I’m new to Computer Vision model structure, and I’m using Tensorflow for Node JS @tensorflow/tfjs-node to make some models detect some objects. With Mobilenet and Resnet SSD, the models are using the Channels Last format, so when I create a Tensor with tf.node.decodeImage the format is by default Channels Last, like shape: [1, 1200, 1200, 3] for 3 channels, and the predictions data work great, able to recognize objects.
Tensors are immutable, so all operations always return new Tensors and never modify input Tensors.
I’m sending the image as base64 string to the node express server to analysis the object detection with tensorflow. How to change the base64 image as tensors for object detection with cocossd model in node js.
I get the error – RangeError: attempting to construct out-of-bounds TypedArray on ArrayBuffer when I try to load a tf-js model into Reactjs.
I am using Keras with TensorFlow to implement a deep neural network. When I plot the loss and number of iterations, there is a significant jump in loss after each epoch. In reality, the loss of each mini-batch should vary from each other, but Keras calculates the moving average of the loss over the mini-batches, that’s why we obtain a smooth curve instead of an arbitrary one. The array of the moving average is reset after each epoch because of which we can observe a jump in the loss.
I am trying to use the KNN classifier inside a Tensorflow session.
I have a CNN model and using it to predict the class of an image:
After you train a model in Tensorflow:
I work in an environment in which computational resources are shared, i.e., we have a few server machines equipped with a few Nvidia Titan X GPUs each.
I have trained a binary classification model with CNN, and here is my code
