deep-learning

问题 I trained one regression network using resnet50 as backbone. The input of the network is image whose size is 224*224*3 , the output of the network is one value , varying from 0 to 1 . but the netwrok can not converge, no matter I use sigmoid or relu as output layer's activation. mae or mse as loss function . For exampple, I use resnet50 as backbone, mae as loss function, sigmoid is the activation function of output layer. SGD as optimizer. The training loss would be: Epoch 1 training loss is

问题 I trained one regression network using resnet50 as backbone. The input of the network is image whose size is 224*224*3 , the output of the network is one value , varying from 0 to 1 . but the netwrok can not converge, no matter I use sigmoid or relu as output layer's activation. mae or mse as loss function . For exampple, I use resnet50 as backbone, mae as loss function, sigmoid is the activation function of output layer. SGD as optimizer. The training loss would be: Epoch 1 training loss is

问题 I have two 60 x 80921 matrices, one filled with data, one with reference. I would like to store the values as key/value pairs in two different LMDBs, one for training (say I'll slice around the 60000 column mark) and one for testing. Here is my idea; does it work? X_train = X[:,:60000] Y_train = Y[:,:60000] X_test = X[:,60000:] Y_test = Y[:,60000:] X_train = X_train.astype(int) X_test = X_test.astype(int) Y_train = Y_train.astype(int) Y_test = Y_test.astype(int) map_size = X_train.nbytes * 10

问题 I want to use the MobileNet model pre-trained on ImageNet for feature extraction. I am loading the model as follows: from keras.applications.mobilenet import MobileNet feature_model = MobileNet(include_top=False, weights='imagenet', input_shape=(200, 200, 3)) The Keras manual clearly says that this input shape is valid: input_shape: optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) (with 'channels_last' data format) or (3,

问题 I want to use the MobileNet model pre-trained on ImageNet for feature extraction. I am loading the model as follows: from keras.applications.mobilenet import MobileNet feature_model = MobileNet(include_top=False, weights='imagenet', input_shape=(200, 200, 3)) The Keras manual clearly says that this input shape is valid: input_shape: optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) (with 'channels_last' data format) or (3,

问题 I have implemented universal sentence encoder in Tensorflow and now I am trying to predict the class probabilities on a sentence. I am converting the string to an array as well. Code: if model.model_type == "universal_classifier_basic": class_probs = model.predict(np.array(['this is a random sentence'], dtype=object) Error Message: InvalidArgumentError (see above for traceback): input must be a vector, got shape: [] [[Node: lambda_1/module_apply_default/tokenize/StringSplit = StringSplit[skip

问题 I followed the guide at this link to build a model and stopped before the finetuning part to test the model on some other images using the following code: img_width, img_height = 150, 150 batch_size = 1 test_model = load_model('dog_cat_model.h5') validation_data_dir = "test1" test_datagen = ImageDataGenerator(rescale=1. / 255) validation_generator = test_datagen.flow_from_directory( validation_data_dir, target_size=(img_width, img_height), batch_size=batch_size, shuffle=False, class_mode=

问题 After trying the Keras example on CIFAR10, I decided to go for something bigger : a VGG-like net on the Tiny Imagenet dataset. This is a subset of the ImageNet dataset with 200 classes (instead of 1000) and 100K images downscaled to 64x64. I got the VGG-like model from the file vgg_like_convnet.py here. Unfortunately, things are going pretty much like here except that this time changing the learning rate or swapping TH for TF does not help. Neither changing the optimizer (see code below).

问题 I'm trying to implement the first part of the google blog entry Inceptionism: Going Deeper into Neural Networks in TensorFlow. So far I have found several resources that either explain it in natural language or focus on other parts or give code snippets for other frameworks. I understand the idea of optimizing a random input image with respect to a class prior and also the maths behind it given in the this paper, section 2, but I'm not able to implement it myself using TensorFlow. From this

问题 I'm trying to implement the first part of the google blog entry Inceptionism: Going Deeper into Neural Networks in TensorFlow. So far I have found several resources that either explain it in natural language or focus on other parts or give code snippets for other frameworks. I understand the idea of optimizing a random input image with respect to a class prior and also the maths behind it given in the this paper, section 2, but I'm not able to implement it myself using TensorFlow. From this