Pyodide里的Tensorflow目录

可以跑通基本的训练、使用模型过程

common/msg/blockly/en.js

@@ -4009,4 +4009,36 @@ En.MIXLY_TINY_WEB_DB_START_NUMBER = 'Start number';
 En.MIXLY_TINY_WEB_DB_VARIABLE_NUMBER = 'Number of variables';
 En.MIXLY_TINY_WEB_DB_SEARCH_VARS = 'Characters in variable names';
 
+
+En.MIXLY_TENSORFLOW_INIT_TENSOR = 'Initialize tensor as';
+En.MIXLY_TENSORFLOW_SEQUENTIAL = 'Initialize sequential model';
+En.MIXLY_TENSORFLOW_INIT_LAYERS_DENSE_LAYER = 'Build dense layer';
+En.MIXLY_TENSORFLOW_OUTPUT_DIMENSION = 'Output dimension';
+En.MIXLY_TENSORFLOW_INPUT_SHAPE = 'Input shape';
+En.MIXLY_TENSORFLOW_MODEL = 'Model';
+En.MIXLY_TENSORFLOW_ADD_LAYER = 'Add layer';
+En.MIXLY_TENSORFLOW_COMPILE_MODEL = 'Compile model';
+En.MIXLY_TENSORFLOW_LOSS_FUNCTION_TYPE = 'Loss function type';
+En.MIXLY_TENSORFLOW_MEAN_SQUARED_ERROR = 'Mean squared error';
+En.MIXLY_TENSORFLOW_OPTIMIZER = 'Optimizer';
+En.MIXLY_TENSORFLOW_SGD = 'Stochastic gradient descent';
+En.MIXLY_TENSORFLOW_FIT_MODEL = 'Train model';
+En.MIXLY_TENSORFLOW_FIT_INPUT_DATA = 'Input data';
+En.MIXLY_TENSORFLOW_FIT_TARGET_DATA = 'Target data';
+En.MIXLY_TENSORFLOW_FIT_EPOCHS = 'Training epochs';
+En.MIXLY_TENSORFLOW_FIT_VERBOSE = 'Verbose level';
+En.MIXLY_TENSORFLOW_FIT_RETURN_HISTORY = 'Return training history object';
+En.MIXLY_TENSORFLOW_GET_LOSS_FROM_HISTORY = 'From training history object';
+En.MIXLY_TENSORFLOW_GET_LOSS_FROM_HISTORY_2 = 'Get training loss values array';
+En.MIXLY_TENSORFLOW_PREDICT = 'Use model for prediction';
+En.MIXLY_TENSORFLOW_PREDICT_INPUT_DATA = 'Input data';
+En.MIXLY_TENSORFLOW_PREDICT_RETURN_RESULT = 'Return prediction result tensor';
+En.MIXLY_TENSORFLOW_GET_TENSOR_DATA = 'Get data from tensor';
+En.MIXLY_TENSORFLOW_SAVE_MODEL = 'Save model';
+En.MIXLY_TENSORFLOW_EXPORT_MODEL = 'Export model';
+En.MIXLY_TENSORFLOW_SAVE_MODEL_NAME = 'Name';
+En.MIXLY_TENSORFLOW_LOAD_MODEL = 'Use imported model';
+En.MIXLY_TENSORFLOW_MODEL_NAME = 'Model name';
+En.MIXLY_TENSORFLOW_PREPARE_PICTURE_TO_TENSOR = 'Preprocess image to tensor';
+En.MIXLY_TENSORFLOW_PREPARE_PICTURE_READ_PICTURE = 'Read image';
 })();

common/msg/blockly/zh-hans.js

@@ -4168,4 +4168,35 @@ ZhHans.MIXLY_TINY_WEB_DB_START_NUMBER = '起始编号';
 ZhHans.MIXLY_TINY_WEB_DB_VARIABLE_NUMBER = '变量个数';
 ZhHans.MIXLY_TINY_WEB_DB_SEARCH_VARS = '变量名包含的字符';
 
+ZhHans.MIXLY_TENSORFLOW_INIT_TENSOR = '初始化张量为';
+ZhHans.MIXLY_TENSORFLOW_SEQUENTIAL = '初始化顺序模型';
+ZhHans.MIXLY_TENSORFLOW_INIT_LAYERS_DENSE_LAYER = '构建全连接层';
+ZhHans.MIXLY_TENSORFLOW_OUTPUT_DIMENSION = '输出维度';
+ZhHans.MIXLY_TENSORFLOW_INPUT_SHAPE = '输入形状';
+ZhHans.MIXLY_TENSORFLOW_MODEL = '模型';
+ZhHans.MIXLY_TENSORFLOW_ADD_LAYER = '添加层';
+ZhHans.MIXLY_TENSORFLOW_COMPILE_MODEL = '编译模型';
+ZhHans.MIXLY_TENSORFLOW_LOSS_FUNCTION_TYPE = '损失函数类型';
+ZhHans.MIXLY_TENSORFLOW_MEAN_SQUARED_ERROR = '均方误差';
+ZhHans.MIXLY_TENSORFLOW_OPTIMIZER = '优化器';
+ZhHans.MIXLY_TENSORFLOW_SGD = '随机梯度下降法';
+ZhHans.MIXLY_TENSORFLOW_FIT_MODEL = '训练模型';
+ZhHans.MIXLY_TENSORFLOW_FIT_INPUT_DATA = '输入数据';
+ZhHans.MIXLY_TENSORFLOW_FIT_TARGET_DATA = '目标数据';
+ZhHans.MIXLY_TENSORFLOW_FIT_EPOCHS = '训练迭代次数';
+ZhHans.MIXLY_TENSORFLOW_FIT_VERBOSE = '日志输出级别';
+ZhHans.MIXLY_TENSORFLOW_FIT_RETURN_HISTORY = '返回训练历史对象';
+ZhHans.MIXLY_TENSORFLOW_GET_LOSS_FROM_HISTORY = '从训练历史对象';
+ZhHans.MIXLY_TENSORFLOW_GET_LOSS_FROM_HISTORY_2 = '获取训练损失值数组';
+ZhHans.MIXLY_TENSORFLOW_PREDICT = '使用模型预测';
+ZhHans.MIXLY_TENSORFLOW_PREDICT_INPUT_DATA = '输入数据';
+ZhHans.MIXLY_TENSORFLOW_PREDICT_RETURN_RESULT = '返回预测结果张量';
+ZhHans.MIXLY_TENSORFLOW_GET_TENSOR_DATA = '获取张量中的数据';
+ZhHans.MIXLY_TENSORFLOW_SAVE_MODEL = '保存模型';
+ZhHans.MIXLY_TENSORFLOW_EXPORT_MODEL = '导出模型';
+ZhHans.MIXLY_TENSORFLOW_SAVE_MODEL_NAME = '名称';
+ZhHans.MIXLY_TENSORFLOW_LOAD_MODEL = '使用导入模型';
+ZhHans.MIXLY_TENSORFLOW_MODEL_NAME = '模型名';
+ZhHans.MIXLY_TENSORFLOW_PREPARE_PICTURE_TO_TENSOR = '预处理图像为张量';
+ZhHans.MIXLY_TENSORFLOW_PREPARE_PICTURE_READ_PICTURE = '读入图像';
 })(); 

common/msg/blockly/zh-hant.js

@@ -4163,4 +4163,35 @@ ZhHant.MIXLY_TINY_WEB_DB_START_NUMBER = '起始編號';
 ZhHant.MIXLY_TINY_WEB_DB_VARIABLE_NUMBER = '變數個數';
 ZhHant.MIXLY_TINY_WEB_DB_SEARCH_VARS = '變數名稱包含的字元';
 
+ZhHant.MIXLY_TENSORFLOW_INIT_TENSOR = '初始化張量為';
+ZhHant.MIXLY_TENSORFLOW_SEQUENTIAL = '初始化順序模型';
+ZhHant.MIXLY_TENSORFLOW_INIT_LAYERS_DENSE_LAYER = '構建全連接層';
+ZhHant.MIXLY_TENSORFLOW_OUTPUT_DIMENSION = '輸出維度';
+ZhHant.MIXLY_TENSORFLOW_INPUT_SHAPE = '輸入形狀';
+ZhHant.MIXLY_TENSORFLOW_MODEL = '模型';
+ZhHant.MIXLY_TENSORFLOW_ADD_LAYER = '添加層';
+ZhHant.MIXLY_TENSORFLOW_COMPILE_MODEL = '編譯模型';
+ZhHant.MIXLY_TENSORFLOW_LOSS_FUNCTION_TYPE = '損失函數類型';
+ZhHant.MIXLY_TENSORFLOW_MEAN_SQUARED_ERROR = '均方誤差';
+ZhHant.MIXLY_TENSORFLOW_OPTIMIZER = '優化器';
+ZhHant.MIXLY_TENSORFLOW_SGD = '隨機梯度下降法';
+ZhHant.MIXLY_TENSORFLOW_FIT_MODEL = '訓練模型';
+ZhHant.MIXLY_TENSORFLOW_FIT_INPUT_DATA = '輸入數據';
+ZhHant.MIXLY_TENSORFLOW_FIT_TARGET_DATA = '目標數據';
+ZhHant.MIXLY_TENSORFLOW_FIT_EPOCHS = '訓練迭代次數';
+ZhHant.MIXLY_TENSORFLOW_FIT_VERBOSE = '日誌輸出級別';
+ZhHant.MIXLY_TENSORFLOW_FIT_RETURN_HISTORY = '返回訓練歷史對象';
+ZhHant.MIXLY_TENSORFLOW_GET_LOSS_FROM_HISTORY = '從訓練歷史對象';
+ZhHant.MIXLY_TENSORFLOW_GET_LOSS_FROM_HISTORY_2 = '獲取訓練損失值數組';
+ZhHant.MIXLY_TENSORFLOW_PREDICT = '使用模型預測';
+ZhHant.MIXLY_TENSORFLOW_PREDICT_INPUT_DATA = '輸入數據';
+ZhHant.MIXLY_TENSORFLOW_PREDICT_RETURN_RESULT = '返回預測結果張量';
+ZhHant.MIXLY_TENSORFLOW_GET_TENSOR_DATA = '獲取張量中的數據';
+ZhHant.MIXLY_TENSORFLOW_SAVE_MODEL = '保存模型';
+ZhHant.MIXLY_TENSORFLOW_EXPORT_MODEL = '導出模型';
+ZhHant.MIXLY_TENSORFLOW_SAVE_MODEL_NAME = '名稱';
+ZhHant.MIXLY_TENSORFLOW_LOAD_MODEL = '使用導入模型';
+ZhHant.MIXLY_TENSORFLOW_MODEL_NAME = '模型名';
+ZhHant.MIXLY_TENSORFLOW_PREPARE_PICTURE_TO_TENSOR = '預處理圖像為張量';
+ZhHant.MIXLY_TENSORFLOW_PREPARE_PICTURE_READ_PICTURE = '讀入圖像';
 })();