яблоко в апельсин
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import glob
%matplotlib inline
H:\Anaconda\lib\site-packages\h5py\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
print("TensorFlow Version: {}".format(tf.__version__))
TensorFlow Version: 1.8.0
Generator
def generator(inputs_real, is_train=True, alpha=0.01, name="generator"):
# 256*256*3
with tf.variable_scope(name, reuse=(not is_train)):
# 128*128*64
conv1 = tf.layers.conv2d(inputs_real, 64, (3,3), padding='same')
conv1 = tf.nn.relu(conv1)
conv1 = tf.layers.max_pooling2d(conv1, (2,2), (2,2), padding='same')
# 64*64*128
conv2 = tf.layers.conv2d(conv1, 128, (3,3), padding='same')
conv2 = tf.nn.relu(conv2)
conv2 = tf.layers.max_pooling2d(conv2, (2,2), (2,2), padding='same')
# 32*32*256
conv3 = tf.layers.conv2d(conv2, 256, (3,3), padding='same')
conv3 = tf.nn.relu(conv3)
conv3 = tf.layers.max_pooling2d(conv3, (2,2), (2,2), padding='same')
# 16*16*512
conv4 = tf.layers.conv2d(conv3, 512, (3,3), padding='same')
conv4 = tf.nn.relu(conv4)
conv4 = tf.layers.max_pooling2d(conv4, (2,2), (2,2), padding='same')
# 8*8*512
conv5 = tf.layers.conv2d(conv4, 512, (3,3), padding='same')
conv5 = tf.nn.relu(conv5)
conv5 = tf.layers.max_pooling2d(conv5, (2,2), (2,2), padding='same')
# 4*4*512
conv6 = tf.layers.conv2d(conv5, 512, (3,3), padding='same')
conv6 = tf.nn.relu(conv6)
conv6 = tf.layers.max_pooling2d(conv6, (2,2), (2,2), padding='same')
# 2*2*512
conv7 = tf.layers.conv2d(conv6, 512, (3,3), padding='same')
conv7 = tf.nn.relu(conv7)
conv7 = tf.layers.max_pooling2d(conv7, (2,2), (2,2), padding='same')
# 1*1*512
conv8 = tf.layers.conv2d(conv7, 512, (3,3), padding='same')
conv8 = tf.nn.relu(conv8)
conv8 = tf.layers.max_pooling2d(conv8, (2,2), (2,2), padding='same')
# 2*2*512
conv9 = tf.layers.conv2d_transpose(conv8, 512, 3, strides=2, padding='same')
conv9 = tf.layers.batch_normalization(conv9, training=is_train)
conv9 = tf.nn.relu(conv9)
conv9 = tf.nn.dropout(conv9, keep_prob=0.5)
# 4*4*512
conv10 = tf.concat([conv9,conv7], 3)
conv10 = tf.layers.conv2d_transpose(conv10, 512, 3, strides=2, padding='same')
conv10 = tf.layers.batch_normalization(conv10, training=is_train)
conv10 = tf.nn.relu(conv10)
conv10 = tf.nn.dropout(conv10, keep_prob=0.5)
# 8*8*512
conv11 = tf.concat([conv10,conv6], 3)
conv11 = tf.layers.conv2d_transpose(conv11, 512, 3, strides=2, padding='same')
conv11 = tf.layers.batch_normalization(conv11, training=is_train)
conv11 = tf.nn.relu(conv11)
conv11 = tf.nn.dropout(conv11, keep_prob=0.5)
# 16*16*512
conv12 = tf.concat([conv11,conv5], 3)
conv12 = tf.layers.conv2d_transpose(conv12, 512, 3, strides=2, padding='same')
conv12 = tf.layers.batch_normalization(conv12, training=is_train)
conv12 = tf.nn.relu(conv12)
# 32*32*256
conv13 = tf.concat([conv12,conv4], 3)
conv13 = tf.layers.conv2d_transpose(conv13, 256, 3, strides=2, padding='same')
conv13 = tf.layers.batch_normalization(conv13, training=is_train)
conv13 = tf.nn.relu(conv13)
# 64*64*128
conv14 = tf.concat([conv13,conv3], 3)
conv14 = tf.layers.conv2d_transpose(conv14, 128, 3, strides=2, padding='same')
conv14 = tf.layers.batch_normalization(conv14, training=is_train)
conv14 = tf.nn.relu(conv14)
# 128*128*64
conv15 = tf.concat([conv14,conv2], 3)
conv15 = tf.layers.conv2d_transpose(conv15, 64, 3, strides=2, padding='same')
conv15 = tf.layers.batch_normalization(conv15, training=is_train)
conv15 = tf.nn.relu(conv15)
# 256*256*3
conv16 = tf.concat([conv15,conv1], 3)
conv16 = tf.layers.conv2d_transpose(conv16, 3, 3, strides=2, padding='same')
# conv16 = tf.layers.batch_normalization(conv16, training=is_train)
# 图片归一化
outputs = tf.nn.tanh(conv16)
return outputs
Discriminator
def discriminator(inputs_image, reuse=False, alpha=0.01, name="discriminator"):
with tf.variable_scope(name, reuse=reuse):
layer1 = tf.layers.conv2d(inputs_image, 64, 3, strides=2, padding='same')
layer1 = tf.layers.batch_normalization(layer1, training=True)
layer1 = tf.maximum(alpha * layer1, layer1)
layer2 = tf.layers.conv2d(layer1, 64, 3, strides=2, padding='same')
layer2 = tf.layers.batch_normalization(layer2, training=True)
layer2 = tf.maximum(alpha * layer2, layer2)
layer3 = tf.layers.conv2d(layer2, 128, 3, strides=2, padding='same')
layer3 = tf.layers.batch_normalization(layer3, training=True)
layer3 = tf.maximum(alpha * layer3, layer3)
layer4 = tf.layers.conv2d(layer3, 128, 3, strides=2, padding='same')
layer4 = tf.layers.batch_normalization(layer4, training=True)
layer4 = tf.maximum(alpha * layer4, layer4)
layer5 = tf.layers.conv2d(layer4, 256, 3, strides=2, padding='same')
layer5 = tf.layers.batch_normalization(layer5, training=True)
layer5 = tf.maximum(alpha * layer5, layer5)
layer6 = tf.layers.conv2d(layer5, 256, 3, strides=2, padding='same')
layer6 = tf.layers.batch_normalization(layer6, training=True)
layer6 = tf.maximum(alpha * layer6, layer6)
layer7 = tf.layers.conv2d(layer6, 512, 3, strides=2, padding='same')
layer7 = tf.layers.batch_normalization(layer7, training=True)
layer7 = tf.maximum(alpha * layer7, layer7)
flatten = tf.reshape(layer7, (-1, 2*2*512))
logits = tf.layers.dense(flatten, 1)
outputs = tf.sigmoid(logits)
return logits, outputs
Loss
def get_loss(inputs_images, inputs_cartoons, smooth=0.1):
fake_cartoons = generator(inputs_images, name="generatorI2C")
fake_images_ = generator(fake_cartoons, name="generatorC2I")
fake_images = generator(inputs_cartoons, False, name="generatorC2I")
fake_cartoons_ = generator(fake_images, False, name="generatorI2C")
discriminator_cartoon_fake, cartoon_fake_logits = discriminator(fake_cartoons, reuse=False, name="discriminator_cartoon")
discriminator_image_fake, image_fake_logits = discriminator(fake_images, reuse=False, name="discriminator_image")
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=cartoon_fake_logits,
labels=tf.ones_like(discriminator_cartoon_fake)*(1-smooth))) \
+ tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=image_fake_logits,
labels=tf.ones_like(discriminator_image_fake)*(1-smooth))) \
+ tf.reduce_mean(tf.abs(inputs_images - fake_images_))\
+ tf.reduce_mean(tf.abs(inputs_cartoons - fake_cartoons_))
discriminator_cartoon_real, cartoon_real_logits = discriminator(inputs_cartoons, reuse=True, name="discriminator_cartoon")
discriminator_image_real, image_real_logits = discriminator(inputs_images, reuse=True, name="discriminator_image")
d_cartoon_real_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=cartoon_real_logits,
labels=tf.ones_like(discriminator_cartoon_real)*(1-smooth)))
d_image_real_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=image_real_logits,
labels=tf.ones_like(discriminator_image_real)*(1-smooth)))
d_cartoon_fake_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=cartoon_fake_logits,
labels=tf.zeros_like(discriminator_cartoon_fake)*(1-smooth)))
d_image_fake_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=image_fake_logits,
labels=tf.zeros_like(discriminator_image_fake)*(1-smooth)))
d_loss = d_cartoon_real_loss + d_image_real_loss + d_cartoon_fake_loss + d_image_fake_loss
return g_loss, d_loss
Optimizer
def get_optimizer(g_loss, d_loss, g_loss_tran, learning_rate=0.0001):
train_vars = tf.trainable_variables()
g_vars = [var for var in train_vars if var.name.startswith("generator")]
d_vars = [var for var in train_vars if var.name.startswith("discriminator")]
# Optimizer
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
g_opt = tf.train.AdamOptimizer(learning_rate*5).minimize(g_loss, var_list=g_vars)
d_opt = tf.train.AdamOptimizer(learning_rate).minimize(d_loss, var_list=d_vars)
return g_opt, d_opt
Вспомогательная функция для отображения изображений во время итерации
def plot_images(samples):
samples = (samples + 1) / 2
fig, axes = plt.subplots(nrows=1, ncols=10, sharex=True, sharey=True, figsize=(10,1))
for img, ax in zip(samples, axes):
ax.imshow(img)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
fig.tight_layout(pad=0)
def show_generator_output(sess, samp_images):
samples = sess.run(generator(samp_images, False , name="generatorI2C"))
samples = sess.run(tf.reshape(samples, [-1, 256, 256, 3]))
return samples
Train
# 定义参数
beta1 = 0.4
learning_rate = 0.0001
def train():
# 存储loss
losses = []
steps = 1000
apple_list = glob.glob('./apple2orange/trainA/*.jpg')
orange_list = glob.glob('./apple2orange/trainB/*.jpg')
image_que = tf.train.slice_input_producer([apple_list, orange_list], shuffle=True)
image_ = tf.read_file(image_que[0])
image = tf.image.decode_jpeg(image_, channels=3)
image = tf.image.resize_image_with_crop_or_pad(image, 256, 256)
new_img = tf.image.convert_image_dtype(image, tf.float32)
new_img = new_img*2 -1
cartoon_ = tf.read_file(image_que[1])
cartoon = tf.image.decode_jpeg(cartoon_, channels=3)
cartoon = tf.image.resize_image_with_crop_or_pad(cartoon, 256, 256)
new_cartoon = tf.image.convert_image_dtype(cartoon, tf.float32)
new_cartoon = new_cartoon*2 -1
batch_size = 10
capacity = 3 + 2 * batch_size
image_batch, cartoon_batch = tf.train.batch([new_img, new_cartoon], batch_size=batch_size, capacity=capacity)
g_loss, d_loss = get_loss(image_batch, cartoon_batch)
g_train_opt, d_train_opt = get_optimizer(g_loss, d_loss, beta1, learning_rate)
saver = tf.train.Saver()
# model_file=tf.train.latest_checkpoint('../input/cycel-apple-to-orange')
with tf.Session() as sess:
# saver.restore(sess, model_file)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
sess.run(tf.global_variables_initializer())
# 迭代epoch
for e in range(steps):
# run optimizer
_ = sess.run(d_train_opt)
_ = sess.run(g_train_opt)
if e % 100 == 0:
# saver.save(sess,'./less96', global_step = e)
train_loss_d = d_loss.eval()
train_loss_g = g_loss.eval()
losses.append((train_loss_d, train_loss_g))
# 显示图片
samples = show_generator_output(sess, image_batch)
plot_images(samples)
print("Epoch {}/{}....".format(e+1, steps),
"Discriminator Loss: {:.4f}....".format(train_loss_d),
"Generator Loss: {:.4f}....". format(train_loss_g))
saver.save(sess,'./less96',global_step = steps)
coord.request_stop()
coord.join(threads)
with tf.Graph().as_default():
train()
Epoch 1/1000.... Discriminator Loss: 2.9407.... Generator Loss: 2.3714....
Epoch 101/1000.... Discriminator Loss: 2.7470.... Generator Loss: 1.8382....
Epoch 201/1000.... Discriminator Loss: 2.6516.... Generator Loss: 1.6317....
Epoch 301/1000.... Discriminator Loss: 2.6107.... Generator Loss: 1.5520....
Epoch 401/1000.... Discriminator Loss: 2.6936.... Generator Loss: 1.5697....
Epoch 501/1000.... Discriminator Loss: 2.4914.... Generator Loss: 1.5345....
Epoch 601/1000.... Discriminator Loss: 2.4656.... Generator Loss: 1.5393....
Epoch 701/1000.... Discriminator Loss: 2.4099.... Generator Loss: 1.5304....
Epoch 801/1000.... Discriminator Loss: 2.3221.... Generator Loss: 1.4708....
Epoch 901/1000.... Discriminator Loss: 2.3139.... Generator Loss: 1.5180....