Python merge.concatenate方法代码示例

# 需要导入模块: from keras.layers import merge [as 别名]
# 或者: from keras.layers.merge import concatenate [as 别名]
def yolo_body(inputs, num_anchors, num_classes):
    """Create YOLO_V2 model CNN body in Keras."""
    darknet = Model(inputs, darknet_body()(inputs))
    conv20 = compose(
        DarknetConv2D_BN_Leaky(1024, (3, 3)),
        DarknetConv2D_BN_Leaky(1024, (3, 3)))(darknet.output)

    conv13 = darknet.layers[43].output
    conv21 = DarknetConv2D_BN_Leaky(64, (1, 1))(conv13)
    # TODO: Allow Keras Lambda to use func arguments for output_shape?
    conv21_reshaped = Lambda(
        space_to_depth_x2,
        output_shape=space_to_depth_x2_output_shape,
        name='space_to_depth')(conv21)

    x = concatenate([conv21_reshaped, conv20])
    x = DarknetConv2D_BN_Leaky(1024, (3, 3))(x)
    x = DarknetConv2D(num_anchors * (num_classes + 5), (1, 1))(x)
    return Model(inputs, x) 

# 需要导入模块: from keras.layers import merge [as 别名]
# 或者: from keras.layers.merge import concatenate [as 别名]
def loss_net(x_in, trux_x_in,width, height,style_image_path,content_weight,style_weight):
    # Append the initial input to the FastNet input to the VGG inputs
    x = concatenate([x_in, trux_x_in], axis=0)
    
    # Normalize the inputs via custom VGG Normalization layer
    x = VGGNormalize(name="vgg_normalize")(x)

    vgg = VGG16(include_top=False,input_tensor=x)

    vgg_output_dict = dict([(layer.name, layer.output) for layer in vgg.layers[-18:]])
    vgg_layers = dict([(layer.name, layer) for layer in vgg.layers[-18:]])

    if style_weight > 0:
        add_style_loss(vgg,style_image_path , vgg_layers, vgg_output_dict, width, height,style_weight)   

    if content_weight > 0:
        add_content_loss(vgg_layers,vgg_output_dict,content_weight)

    # Freeze all VGG layers
    for layer in vgg.layers[-19:]:
        layer.trainable = False

    return vgg 

# 需要导入模块: from keras.layers import merge [as 别名]
# 或者: from keras.layers.merge import concatenate [as 别名]
def block_inception_a(input):
    if K.image_data_format() == 'channels_first':
        channel_axis = 1
    else:
        channel_axis = -1

    branch_0 = conv2d_bn(input, 96, 1, 1)

    branch_1 = conv2d_bn(input, 64, 1, 1)
    branch_1 = conv2d_bn(branch_1, 96, 3, 3)

    branch_2 = conv2d_bn(input, 64, 1, 1)
    branch_2 = conv2d_bn(branch_2, 96, 3, 3)
    branch_2 = conv2d_bn(branch_2, 96, 3, 3)

    branch_3 = AveragePooling2D((3,3), strides=(1,1), padding='same')(input)
    branch_3 = conv2d_bn(branch_3, 96, 1, 1)

    x = concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis)
    return x 

# 需要导入模块: from keras.layers import merge [as 别名]
# 或者: from keras.layers.merge import concatenate [as 别名]
def block_reduction_a(input):
    if K.image_data_format() == 'channels_first':
        channel_axis = 1
    else:
        channel_axis = -1

    branch_0 = conv2d_bn(input, 384, 3, 3, strides=(2,2), padding='valid')

    branch_1 = conv2d_bn(input, 192, 1, 1)
    branch_1 = conv2d_bn(branch_1, 224, 3, 3)
    branch_1 = conv2d_bn(branch_1, 256, 3, 3, strides=(2,2), padding='valid')

    branch_2 = MaxPooling2D((3,3), strides=(2,2), padding='valid')(input)

    x = concatenate([branch_0, branch_1, branch_2], axis=channel_axis)
    return x 

# 需要导入模块: from keras.layers import merge [as 别名]
# 或者: from keras.layers.merge import concatenate [as 别名]
def block_inception_b(input):
    if K.image_data_format() == 'channels_first':
        channel_axis = 1
    else:
        channel_axis = -1

    branch_0 = conv2d_bn(input, 384, 1, 1)

    branch_1 = conv2d_bn(input, 192, 1, 1)
    branch_1 = conv2d_bn(branch_1, 224, 1, 7)
    branch_1 = conv2d_bn(branch_1, 256, 7, 1)

    branch_2 = conv2d_bn(input, 192, 1, 1)
    branch_2 = conv2d_bn(branch_2, 192, 7, 1)
    branch_2 = conv2d_bn(branch_2, 224, 1, 7)
    branch_2 = conv2d_bn(branch_2, 224, 7, 1)
    branch_2 = conv2d_bn(branch_2, 256, 1, 7)

    branch_3 = AveragePooling2D((3,3), strides=(1,1), padding='same')(input)
    branch_3 = conv2d_bn(branch_3, 128, 1, 1)

    x = concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis)
    return x 

# 需要导入模块: from keras.layers import merge [as 别名]
# 或者: from keras.layers.merge import concatenate [as 别名]
def block_reduction_b(input):
    if K.image_data_format() == 'channels_first':
        channel_axis = 1
    else:
        channel_axis = -1

    branch_0 = conv2d_bn(input, 192, 1, 1)
    branch_0 = conv2d_bn(branch_0, 192, 3, 3, strides=(2, 2), padding='valid')

    branch_1 = conv2d_bn(input, 256, 1, 1)
    branch_1 = conv2d_bn(branch_1, 256, 1, 7)
    branch_1 = conv2d_bn(branch_1, 320, 7, 1)
    branch_1 = conv2d_bn(branch_1, 320, 3, 3, strides=(2,2), padding='valid')

    branch_2 = MaxPooling2D((3, 3), strides=(2, 2), padding='valid')(input)

    x = concatenate([branch_0, branch_1, branch_2], axis=channel_axis)
    return x 

# 需要导入模块: from keras.layers import merge [as 别名]
# 或者: from keras.layers.merge import concatenate [as 别名]
def concat_images_with_tiled_vector(images, vector):
    """Combine a set of images with a vector, tiling the vector at each pixel in the images and concatenating on the channel axis.

    # Params

        images: list of images with the same dimensions
        vector: vector to tile on each image. If you have
            more than one vector, simply concatenate them
            all before calling this function.

    # Returns

    """
    with K.name_scope('concat_images_with_tiled_vector'):
        if not isinstance(images, list):
            images = [images]
        image_shape = K.int_shape(images[0])
        tiled_vector = tile_vector_as_image_channels(vector, image_shape)
        images.append(tiled_vector)
        combined = K.concatenate(images)

        return combined 

# 需要导入模块: from keras.layers import merge [as 别名]
# 或者: from keras.layers.merge import concatenate [as 别名]
def forward(self):
        model_input = Input(shape=(self.maxlen,), dtype='int32', name='token')
        x = Token_Embedding(model_input, self.nb_tokens, self.embedding_dim,
                            self.token_embeddings, True, self.maxlen,
                            self.embed_dropout_rate, name='token_embeddings')
        x = Activation('tanh')(x)

        # skip-connection from embedding to output eases gradient-flow and allows access to lower-level features
        # ordering of the way the merge is done is important for consistency with the pretrained model
        lstm_0_output = Bidirectional(
            LSTM(self.rnn_size, return_sequences=True), name="bi_lstm_0")(x)
        lstm_1_output = Bidirectional(
            LSTM(self.rnn_size, return_sequences=True), name="bi_lstm_1")(lstm_0_output)
        x = concatenate([lstm_1_output, lstm_0_output, x], name='concatenate')

        x = self.attention_layer(x)
        if self.return_attention:
            x, weights = x
        outputs = tc_output_logits(x, self.nb_classes, self.final_dropout_rate)
        if self.return_attention:
            outputs.append(weights)
            outputs = concatenate(outputs, axis=-1, name='outputs')

        self.model = Model(inputs=model_input,
                           outputs=outputs, name="Bi_LSTM_Attention") 

# 需要导入模块: from keras.layers import merge [as 别名]
# 或者: from keras.layers.merge import concatenate [as 别名]
def forward(self):
        model_input = Input(shape=(self.maxlen,), dtype='int32', name='token')
        x = Token_Embedding(model_input, self.nb_tokens, self.embedding_dim,
                            self.token_embeddings, False, self.maxlen,
                            self.embed_dropout_rate, name='token_embeddings')
        cnn_combine = []
        for i in range(len(self.conv_kernel_size)):
            cnn = self.cnn_list[i](x)
            pool = self.pool_list[i](cnn)
            cnn_combine.append(pool)
        x = concatenate(cnn_combine, axis=-1)

        x = Flatten()(x)
        x = Dropout(self.final_dropout_rate)(x)
        x = self.fc(x)

        outputs = tc_output_logits(x, self.nb_classes, self.final_dropout_rate)

        self.model = Model(inputs=model_input,
                           outputs=outputs, name="TextCNN") 

# 需要导入模块: from keras.layers import merge [as 别名]
# 或者: from keras.layers.merge import concatenate [as 别名]
def block_inception_a(input):
    if K.image_data_format() == 'channels_first':
        channel_axis = 1
    else:
        channel_axis = -1

    branch_0 = conv2d_bn(input, 96, 1, 1)

    branch_1 = conv2d_bn(input, 64, 1, 1)
    branch_1 = conv2d_bn(branch_1, 96, 3, 3)

    branch_2 = conv2d_bn(input, 64, 1, 1)
    branch_2 = conv2d_bn(branch_2, 96, 3, 3)
    branch_2 = conv2d_bn(branch_2, 96, 3, 3)

    branch_3 = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(input)
    branch_3 = conv2d_bn(branch_3, 96, 1, 1)

    x = concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis)
    return x 

# 需要导入模块: from keras.layers import merge [as 别名]
# 或者: from keras.layers.merge import concatenate [as 别名]
def block_reduction_a(input):
    if K.image_data_format() == 'channels_first':
        channel_axis = 1
    else:
        channel_axis = -1

    branch_0 = conv2d_bn(input, 384, 3, 3, strides=(2, 2), padding='valid')

    branch_1 = conv2d_bn(input, 192, 1, 1)
    branch_1 = conv2d_bn(branch_1, 224, 3, 3)
    branch_1 = conv2d_bn(branch_1, 256, 3, 3, strides=(2, 2), padding='valid')

    branch_2 = MaxPooling2D((3, 3), strides=(2, 2), padding='valid')(input)

    x = concatenate([branch_0, branch_1, branch_2], axis=channel_axis)
    return x 

# 需要导入模块: from keras.layers import merge [as 别名]
# 或者: from keras.layers.merge import concatenate [as 别名]
def block_inception_b(input):
    if K.image_data_format() == 'channels_first':
        channel_axis = 1
    else:
        channel_axis = -1

    branch_0 = conv2d_bn(input, 384, 1, 1)

    branch_1 = conv2d_bn(input, 192, 1, 1)
    branch_1 = conv2d_bn(branch_1, 224, 1, 7)
    branch_1 = conv2d_bn(branch_1, 256, 7, 1)

    branch_2 = conv2d_bn(input, 192, 1, 1)
    branch_2 = conv2d_bn(branch_2, 192, 7, 1)
    branch_2 = conv2d_bn(branch_2, 224, 1, 7)
    branch_2 = conv2d_bn(branch_2, 224, 7, 1)
    branch_2 = conv2d_bn(branch_2, 256, 1, 7)

    branch_3 = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(input)
    branch_3 = conv2d_bn(branch_3, 128, 1, 1)

    x = concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis)
    return x 

# 需要导入模块: from keras.layers import merge [as 别名]
# 或者: from keras.layers.merge import concatenate [as 别名]
def block_reduction_b(input):
    if K.image_data_format() == 'channels_first':
        channel_axis = 1
    else:
        channel_axis = -1

    branch_0 = conv2d_bn(input, 192, 1, 1)
    branch_0 = conv2d_bn(branch_0, 192, 3, 3, strides=(2, 2), padding='valid')

    branch_1 = conv2d_bn(input, 256, 1, 1)
    branch_1 = conv2d_bn(branch_1, 256, 1, 7)
    branch_1 = conv2d_bn(branch_1, 320, 7, 1)
    branch_1 = conv2d_bn(branch_1, 320, 3, 3, strides=(2, 2), padding='valid')

    branch_2 = MaxPooling2D((3, 3), strides=(2, 2), padding='valid')(input)

    x = concatenate([branch_0, branch_1, branch_2], axis=channel_axis)
    return x 

# 需要导入模块: from keras.layers import merge [as 别名]
# 或者: from keras.layers.merge import concatenate [as 别名]
def block_inception_c(input):
    if K.image_data_format() == 'channels_first':
        channel_axis = 1
    else:
        channel_axis = -1

    branch_0 = conv2d_bn(input, 256, 1, 1)

    branch_1 = conv2d_bn(input, 384, 1, 1)
    branch_10 = conv2d_bn(branch_1, 256, 1, 3)
    branch_11 = conv2d_bn(branch_1, 256, 3, 1)
    branch_1 = concatenate([branch_10, branch_11], axis=channel_axis)

    branch_2 = conv2d_bn(input, 384, 1, 1)
    branch_2 = conv2d_bn(branch_2, 448, 3, 1)
    branch_2 = conv2d_bn(branch_2, 512, 1, 3)
    branch_20 = conv2d_bn(branch_2, 256, 1, 3)
    branch_21 = conv2d_bn(branch_2, 256, 3, 1)
    branch_2 = concatenate([branch_20, branch_21], axis=channel_axis)

    branch_3 = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(input)
    branch_3 = conv2d_bn(branch_3, 256, 1, 1)

    x = concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis)
    return x 

# 需要导入模块: from keras.layers import merge [as 别名]
# 或者: from keras.layers.merge import concatenate [as 别名]
def get_unet_resnet(input_shape):
    resnet_base = ResNet50(input_shape=input_shape, include_top=False)

    if args.show_summary:
        resnet_base.summary()

    for l in resnet_base.layers:
        l.trainable = True
    conv1 = resnet_base.get_layer("activation_1").output
    conv2 = resnet_base.get_layer("activation_10").output
    conv3 = resnet_base.get_layer("activation_22").output
    conv4 = resnet_base.get_layer("activation_40").output
    conv5 = resnet_base.get_layer("activation_49").output

    up6 = concatenate([UpSampling2D()(conv5), conv4], axis=-1)
    conv6 = conv_block_simple(up6, 256, "conv6_1")
    conv6 = conv_block_simple(conv6, 256, "conv6_2")

    up7 = concatenate([UpSampling2D()(conv6), conv3], axis=-1)
    conv7 = conv_block_simple(up7, 192, "conv7_1")
    conv7 = conv_block_simple(conv7, 192, "conv7_2")

    up8 = concatenate([UpSampling2D()(conv7), conv2], axis=-1)
    conv8 = conv_block_simple(up8, 128, "conv8_1")
    conv8 = conv_block_simple(conv8, 128, "conv8_2")

    up9 = concatenate([UpSampling2D()(conv8), conv1], axis=-1)
    conv9 = conv_block_simple(up9, 64, "conv9_1")
    conv9 = conv_block_simple(conv9, 64, "conv9_2")

    vgg = VGG16(input_shape=input_shape, input_tensor=resnet_base.input, include_top=False)
    for l in vgg.layers:
        l.trainable = False
    vgg_first_conv = vgg.get_layer("block1_conv2").output
    up10 = concatenate([UpSampling2D()(conv9), resnet_base.input, vgg_first_conv], axis=-1)
    conv10 = conv_block_simple(up10, 32, "conv10_1")
    conv10 = conv_block_simple(conv10, 32, "conv10_2")
    conv10 = SpatialDropout2D(0.2)(conv10)
    x = Conv2D(1, (1, 1), activation="sigmoid", name="prediction")(conv10)
    model = Model(resnet_base.input, x)
    return model