@tensorflow/tfjs-core - npm Package Compare versions

Comparing version 0.0.2 to 0.6.0-alpha7

dist/index.d.ts

 import { BrowserUtil } from './browser_util';
-import * as contrib from './contrib';
 import * as xhr_dataset from './data/xhr-dataset';
@@ -34,3 +33,3 @@ import * as environment from './environment';
 export { version as version_core };
-export { contrib, conv_util, environment, gpgpu_util, test_util, util, webgl_util, xhr_dataset };
+export { conv_util, environment, gpgpu_util, test_util, util, webgl_util, xhr_dataset };
 export * from './ops/ops';
@@ -37,0 +36,0 @@ export * from './train';

dist/index.js

@@ -7,4 +7,2 @@ "use strict";
 var browser_util_1 = require("./browser_util");
-var contrib = require("./contrib");
-exports.contrib = contrib;
 var xhr_dataset = require("./data/xhr-dataset");
@@ -11,0 +9,0 @@ exports.xhr_dataset = xhr_dataset;

dist/kernels/backend_cpu.d.ts

 import { Conv2DInfo } from '../ops/conv_util';
 import { DataId, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D } from '../tensor';
+import * as types from '../types';
 import { DataType, TypedArray } from '../types';
@@ -92,2 +93,4 @@ import { BackendTimingInfo, KernelBackend } from './backend';
     avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    cast<T extends Tensor<types.Rank>>(x: T, dtype: DataType): T;
+    reshape<T extends Tensor<types.Rank>, R extends types.Rank>(x: T, shape: types.ShapeMap[R]): Tensor<R>;
     minPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
@@ -94,0 +97,0 @@ avgPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D;

dist/kernels/backend_cpu.js

@@ -49,2 +49,3 @@ "use strict";
 var util = require("../util");
+var backend_util = require("./backend_util");
 var MathBackendCPU = (function () {
@@ -1191,2 +1192,8 @@ function MathBackendCPU() {
     };
+    MathBackendCPU.prototype.cast = function (x, dtype) {
+        return backend_util.castTensor(x, dtype, this);
+    };
+    MathBackendCPU.prototype.reshape = function (x, shape) {
+        return backend_util.reshapeTensor(x, shape);
+    };
     MathBackendCPU.prototype.minPool = function (x, convInfo) {
@@ -1193,0 +1200,0 @@ return this.pool(x, convInfo, 'min');

dist/kernels/backend_webgl.d.ts

 import { TimingInfo } from '../engine';
 import { Conv2DInfo } from '../ops/conv_util';
 import { DataId, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D } from '../tensor';
+import * as types from '../types';
 import { DataType, TypedArray } from '../types';
@@ -121,2 +122,4 @@ import { KernelBackend } from './backend';
     avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    cast<T extends Tensor<types.Rank>>(x: T, dtype: DataType): T;
+    reshape<T extends Tensor<types.Rank>, R extends types.Rank>(x: T, shape: types.ShapeMap[R]): Tensor<R>;
     resizeBilinear(x: Tensor4D, newHeight: number, newWidth: number, alignCorners: boolean): Tensor4D;
@@ -123,0 +126,0 @@ multinomial(probs: Tensor2D, numSamples: number, seed: number): Tensor2D;

dist/kernels/backend_webgl.js

@@ -44,2 +44,3 @@ "use strict";
 var util = require("../util");
+var backend_util = require("./backend_util");
 var argminmax_gpu_1 = require("./webgl/argminmax_gpu");
@@ -681,2 +682,8 @@ var avg_pool_backprop_gpu_1 = require("./webgl/avg_pool_backprop_gpu");
     };
+    MathBackendWebGL.prototype.cast = function (x, dtype) {
+        return backend_util.castTensor(x, dtype, this);
+    };
+    MathBackendWebGL.prototype.reshape = function (x, shape) {
+        return backend_util.reshapeTensor(x, shape);
+    };
     MathBackendWebGL.prototype.resizeBilinear = function (x, newHeight, newWidth, alignCorners) {
@@ -683,0 +690,0 @@ var program = new resize_bilinear_gpu_1.ResizeBilinearProgram(x.shape, newHeight, newWidth, alignCorners);

dist/kernels/backend.d.ts

 import { Conv2DInfo } from '../ops/conv_util';
 import { DataId, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D } from '../tensor';
-import { DataType, TypedArray } from '../types';
+import { DataType, Rank, ShapeMap, TypedArray } from '../types';
 export interface BackendTimingInfo {
@@ -89,2 +89,4 @@ kernelMs: number;
     avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    reshape<T extends Tensor, R extends Rank>(x: T, shape: ShapeMap[R]): Tensor<R>;
+    cast<T extends Tensor>(x: T, dtype: DataType): T;
     tile<T extends Tensor>(x: T, reps: number[]): T;
@@ -91,0 +93,0 @@ pad<T extends Tensor>(x: T, paddings: Array<[number, number]>, constantValue: number): T;

dist/ops/array_ops.js

@@ -203,3 +203,3 @@ "use strict";
         };
-        return environment_1.ENV.engine.runKernel(function (backend) { return tensor_1.Tensor.make(shape, { dataId: x.dataId }, x.dtype); }, { x: x }, grad);
+        return environment_1.ENV.engine.runKernel(function (backend) { return backend.reshape(x, shape); }, { x: x }, grad);
     };
@@ -210,20 +210,6 @@ ArrayOps.squeeze = function (x, axis) {
     ArrayOps.cast = function (x, dtype) {
-        var forw = function (backend) {
-            if (!util.hasEncodingLoss(x.dtype, dtype)) {
-                return tensor_1.Tensor.make(x.shape, { dataId: x.dataId }, dtype);
-            }
-            if (dtype === 'int32') {
-                return backend.int(x);
-            }
-            else if (dtype === 'bool') {
-                return backend.notEqual(x, ArrayOps.scalar(0, x.dtype));
-            }
-            else {
-                throw new Error("Error in Cast: unknown dtype argument (" + dtype + ")");
-            }
-        };
         var grad = function (dy) {
             return { x: function () { return dy.clone(); } };
         };
-        return environment_1.ENV.engine.runKernel(forw, { x: x }, grad);
+        return environment_1.ENV.engine.runKernel(function (backend) { return backend.cast(x, dtype); }, { x: x }, grad);
     };
@@ -230,0 +216,0 @@ ArrayOps.tile = function (x, reps) {

dist/version.d.ts

@@ -1,2 +0,2 @@
-declare const version = "0.0.2";
+declare const version = "0.6.0-alpha7";
 export { version };

dist/version.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
-var version = '0.0.2';
+var version = '0.6.0-alpha7';
 exports.version = version;

package.json

 {
   "name": "@tensorflow/tfjs-core",
-  "version": "0.0.2",
+  "version": "0.6.0-alpha7",
   "description": "Hardware-accelerated JavaScript library for machine intelligence",
@@ -5,0 +5,0 @@ "private": false,

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1596966

decreased by-11.9%

Number of package files

214

decreased by-13.01%

Lines of code

26941

decreased by-12.9%

No dependency changes