I am using the keras API to load in the MNIST dataset. My problem is I need to use AlexNet as my algorithm. Understanding the AlexNet model, I require to start with 277x277 images but the MINST dataset has 28x28. How can I reshape the numpy array so that each image is 227x277 to then use the full AlexNet model? (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data() This is how I load my data in. Could someone show me the solution …

In the Alexnet model, after the encoder steps are completed, you end up with a 6x6x256 tensor. Now this needs to be flattened before we go to the ANN part of the network. However, the flattening results in a length of 4096. How did the size of the tensor reduce? In a few tutorials I read about these flatten steps, there is no loss of size when you flatten the tensor so I was expecting the length of the flattened …

I'm trying to do image classification using CNN. The exact model isn't important but I decided to try use AlexNet and I'm getting abysmal accuracy. I believe the issue might be with my data preprocessing. My dataset directory contains a Training and Test folder but no validation folder (I have to split the dataset myself) and they are layed out like this: Training ├── class0 │ ├── image1 │ ├── .... │ └── image20 │ ├── .... │ ├── image1 …

I have a question targeting some basics of CNN. I came across various CNN networks like AlexNet, GoogLeNet and LeNet. I read at a lot of places that AlexNet has 3 Fully Connected layers with 4096, 4096, 1000 layers each. The layer containing 1000 nodes is the classification layer and each neuron represents the each class. Now I came across GoogLeNet. I read about its architecture here. It says that GoogLeNet has 0 FC layers. However, you do need the …

On the abstract of the AlexNet paper, they claimed to have 60 million parameters: The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. When I implement the model with Keras, I get ~25 million params. model = tf.keras.models.Sequential([ tf.keras.layers.Conv2D(96, 11, strides=4, activation="relu", input_shape=[227,227,3]), tf.keras.layers.MaxPooling2D(pool_size=(3,3), strides=(2,2)), tf.keras.layers.Conv2D(256, 5, activation="relu", padding="SAME"), tf.keras.layers.MaxPooling2D(pool_size=(3,3), strides=(2,2)), tf.keras.layers.Conv2D(384, 3, activation="relu", padding="SAME"), tf.keras.layers.Conv2D(384, …

I am building a model based on ZFNet in Tensorflow 2.0. I am using the Petal images dataset. The images are of size 224x244x3. So my question is when implementing the first layer (conv2d) with filter size = 7 and a stride of 3 and padding of 0. I am getting the output dimension of 109.5 using formula (n+2p-f/S + 1). So if I use the above-mentioned values what will be the dimension returned by TensorFlow in the first layer. …

I'm working on my last year project where I'm given digitized WSI (Whole Slide Images), though they're fairly small around 1390x1040 size (which is unusual). These images are of cases of Glioblastoma Multiforme (brain cancer) which is stained with Ki-64 index, which results in what I assume malignant parts being marked as brown. Here's a small example of what I'm looking at. My objective in simple terms is to count the blue and brown cells (estimation of proliferation indices) which …

I'm making my way through Deep Learning research papers, starting with AlexNet, and I found differences in the implementation of PyTorch and Tensorflow that I can't explain. In the research paper, they define the model architecture with: The first convolutional layer filters the 224x224x3 input image with 96 kernels of size 11x11x3 with a stride of 4 pixels (this is the distance between the > receptive field centers of neighboring neurons in a kernel map). The second convolutional layer takes …

A lot of websites on CNN for large datasets of images talk about starting with the pretrained model for 1.2 million images in 1000 categories available via AlexNet / Imagenet. These sites seem to imply that this dataset is freely available, but I'm having trouble actually getting access to it. For example, I tried going to https://github.com/deep-diver/AlexNet but couldn't get the code in alexnet.ipynb to run. Consider the following code: for f in data_file.iterdir(): data = pickle.loads(f.read_bytes(), encoding='bytes') if 'meta' …

I know AlexNet does object classification in images [categories] and R-CNN does object localization [category and bounding box]. How does R-CNN and AlexNet compare? Are they used for the same purpose or R-CNN does more? Does R-CNN use Alexnet as a sub-module?

I have some questions if someone can answer me or guide me articles to understand them. I investigated different pre-trained model i.e. AlexNet, VGG, GoogLeNet, InceptionV3 and ResNet. I have retrained these models(In MATLAB2018b using single CPU) on my disease dataset and the retrained models are of following sizes: AlexNet: 207.266MB VGG: 407.981MB GoogLeNet:22MB Inception V3: 79.46MB ResNet: 155MB **Q1) Among all the size of the GoogLeNet and InceptionV3 is less among all? What can be the be the possible …

I've been reviewing performance of several NVIDIA GPU's and I see that typically results are presented in terms of "images per second" that can be processed. Experiments are typically being performed on classical network architectures such as Alex Net or GoogLeNet. I'm wondering if a given number of images per second, say 15000, means that 15000 images can be processed by iteration or for fully learning the network with that amount of images?. I suppose that if I have 15000 …

I am working on texture classification and based on previous works, I am trying to modify the final layer of AlexNET to have 20 classes, and train only that layer for my multi class classification problem. I am using Tensorflow-GPU on an NVIDIA GTX 1080, Python3.6 on Ubuntu 16.04. I am using the Gradient Descent Optimiser and the class Estimator to build this. I am also using two dropout layers for regularization. Therefore, my hyper parameters are the learning rate, …