Question

我使用Yeoman Generator制作了自己的扩展包，但是我不知道如何在vscode中安装它。

也许我可以将扩展包导出到Marketplace了，但是我不想这么做

Answer 1

您可以package an extension into a vsix file自己安装或分发给其他人。

要打包扩展程序，请在扩展程序目录中运行：

# create first conv layers
first_input = Input(shape=input_shape, dtype='int32', name='first_input')
x = Embedding(input_dim=num_features,output_dim=embedding_dim,input_length=input_shape[0])(first_input)
#x = Dropout(rate = dropout_rate)(x)
x = Conv1D(filters=filters,
                                  kernel_size=kernel_size,
                                   strides = 1,
                                  activation='relu',
                                  bias_initializer='random_uniform',
                                  padding='same')(x)
x = Conv1D(filters=filters,
                                  kernel_size=kernel_size,
                                   strides = 1,
                                  activation='relu',
                                  bias_initializer='random_uniform',
                                  padding='same')(x)
x = MaxPooling1D(pool_size=pool_size)(x)

x = Conv1D(filters=filters * 2,
                                  kernel_size=kernel_size,
                                   strides = 1,
                                  activation='relu',
                                  bias_initializer='random_uniform',
                                  padding='same')(x)
x = Conv1D(filters=filters * 2,
                                  kernel_size=kernel_size,
                                   strides = 1,
                                  activation='relu',
                                  bias_initializer='random_uniform',
                                  padding='same')(x)
x = GlobalAveragePooling1D()(x)

aux_predictions = Dense(op_units, activation=op_activation)(x)

# now create a convolutional model for second
second_input = Input(shape=input_shape, dtype='int32', name='second_input')
x = Embedding(input_dim=num_features,output_dim=embedding_dim,input_length=input_shape[0])(second_input)
#x = Dropout(rate = dropout_rate)(x)
x = Conv1D(filters=filters,
                                  kernel_size=kernel_size,
                                   strides = 1,
                                  activation='relu',
                                  bias_initializer='random_uniform',
                                  padding='same')(x)
x = Conv1D(filters=filters,
                                  kernel_size=kernel_size,
                                   strides = 1,
                                  activation='relu',
                                  bias_initializer='random_uniform',
                                  padding='same')(x)
x = MaxPooling1D(pool_size=pool_size)(x)

x = Conv1D(filters=filters * 2,
                                  kernel_size=kernel_size,
                                   strides = 1,
                                  activation='relu',
                                  bias_initializer='random_uniform',
                                  padding='same')(x)
x = Conv1D(filters=filters * 2,
                                  kernel_size=kernel_size,
                                   strides = 1,
                                  activation='relu',
                                  bias_initializer='random_uniform',
                                  padding='same')(x)
x = GlobalAveragePooling1D()(x)

aux_predictions2 = Dense(op_units, activation=op_activation)(x)

# now create a convolutional model for second
third_input = Input(shape=input_shape, dtype='int32', name='third_input')
x = Embedding(input_dim=num_features,output_dim=embedding_dim,input_length=input_shape[0])(third_input)
#x = Dropout(rate = dropout_rate)(x)
x = Conv1D(filters=filters,
                                  kernel_size=kernel_size,
                                   strides = 1,
                                  activation='relu',
                                  bias_initializer='random_uniform',
                                  padding='same')(x)
x = Conv1D(filters=filters,
                                  kernel_size=kernel_size,
                                   strides = 1,
                                  activation='relu',
                                  bias_initializer='random_uniform',
                                  padding='same')(x)
x = MaxPooling1D(pool_size=pool_size)(x)

x = Conv1D(filters=filters * 2,
                                  kernel_size=kernel_size,
                                   strides = 1,
                                  activation='relu',
                                  bias_initializer='random_uniform',
                                  padding='same')(x)
x = Conv1D(filters=filters * 2,
                                  kernel_size=kernel_size,
                                   strides = 1,
                                  activation='relu',
                                  bias_initializer='random_uniform',
                                  padding='same')(x)
x = GlobalAveragePooling1D()(x)

aux_predictions3 = Dense(op_units, activation=op_activation)(x)

# Now combine three CNN layers with metadata
auxiliary_input = Input(shape=metadata_dim, name='aux_input')
x = keras.layers.concatenate([aux_predictions, aux_predictions2, aux_predictions3, auxiliary_input ])
#x = Dropout(rate = dropout_rate)(x)
x = Dense(64, activation='relu')(x)
x = Dense(64, activation='relu')(x)
x = Dense(64, activation='relu')(x)

model_output = Dense(op_units, activation=op_activation, name='model_output')(x)



opt = SGD(lr=0.01)

fullmetamodel = Model(inputs=[first_input, second_input, third_input, auxiliary_input], outputs=[aux_predictions, aux_predictions2, aux_predictions3, model_output])
fullmetamodel.compile(
              metrics=['categorical_accuracy'], loss='categorical_crossentropy',
              loss_weights=[0.2, 0.2, 0.2, 1.], optimizer = opt)
callbacks = [keras.callbacks.EarlyStopping(monitor='val_loss', patience=2), TQDMNotebookCallback(leave_inner=False, leave_outer = True)] 
fullmetamodel.fit(x=[first_x_train, second_x_train, third_x_train, training_meta], y=[training_labels,training_labels,training_labels, training_labels],
                  batch_size=32, epochs=40, validation_data=([first_x_val, second_x_val, third_x_val, val_meta], [val_labels, val_labels, val_labels, val_labels])
                  , verbose = 0, callbacks = callbacks)  # starts training


# Output, three conv layers working ok, concatenated model performing poorly
Training
50% 20/40 [2:49:34<2:49:23, 508.20s/it]
Epoch 20
[loss: 8.002, dense_118_loss: 0.749, dense_119_loss: 0.769, dense_120_loss: 0.876, model_output_loss: 7.523, dense_118_categorical_accuracy: 0.686
  , dense_119_categorical_accuracy: 0.626, dense_120_categorical_accuracy: 0.620, model_output_categorical_accuracy: 0.532] : 66% 265184/400000 [05:13<02:40, 840.90it/s]

这将产生一个名为$ vsce package的{{1}}文件。

然后从命令行运行vsix或在扩展程序视图（位于my-extension-0.0.1.vsix下）中使用code --install-extension my-extension-0.0.1.vsix命令来安装扩展程序：

Answer 2

您可以通过将扩展目录放置在/home/your-username/.vscode/extensions/（在OS X / Linux中）或Windows中的等效目录（只是猜测但也许是/Users/your-username/.vscode）中来手动安装扩展。

如何安装自己的扩展程序？代码

2 个答案: