CNN

问题 System information Colab tensorflow 2.2.0 Describe the current behavior: I faced this error when i tried to solve my own data issues, which is multiple label semantic segmentations. Below is the code import tensorflow as tf import tensorflow.keras.backend as K IMG_WIDTH = 512 IMG_HEIGHT = 512 IMG_CHANNELS = 3 # batch_shape=(512,512,3) # inputs = Input(batch_shape=(4, 512, 512, 3)) #Build the model inputs = tf.keras.layers.Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS)) #s = tf.keras.layers

问题 System information Colab tensorflow 2.2.0 Describe the current behavior: I faced this error when i tried to solve my own data issues, which is multiple label semantic segmentations. Below is the code import tensorflow as tf import tensorflow.keras.backend as K IMG_WIDTH = 512 IMG_HEIGHT = 512 IMG_CHANNELS = 3 # batch_shape=(512,512,3) # inputs = Input(batch_shape=(4, 512, 512, 3)) #Build the model inputs = tf.keras.layers.Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS)) #s = tf.keras.layers

问题 I am trying to implement Bayesian CNN using Mc Dropout on Pytorch, the main idea is that by applying dropout at test time and running over many forward passes , you get predictions from a variety of different models. I’ve found an application of the Mc Dropout and I really did not get how they applied this method and how exactly they did choose the correct prediction from the list of predictions here is the code def mcdropout_test(model): model.train() test_loss = 0 correct = 0 T = 100 for

问题 I have trained a keras CNN monitoring the metrics as follow: METRICS = [ TruePositives(name='tp'), FalsePositives(name='fp'), TrueNegatives(name='tn'), FalseNegatives(name='fn'), BinaryAccuracy(name='accuracy'), Precision(name='precision'), Recall(name='recall'), AUC(name='auc'), ] and then the model.compile: model.compile(optimizer='nadam', loss='binary_crossentropy', metrics=METRICS) it works perfectly and I saved my h5 model (model.h5). Now I have downloaded the model and I would like to

问题 I have trained a keras CNN monitoring the metrics as follow: METRICS = [ TruePositives(name='tp'), FalsePositives(name='fp'), TrueNegatives(name='tn'), FalseNegatives(name='fn'), BinaryAccuracy(name='accuracy'), Precision(name='precision'), Recall(name='recall'), AUC(name='auc'), ] and then the model.compile: model.compile(optimizer='nadam', loss='binary_crossentropy', metrics=METRICS) it works perfectly and I saved my h5 model (model.h5). Now I have downloaded the model and I would like to

问题 I have trained a keras CNN monitoring the metrics as follow: METRICS = [ TruePositives(name='tp'), FalsePositives(name='fp'), TrueNegatives(name='tn'), FalseNegatives(name='fn'), BinaryAccuracy(name='accuracy'), Precision(name='precision'), Recall(name='recall'), AUC(name='auc'), ] and then the model.compile: model.compile(optimizer='nadam', loss='binary_crossentropy', metrics=METRICS) it works perfectly and I saved my h5 model (model.h5). Now I have downloaded the model and I would like to

问题 I have trained a keras CNN monitoring the metrics as follow: METRICS = [ TruePositives(name='tp'), FalsePositives(name='fp'), TrueNegatives(name='tn'), FalseNegatives(name='fn'), BinaryAccuracy(name='accuracy'), Precision(name='precision'), Recall(name='recall'), AUC(name='auc'), ] and then the model.compile: model.compile(optimizer='nadam', loss='binary_crossentropy', metrics=METRICS) it works perfectly and I saved my h5 model (model.h5). Now I have downloaded the model and I would like to

来源： https://stackoverflow.com/questions/64476565/good-training-accuracy-and-validaiton-accuracy-but-poor-prediction-accuracy

来源： https://stackoverflow.com/questions/64476565/good-training-accuracy-and-validaiton-accuracy-but-poor-prediction-accuracy

来源： https://stackoverflow.com/questions/63034278/error-in-multiplying-2-tensorflow-cnn-layers-with-different-dimensions-for-atten