deep-learning

问题 I have trained a fully convolutional neural network with Keras. I have used the Functional API and have defined the input layer as Input(shape=(128,128,3)) , corresponding to the size of the images in my training set. However, I want to use the trained model on images of variable sizes (which should be ok because the network is fully convolutional). To do this, I need to change my input layer to Input(shape=(None,None,3)) . The obvious way to solve the problem would have been to train my

问题 I have trained a fully convolutional neural network with Keras. I have used the Functional API and have defined the input layer as Input(shape=(128,128,3)) , corresponding to the size of the images in my training set. However, I want to use the trained model on images of variable sizes (which should be ok because the network is fully convolutional). To do this, I need to change my input layer to Input(shape=(None,None,3)) . The obvious way to solve the problem would have been to train my

问题 I have trained a fully convolutional neural network with Keras. I have used the Functional API and have defined the input layer as Input(shape=(128,128,3)) , corresponding to the size of the images in my training set. However, I want to use the trained model on images of variable sizes (which should be ok because the network is fully convolutional). To do this, I need to change my input layer to Input(shape=(None,None,3)) . The obvious way to solve the problem would have been to train my

问题 I have the following model - this is LSTM + CNN with 3 inputs. And I built this generator function to train the model using fit_generator (based on this: https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly): class MultiInputDataGenerator(keras.utils.Sequence): 'Generates data for Keras' def __init__(self, list_IDs, labels, shuffle=True): 'Initialization' self.batch_size = 8 self.labels = labels self.list_IDs = list_IDs self.n_classes = 5 self.shuffle = shuffle self.on

问题 I have the following model - this is LSTM + CNN with 3 inputs. And I built this generator function to train the model using fit_generator (based on this: https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly): class MultiInputDataGenerator(keras.utils.Sequence): 'Generates data for Keras' def __init__(self, list_IDs, labels, shuffle=True): 'Initialization' self.batch_size = 8 self.labels = labels self.list_IDs = list_IDs self.n_classes = 5 self.shuffle = shuffle self.on

来源： https://stackoverflow.com/questions/64794996/incompatible-array-types-are-mixed-in-the-forward-input-linearfunction-in-mach

来源： https://stackoverflow.com/questions/64794996/incompatible-array-types-are-mixed-in-the-forward-input-linearfunction-in-mach

来源： https://stackoverflow.com/questions/64794996/incompatible-array-types-are-mixed-in-the-forward-input-linearfunction-in-mach

来源： https://stackoverflow.com/questions/64644565/merge-sequential-models-in-keras

来源： https://stackoverflow.com/questions/56589726/valueerror-graph-disconnected-cannot-obtain-value-for-tensor-tensor-the-foll