Feature/simple gan for api

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

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+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()