Problema :

• un ValueError dice que conv2d_Conv2D1_input espera tener 4 dimensiones, pero obtuvo una matriz con forma [475,475,3]

Sin embargo:

• La forma de entrada se establece en [475,475,3]
• cuando se registran, los tensores tienen la forma [475,475,3]

Error: ValueError: Error when checking : expected conv2d_Conv2D1_input to have 4 dimension(s), but got array with shape [475,475,3]

Tensor:

 Tensor { kept: false, isDisposedInternal: false, shape: [ 475, 475, 3 ], dtype: 'int32', size: 676875, strides: [ 1425, 3 ], dataId: {}, id: 8, rankType: '3', scopeId: 4 }

Código completo:

 var tf = require('@tensorflow/tfjs'); var tfnode = require('@tensorflow/tfjs-node'); var fs = require(`fs`) const main = async () => { const loadImage = async (file) => { const imageBuffer = await fs.readFileSync(file) const tensorFeature = await tfnode.node.decodeImage(imageBuffer, 3) return tensorFeature; } const tensorFeature = await loadImage(`./1.png`) const tensorFeature2 = await loadImage(`./4.png`) const tensorFeature3 = await loadImage(`./7.png`) console.log(tensorFeature) console.log(tensorFeature2) console.log(tensorFeature3) tensorFeatures = [tensorFeature, tensorFeature2, tensorFeature3] labelArray = [0, 1, 2] tensorLabels = tf.oneHot(tf.tensor1d(labelArray, 'int32'), 3); const model = tf.sequential(); model.add(tf.layers.conv2d({ inputShape: [475, 475, 3], filters: 32, kernelSize: 3, activation: 'relu', })); model.add(tf.layers.flatten()); model.add(tf.layers.dense({units: 3, activation: 'softmax'})); model.compile({ optimizer: 'sgd', loss: 'categoricalCrossentropy', metrics: ['accuracy'] }); model.summary() model.fit(tf.stack(tensorFeatures), tensorLabels) const im = await loadImage(`./2.png`) model.predict(im) } main()