本文整理汇总了Python中keras.layers.convolutional.Conv2D方法的典型用法代码示例。如果您正苦于以下问题：Python convolutional.Conv2D方法的具体用法？Python convolutional.Conv2D怎么用？Python convolutional.Conv2D使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类keras.layers.convolutional的用法示例。
在下文中一共展示了convolutional.Conv2D方法的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: build_generator
# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import Conv2D [as 别名]
def build_generator(self):
model = Sequential()
model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
model.add(Reshape((7, 7, 128)))
model.add(BatchNormalization(momentum=0.8))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(momentum=0.8))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(1, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
model.summary()
noise = Input(shape=(self.latent_dim,))
img = model(noise)
return Model(noise, img) 示例2: build_discriminator
# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import Conv2D [as 别名]
def build_discriminator(self):
model = Sequential()
model.add(Conv2D(64, kernel_size=3, strides=2, input_shape=self.missing_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(256, kernel_size=3, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
model.summary()
img = Input(shape=self.missing_shape)
validity = model(img)
return Model(img, validity) 示例3: build_discriminator
# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import Conv2D [as 别名]
def build_discriminator(self):
img = Input(shape=self.img_shape)
model = Sequential()
model.add(Conv2D(64, kernel_size=4, strides=2, padding='same', input_shape=self.img_shape))
model.add(LeakyReLU(alpha=0.8))
model.add(Conv2D(128, kernel_size=4, strides=2, padding='same'))
model.add(LeakyReLU(alpha=0.2))
model.add(InstanceNormalization())
model.add(Conv2D(256, kernel_size=4, strides=2, padding='same'))
model.add(LeakyReLU(alpha=0.2))
model.add(InstanceNormalization())
model.summary()
img = Input(shape=self.img_shape)
features = model(img)
validity = Conv2D(1, kernel_size=4, strides=1, padding='same')(features)
label = Flatten()(features)
label = Dense(self.num_classes+1, activation="softmax")(label)
return Model(img, [validity, label]) 示例4: build_classifier
# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import Conv2D [as 别名]
def build_classifier(self):
def clf_layer(layer_input, filters, f_size=4, normalization=True):
"""Classifier layer"""
d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)
d = LeakyReLU(alpha=0.2)(d)
if normalization:
d = InstanceNormalization()(d)
return d
img = Input(shape=self.img_shape)
c1 = clf_layer(img, self.cf, normalization=False)
c2 = clf_layer(c1, self.cf*2)
c3 = clf_layer(c2, self.cf*4)
c4 = clf_layer(c3, self.cf*8)
c5 = clf_layer(c4, self.cf*8)
class_pred = Dense(self.num_classes, activation='softmax')(Flatten()(c5))
return Model(img, class_pred) 示例5: build_generator
# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import Conv2D [as 别名]
def build_generator(self):
model = Sequential()
model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
model.add(Reshape((7, 7, 128)))
model.add(BatchNormalization(momentum=0.8))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(momentum=0.8))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(self.channels, kernel_size=3, padding='same'))
model.add(Activation("tanh"))
gen_input = Input(shape=(self.latent_dim,))
img = model(gen_input)
model.summary()
return Model(gen_input, img) 示例6: build_generator
# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import Conv2D [as 别名]
def build_generator(self):
model = Sequential()
model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
model.add(Reshape((7, 7, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=4, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=4, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(Activation("relu"))
model.add(Conv2D(self.channels, kernel_size=4, padding="same"))
model.add(Activation("tanh"))
model.summary()
noise = Input(shape=(self.latent_dim,))
img = model(noise)
return Model(noise, img) 示例7: build_discriminator
# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import Conv2D [as 别名]
def build_discriminator(self):
def d_layer(layer_input, filters, f_size=4, normalization=True):
"""Discriminator layer"""
d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)
d = LeakyReLU(alpha=0.2)(d)
if normalization:
d = InstanceNormalization()(d)
return d
img = Input(shape=self.img_shape)
d1 = d_layer(img, self.df, normalization=False)
d2 = d_layer(d1, self.df*2)
d3 = d_layer(d2, self.df*4)
d4 = d_layer(d3, self.df*8)
validity = Conv2D(1, kernel_size=4, strides=1, padding='same')(d4)
return Model(img, validity) 示例8: build_discriminator
# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import Conv2D [as 别名]
def build_discriminator(self):
def d_layer(layer_input, filters, f_size=4, bn=True):
"""Discriminator layer"""
d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)
d = LeakyReLU(alpha=0.2)(d)
if bn:
d = BatchNormalization(momentum=0.8)(d)
return d
img_A = Input(shape=self.img_shape)
img_B = Input(shape=self.img_shape)
# Concatenate image and conditioning image by channels to produce input
combined_imgs = Concatenate(axis=-1)([img_A, img_B])
d1 = d_layer(combined_imgs, self.df, bn=False)
d2 = d_layer(d1, self.df*2)
d3 = d_layer(d2, self.df*4)
d4 = d_layer(d3, self.df*8)
validity = Conv2D(1, kernel_size=4, strides=1, padding='same')(d4)
return Model([img_A, img_B], validity) 示例9: build_generator
# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import Conv2D [as 别名]
def build_generator(self):
model = Sequential()
model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
model.add(Reshape((7, 7, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(Activation("relu"))
model.add(Conv2D(self.channels, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
model.summary()
noise = Input(shape=(self.latent_dim,))
img = model(noise)
return Model(noise, img) 示例10: build_model
# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import Conv2D [as 别名]
def build_model(self):
input = Input(shape=self.state_size)
conv = Conv2D(16, (8, 8), strides=(4, 4), activation='relu')(input)
conv = Conv2D(32, (4, 4), strides=(2, 2), activation='relu')(conv)
conv = Flatten()(conv)
fc = Dense(256, activation='relu')(conv)
policy = Dense(self.action_size, activation='softmax')(fc)
value = Dense(1, activation='linear')(fc)
actor = Model(inputs=input, outputs=policy)
critic = Model(inputs=input, outputs=value)
actor.summary()
critic.summary()
return actor, critic 示例11: build_model
# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import Conv2D [as 别名]
def build_model(self):
input = Input(shape=self.state_size)
conv = Conv2D(16, (8, 8), strides=(4, 4), activation='relu')(input)
conv = Conv2D(32, (4, 4), strides=(2, 2), activation='relu')(conv)
conv = Flatten()(conv)
fc = Dense(256, activation='relu')(conv)
policy = Dense(self.action_size, activation='softmax')(fc)
value = Dense(1, activation='linear')(fc)
actor = Model(inputs=input, outputs=policy)
critic = Model(inputs=input, outputs=value)
# 가치와 정책을 예측하는 함수를 만들어냄
actor._make_predict_function()
critic._make_predict_function()
actor.summary()
critic.summary()
return actor, critic
# 정책신경망을 업데이트하는 함수 示例12: build_local_model
# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import Conv2D [as 别名]
def build_local_model(self):
input = Input(shape=self.state_size)
conv = Conv2D(16, (8, 8), strides=(4, 4), activation='relu')(input)
conv = Conv2D(32, (4, 4), strides=(2, 2), activation='relu')(conv)
conv = Flatten()(conv)
fc = Dense(256, activation='relu')(conv)
policy = Dense(self.action_size, activation='softmax')(fc)
value = Dense(1, activation='linear')(fc)
local_actor = Model(inputs=input, outputs=policy)
local_critic = Model(inputs=input, outputs=value)
local_actor._make_predict_function()
local_critic._make_predict_function()
local_actor.set_weights(self.actor.get_weights())
local_critic.set_weights(self.critic.get_weights())
local_actor.summary()
local_critic.summary()
return local_actor, local_critic
# 로컬신경망을 글로벌신경망으로 업데이트 示例13: Decoder
# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import Conv2D [as 别名]
def Decoder(name):
input_ = Input( shape=(8,8,512) )
skip_in = Input( shape=(8,8,512) )
x = input_
x = upscale(512)(x)
x = res_block(x, 512)
x = upscale(256)(x)
x = res_block(x, 256)
x = upscale(128)(x)
x = res_block(x, 128)
x = upscale(64)(x)
x = Conv2D( 3, kernel_size=5, padding='same', activation='sigmoid' )(x)
y = input_
y = upscale(512)(y)
y = upscale(256)(y)
y = upscale(128)(y)
y = upscale(64)(y)
y = Conv2D( 1, kernel_size=5, padding='same', activation='sigmoid' )(y)
return Model( [input_], outputs=[x,y] ) 示例14: build_model
# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import Conv2D [as 别名]
def build_model(self):
input = Input(shape=self.state_size)
conv = Conv2D(16, (8, 8), strides=(4, 4), activation='relu')(input)
conv = Conv2D(32, (4, 4), strides=(2, 2), activation='relu')(conv)
conv = Flatten()(conv)
fc = Dense(256, activation='relu')(conv)
policy = Dense(self.action_size, activation='softmax')(fc)
value = Dense(1, activation='linear')(fc)
actor = Model(inputs=input, outputs=policy)
critic = Model(inputs=input, outputs=value)
actor._make_predict_function()
critic._make_predict_function()
actor.summary()
critic.summary()
return actor, critic
# make loss function for Policy Gradient
# [log(action probability) * advantages] will be input for the back prop
# we add entropy of action probability to loss 示例15: build_localmodel
# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import Conv2D [as 别名]
def build_localmodel(self):
input = Input(shape=self.state_size)
conv = Conv2D(16, (8, 8), strides=(4, 4), activation='relu')(input)
conv = Conv2D(32, (4, 4), strides=(2, 2), activation='relu')(conv)
conv = Flatten()(conv)
fc = Dense(256, activation='relu')(conv)
policy = Dense(self.action_size, activation='softmax')(fc)
value = Dense(1, activation='linear')(fc)
actor = Model(inputs=input, outputs=policy)
critic = Model(inputs=input, outputs=value)
actor._make_predict_function()
critic._make_predict_function()
actor.set_weights(self.actor.get_weights())
critic.set_weights(self.critic.get_weights())
actor.summary()
critic.summary()
return actor, critic