苹果转橘子
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import glob
%matplotlib inline
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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
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print("TensorFlow Version: {}" .format(tf.__version__))
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TensorFlow Version: 1.8.0
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Generator
def generator (inputs_real, is_train=True, alpha=0.01 , name="generator" ) :
with tf.variable_scope(name, reuse=(not is_train)):
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' )
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' )
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' )
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' )
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' )
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' )
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' )
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' )
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 )
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 )
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 )
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)
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)
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)
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)
conv16 = tf.concat([conv15,conv1], 3 )
conv16 = tf.layers.conv2d_transpose(conv16, 3 , 3 , strides=2 , padding='same' )
outputs = tf.nn.tanh(conv16)
return outputs
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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
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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
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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" )]
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
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辅助函数,用来在迭代中显示图片
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 )
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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
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Train
beta1 = 0.4
learning_rate = 0.0001
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def train () :
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()
with tf.Session() as sess:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
sess.run(tf.global_variables_initializer())
for e in range(steps):
_ = sess.run(d_train_opt)
_ = sess.run(g_train_opt)
if e % 100 == 0 :
train_loss_d = d_loss.eval()
train_loss_g = g_loss.eval()
losses.append((train_loss_d, train_loss_g))
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)
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with tf.Graph().as_default():
train()
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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....
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