)
+
+ with fluid.program_guard(dg_program, startup_program):
+ noise = fluid.layers.data(
+ name='noise', shape=[NOISE_SIZE], dtype='float32')
+ g_img = G(x=noise)
+ g_program = dg_program.clone()
+ dg_loss = fluid.layers.sigmoid_cross_entropy_with_logits(
+ x=D(g_img),
+ label=fluid.layers.fill_constant_batch_size_like(
+ input=noise, dtype='float32', shape=[-1, 1], value=1.0))
+ dg_loss = fluid.layers.mean(x=dg_loss)
+
+ opt = fluid.optimizer.Adam(learning_rate=LEARNING_RATE)
+
+ opt.minimize(loss=d_loss, startup_program=startup_program)
+ opt.minimize(
+ loss=dg_loss,
+ startup_program=startup_program,
+ parameter_list=[
+ p.name for p in g_program.global_block().all_parameters()
+ ])
+ exe = fluid.Executor(fluid.CPUPlace())
+ exe.run(startup_program)
+
+ num_true = NUM_REAL_IMGS_IN_BATCH
+ train_reader = paddle.batch(
+ paddle.reader.shuffle(
+ paddle.dataset.mnist.train(), buf_size=60000),
+ batch_size=num_true)
+
+ for pass_id in range(NUM_PASS):
+ for batch_id, data in enumerate(train_reader()):
+ num_true = len(data)
+ n = numpy.random.uniform(
+ low=-1.0, high=1.0,
+ size=[num_true * NOISE_SIZE]).astype('float32').reshape(
+ [num_true, NOISE_SIZE])
+ generated_img = exe.run(g_program,
+ feed={'noise': n},
+ fetch_list={g_img})[0]
+ real_data = numpy.array(map(lambda x: x[0], data)).astype('float32')
+ real_data = real_data.reshape(num_true, 784)
+ total_data = numpy.concatenate([real_data, generated_img])
+ total_label = numpy.concatenate([
+ numpy.ones(
+ shape=[real_data.shape[0], 1], dtype='float32'),
+ numpy.zeros(
+ shape=[real_data.shape[0], 1], dtype='float32')
+ ])
+ d_loss_np = exe.run(d_program,
+ feed={'img': total_data,
+ 'label': total_label},
+ fetch_list={d_loss})[0]
+ for _ in xrange(NUM_TRAIN_TIMES_OF_DG):
+ n = numpy.random.uniform(
+ low=-1.0, high=1.0,
+ size=[2 * num_true * NOISE_SIZE]).astype('float32').reshape(
+ [2 * num_true, NOISE_SIZE, 1, 1])
+ dg_loss_np = exe.run(dg_program,
+ feed={'noise': n},
+ fetch_list={dg_loss})[0]
+ print("Pass ID={0}, Batch ID={1}, D-Loss={2}, DG-Loss={3}".format(
+ pass_id, batch_id, d_loss_np, dg_loss_np))
+ # generate image each batch
+ fig = plot(generated_img)
+ plt.savefig(
+ 'out/{0}.png'.format(str(pass_id).zfill(3)), bbox_inches='tight')
+ plt.close(fig)
+
+
+if __name__ == '__main__':
+ main()