keras: false optimize: false int8: false dynamic: false simplify: false opset: null workspace: 4 nms: false lr0: 0.01 lrf: 0.01 momentum: 0.937 weight_decay: 0.0005 warmup_epochs: 3.0 warmup_momentum: 0.8 warmup_bias_lr: 0.1 box: 7.5 cls: 0.5 dfl: 1.5 pose: 12.0 kobj: 1.0 label...
The UNet network uses Python compiler based on Keras/Tensorflow 2.0 Library, and the GPU edition is a NVIDIA RTX 3060 laptop. The total number of training epochs is 50, and the learning rate at the beginning is set as 2 × 10− 4. After 5 epochs, if the loss function value ...
KerasDeep neural networkThis research aims at investigating the capability of Keras's deep learning models with three robust optimization algorithms (stochastic gradient descent, root mean square propagation, and adaptive moment optimization) and two-loss functions for spatial modeling of landslide hazard ...
I am using Keras 2.0 with Tensorflow 1.0 setup. I am building model in Keras and using Tensorflow pipeline for training and testing. When you load the keras model, it might reinitialize the weights. I avoided tf.global_variables_initializer() and used load_weights('saved_model.h5'). Then ...
In this work, we use the interior point solver Ipopt to solve the resulting OCP [60]. On the other hand, the training utilizes specialized tools like scikit-learn [72] or Keras [73]. Additionally, the framework automatically connects the different inputs of the machine learning models with ...
This research aims at investigating the capability of Keras’s deep learning models with three robust optimization algorithms (stochastic gradient descent, root mean square propagation, and adaptive moment optimization) and two-loss functions for spatial modeling of landslide hazard at a regional scale....
Additionally, we conducted experiments using a deep-learning model (KerasReressor) and an ensemble-learning approach (XGBoost). 4.3.1. Lasso Regression While the baseline approach relied on the linear regression model implementing the L2 regularization norm, due to the large number n-gram features ...
The main hyper-parameters of the feedforward neural network include the number of hidden layers, the number of hidden layer neurons, the loss function, and the number of iterations. The DNN parameter adjustment method generally adopts empirical adjustment. The keras and tensorflow packages in the ...
pytorch-summary: Model summary in PyTorch similar to model.summary() in Keras mpl.pytorch: Pytorch implementation of MaxPoolingLoss. scVI-dev: Development branch of the scVI project in PyTorch apex: An Experimental PyTorch Extension(will be deprecated at a later point) ELF: ELF: a platform for...
() self.d1 = Dense(128, activation='relu') self.d2 = Dense(10, activation='softmax') def call(self, x): x = self.conv1(x) x = self.flatten(x) x = self.d1(x) return self.d2(x) model = MyModel() model.build((512, 28, 28, 1)) loss_object = tf.keras.losses....