deep-learning

问题 I trained an SSD MobileNet v2 network using the TensorFlow Object Detection API with TensorFlow 2 and then converted the trained model into a SavedModel. Now I need to convert the SavedModel to a FrozenGraph in order to make the model compatible with external libraries like OpenCV. I use this example for conversion and I cannot even load the Keras model. from keras.models import load_model model = load_model("training/model/saved_model") Calling load_model() produces an exception: Exception

问题 I trained an SSD MobileNet v2 network using the TensorFlow Object Detection API with TensorFlow 2 and then converted the trained model into a SavedModel. Now I need to convert the SavedModel to a FrozenGraph in order to make the model compatible with external libraries like OpenCV. I use this example for conversion and I cannot even load the Keras model. from keras.models import load_model model = load_model("training/model/saved_model") Calling load_model() produces an exception: Exception

问题 Bahdanau's Additive Attention is recognized as the second part of equation 4 in the below image. I am trying to figure out the shapes of the matrices w1 , w2 , ht , hs and v in order to figure out how this mechanism is used in this paper Can ht and hs have different final dimensions? say (batch size, total units) and (batch size, time window). Equation 8 in the mentioned paper above seem to be doing this. Equation 8 in the above paper has the below notation: what does this expand to exactly?

问题 I'd like to load a keras model that i've trained and saved it as .pb . Here's the code, Am using a jupyter notebook. The model is successfully saved as saved_model.pb under the same directory. But the code is unable to access it. Can anybody see to it, how can i access this keras model that's saved in .pb extension. I checked at several other places for solution but no luck. Model is saved at model/saved_model.pb . I've taken out the .pb file and placed it in the same directory where my code

问题 I am trying to predict a continuous value (using a Neural Network for the first time). I have normalized the input data. I can't figure out why I am getting a loss: nan output starting with the first epoch. I read and tried many suggestions from previous answers to the same question but that none of them helped me. My training data shape is: (201917, 64) . Here's my code: model = Sequential() model.add(Dense(100, input_dim=X.shape[1], activation='relu')) model.add(Dense(100, activation='relu'

问题 I'm fairly new to Keras, please excuse me if I made a fundamental error. So, my model has 3 Convolutional (2D) layers and 4 Dense Layers, interspersed with Dropout Layers. I am trying to train a Regression Model using images. X_train.shape = (5164, 160, 320, 3) y_train.shape = (5164) from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense, Flatten, Conv2D, Activation, MaxPooling2D, Dropout import tensorflow.compat.v1 as tf tf.disable_v2_behavior() from tensorflow

问题 I'm fairly new to Keras, please excuse me if I made a fundamental error. So, my model has 3 Convolutional (2D) layers and 4 Dense Layers, interspersed with Dropout Layers. I am trying to train a Regression Model using images. X_train.shape = (5164, 160, 320, 3) y_train.shape = (5164) from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense, Flatten, Conv2D, Activation, MaxPooling2D, Dropout import tensorflow.compat.v1 as tf tf.disable_v2_behavior() from tensorflow

问题 I have the following pieces of code: # Device configuration device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') seed = 42 np.random.seed(seed) torch.manual_seed(seed) # split the dataset into validation and test sets len_valid_set = int(0.1*len(dataset)) len_train_set = len(dataset) - len_valid_set print("The length of Train set is {}".format(len_train_set)) print("The length of Test set is {}".format(len_valid_set)) train_dataset , valid_dataset, = torch.utils.data.random

问题 I have the following pieces of code: # Device configuration device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') seed = 42 np.random.seed(seed) torch.manual_seed(seed) # split the dataset into validation and test sets len_valid_set = int(0.1*len(dataset)) len_train_set = len(dataset) - len_valid_set print("The length of Train set is {}".format(len_train_set)) print("The length of Test set is {}".format(len_valid_set)) train_dataset , valid_dataset, = torch.utils.data.random

问题 I Want to Combine Two CNN Into Just One In Keras, What I Mean Is that I Want The Neural Network To Take Two Images And Process Each One in Separate CNN, and Then Concatenate Them Together Into The Flattening Layer and Use Fully Connected Layer to Do The Last Work, Here What I Did: # Start With First Branch ############################################################ branch_one = Sequential() # Adding The Convolution branch_one.add(Conv2D(32, (3,3),input_shape = (64,64,3) , activation = 'relu'