Python convolutional.Conv2D方法代碼示例

本文整理匯總了Python中keras.layers.convolutional.Conv2D方法的典型用法代碼示例。如果您正苦於以下問題：Python convolutional.Conv2D方法的具體用法？Python convolutional.Conv2D怎麽用？Python convolutional.Conv2D使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類keras.layers.convolutional的用法示例。

在下文中一共展示了convolutional.Conv2D方法的15個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: build_generator

# 需要導入模塊: from keras.layers import convolutional [as 別名]
# 或者: from keras.layers.convolutional import Conv2D [as 別名]
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)

開發者ID:eriklindernoren，項目名稱:Keras-GAN，代碼行數:26，代碼來源:sgan.py

示例2: build_discriminator

# 需要導入模塊: from keras.layers import convolutional [as 別名]
# 或者: from keras.layers.convolutional import Conv2D [as 別名]
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)

開發者ID:eriklindernoren，項目名稱:Keras-GAN，代碼行數:23，代碼來源:context_encoder.py

示例3: build_discriminator

# 需要導入模塊: from keras.layers import convolutional [as 別名]
# 或者: from keras.layers.convolutional import Conv2D [as 別名]
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])

開發者ID:eriklindernoren，項目名稱:Keras-GAN，代碼行數:27，代碼來源:ccgan.py

示例4: build_classifier

# 需要導入模塊: from keras.layers import convolutional [as 別名]
# 或者: from keras.layers.convolutional import Conv2D [as 別名]
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)

開發者ID:eriklindernoren，項目名稱:Keras-GAN，代碼行數:23，代碼來源:pixelda.py

示例5: build_generator

# 需要導入模塊: from keras.layers import convolutional [as 別名]
# 或者: from keras.layers.convolutional import Conv2D [as 別名]
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)

開發者ID:eriklindernoren，項目名稱:Keras-GAN，代碼行數:26，代碼來源:infogan.py

示例6: build_generator

# 需要導入模塊: from keras.layers import convolutional [as 別名]
# 或者: from keras.layers.convolutional import Conv2D [as 別名]
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)

開發者ID:eriklindernoren，項目名稱:Keras-GAN，代碼行數:25，代碼來源:wgan.py

示例7: build_discriminator

# 需要導入模塊: from keras.layers import convolutional [as 別名]
# 或者: from keras.layers.convolutional import Conv2D [as 別名]
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)

開發者ID:eriklindernoren，項目名稱:Keras-GAN，代碼行數:22，代碼來源:cyclegan.py

示例8: build_discriminator

# 需要導入模塊: from keras.layers import convolutional [as 別名]
# 或者: from keras.layers.convolutional import Conv2D [as 別名]
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)

開發者ID:eriklindernoren，項目名稱:Keras-GAN，代碼行數:26，代碼來源:pix2pix.py

示例9: build_generator

# 需要導入模塊: from keras.layers import convolutional [as 別名]
# 或者: from keras.layers.convolutional import Conv2D [as 別名]
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)

開發者ID:eriklindernoren，項目名稱:Keras-GAN，代碼行數:25，代碼來源:dcgan.py

示例10: build_model

# 需要導入模塊: from keras.layers import convolutional [as 別名]
# 或者: from keras.layers.convolutional import Conv2D [as 別名]
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

開發者ID:rlcode，項目名稱:reinforcement-learning-kr，代碼行數:18，代碼來源:play_a3c_model.py

示例11: build_model

# 需要導入模塊: from keras.layers import convolutional [as 別名]
# 或者: from keras.layers.convolutional import Conv2D [as 別名]
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

開發者ID:rlcode，項目名稱:reinforcement-learning-kr，代碼行數:25，代碼來源:breakout_a3c.py

示例12: build_local_model

# 需要導入模塊: from keras.layers import convolutional [as 別名]
# 或者: from keras.layers.convolutional import Conv2D [as 別名]
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

開發者ID:rlcode，項目名稱:reinforcement-learning-kr，代碼行數:26，代碼來源:breakout_a3c.py

示例13: Decoder

# 需要導入模塊: from keras.layers import convolutional [as 別名]
# 或者: from keras.layers.convolutional import Conv2D [as 別名]
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] )

開發者ID:dfaker，項目名稱:df，代碼行數:24，代碼來源:model.py

示例14: build_model

# 需要導入模塊: from keras.layers import convolutional [as 別名]
# 或者: from keras.layers.convolutional import Conv2D [as 別名]
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

開發者ID:rlcode，項目名稱:reinforcement-learning，代碼行數:25，代碼來源:breakout_a3c.py

示例15: build_localmodel

# 需要導入模塊: from keras.layers import convolutional [as 別名]
# 或者: from keras.layers.convolutional import Conv2D [as 別名]
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

開發者ID:rlcode，項目名稱:reinforcement-learning，代碼行數:24，代碼來源:breakout_a3c.py

注：本文中的keras.layers.convolutional.Conv2D方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。