DCGAN生成式对抗网络--keras实现
本文针对cifar10 图集进行了DCGAN的复现。
其中库中的SpectralNormalizationKeras需添加至python环境中 该篇代码如下:
from keras import backend as K
from keras.engine import *
from keras.legacy import interfaces
from keras import activations
from keras import initializers
from keras import regularizers
from keras import constraints
from keras.utils.generic_utils import func_dump
from keras.utils.generic_utils import func_load
from keras.utils.generic_utils import deserialize_keras_object
from keras.utils.generic_utils import has_arg
from keras.utils import conv_utils
from keras.legacy import interfaces
from keras.layers import Dense, Conv1D, Conv2D, Conv3D, Conv2DTranspose, Embedding
import tensorflow as tf class DenseSN(Dense):
def build(self, input_shape):
assert len(input_shape) >= 2
input_dim = input_shape[-1]
self.kernel = self.add_weight(shape=(input_dim, self.units),
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(self.units,),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
self.u = self.add_weight(shape=tuple([1, self.kernel.shape.as_list()[-1]]),
initializer=initializers.RandomNormal(0, 1),
name='sn',
trainable=False)
self.input_spec = InputSpec(min_ndim=2, axes={-1: input_dim})
self.built = True def call(self, inputs, training=None):
def _l2normalize(v, eps=1e-12):
return v / (K.sum(v ** 2) ** 0.5 + eps)
def power_iteration(W, u):
_u = u
_v = _l2normalize(K.dot(_u, K.transpose(W)))
_u = _l2normalize(K.dot(_v, W))
return _u, _v
W_shape = self.kernel.shape.as_list()
#Flatten the Tensor
W_reshaped = K.reshape(self.kernel, [-1, W_shape[-1]])
_u, _v = power_iteration(W_reshaped, self.u)
#Calculate Sigma
sigma=K.dot(_v, W_reshaped)
sigma=K.dot(sigma, K.transpose(_u))
#normalize it
W_bar = W_reshaped / sigma
#reshape weight tensor
if training in {0, False}:
W_bar = K.reshape(W_bar, W_shape)
else:
with tf.control_dependencies([self.u.assign(_u)]):
W_bar = K.reshape(W_bar, W_shape)
output = K.dot(inputs, W_bar)
if self.use_bias:
output = K.bias_add(output, self.bias, data_format='channels_last')
if self.activation is not None:
output = self.activation(output)
return output class _ConvSN(Layer): def __init__(self, rank,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
dilation_rate=1,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
spectral_normalization=True,
**kwargs):
super(_ConvSN, self).__init__(**kwargs)
self.rank = rank
self.filters = filters
self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, 'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, rank, 'dilation_rate')
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(ndim=self.rank + 2)
self.spectral_normalization = spectral_normalization
self.u = None def _l2normalize(self, v, eps=1e-12):
return v / (K.sum(v ** 2) ** 0.5 + eps) def power_iteration(self, u, W):
'''
Accroding the paper, we only need to do power iteration one time.
'''
v = self._l2normalize(K.dot(u, K.transpose(W)))
u = self._l2normalize(K.dot(v, W))
return u, v
def build(self, input_shape):
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = input_shape[channel_axis]
kernel_shape = self.kernel_size + (input_dim, self.filters) self.kernel = self.add_weight(shape=kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint) #Spectral Normalization
if self.spectral_normalization:
self.u = self.add_weight(shape=tuple([1, self.kernel.shape.as_list()[-1]]),
initializer=initializers.RandomNormal(0, 1),
name='sn',
trainable=False) if self.use_bias:
self.bias = self.add_weight(shape=(self.filters,),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
# Set input spec.
self.input_spec = InputSpec(ndim=self.rank + 2,
axes={channel_axis: input_dim})
self.built = True def call(self, inputs):
def _l2normalize(v, eps=1e-12):
return v / (K.sum(v ** 2) ** 0.5 + eps)
def power_iteration(W, u):
_u = u
_v = _l2normalize(K.dot(_u, K.transpose(W)))
_u = _l2normalize(K.dot(_v, W))
return _u, _v if self.spectral_normalization:
W_shape = self.kernel.shape.as_list()
#Flatten the Tensor
W_reshaped = K.reshape(self.kernel, [-1, W_shape[-1]])
_u, _v = power_iteration(W_reshaped, self.u)
#Calculate Sigma
sigma=K.dot(_v, W_reshaped)
sigma=K.dot(sigma, K.transpose(_u))
#normalize it
W_bar = W_reshaped / sigma
#reshape weight tensor
if training in {0, False}:
W_bar = K.reshape(W_bar, W_shape)
else:
with tf.control_dependencies([self.u.assign(_u)]):
W_bar = K.reshape(W_bar, W_shape) #update weitht
self.kernel = W_bar if self.rank == 1:
outputs = K.conv1d(
inputs,
self.kernel,
strides=self.strides[0],
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate[0])
if self.rank == 2:
outputs = K.conv2d(
inputs,
self.kernel,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
if self.rank == 3:
outputs = K.conv3d(
inputs,
self.kernel,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate) if self.use_bias:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format) if self.activation is not None:
return self.activation(outputs)
return outputs def compute_output_shape(self, input_shape):
if self.data_format == 'channels_last':
space = input_shape[1:-1]
new_space = []
for i in range(len(space)):
new_dim = conv_utils.conv_output_length(
space[i],
self.kernel_size[i],
padding=self.padding,
stride=self.strides[i],
dilation=self.dilation_rate[i])
new_space.append(new_dim)
return (input_shape[0],) + tuple(new_space) + (self.filters,)
if self.data_format == 'channels_first':
space = input_shape[2:]
new_space = []
for i in range(len(space)):
new_dim = conv_utils.conv_output_length(
space[i],
self.kernel_size[i],
padding=self.padding,
stride=self.strides[i],
dilation=self.dilation_rate[i])
new_space.append(new_dim)
return (input_shape[0], self.filters) + tuple(new_space) def get_config(self):
config = {
'rank': self.rank,
'filters': self.filters,
'kernel_size': self.kernel_size,
'strides': self.strides,
'padding': self.padding,
'data_format': self.data_format,
'dilation_rate': self.dilation_rate,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)
}
base_config = super(_Conv, self).get_config()
return dict(list(base_config.items()) + list(config.items())) class ConvSN2D(Conv2D): def build(self, input_shape):
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = input_shape[channel_axis]
kernel_shape = self.kernel_size + (input_dim, self.filters) self.kernel = self.add_weight(shape=kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint) if self.use_bias:
self.bias = self.add_weight(shape=(self.filters,),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None self.u = self.add_weight(shape=tuple([1, self.kernel.shape.as_list()[-1]]),
initializer=initializers.RandomNormal(0, 1),
name='sn',
trainable=False) # Set input spec.
self.input_spec = InputSpec(ndim=self.rank + 2,
axes={channel_axis: input_dim})
self.built = True
def call(self, inputs, training=None):
def _l2normalize(v, eps=1e-12):
return v / (K.sum(v ** 2) ** 0.5 + eps)
def power_iteration(W, u):
#Accroding the paper, we only need to do power iteration one time.
_u = u
_v = _l2normalize(K.dot(_u, K.transpose(W)))
_u = _l2normalize(K.dot(_v, W))
return _u, _v
#Spectral Normalization
W_shape = self.kernel.shape.as_list()
#Flatten the Tensor
W_reshaped = K.reshape(self.kernel, [-1, W_shape[-1]])
_u, _v = power_iteration(W_reshaped, self.u)
#Calculate Sigma
sigma=K.dot(_v, W_reshaped)
sigma=K.dot(sigma, K.transpose(_u))
#normalize it
W_bar = W_reshaped / sigma
#reshape weight tensor
if training in {0, False}:
W_bar = K.reshape(W_bar, W_shape)
else:
with tf.control_dependencies([self.u.assign(_u)]):
W_bar = K.reshape(W_bar, W_shape) outputs = K.conv2d(
inputs,
W_bar,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
if self.use_bias:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs class ConvSN1D(Conv1D): def build(self, input_shape):
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = input_shape[channel_axis]
kernel_shape = self.kernel_size + (input_dim, self.filters) self.kernel = self.add_weight(shape=kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint) if self.use_bias:
self.bias = self.add_weight(shape=(self.filters,),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None self.u = self.add_weight(shape=tuple([1, self.kernel.shape.as_list()[-1]]),
initializer=initializers.RandomNormal(0, 1),
name='sn',
trainable=False)
# Set input spec.
self.input_spec = InputSpec(ndim=self.rank + 2,
axes={channel_axis: input_dim})
self.built = True def call(self, inputs, training=None):
def _l2normalize(v, eps=1e-12):
return v / (K.sum(v ** 2) ** 0.5 + eps)
def power_iteration(W, u):
#Accroding the paper, we only need to do power iteration one time.
_u = u
_v = _l2normalize(K.dot(_u, K.transpose(W)))
_u = _l2normalize(K.dot(_v, W))
return _u, _v
#Spectral Normalization
W_shape = self.kernel.shape.as_list()
#Flatten the Tensor
W_reshaped = K.reshape(self.kernel, [-1, W_shape[-1]])
_u, _v = power_iteration(W_reshaped, self.u)
#Calculate Sigma
sigma=K.dot(_v, W_reshaped)
sigma=K.dot(sigma, K.transpose(_u))
#normalize it
W_bar = W_reshaped / sigma
#reshape weight tensor
if training in {0, False}:
W_bar = K.reshape(W_bar, W_shape)
else:
with tf.control_dependencies([self.u.assign(_u)]):
W_bar = K.reshape(W_bar, W_shape) outputs = K.conv1d(
inputs,
W_bar,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
if self.use_bias:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs class ConvSN3D(Conv3D):
def build(self, input_shape):
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = input_shape[channel_axis]
kernel_shape = self.kernel_size + (input_dim, self.filters) self.kernel = self.add_weight(shape=kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint) self.u = self.add_weight(shape=tuple([1, self.kernel.shape.as_list()[-1]]),
initializer=initializers.RandomNormal(0, 1),
name='sn',
trainable=False) if self.use_bias:
self.bias = self.add_weight(shape=(self.filters,),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
# Set input spec.
self.input_spec = InputSpec(ndim=self.rank + 2,
axes={channel_axis: input_dim})
self.built = True def call(self, inputs, training=None):
def _l2normalize(v, eps=1e-12):
return v / (K.sum(v ** 2) ** 0.5 + eps)
def power_iteration(W, u):
#Accroding the paper, we only need to do power iteration one time.
_u = u
_v = _l2normalize(K.dot(_u, K.transpose(W)))
_u = _l2normalize(K.dot(_v, W))
return _u, _v
#Spectral Normalization
W_shape = self.kernel.shape.as_list()
#Flatten the Tensor
W_reshaped = K.reshape(self.kernel, [-1, W_shape[-1]])
_u, _v = power_iteration(W_reshaped, self.u)
#Calculate Sigma
sigma=K.dot(_v, W_reshaped)
sigma=K.dot(sigma, K.transpose(_u))
#normalize it
W_bar = W_reshaped / sigma
#reshape weight tensor
if training in {0, False}:
W_bar = K.reshape(W_bar, W_shape)
else:
with tf.control_dependencies([self.u.assign(_u)]):
W_bar = K.reshape(W_bar, W_shape) outputs = K.conv3d(
inputs,
W_bar,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
if self.use_bias:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs class EmbeddingSN(Embedding): def build(self, input_shape):
self.embeddings = self.add_weight(
shape=(self.input_dim, self.output_dim),
initializer=self.embeddings_initializer,
name='embeddings',
regularizer=self.embeddings_regularizer,
constraint=self.embeddings_constraint,
dtype=self.dtype) self.u = self.add_weight(shape=tuple([1, self.embeddings.shape.as_list()[-1]]),
initializer=initializers.RandomNormal(0, 1),
name='sn',
trainable=False) self.built = True def call(self, inputs):
if K.dtype(inputs) != 'int32':
inputs = K.cast(inputs, 'int32') def _l2normalize(v, eps=1e-12):
return v / (K.sum(v ** 2) ** 0.5 + eps)
def power_iteration(W, u):
#Accroding the paper, we only need to do power iteration one time.
_u = u
_v = _l2normalize(K.dot(_u, K.transpose(W)))
_u = _l2normalize(K.dot(_v, W))
return _u, _v
W_shape = self.embeddings.shape.as_list()
#Flatten the Tensor
W_reshaped = K.reshape(self.embeddings, [-1, W_shape[-1]])
_u, _v = power_iteration(W_reshaped, self.u)
#Calculate Sigma
sigma=K.dot(_v, W_reshaped)
sigma=K.dot(sigma, K.transpose(_u))
#normalize it
W_bar = W_reshaped / sigma
#reshape weight tensor
if training in {0, False}:
W_bar = K.reshape(W_bar, W_shape)
else:
with tf.control_dependencies([self.u.assign(_u)]):
W_bar = K.reshape(W_bar, W_shape)
self.embeddings = W_bar out = K.gather(self.embeddings, inputs)
return out class ConvSN2DTranspose(Conv2DTranspose): def build(self, input_shape):
if len(input_shape) != 4:
raise ValueError('Inputs should have rank ' +
str(4) +
'; Received input shape:', str(input_shape))
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = input_shape[channel_axis]
kernel_shape = self.kernel_size + (self.filters, input_dim) self.kernel = self.add_weight(shape=kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(self.filters,),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None self.u = self.add_weight(shape=tuple([1, self.kernel.shape.as_list()[-1]]),
initializer=initializers.RandomNormal(0, 1),
name='sn',
trainable=False) # Set input spec.
self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim})
self.built = True def call(self, inputs):
input_shape = K.shape(inputs)
batch_size = input_shape[0]
if self.data_format == 'channels_first':
h_axis, w_axis = 2, 3
else:
h_axis, w_axis = 1, 2 height, width = input_shape[h_axis], input_shape[w_axis]
kernel_h, kernel_w = self.kernel_size
stride_h, stride_w = self.strides
if self.output_padding is None:
out_pad_h = out_pad_w = None
else:
out_pad_h, out_pad_w = self.output_padding # Infer the dynamic output shape:
out_height = conv_utils.deconv_length(height,
stride_h, kernel_h,
self.padding,
out_pad_h)
out_width = conv_utils.deconv_length(width,
stride_w, kernel_w,
self.padding,
out_pad_w)
if self.data_format == 'channels_first':
output_shape = (batch_size, self.filters, out_height, out_width)
else:
output_shape = (batch_size, out_height, out_width, self.filters) #Spectral Normalization
def _l2normalize(v, eps=1e-12):
return v / (K.sum(v ** 2) ** 0.5 + eps)
def power_iteration(W, u):
#Accroding the paper, we only need to do power iteration one time.
_u = u
_v = _l2normalize(K.dot(_u, K.transpose(W)))
_u = _l2normalize(K.dot(_v, W))
return _u, _v
W_shape = self.kernel.shape.as_list()
#Flatten the Tensor
W_reshaped = K.reshape(self.kernel, [-1, W_shape[-1]])
_u, _v = power_iteration(W_reshaped, self.u)
#Calculate Sigma
sigma=K.dot(_v, W_reshaped)
sigma=K.dot(sigma, K.transpose(_u))
#normalize it
W_bar = W_reshaped / sigma
#reshape weight tensor
if training in {0, False}:
W_bar = K.reshape(W_bar, W_shape)
else:
with tf.control_dependencies([self.u.assign(_u)]):
W_bar = K.reshape(W_bar, W_shape)
self.kernel = W_bar outputs = K.conv2d_transpose(
inputs,
self.kernel,
output_shape,
self.strides,
padding=self.padding,
data_format=self.data_format) if self.use_bias:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format) if self.activation is not None:
return self.activation(outputs)
return outputs
完成了该部之后开始正文。
首先是导入数据集：
# 导入CIFAR10数据集
# 读取数据
def unpickle(file):
import pickle
with open(file, 'rb') as fo:
dict = pickle.load(fo, encoding='bytes')
return dict cifar={}
for i in range(5):
cifar1=unpickle('data_batch_'+str(i+1))
if i==0:
cifar[b'data']=cifar1[b'data']
cifar[b'labels']=cifar1[b'labels']
else:
cifar[b'data']=np.vstack([cifar1[b'data'],cifar[b'data']])
cifar[b'labels']=np.hstack([cifar1[b'labels'],cifar[b'labels']])
target_name=unpickle('batches.meta')
cifar[b'label_names']=target_name[b'label_names']
del cifar1 # 定义数据格式
blank_image= np.zeros((len(cifar[b'data']),32,32,3), np.uint8)
for i in range(len(cifar[b'data'])):
blank_image[i] = np.zeros((32,32,3), np.uint8)
blank_image[i][:,:,0]=cifar[b'data'][i][0:1024].reshape(32,32)
blank_image[i][:,:,1]=cifar[b'data'][i][1024:1024*2].reshape(32,32)
blank_image[i][:,:,2]=cifar[b'data'][i][1024*2:1024*3].reshape(32,32)
cifar[b'data']=blank_image cifar_test=unpickle('test_batch')
cifar_test[b'labels']=np.array(cifar_test[b'labels'])
blank_image= np.zeros((len(cifar_test[b'data']),32,32,3), np.uint8)
for i in range(len(cifar_test[b'data'])):
blank_image[i] = np.zeros((32,32,3), np.uint8)
blank_image[i][:,:,0]=cifar_test[b'data'][i][0:1024].reshape(32,32)
blank_image[i][:,:,1]=cifar_test[b'data'][i][1024:1024*2].reshape(32,32)
blank_image[i][:,:,2]=cifar_test[b'data'][i][1024*2:1024*3].reshape(32,32)
cifar_test[b'data']=blank_image x_train=cifar[b'data']
x_test=cifar[b'data']
x_test=cifar_test[b'data']
y_test=cifar_test[b'labels']
X = np.concatenate((x_test,x_train))
以上是在cifar 10 官方网站下载的数据文件。也可以使用keras官方的cifar10导入代码:
from keras.datasets import cifar100, cifar10 (x_train, y_train), (x_test, y_test) = cifar10.load_data()
接下来是生成器与判别器的构造:
# Hyperperemeter
# 生成器与判别器构造
BATCHSIZE=64
LEARNING_RATE = 0.0002
TRAINING_RATIO = 1
BETA_1 = 0.0
BETA_2 = 0.9
EPOCHS = 500
BN_MIMENTUM = 0.1
BN_EPSILON = 0.00002
SAVE_DIR = 'img/generated_img_CIFAR10_DCGAN/' GENERATE_ROW_NUM = 8
GENERATE_BATCHSIZE = GENERATE_ROW_NUM*GENERATE_ROW_NUM def BuildGenerator(summary=True):
model = Sequential()
model.add(Dense(4*4*512, kernel_initializer='glorot_uniform' , input_dim=128))
model.add(Reshape((4,4,512)))
model.add(Conv2DTranspose(256, kernel_size=4, strides=2, padding='same', activation='relu',kernel_initializer='glorot_uniform'))
model.add(BatchNormalization(epsilon=BN_EPSILON, momentum=BN_MIMENTUM))
model.add(Conv2DTranspose(128, kernel_size=4, strides=2, padding='same', activation='relu',kernel_initializer='glorot_uniform'))
model.add(BatchNormalization(epsilon=BN_EPSILON, momentum=BN_MIMENTUM))
model.add(Conv2DTranspose(64, kernel_size=4, strides=2, padding='same', activation='relu',kernel_initializer='glorot_uniform'))
model.add(BatchNormalization(epsilon=BN_EPSILON, momentum=BN_MIMENTUM))
model.add(Conv2DTranspose(3, kernel_size=3, strides=1, padding='same', activation='tanh'))
if summary:
print("Generator")
model.summary()
return model def BuildDiscriminator(summary=True, spectral_normalization=True):
if spectral_normalization:
model = Sequential()
model.add(ConvSN2D(64, kernel_size=3, strides=1,kernel_initializer='glorot_uniform', padding='same', input_shape=(32,32,3) ))
model.add(LeakyReLU(0.1))
model.add(ConvSN2D(64, kernel_size=4, strides=2,kernel_initializer='glorot_uniform', padding='same'))
model.add(LeakyReLU(0.1))
model.add(ConvSN2D(128, kernel_size=3, strides=1,kernel_initializer='glorot_uniform', padding='same'))
model.add(LeakyReLU(0.1))
model.add(ConvSN2D(128, kernel_size=4, strides=2,kernel_initializer='glorot_uniform', padding='same'))
model.add(LeakyReLU(0.1))
model.add(ConvSN2D(256, kernel_size=3, strides=1,kernel_initializer='glorot_uniform', padding='same'))
model.add(LeakyReLU(0.1))
model.add(ConvSN2D(256, kernel_size=4, strides=2,kernel_initializer='glorot_uniform', padding='same'))
model.add(LeakyReLU(0.1))
model.add(ConvSN2D(512, kernel_size=3, strides=1,kernel_initializer='glorot_uniform', padding='same'))
model.add(LeakyReLU(0.1))
model.add(Flatten())
model.add(DenseSN(1,kernel_initializer='glorot_uniform'))
else:
model = Sequential()
model.add(Conv2D(64, kernel_size=3, strides=1,kernel_initializer='glorot_uniform', padding='same', input_shape=(32,32,3) ))
model.add(LeakyReLU(0.1))
model.add(Conv2D(64, kernel_size=4, strides=2,kernel_initializer='glorot_uniform', padding='same'))
model.add(LeakyReLU(0.1))
model.add(Conv2D(128, kernel_size=3, strides=1,kernel_initializer='glorot_uniform', padding='same'))
model.add(LeakyReLU(0.1))
model.add(Conv2D(128, kernel_size=4, strides=2,kernel_initializer='glorot_uniform', padding='same'))
model.add(LeakyReLU(0.1))
model.add(Conv2D(256, kernel_size=3, strides=1,kernel_initializer='glorot_uniform', padding='same'))
model.add(LeakyReLU(0.1))
model.add(Conv2D(256, kernel_size=4, strides=2,kernel_initializer='glorot_uniform', padding='same'))
model.add(LeakyReLU(0.1))
model.add(Conv2D(512, kernel_size=3, strides=1,kernel_initializer='glorot_uniform', padding='same'))
model.add(LeakyReLU(0.1))
model.add(Flatten())
model.add(Dense(1,kernel_initializer='glorot_uniform'))
if summary:
print('Discriminator')
print('Spectral Normalization: {}'.format(spectral_normalization))
model.summary()
return model def wasserstein_loss(y_true, y_pred):
return K.mean(y_true*y_pred) generator = BuildGenerator()
discriminator = BuildDiscriminator()
然后是训练器的构造:
# 生成器训练模型
Noise_input_for_training_generator = Input(shape=(128,))
Generated_image = generator(Noise_input_for_training_generator)
Discriminator_output = discriminator(Generated_image)
model_for_training_generator = Model(Noise_input_for_training_generator, Discriminator_output)
print("model_for_training_generator")
model_for_training_generator.summary() discriminator.trainable = False
model_for_training_generator.summary() model_for_training_generator.compile(optimizer=Adam(LEARNING_RATE, beta_1=BETA_1, beta_2=BETA_2), loss=wasserstein_loss) # 判别器训练模型
Real_image = Input(shape=(32,32,3))
Noise_input_for_training_discriminator = Input(shape=(128,))
Fake_image = generator(Noise_input_for_training_discriminator)
Discriminator_output_for_real = discriminator(Real_image)
Discriminator_output_for_fake = discriminator(Fake_image) model_for_training_discriminator = Model([Real_image,
Noise_input_for_training_discriminator],
[Discriminator_output_for_real,
Discriminator_output_for_fake])
print("model_for_training_discriminator")
generator.trainable = False
discriminator.trainable = True
model_for_training_discriminator.compile(optimizer=Adam(LEARNING_RATE, beta_1=BETA_1, beta_2=BETA_2), loss=[wasserstein_loss, wasserstein_loss])
model_for_training_discriminator.summary() real_y = np.ones((BATCHSIZE, 1), dtype=np.float32)
fake_y = -real_y X = X/255*2-1 plt.imshow((X[8787]+1)/2)
最后是重复训练：
test_noise = np.random.randn(GENERATE_BATCHSIZE, 128)
W_loss = []
discriminator_loss = []
generator_loss = []
for epoch in range(EPOCHS):
np.random.shuffle(X) print("epoch {} of {}".format(epoch+1, EPOCHS))
num_batches = int(X.shape[0] // BATCHSIZE) print("number of batches: {}".format(int(X.shape[0] // (BATCHSIZE)))) progress_bar = Progbar(target=int(X.shape[0] // (BATCHSIZE * TRAINING_RATIO)))
minibatches_size = BATCHSIZE * TRAINING_RATIO start_time = time()
for index in range(int(X.shape[0] // (BATCHSIZE * TRAINING_RATIO))):
progress_bar.update(index)
discriminator_minibatches = X[index * minibatches_size:(index + 1) * minibatches_size] for j in range(TRAINING_RATIO):
image_batch = discriminator_minibatches[j * BATCHSIZE : (j + 1) * BATCHSIZE]
noise = np.random.randn(BATCHSIZE, 128).astype(np.float32)
discriminator.trainable = True
generator.trainable = False
discriminator_loss.append(model_for_training_discriminator.train_on_batch([image_batch, noise],
[real_y, fake_y]))
discriminator.trainable = False
generator.trainable = True
generator_loss.append(model_for_training_generator.train_on_batch(np.random.randn(BATCHSIZE, 128), real_y)) print('\nepoch time: {}'.format(time()-start_time)) W_real = model_for_training_generator.evaluate(test_noise, real_y)
print(W_real)
W_fake = model_for_training_generator.evaluate(test_noise, fake_y)
print(W_fake)
W_l = W_real+W_fake
print('wasserstein_loss: {}'.format(W_l))
W_loss.append(W_l)
#Generate image
generated_image = generator.predict(test_noise)
generated_image = (generated_image+1)/2
for i in range(GENERATE_ROW_NUM):
new = generated_image[i*GENERATE_ROW_NUM:i*GENERATE_ROW_NUM+GENERATE_ROW_NUM].reshape(32*GENERATE_ROW_NUM,32,3)
if i!=0:
old = np.concatenate((old,new),axis=1)
else:
old = new
print('plot generated_image')
plt.imsave('{}/SN_epoch_{}.png'.format(SAVE_DIR, epoch), old)
训练十轮之后生成的图片有显著的提升，结果如下：
第一轮：
第10轮：
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