本文整理汇总了TypeScript中@tensorflow/tfjs-core.tensor4d函数的典型用法代码示例。如果您正苦于以下问题：TypeScript tensor4d函数的具体用法？TypeScript tensor4d怎么用？TypeScript tensor4d使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了tensor4d函数的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的TypeScript代码示例。
示例1: extractFilterValues
function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {
const weights = extractWeights(numFilterValues)
const depth = weights.length / (numFilters * filterSize * filterSize)
if (isFloat(depth)) {
throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)
}
return tf.transpose(
tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),
[2, 3, 1, 0]
)
}示例2: it
it('is padded to square by 2 columns', () => tf.tidy(() => {
const imgTensor = tf.tensor4d(Array(24).fill(1), [1, 4, 2, 3])
const result = padToSquare(imgTensor)
expect(result.shape).toEqual([1, 4, 4, 3])
const paddedCols = tf.unstack(result, 2)
expect(paddedCols.length).toEqual(4)
expect(paddedCols[0].dataSync()).toEqual(ones(12))
expect(paddedCols[1].dataSync()).toEqual(ones(12))
expect(paddedCols[2].dataSync()).toEqual(zeros(12))
expect(paddedCols[3].dataSync()).toEqual(zeros(12))
}))示例3: extractDepthwiseConvParams
function extractDepthwiseConvParams(numChannels: number): MobileNetV1.DepthwiseConvParams {
const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])
const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))
const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))
const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))
const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))
return {
filters,
batch_norm_scale,
batch_norm_offset,
batch_norm_mean,
batch_norm_variance
}
}示例4: function
return function (
channelsIn: number,
channelsOut: number,
filterSize: number
): ConvParams {
const filters = tf.tensor4d(
extractWeights(channelsIn * channelsOut * filterSize * filterSize),
[filterSize, filterSize, channelsIn, channelsOut]
)
const bias = tf.tensor1d(extractWeights(channelsOut))
return {
filters,
bias
}
}示例5: it
it('should not throw exception if inputs shapes is dynamic', () => {
inputNode.params['shape'] = {value: [-1, 1, 1, 1], type: 'shape'};
const inputTensor = tfc.tensor4d([1, 1], [2, 1, 1, 1], 'float32');
expect(() => executor.execute({input: [inputTensor]})).not.toThrow();
});