本文整理匯總了TypeScript中@tensorflow/tfjs-core.scalar函數的典型用法代碼示例。如果您正苦於以下問題：TypeScript scalar函數的具體用法？TypeScript scalar怎麽用？TypeScript scalar使用的例子？那麽, 這裏精選的函數代碼示例或許可以為您提供幫助。
在下文中一共展示了scalar函數的8個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的TypeScript代碼示例。
示例1: describe
describe('arithmetic', () => {
let node: Node;
const input1 = [tfc.scalar(1)];
const input2 = [tfc.scalar(1)];
const context = new ExecutionContext({}, {});
beforeEach(() => {
node = {
name: 'test',
op: '',
category: 'arithmetic',
inputNames: ['input1', 'input2'],
inputs: [],
params: {a: createTensorAttr(0), b: createTensorAttr(1)},
children: []
};
});
describe('executeOp', () => {
['add', 'mul', 'div', 'sub', 'maximum', 'minimum', 'pow',
'squaredDifference', 'mod', 'floorDiv']
.forEach((op => {
it('should call tfc.' + op, () => {
const spy = spyOn(tfc, op as 'add');
node.op = op;
executeOp(node, {input1, input2}, context);
expect(spy).toHaveBeenCalledWith(input1[0], input2[0]);
});
}));
});
});示例2: gLoss
gLoss(generatedPred: tf.Tensor1D) {
if (this.lossType === 'LeastSq loss') {
return generatedPred.sub(tf.scalar(1)).square().mean() as tf.Scalar;
} else {
return generatedPred.log().mean().mul(tf.scalar(-1)) as tf.Scalar;
}
}示例3: it
it('should execute control flow graph', async (done) => {
inputNode = {
inputNames: [],
inputs: [],
children: [],
name: 'input',
op: 'placeholder',
category: 'graph',
params: {}
};
constNode = {
inputNames: [],
inputs: [],
children: [],
name: 'const',
op: 'const',
category: 'graph',
params: {}
};
outputNode = {
inputNames: ['input', 'const'],
inputs: [inputNode, constNode],
children: [],
name: 'output',
op: 'switch',
category: 'control',
params: {}
};
inputNode.children.push(outputNode);
constNode.children.push(outputNode);
graphWithControlFlow = {
inputs: [constNode, inputNode],
nodes:
{'input': inputNode, 'const': constNode, 'output': outputNode},
outputs: [outputNode],
withControlFlow: true,
withDynamicShape: false,
placeholders: [inputNode]
};
executor = new GraphExecutor(graphWithControlFlow);
const inputTensor = tfc.scalar(1);
const constTensor = tfc.scalar(2);
executor.weightMap = {const : [constTensor]};
const spy =
spyOn(operations, 'executeOp').and.callFake((node: Node) => {
return node.op === 'const' ? [constTensor] : [inputTensor];
});
await executor.executeAsync({input: [inputTensor]}).then(result => {
expect(spy.calls.allArgs()).toEqual([
[inputNode, jasmine.any(Object), jasmine.any(ExecutionContext)],
[outputNode, jasmine.any(Object), jasmine.any(ExecutionContext)],
[constNode, jasmine.any(Object), jasmine.any(ExecutionContext)],
]);
done();
});
});示例4: switch
export let executeOp: OpExecutor = (node: Node, tensorMap: NamedTensorsMap,
context: ExecutionContext):
tfc.Tensor[] => {
switch (node.op) {
case 'const': {
return tensorMap[node.name];
}
case 'placeholder':
const def =
getParamValue('default', node, tensorMap, context) as tfc.Tensor;
return [getTensor(node.name, tensorMap, context) || def];
case 'identity':
case 'stopGradient':
case 'fakeQuantWithMinMaxVars': // This op is currently ignored.
return [getParamValue('x', node, tensorMap, context) as tfc.Tensor];
case 'snapshot':
const snapshot =
(getParamValue('x', node, tensorMap, context) as tfc.Tensor);
return [snapshot.clone()];
case 'shape':
return [tfc.tensor1d(
(getParamValue('x', node, tensorMap, context) as tfc.Tensor).shape,
'int32')];
case 'size':
return [tfc.scalar(
(getParamValue('x', node, tensorMap, context) as tfc.Tensor).size,
'int32')];
case 'rank':
return [tfc.scalar(
(getParamValue('x', node, tensorMap, context) as tfc.Tensor).rank,
'int32')];
case 'noop':
return [];
case 'print':
const input = getParamValue('x', node, tensorMap, context) as tfc.Tensor;
const data =
getParamValue('data', node, tensorMap, context) as tfc.Tensor[];
const message =
getParamValue('message', node, tensorMap, context) as string;
const summarize =
getParamValue('summarize', node, tensorMap, context) as number;
console.warn(
'The graph has a tf.print() operation,' +
'usually used for debugging, which slows down performance.');
console.log(message);
for (let i = 0; i < data.length; i++) {
console.log(
Array.prototype.slice.call(data[0].dataSync()).slice(0, summarize));
}
return [input];
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};示例5: describe
describe('logical', () => {
let node: Node;
const input1 = [tfc.scalar(1)];
const input2 = [tfc.scalar(2)];
const context = new ExecutionContext({}, {});
beforeEach(() => {
node = {
name: 'test',
op: '',
category: 'logical',
inputNames: ['input1', 'input2'],
inputs: [],
params: {a: createTensorAttr(0), b: createTensorAttr(1)},
children: []
};
});
describe('executeOp', () => {
['equal', 'notEqual', 'greater', 'greaterEqual', 'less', 'lessEqual',
'logicalAnd', 'logicalOr']
.forEach(op => {
it('should call tfc.' + op, () => {
const spy = spyOn(tfc, op as 'equal');
node.op = op;
executeOp(node, {input1, input2}, context);
expect(spy).toHaveBeenCalledWith(input1[0], input2[0]);
});
});
describe('logicalNot', () => {
it('should call tfc.logicalNot', () => {
spyOn(tfc, 'logicalNot');
node.op = 'logicalNot';
executeOp(node, {input1}, context);
expect(tfc.logicalNot).toHaveBeenCalledWith(input1[0]);
});
});
describe('where', () => {
it('should call tfc.where', () => {
spyOn(tfc, 'where');
node.op = 'where';
node.inputNames = ['input1', 'input2', 'input3'];
node.params.condition = createTensorAttr(2);
const input3 = [tfc.scalar(1)];
executeOp(node, {input1, input2, input3}, context);
expect(tfc.where).toHaveBeenCalledWith(input3[0], input1[0], input2[0]);
});
});
});
});示例6: it
it('should call tfc.linspace', () => {
spyOn(tfc, 'linspace');
node.op = 'linspace';
node.params['start'] = createNumberAttrFromIndex(0);
node.params['stop'] = createNumberAttrFromIndex(1);
node.params['num'] = createNumberAttrFromIndex(2);
node.inputNames = ['input', 'input2', 'input3'];
const input = [tfc.scalar(0)];
const input3 = [tfc.scalar(2)];
executeOp(node, {input, input2, input3}, context);
expect(tfc.linspace).toHaveBeenCalledWith(0, 1, 2);
});示例7: dLoss
// Define losses.
dLoss(truePred: tf.Tensor1D, generatedPred: tf.Tensor1D) {
if (this.lossType === 'LeastSq loss') {
return tf.add(
truePred.sub(tf.scalar(1)).square().mean(),
generatedPred.square().mean()
) as tf.Scalar;
} else {
return tf.add(
truePred.log().mul(tf.scalar(0.95)).mean(),
tf.sub(tf.scalar(1), generatedPred).log().mean()
).mul(tf.scalar(-1)) as tf.Scalar;
}
}示例8: it
it('should write the tensor to tensorArray', async () => {
const tensorArray =
new TensorArray('', 'int32', 5, [], true, false, true);
context.addTensorArray(tensorArray);
node.op = 'tensorArrayWrite';
node.params['tensorArrayId'] = createNumberAttrFromIndex(0);
node.params['index'] = createNumberAttrFromIndex(1);
node.params['tensor'] = createTensorAttr(2);
node.inputNames = ['input2', 'input3', 'input1'];
const input2 = [scalar(tensorArray.id)];
const input3 = [scalar(0)];
await executeOp(node, {input1, input2, input3}, context);
expect(tensorArray.size()).toEqual(1);
});