vgg-net

I am using pre-trained VGG-16 model for image classification. I am adding custom last layer as the number of my classification classes are 10. I am training the model for 200 epochs. My question is: is there any way if I randomly stop (by closing python window) the training at some epoch, let's say epoch no. 50 and resume from there? I have read about saving and reloading model but my understanding is that works for our custom models only instead of pre-trained models like VGG-16. You can use ModelCheckpoint callback to save your model regularly. To use it, pass a callbacks parameter to the

There is an example of VGG16 fine-tuning on keras blog , but I can't reproduce it. More precisely, here is code used to init VGG16 without top layer and to freeze all blocks except the topmost: WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5' weights_path = get_file('vgg16_weights.h5', WEIGHTS_PATH_NO_TOP) model = Sequential() model.add(InputLayer(input_shape=(150, 150, 3))) model.add(Conv2D(64, (3, 3), activation='relu', padding='same')) model.add(Conv2D(64, (3, 3), activation='relu', padding=

I am trying to use a pre-trained Keras model within TensorFlow code, as described in this Keras blog post under section II: Using Keras models with TensorFlow. I want to use the pre-trained VGG16 network available in Keras to extract convolutional feature maps from images, and add my own TensorFlow code over that. So I've done this: import tensorflow as tf from tensorflow.python.keras.applications.vgg16 import VGG16, preprocess_input from tensorflow.python.keras import backend as K # images = a NumPy array containing 8 images model = VGG16(include_top=False, weights='imagenet') inputs = tf

I can't give the correct number of parameters of AlexNet or VGG Net . For example, to calculate the number of parameters of a conv3-256 layer of VGG Net, the answer is 0.59M = (3*3)*(256*256), that is (kernel size) * (product of both number of channels in the joint layers), however in that way, I can't get the 138M parameters. So could you please show me where is wrong with my calculation, or show me the right calculation procedure? If you refer to VGG Net with 16-layer (table 1, column D) then 138M refers to the total number of parameters of this network, i.e including all convolutional