@tensorflow/tfjs-layers - npm Package Compare versions

Comparing version 0.0.4 to 0.0.5

dist/backend/common.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
-var common_1 = require("../common");
 var _epsilon = 1e-7;
@@ -14,4 +13,4 @@ function epsilon() {
 function imageDataFormat() {
-    return common_1.DataFormat.CHANNEL_LAST;
+    return 'channelLast';
 }
 exports.imageDataFormat = imageDataFormat;

dist/backend/deeplearnjs_backend.d.ts

@@ -93,7 +93,7 @@ import { Scalar, Tensor, Tensor1D } from '@tensorflow/tfjs-core';
 export declare function l2Normalize(x: Tensor, axis?: number): Tensor;
-export declare function conv1dWithBias(x: Tensor, kernel: Tensor, bias: Tensor, strides?: number, padding?: PaddingMode, dataFormat?: DataFormat, dilationRate?: number): Tensor;
-export declare function conv1d(x: Tensor, kernel: Tensor, strides?: number, padding?: PaddingMode, dataFormat?: DataFormat, dilationRate?: number): Tensor;
-export declare function conv2d(x: Tensor, kernel: Tensor, strides?: number[], padding?: PaddingMode, dataFormat?: DataFormat, dilationRate?: [number, number]): Tensor;
-export declare function conv2dWithBias(x: Tensor, kernel: Tensor, bias: Tensor, strides?: number[], padding?: PaddingMode, dataFormat?: DataFormat, dilationRate?: [number, number]): Tensor;
-export declare function depthwiseConv2d(x: Tensor, depthwiseKernel: Tensor, strides?: [number, number], padding?: PaddingMode, dataFormat?: DataFormat, dilationRate?: [number, number]): Tensor;
+export declare function conv1dWithBias(x: Tensor, kernel: Tensor, bias: Tensor, strides?: number, padding?: string, dataFormat?: DataFormat, dilationRate?: number): Tensor;
+export declare function conv1d(x: Tensor, kernel: Tensor, strides?: number, padding?: string, dataFormat?: DataFormat, dilationRate?: number): Tensor;
+export declare function conv2d(x: Tensor, kernel: Tensor, strides?: number[], padding?: string, dataFormat?: DataFormat, dilationRate?: [number, number]): Tensor;
+export declare function conv2dWithBias(x: Tensor, kernel: Tensor, bias: Tensor, strides?: number[], padding?: string, dataFormat?: DataFormat, dilationRate?: [number, number]): Tensor;
+export declare function depthwiseConv2d(x: Tensor, depthwiseKernel: Tensor, strides?: [number, number], padding?: string, dataFormat?: DataFormat, dilationRate?: [number, number]): Tensor;
 export declare function pool2d(x: Tensor, poolSize: [number, number], strides?: [number, number], padding?: PaddingMode, dataFormat?: DataFormat, poolMode?: PoolMode): Tensor;
@@ -100,0 +100,0 @@ export declare function nameScope<T>(name: string, fn: () => T): T;

dist/backend/deeplearnjs_backend.js

@@ -512,2 +512,3 @@ "use strict";
 function biasAdd(x, bias, dataFormat) {
+    common_1.checkDataFormat(dataFormat);
     if (ndim(bias) !== 1 && ndim(bias) !== ndim(x)) {
@@ -573,3 +574,4 @@ throw new errors_1.ValueError('Unexpected bias dimensions: ' + ndim(bias) +
 function preprocessConv2DInput(x, dataFormat) {
-    if (dataFormat === common_1.DataFormat.CHANNEL_FIRST) {
+    common_1.checkDataFormat(dataFormat);
+    if (dataFormat === 'channelFirst') {
         return tfc.transpose(x, [0, 2, 3, 1]);
@@ -583,3 +585,3 @@ }
     if (strides === void 0) { strides = 1; }
-    if (padding === void 0) { padding = common_1.PaddingMode.VALID; }
+    if (padding === void 0) { padding = 'valid'; }
     if (dilationRate === void 0) { dilationRate = 1; }
@@ -589,2 +591,3 @@ if (dataFormat == null) {
     }
+    common_1.checkDataFormat(dataFormat);
     if (dilationRate !== 1) {
@@ -606,10 +609,10 @@ throw new errors_1.NotImplementedError("dilationRate = " + dilationRate + " is not implemented for 1D " +
     }
-    if (dataFormat === common_1.DataFormat.CHANNEL_FIRST) {
+    if (dataFormat === 'channelFirst') {
         x = transpose(x, [0, 2, 1]);
     }
-    if (padding === common_1.PaddingMode.CASUAL) {
+    if (padding === 'casual') {
         throw new errors_1.NotImplementedError('The support for CASUAL padding mode in conv1dWithBias is not ' +
             'implemented yet.');
     }
-    var y = tfc.conv1d(x, kernel, strides, padding === common_1.PaddingMode.SAME ? 'same' : 'valid');
+    var y = tfc.conv1d(x, kernel, strides, padding === 'same' ? 'same' : 'valid');
     if (bias != null) {
@@ -623,4 +626,5 @@ y = biasAdd(y, bias);
     if (strides === void 0) { strides = 1; }
-    if (padding === void 0) { padding = common_1.PaddingMode.VALID; }
+    if (padding === void 0) { padding = 'valid'; }
     if (dilationRate === void 0) { dilationRate = 1; }
+    common_1.checkDataFormat(dataFormat);
     return conv1dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);
@@ -631,3 +635,4 @@ }
     if (strides === void 0) { strides = [1, 1]; }
-    if (padding === void 0) { padding = common_1.PaddingMode.VALID; }
+    if (padding === void 0) { padding = 'valid'; }
+    common_1.checkDataFormat(dataFormat);
     return conv2dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);
@@ -638,6 +643,7 @@ }
     if (strides === void 0) { strides = [1, 1]; }
-    if (padding === void 0) { padding = common_1.PaddingMode.VALID; }
+    if (padding === void 0) { padding = 'valid'; }
     if (dataFormat == null) {
         dataFormat = common_3.imageDataFormat();
     }
+    common_1.checkDataFormat(dataFormat);
     if (dilationRate != null) {
@@ -655,11 +661,11 @@ throw new errors_1.NotImplementedError('Support for non-default dilation rate is not implemented yet.');
     var y = preprocessConv2DInput(x, dataFormat);
-    if (padding === common_1.PaddingMode.CASUAL) {
+    if (padding === 'casual') {
         throw new errors_1.NotImplementedError('The support for CASUAL padding mode in conv1dWithBias is not ' +
             'implemented yet.');
     }
-    y = tfc.conv2d(y, kernel, strides, padding === common_1.PaddingMode.SAME ? 'same' : 'valid');
+    y = tfc.conv2d(y, kernel, strides, padding === 'same' ? 'same' : 'valid');
     if (bias != null) {
         y = biasAdd(y, bias);
     }
-    if (dataFormat === common_1.DataFormat.CHANNEL_FIRST) {
+    if (dataFormat === 'channelFirst') {
         y = tfc.transpose(y, [0, 3, 1, 2]);
@@ -672,6 +678,7 @@ }
     if (strides === void 0) { strides = [1, 1]; }
-    if (padding === void 0) { padding = common_1.PaddingMode.VALID; }
+    if (padding === void 0) { padding = 'valid'; }
     if (dataFormat == null) {
         dataFormat = common_3.imageDataFormat();
     }
+    common_1.checkDataFormat(dataFormat);
     var y = preprocessConv2DInput(x, dataFormat);
@@ -686,4 +693,4 @@ if (ndim(x) !== 4) {
     }
-    y = tfc.depthwiseConv2d(y, depthwiseKernel, strides, padding === common_1.PaddingMode.SAME ? 'same' : 'valid', dilationRate);
-    if (dataFormat === common_1.DataFormat.CHANNEL_FIRST) {
+    y = tfc.depthwiseConv2d(y, depthwiseKernel, strides, padding === 'same' ? 'same' : 'valid', dilationRate);
+    if (dataFormat === 'channelFirst') {
         y = tfc.transpose(y, [0, 3, 1, 2]);
@@ -695,2 +702,5 @@ }
 function pool2d(x, poolSize, strides, padding, dataFormat, poolMode) {
+    common_1.checkDataFormat(dataFormat);
+    common_1.checkPoolMode(poolMode);
+    common_1.checkPaddingMode(padding);
     if (strides == null) {
@@ -700,3 +710,3 @@ strides = [1, 1];
     if (padding == null) {
-        padding = common_1.PaddingMode.VALID;
+        padding = 'valid';
     }
@@ -707,8 +717,8 @@ if (dataFormat == null) {
     if (poolMode == null) {
-        poolMode = common_1.PoolMode.MAX;
+        poolMode = 'max';
     }
     x = preprocessConv2DInput(x, dataFormat);
     var y;
-    var paddingString = (padding === common_1.PaddingMode.SAME) ? 'same' : 'valid';
-    if (poolMode === common_1.PoolMode.MAX) {
+    var paddingString = (padding === 'same') ? 'same' : 'valid';
+    if (poolMode === 'max') {
         y = tfc.maxPool(x, poolSize, strides, paddingString);
@@ -719,3 +729,3 @@ }
     }
-    if (dataFormat === common_1.DataFormat.CHANNEL_FIRST) {
+    if (dataFormat === 'channelFirst') {
         y = tfc.transpose(y, [0, 3, 1, 2]);
@@ -722,0 +732,0 @@ }

dist/callbacks.d.ts

@@ -49,2 +49,3 @@ import { Scalar, Tensor } from '@tensorflow/tfjs-core';
 export declare function resolveScalarsInLogs(logs: UnresolvedLogs): Promise<void>;
+export declare function disposeTensorsInLogs(logs: UnresolvedLogs): void;
 export declare class History extends Callback {
@@ -51,0 +52,0 @@ epoch: number[];

dist/callbacks.js

@@ -396,2 +396,14 @@ "use strict";
 exports.resolveScalarsInLogs = resolveScalarsInLogs;
+function disposeTensorsInLogs(logs) {
+    if (logs == null) {
+        return;
+    }
+    for (var key in logs) {
+        var value = logs[key];
+        if (typeof value !== 'number') {
+            value.dispose();
+        }
+    }
+}
+exports.disposeTensorsInLogs = disposeTensorsInLogs;
 var History = (function (_super) {
@@ -398,0 +410,0 @@ __extends(History, _super);

dist/common.d.ts

@@ -1,16 +0,12 @@
-export declare enum DataFormat {
-    CHANNEL_FIRST = 0,
-    CHANNEL_LAST = 1,
-}
-export declare enum PaddingMode {
-    VALID = 0,
-    SAME = 1,
-    CASUAL = 2,
-}
-export declare enum PoolMode {
-    MAX = 0,
-    AVG = 1,
-}
+export declare type DataFormat = 'channelFirst' | 'channelLast';
+export declare const VALID_DATA_FORMAT_VALUES: string[];
+export declare function checkDataFormat(value?: string): void;
+export declare type PaddingMode = 'valid' | 'same' | 'casual';
+export declare const VALID_PADDING_MODE_VALUES: string[];
+export declare function checkPaddingMode(value?: string): void;
+export declare type PoolMode = 'max' | 'avg';
+export declare const VALID_POOL_MODE_VALUES: string[];
+export declare function checkPoolMode(value?: string): void;
 export declare function nameScope<T>(name: string, fn: () => T): T;
 export declare function getUniqueTensorName(prefix: string): string;
 export declare function isValidTensorName(name: string): boolean;

dist/common.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
+var errors_1 = require("./errors");
 var generic_utils_1 = require("./utils/generic_utils");
 var nameMap = new Map();
-var DataFormat;
-(function (DataFormat) {
-    DataFormat[DataFormat["CHANNEL_FIRST"] = 0] = "CHANNEL_FIRST";
-    DataFormat[DataFormat["CHANNEL_LAST"] = 1] = "CHANNEL_LAST";
-})(DataFormat = exports.DataFormat || (exports.DataFormat = {}));
-generic_utils_1.SerializableEnumRegistry.register('data_format', {
-    'channels_first': DataFormat.CHANNEL_FIRST,
-    'channels_last': DataFormat.CHANNEL_LAST
-});
-var PaddingMode;
-(function (PaddingMode) {
-    PaddingMode[PaddingMode["VALID"] = 0] = "VALID";
-    PaddingMode[PaddingMode["SAME"] = 1] = "SAME";
-    PaddingMode[PaddingMode["CASUAL"] = 2] = "CASUAL";
-})(PaddingMode = exports.PaddingMode || (exports.PaddingMode = {}));
-generic_utils_1.SerializableEnumRegistry.register('padding', { 'valid': PaddingMode.VALID, 'same': PaddingMode.SAME });
-var PoolMode;
-(function (PoolMode) {
-    PoolMode[PoolMode["MAX"] = 0] = "MAX";
-    PoolMode[PoolMode["AVG"] = 1] = "AVG";
-})(PoolMode = exports.PoolMode || (exports.PoolMode = {}));
+generic_utils_1.SerializableEnumRegistry.register('data_format', { 'channels_first': 'channelFirst', 'channels_last': 'channelLast' });
+exports.VALID_DATA_FORMAT_VALUES = ['channelFirst', 'channelLast', undefined, null];
+function checkDataFormat(value) {
+    if (value == null) {
+        return;
+    }
+    if (exports.VALID_DATA_FORMAT_VALUES.indexOf(value) < 0) {
+        throw new errors_1.ValueError(value + " is not a valid DataFormat.  Valid values as " + exports.VALID_DATA_FORMAT_VALUES);
+    }
+}
+exports.checkDataFormat = checkDataFormat;
+generic_utils_1.SerializableEnumRegistry.register('padding', { 'valid': 'valid', 'same': 'same', 'casual': 'casual' });
+exports.VALID_PADDING_MODE_VALUES = ['valid', 'same', 'casual', undefined, null];
+function checkPaddingMode(value) {
+    if (value == null) {
+        return;
+    }
+    if (exports.VALID_PADDING_MODE_VALUES.indexOf(value) < 0) {
+        throw new errors_1.ValueError(value + " is not a valid PaddingMode.  Valid values as " + exports.VALID_PADDING_MODE_VALUES);
+    }
+}
+exports.checkPaddingMode = checkPaddingMode;
+exports.VALID_POOL_MODE_VALUES = ['max', 'avg', undefined, null];
+function checkPoolMode(value) {
+    if (value == null) {
+        return;
+    }
+    if (exports.VALID_POOL_MODE_VALUES.indexOf(value) < 0) {
+        throw new errors_1.ValueError(value + " is not a valid PoolMode.  Valid values as " + exports.VALID_POOL_MODE_VALUES);
+    }
+}
+exports.checkPoolMode = checkPoolMode;
 var _nameScopeStack = [];
@@ -27,0 +39,0 @@ var _nameScopeDivider = '/';

dist/engine/training.d.ts

@@ -81,6 +81,6 @@ import { Optimizer, Scalar, Tensor, Tensor1D } from '@tensorflow/tfjs-core';
     private checkTrainableWeightsConsistency();
-    evaluate(x: Tensor | Tensor[], y: Tensor | Tensor[], config?: ModelEvaluateConfig): Promise<Scalar | Scalar[]>;
+    evaluate(x: Tensor | Tensor[], y: Tensor | Tensor[], config?: ModelEvaluateConfig): Scalar | Scalar[];
     private checkNumSamples(ins, batchSize?, steps?, stepsName?);
     private predictLoop(ins, batchSize?, verbose?);
-    predict(x: Tensor | Tensor[], config?: ModelPredictConfig): Promise<Tensor | Tensor[]>;
+    predict(x: Tensor | Tensor[], config?: ModelPredictConfig): Tensor | Tensor[];
     predictOnBatch(x: Tensor): Tensor | Tensor[];
@@ -87,0 +87,0 @@ protected standardizeUserData(x: Tensor | Tensor[] | {

dist/engine/training.js

@@ -519,14 +519,9 @@ "use strict";
         if (config === void 0) { config = {}; }
-        return __awaiter(this, void 0, void 0, function () {
-            var batchSize, standardizedOuts, ins, f, testOuts;
-            return __generator(this, function (_a) {
-                batchSize = config.batchSize == null ? 32 : config.batchSize;
-                standardizedOuts = this.standardizeUserData(x, y, true, batchSize);
-                ins = standardizedOuts[0].concat(standardizedOuts[1]);
-                this.makeTestFunction();
-                f = this.testFunction;
-                testOuts = this.testLoop(f, ins, batchSize, config.verbose, config.steps);
-                return [2, generic_utils_1.singletonOrArray(testOuts)];
-            });
-        });
+        var batchSize = config.batchSize == null ? 32 : config.batchSize;
+        var standardizedOuts = this.standardizeUserData(x, y, true, batchSize);
+        var ins = standardizedOuts[0].concat(standardizedOuts[1]);
+        this.makeTestFunction();
+        var f = this.testFunction;
+        var testOuts = this.testLoop(f, ins, batchSize, config.verbose, config.steps);
+        return generic_utils_1.singletonOrArray(testOuts);
     };
@@ -603,10 +598,5 @@ Model.prototype.checkNumSamples = function (ins, batchSize, steps, stepsName) {
         if (config === void 0) { config = {}; }
-        return __awaiter(this, void 0, void 0, function () {
-            var batchSize;
-            return __generator(this, function (_a) {
-                checkInputData(x, this.inputNames, this.feedInputShapes, false);
-                batchSize = config.batchSize == null ? 32 : config.batchSize;
-                return [2, this.predictLoop(x, batchSize)];
-            });
-        });
+        checkInputData(x, this.inputNames, this.feedInputShapes, false);
+        var batchSize = config.batchSize == null ? 32 : config.batchSize;
+        return this.predictLoop(x, batchSize);
     };
@@ -759,3 +749,2 @@ Model.prototype.predictOnBatch = function (x) {
                                                             }
-                                                            return outs;
                                                         });
@@ -765,2 +754,3 @@ return [4, callbackList.onBatchEnd(batchIndex, batchLogs)];
                                                         _a.sent();
+                                                        callbacks_1.disposeTensorsInLogs(batchLogs);
                                                         return [2];
@@ -781,4 +771,7 @@ }
                                         return [3, 3];
-                                    case 6: return [4, callbackList.onEpochEnd(epoch, epochLogs)];
-                                    case 7:
+                                    case 6:
+                                        epochIndexArray1D_1.dispose();
+                                        _a.label = 7;
+                                    case 7: return [4, callbackList.onEpochEnd(epoch, epochLogs)];
+                                    case 8:
                                         _a.sent();
@@ -961,3 +954,2 @@ return [2];
                             var meanLoss = K.mean(loss);
-                            K.keep(meanLoss);
                             lossValues.push(meanLoss);
@@ -981,8 +973,3 @@ if (i === 0) {
                     };
-                    _this.optimizer.updateVariables(totalLossFunction, _this.collectedTrainableWeights);
-                    var totalLossValue = lossValues[0];
-                    for (var i = 1; i < lossValues.length; ++i) {
-                        totalLossValue =
-                            K.scalarPlusArray(totalLossValue, lossValues[i]);
-                    }
+                    var totalLossValue = _this.optimizer.updateVariables(totalLossFunction, _this.collectedTrainableWeights);
                     return [totalLossValue].concat(metricsValues);
@@ -989,0 +976,0 @@ };

dist/exports.d.ts

@@ -9,3 +9,3 @@ import { MaxNorm, MaxNormConfig, MinMaxNorm, MinMaxNormConfig, NonNeg, UnitNorm, UnitNormConfig } from './constraints';
 import { EmbeddingLayerConfig } from './layers/embeddings';
-import { ConcatenateLayerConfig, MergeLayerConfig } from './layers/merge';
+import { ConcatenateLayerConfig } from './layers/merge';
 import { BatchNormalizationLayerConfig } from './layers/normalization';
@@ -26,2 +26,4 @@ import { GlobalPooling2DLayerConfig, Pooling1DLayerConfig, Pooling2DLayerConfig } from './layers/pooling';
 export declare class LayerExports {
+    static Layer: typeof Layer;
+    static RNNCell: typeof RNNCell;
     static conv1d(config: ConvLayerConfig): Layer;
@@ -36,8 +38,8 @@ static conv2d(config: ConvLayerConfig): Layer;
     static embedding(config: EmbeddingLayerConfig): Layer;
-    static add(config: MergeLayerConfig): Layer;
-    static average(config: MergeLayerConfig): Layer;
+    static add(config: LayerConfig): Layer;
+    static average(config: LayerConfig): Layer;
     static concatenate(config: ConcatenateLayerConfig): Layer;
-    static maximum(config: MergeLayerConfig): Layer;
-    static minimum(config: MergeLayerConfig): Layer;
-    static multiply(config: MergeLayerConfig): Layer;
+    static maximum(config: LayerConfig): Layer;
+    static minimum(config: LayerConfig): Layer;
+    static multiply(config: LayerConfig): Layer;
     static batchNormalization(config: BatchNormalizationLayerConfig): Layer;
@@ -44,0 +46,0 @@ static avgPooling1d(config: Pooling1DLayerConfig): Layer;

dist/exports.js

@@ -190,2 +190,4 @@ "use strict";
     };
+    LayerExports.Layer = topology_1.Layer;
+    LayerExports.RNNCell = recurrent_1.RNNCell;
     __decorate([
@@ -192,0 +194,0 @@ tfjs_core_1.doc({

dist/index.d.ts

@@ -5,5 +5,4 @@ import * as dl from '@tensorflow/tfjs-core';
 export { Callback, CallbackList, CustomCallback, CustomCallbackConfig, Logs } from './callbacks';
-export { Layer } from './engine/topology';
 export { Model, ModelCompileConfig, ModelEvaluateConfig, ModelFitConfig, ModelPredictConfig } from './engine/training';
-export { GRUCellLayerConfig, GRULayerConfig, LSTMCellLayerConfig, LSTMLayerConfig, RNN, RNNCell, RNNLayerConfig, SimpleRNNCellLayerConfig, SimpleRNNLayerConfig } from './layers/recurrent';
+export { GRUCellLayerConfig, GRULayerConfig, LSTMCellLayerConfig, LSTMLayerConfig, RNN, RNNLayerConfig, SimpleRNNCellLayerConfig, SimpleRNNLayerConfig } from './layers/recurrent';
 export { ModelAndWeightsConfig, Sequential, SequentialConfig } from './models';
@@ -10,0 +9,0 @@ export { SymbolicTensor } from './types';

dist/index.js

@@ -12,4 +12,2 @@ "use strict";
 exports.CustomCallback = callbacks_1.CustomCallback;
-var topology_1 = require("./engine/topology");
-exports.Layer = topology_1.Layer;
 var training_1 = require("./engine/training");
@@ -19,3 +17,2 @@ exports.Model = training_1.Model;
 exports.RNN = recurrent_1.RNN;
-exports.RNNCell = recurrent_1.RNNCell;
 var models_1 = require("./models");
@@ -22,0 +19,0 @@ exports.Sequential = models_1.Sequential;

dist/initializers.d.ts

@@ -5,11 +5,8 @@ import { Tensor } from '@tensorflow/tfjs-core';
 import { Constructor } from './utils/generic_utils';
-export declare enum FanMode {
-    FAN_IN = 0,
-    FAN_OUT = 1,
-    FAN_AVG = 2,
-}
-export declare enum Distribution {
-    NORMAL = 0,
-    UNIFORM = 1,
-}
+export declare type FanMode = 'fanIn' | 'fanOut' | 'fanAvg';
+export declare const VALID_FAN_MODE_VALUES: string[];
+export declare function checkFanMode(value?: string): void;
+export declare type Distribution = 'normal' | 'uniform';
+export declare const VALID_DISTRIBUTION_VALUES: string[];
+export declare function checkDistribution(value?: string): void;
 export declare abstract class Initializer {
@@ -16,0 +13,0 @@ static fromConfig<T>(cls: Constructor<T>, config: ConfigDict): T;

dist/initializers.js

@@ -26,19 +26,24 @@ "use strict";
 var math_utils_1 = require("./utils/math_utils");
-var FanMode;
-(function (FanMode) {
-    FanMode[FanMode["FAN_IN"] = 0] = "FAN_IN";
-    FanMode[FanMode["FAN_OUT"] = 1] = "FAN_OUT";
-    FanMode[FanMode["FAN_AVG"] = 2] = "FAN_AVG";
-})(FanMode = exports.FanMode || (exports.FanMode = {}));
-generic_utils_1.SerializableEnumRegistry.register('mode', {
-    'fan_in': FanMode.FAN_IN,
-    'fan_out': FanMode.FAN_OUT,
-    'fan_avg': FanMode.FAN_AVG
-});
-var Distribution;
-(function (Distribution) {
-    Distribution[Distribution["NORMAL"] = 0] = "NORMAL";
-    Distribution[Distribution["UNIFORM"] = 1] = "UNIFORM";
-})(Distribution = exports.Distribution || (exports.Distribution = {}));
-generic_utils_1.SerializableEnumRegistry.register('distribution', { 'normal': Distribution.NORMAL, 'uniform': Distribution.UNIFORM });
+generic_utils_1.SerializableEnumRegistry.register('mode', { 'fan_in': 'fanIn', 'fan_out': 'fanOut', 'fan_avg': 'fanAvg' });
+exports.VALID_FAN_MODE_VALUES = ['fanIn', 'fanOut', 'fanAvg', undefined, null];
+function checkFanMode(value) {
+    if (value == null) {
+        return;
+    }
+    if (exports.VALID_FAN_MODE_VALUES.indexOf(value) < 0) {
+        throw new errors_1.ValueError(value + " is not a valid FanMode.  Valid values as " + exports.VALID_FAN_MODE_VALUES);
+    }
+}
+exports.checkFanMode = checkFanMode;
+generic_utils_1.SerializableEnumRegistry.register('distribution', { 'normal': 'normal', 'uniform': 'uniform' });
+exports.VALID_DISTRIBUTION_VALUES = ['normal', 'uniform', undefined, null];
+function checkDistribution(value) {
+    if (value == null) {
+        return;
+    }
+    if (exports.VALID_DISTRIBUTION_VALUES.indexOf(value) < 0) {
+        throw new errors_1.ValueError(value + " is not a valid Distribution.  Valid values as " + exports.VALID_DISTRIBUTION_VALUES);
+    }
+}
+exports.checkDistribution = checkDistribution;
 var Initializer = (function () {
@@ -192,5 +197,6 @@ function Initializer() {
 function computeFans(shape, dataFormat) {
-    if (dataFormat === void 0) { dataFormat = common_1.DataFormat.CHANNEL_LAST; }
+    if (dataFormat === void 0) { dataFormat = 'channelLast'; }
     var fanIn;
     var fanOut;
+    common_1.checkDataFormat(dataFormat);
     if (shape.length === 2) {
@@ -201,3 +207,3 @@ fanIn = shape[0];
     else if (_.contains([3, 4, 5], shape.length)) {
-        if (dataFormat === common_1.DataFormat.CHANNEL_FIRST) {
+        if (dataFormat === 'channelFirst') {
             var receptiveFieldSize = math_utils_1.arrayProd(shape, 2);
@@ -207,3 +213,3 @@ fanIn = shape[1] * receptiveFieldSize;
         }
-        else if (dataFormat === common_1.DataFormat.CHANNEL_LAST) {
+        else if (dataFormat === 'channelLast') {
             var receptiveFieldSize = math_utils_1.arrayProd(shape, 0, shape.length - 2);
@@ -213,5 +219,2 @@ fanIn = shape[shape.length - 2] * receptiveFieldSize;
         }
-        else {
-            throw new errors_1.ValueError("Invalid dataFormat: " + dataFormat);
-        }
     }
@@ -234,3 +237,5 @@ else {
         _this.mode = config.mode;
+        checkFanMode(_this.mode);
         _this.distribution = config.distribution;
+        checkDistribution(_this.distribution);
         _this.seed = config.seed;
@@ -244,6 +249,6 @@ return _this;
         var scale = this.scale;
-        if (this.mode === FanMode.FAN_IN) {
+        if (this.mode === 'fanIn') {
             scale /= Math.max(1, fanIn);
         }
-        else if (this.mode === FanMode.FAN_OUT) {
+        else if (this.mode === 'fanOut') {
             scale /= Math.max(1, fanOut);
@@ -254,3 +259,3 @@ }
         }
-        if (this.distribution === Distribution.NORMAL) {
+        if (this.distribution === 'normal') {
             var stddev = Math.sqrt(scale);
@@ -281,4 +286,4 @@ return K.truncatedNormal(shape, 0, stddev, dtype, this.seed);
             scale: 1.0,
-            mode: FanMode.FAN_AVG,
-            distribution: Distribution.UNIFORM,
+            mode: 'fanAvg',
+            distribution: 'uniform',
             seed: config.seed
@@ -296,4 +301,4 @@ }) || this;
             scale: 1.0,
-            mode: FanMode.FAN_AVG,
-            distribution: Distribution.NORMAL,
+            mode: 'fanAvg',
+            distribution: 'normal',
             seed: config.seed
@@ -309,8 +314,3 @@ }) || this;
     function HeNormal(config) {
-        return _super.call(this, {
-            scale: 2.0,
-            mode: FanMode.FAN_IN,
-            distribution: Distribution.NORMAL,
-            seed: config.seed
-        }) || this;
+        return _super.call(this, { scale: 2.0, mode: 'fanIn', distribution: 'normal', seed: config.seed }) || this;
     }
@@ -324,8 +324,3 @@ return HeNormal;
     function LeCunNormal(config) {
-        return _super.call(this, {
-            scale: 1.0,
-            mode: FanMode.FAN_IN,
-            distribution: Distribution.NORMAL,
-            seed: config.seed
-        }) || this;
+        return _super.call(this, { scale: 1.0, mode: 'fanIn', distribution: 'normal', seed: config.seed }) || this;
     }
@@ -332,0 +327,0 @@ return LeCunNormal;

dist/layers/convolutional_depthwise.js

@@ -14,3 +14,2 @@ "use strict";
 var K = require("../backend/deeplearnjs_backend");
-var common_1 = require("../common");
 var constraints_1 = require("../constraints");
@@ -42,3 +41,3 @@ var errors_1 = require("../errors");
         }
-        var channelAxis = this.dataFormat === common_1.DataFormat.CHANNEL_FIRST ? 1 : 3;
+        var channelAxis = this.dataFormat === 'channelFirst' ? 1 : 3;
         if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {
@@ -74,7 +73,5 @@ throw new errors_1.ValueError('The channel dimension of the inputs to DepthwiseConv2D should ' +
         inputShape = generic_utils_1.getExactlyOneShape(inputShape);
-        var rows = this.dataFormat === common_1.DataFormat.CHANNEL_FIRST ? inputShape[2] :
-            inputShape[1];
-        var cols = this.dataFormat === common_1.DataFormat.CHANNEL_FIRST ? inputShape[3] :
-            inputShape[2];
-        var outFilters = this.dataFormat === common_1.DataFormat.CHANNEL_FIRST ?
+        var rows = this.dataFormat === 'channelFirst' ? inputShape[2] : inputShape[1];
+        var cols = this.dataFormat === 'channelFirst' ? inputShape[3] : inputShape[2];
+        var outFilters = this.dataFormat === 'channelFirst' ?
             inputShape[1] * this.depthMultiplier :
@@ -84,3 +81,3 @@ inputShape[3] * this.depthMultiplier;
         var outCols = conv_utils_1.convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]);
-        if (this.dataFormat === common_1.DataFormat.CHANNEL_FIRST) {
+        if (this.dataFormat === 'channelFirst') {
             return [inputShape[0], outFilters, outRows, outCols];
@@ -87,0 +84,0 @@ }

dist/layers/convolutional.js

@@ -40,5 +40,7 @@ "use strict";
         _this.strides = conv_utils_1.normalizeArray(config.strides == null ? 1 : config.strides, rank, 'strides');
-        _this.padding = config.padding == null ? common_1.PaddingMode.VALID : config.padding;
+        _this.padding = config.padding == null ? 'valid' : config.padding;
+        common_1.checkPaddingMode(_this.padding);
         _this.dataFormat =
-            config.dataFormat == null ? common_1.DataFormat.CHANNEL_LAST : config.dataFormat;
+            config.dataFormat == null ? 'channelLast' : config.dataFormat;
+        common_1.checkDataFormat(_this.dataFormat);
         _this.dilationRate = config.dilationRate == null ? 1 : config.dilationRate;
@@ -65,5 +67,3 @@ if (!(_this.dilationRate === 1 ||
         inputShape = generic_utils.getExactlyOneShape(inputShape);
-        var channelAxis = this.dataFormat === common_1.DataFormat.CHANNEL_FIRST ?
-            1 :
-            inputShape.length - 1;
+        var channelAxis = this.dataFormat === 'channelFirst' ? 1 : inputShape.length - 1;
         if (inputShape[channelAxis] == null) {
@@ -104,3 +104,3 @@ throw new errors_1.ValueError("The channel dimension of the input should be defined. " +
         var newSpace = [];
-        var space = (this.dataFormat === common_1.DataFormat.CHANNEL_LAST) ?
+        var space = (this.dataFormat === 'channelLast') ?
             inputShape.slice(1, inputShape.length - 1) :
@@ -114,3 +114,3 @@ inputShape.slice(2);
         var outputShape = [inputShape[0]];
-        if (this.dataFormat === common_1.DataFormat.CHANNEL_LAST) {
+        if (this.dataFormat === 'channelLast') {
             outputShape = outputShape.concat(newSpace);
@@ -117,0 +117,0 @@ outputShape.push(this.filters);

dist/layers/core.d.ts

@@ -63,3 +63,3 @@ import { Tensor } from '@tensorflow/tfjs-core';
 export interface ActivationLayerConfig extends LayerConfig {
-    activation: string;
+    activation: ActivationIdentifier;
 }
@@ -66,0 +66,0 @@ export declare class Activation extends Layer {

dist/layers/merge.d.ts

 import { Tensor } from '@tensorflow/tfjs-core';
 import { Layer, LayerConfig } from '../engine/topology';
 import { Shape } from '../types';
-export interface MergeLayerConfig extends LayerConfig {
-}
 export declare class Merge extends Layer {
     protected reshapeRequired: boolean;
-    constructor(config: MergeLayerConfig);
+    constructor(config?: LayerConfig);
     protected mergeFunction(inputs: Tensor[]): Tensor;
@@ -16,17 +14,22 @@ private computeElementwiseOpOutputShape(shape1, shape2);
 export declare class Add extends Merge {
+    constructor(config?: LayerConfig);
     protected mergeFunction(inputs: Tensor[]): Tensor;
 }
 export declare class Multiply extends Merge {
+    constructor(config?: LayerConfig);
     protected mergeFunction(inputs: Tensor[]): Tensor;
 }
 export declare class Average extends Merge {
+    constructor(config?: LayerConfig);
     protected mergeFunction(inputs: Tensor[]): Tensor;
 }
 export declare class Maximum extends Merge {
+    constructor(config?: LayerConfig);
     protected mergeFunction(inputs: Tensor[]): Tensor;
 }
 export declare class Minimum extends Merge {
+    constructor(config?: LayerConfig);
     protected mergeFunction(inputs: Tensor[]): Tensor;
 }
-export interface ConcatenateLayerConfig extends MergeLayerConfig {
+export interface ConcatenateLayerConfig extends LayerConfig {
     axis?: number;
@@ -33,0 +36,0 @@ }

dist/layers/merge.js

@@ -22,3 +22,3 @@ "use strict";
     function Merge(config) {
-        var _this = _super.call(this, config) || this;
+        var _this = _super.call(this, config || {}) || this;
         _this.supportsMasking = true;
@@ -194,4 +194,4 @@ return _this;
     __extends(Add, _super);
-    function Add() {
-        return _super !== null && _super.apply(this, arguments) || this;
+    function Add(config) {
+        return _super.call(this, config) || this;
     }
@@ -212,4 +212,4 @@ Add.prototype.mergeFunction = function (inputs) {
     __extends(Multiply, _super);
-    function Multiply() {
-        return _super !== null && _super.apply(this, arguments) || this;
+    function Multiply(config) {
+        return _super.call(this, config) || this;
     }
@@ -230,4 +230,4 @@ Multiply.prototype.mergeFunction = function (inputs) {
     __extends(Average, _super);
-    function Average() {
-        return _super !== null && _super.apply(this, arguments) || this;
+    function Average(config) {
+        return _super.call(this, config) || this;
     }
@@ -248,4 +248,4 @@ Average.prototype.mergeFunction = function (inputs) {
     __extends(Maximum, _super);
-    function Maximum() {
-        return _super !== null && _super.apply(this, arguments) || this;
+    function Maximum(config) {
+        return _super.call(this, config) || this;
     }
@@ -266,4 +266,4 @@ Maximum.prototype.mergeFunction = function (inputs) {
     __extends(Minimum, _super);
-    function Minimum() {
-        return _super !== null && _super.apply(this, arguments) || this;
+    function Minimum(config) {
+        return _super.call(this, config) || this;
     }
@@ -270,0 +270,0 @@ Minimum.prototype.mergeFunction = function (inputs) {

dist/layers/pooling.js

@@ -30,3 +30,4 @@ "use strict";
         _this.strides = config.strides == null ? _this.poolSize : [config.strides];
-        _this.padding = config.padding == null ? common_1.PaddingMode.VALID : config.padding;
+        _this.padding = config.padding == null ? 'valid' : config.padding;
+        common_1.checkPaddingMode(_this.padding);
         _this.inputSpec = [new topology_1.InputSpec({ ndim: 3 })];
@@ -43,3 +44,3 @@ return _this;
         inputs = K.expandDims(generic_utils.getExactlyOneTensor(inputs), 2);
-        var output = this.poolingFunction(generic_utils.getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, common_1.DataFormat.CHANNEL_LAST);
+        var output = this.poolingFunction(generic_utils.getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, 'channelLast');
         return K.squeeze(output, 2);
@@ -66,3 +67,5 @@ };
     MaxPooling1D.prototype.poolingFunction = function (inputs, poolSize, strides, padding, dataFormat) {
-        return K.pool2d(inputs, poolSize, strides, padding, dataFormat, common_1.PoolMode.MAX);
+        common_1.checkDataFormat(dataFormat);
+        common_1.checkPaddingMode(padding);
+        return K.pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');
     };
@@ -79,3 +82,5 @@ return MaxPooling1D;
     AvgPooling1D.prototype.poolingFunction = function (inputs, poolSize, strides, padding, dataFormat) {
-        return K.pool2d(inputs, poolSize, strides, padding, dataFormat, common_1.PoolMode.AVG);
+        common_1.checkDataFormat(dataFormat);
+        common_1.checkPaddingMode(padding);
+        return K.pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');
     };
@@ -98,5 +103,7 @@ return AvgPooling1D;
         _this.strides = config.strides == null ? _this.poolSize : config.strides;
-        _this.padding = config.padding == null ? common_1.PaddingMode.VALID : config.padding;
+        _this.padding = config.padding == null ? 'valid' : config.padding;
         _this.dataFormat =
-            config.dataFormat == null ? common_1.DataFormat.CHANNEL_LAST : config.dataFormat;
+            config.dataFormat == null ? 'channelLast' : config.dataFormat;
+        common_1.checkDataFormat(_this.dataFormat);
+        common_1.checkPaddingMode(_this.padding);
         _this.inputSpec = [new topology_1.InputSpec({ ndim: 4 })];
@@ -107,6 +114,4 @@ return _this;
         inputShape = generic_utils.getExactlyOneShape(inputShape);
-        var rows = this.dataFormat === common_1.DataFormat.CHANNEL_FIRST ? inputShape[2] :
-            inputShape[1];
-        var cols = this.dataFormat === common_1.DataFormat.CHANNEL_FIRST ? inputShape[3] :
-            inputShape[2];
+        var rows = this.dataFormat === 'channelFirst' ? inputShape[2] : inputShape[1];
+        var cols = this.dataFormat === 'channelFirst' ? inputShape[3] : inputShape[2];
         rows =
@@ -116,3 +121,3 @@ conv_utils_1.convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]);
             conv_utils_1.convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]);
-        if (this.dataFormat === common_1.DataFormat.CHANNEL_FIRST) {
+        if (this.dataFormat === 'channelFirst') {
             return [inputShape[0], inputShape[1], rows, cols];
@@ -148,3 +153,5 @@ }
     MaxPooling2D.prototype.poolingFunction = function (inputs, poolSize, strides, padding, dataFormat) {
-        return K.pool2d(inputs, poolSize, strides, padding, dataFormat, common_1.PoolMode.MAX);
+        common_1.checkDataFormat(dataFormat);
+        common_1.checkPaddingMode(padding);
+        return K.pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');
     };
@@ -161,3 +168,5 @@ return MaxPooling2D;
     AvgPooling2D.prototype.poolingFunction = function (inputs, poolSize, strides, padding, dataFormat) {
-        return K.pool2d(inputs, poolSize, strides, padding, dataFormat, common_1.PoolMode.AVG);
+        common_1.checkDataFormat(dataFormat);
+        common_1.checkPaddingMode(padding);
+        return K.pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');
     };
@@ -215,3 +224,4 @@ return AvgPooling2D;
         _this.dataFormat =
-            config.dataFormat == null ? common_1.DataFormat.CHANNEL_LAST : config.dataFormat;
+            config.dataFormat == null ? 'channelLast' : config.dataFormat;
+        common_1.checkDataFormat(_this.dataFormat);
         _this.inputSpec = [new topology_1.InputSpec({ ndim: 4 })];
@@ -222,3 +232,3 @@ return _this;
         inputShape = inputShape;
-        if (this.dataFormat === common_1.DataFormat.CHANNEL_LAST) {
+        if (this.dataFormat === 'channelLast') {
             return [inputShape[0], inputShape[3]];
@@ -249,3 +259,3 @@ }
         var input = generic_utils.getExactlyOneTensor(inputs);
-        if (this.dataFormat === common_1.DataFormat.CHANNEL_LAST) {
+        if (this.dataFormat === 'channelLast') {
             return K.mean(input, [1, 2]);
@@ -268,3 +278,3 @@ }
         var input = generic_utils.getExactlyOneTensor(inputs);
-        if (this.dataFormat === common_1.DataFormat.CHANNEL_LAST) {
+        if (this.dataFormat === 'channelLast') {
             return K.max(input, [1, 2]);
@@ -271,0 +281,0 @@ }

dist/layers/recurrent.d.ts

@@ -193,3 +193,3 @@ import { Tensor } from '@tensorflow/tfjs-core';
     readonly recurrentDropout: number;
-    readonly implementatin: number;
+    readonly implementation: number;
     getConfig(): ConfigDict;
@@ -259,3 +259,3 @@ static fromConfig<T>(cls: generic_utils.Constructor<T>, config: ConfigDict): T;
     readonly recurrentDropout: number;
-    readonly implementatin: number;
+    readonly implementation: number;
     getConfig(): ConfigDict;
@@ -262,0 +262,0 @@ static fromConfig<T>(cls: generic_utils.Constructor<T>, config: ConfigDict): T;

dist/layers/recurrent.js

@@ -867,3 +867,3 @@ "use strict";
     });
-    Object.defineProperty(GRU.prototype, "implementatin", {
+    Object.defineProperty(GRU.prototype, "implementation", {
         get: function () {
@@ -892,3 +892,3 @@ return this.cell.implementation;
             recurrentDropout: this.recurrentDropout,
-            implementation: this.implementatin,
+            implementation: this.implementation,
         };
@@ -1201,3 +1201,3 @@ var baseConfig = _super.prototype.getConfig.call(this);
     });
-    Object.defineProperty(LSTM.prototype, "implementatin", {
+    Object.defineProperty(LSTM.prototype, "implementation", {
         get: function () {
@@ -1227,3 +1227,3 @@ return this.cell.implementation;
             recurrentDropout: this.recurrentDropout,
-            implementation: this.implementatin,
+            implementation: this.implementation,
         };
@@ -1230,0 +1230,0 @@ var baseConfig = _super.prototype.getConfig.call(this);

dist/models.d.ts

@@ -29,4 +29,4 @@ import { Scalar, Tensor, WeightsManifestConfig } from '@tensorflow/tfjs-core';
     updatable: boolean;
-    evaluate(x: Tensor | Tensor[], y: Tensor | Tensor[], config?: ModelEvaluateConfig): Promise<Scalar | Scalar[]>;
-    predict(x: Tensor | Tensor[], config?: ModelPredictConfig): Promise<Tensor | Tensor[]>;
+    evaluate(x: Tensor | Tensor[], y: Tensor | Tensor[], config?: ModelEvaluateConfig): Scalar | Scalar[];
+    predict(x: Tensor | Tensor[], config?: ModelPredictConfig): Tensor | Tensor[];
     predictOnBatch(x: Tensor): Tensor | Tensor[];
@@ -33,0 +33,0 @@ compile(config: ModelCompileConfig): void;

dist/models.js

@@ -259,21 +259,13 @@ "use strict";
         if (config === void 0) { config = {}; }
-        return __awaiter(this, void 0, void 0, function () {
-            return __generator(this, function (_a) {
-                if (!this.built) {
-                    throw new errors_1.RuntimeError('The model needs to be compiled before being used.');
-                }
-                return [2, this.model.evaluate(x, y, config)];
-            });
-        });
+        if (!this.built) {
+            throw new errors_1.RuntimeError('The model needs to be compiled before being used.');
+        }
+        return this.model.evaluate(x, y, config);
     };
     Sequential.prototype.predict = function (x, config) {
         if (config === void 0) { config = {}; }
-        return __awaiter(this, void 0, void 0, function () {
-            return __generator(this, function (_a) {
-                if (this.model == null) {
-                    this.build();
-                }
-                return [2, this.model.predict(x, config)];
-            });
-        });
+        if (this.model == null) {
+            this.build();
+        }
+        return this.model.predict(x, config);
     };
@@ -280,0 +272,0 @@ Sequential.prototype.predictOnBatch = function (x) {

dist/optimizers.d.ts

@@ -17,3 +17,3 @@ import { AdagradOptimizer, AdamOptimizer, Optimizer as CoreOptimizer, RMSPropOptimizer, Scalar, SGDOptimizer } from '@tensorflow/tfjs-core';
     getConfig(): ConfigDict;
-    updateVariables(lossFn: () => Scalar, params: LayerVariable[]): void;
+    updateVariables(lossFn: () => Scalar, params: LayerVariable[]): Scalar;
     static fromConfig<T>(cls: Constructor<T>, config: ConfigDict): T;
@@ -20,0 +20,0 @@ }

dist/optimizers.js

@@ -46,3 +46,3 @@ "use strict";
         var variables = params.map(function (param) { return param.read(); });
-        this.optimizer.minimize(lossFn, false, variables);
+        return this.optimizer.minimize(lossFn, true, variables);
     };
@@ -49,0 +49,0 @@ LayersOptimizer.fromConfig = function (cls, config) {

dist/utils/conv_utils.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
-var common_1 = require("../common");
 var errors_1 = require("../errors");
@@ -35,3 +34,3 @@ var generic_utils_1 = require("./generic_utils");
     var outputLength;
-    if (padding === common_1.PaddingMode.SAME) {
+    if (padding === 'same') {
         outputLength = inputLength;
@@ -38,0 +37,0 @@ }

dist/version.d.ts

@@ -1,2 +0,2 @@
-declare const version = "0.0.4";
+declare const version = "0.0.5";
 export { version };

dist/version.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
-var version = '0.0.4';
+var version = '0.0.5';
 exports.version = version;

package.json

 {
   "name": "@tensorflow/tfjs-layers",
-  "version": "0.0.4",
+  "version": "0.0.5",
   "description": "TensorFlow layers API in JavaScript",
@@ -5,0 +5,0 @@ "private": false,

README.md

@@ -56,3 +56,3 @@ # TensorFlow.js Layers: High-Level Machine Learning Model API
 // Ater the training, perform inference.
-const output = await model.predict(tf.tensor2d([[5]], [1, 1]));
+const output = model.predict(tf.tensor2d([[5]], [1, 1]));
 output.print();
@@ -59,0 +59,0 @@ ```

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