I'm still in over my head here, but here's how things appear to me. Can anyone confirm I'm on the right track? This is all tied to learning_phase (see https://keras.io/backend/) and loss/metric estimation based on batches. Dropout is only active when the learning_phase is set ...
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 ...
We used K.clear_session() in order to make sure we are in a new session in each combination in the experiment (We imported consider K as tensorflow.keras.backend). We initialized the seeds using these lines: os.environ['PYTHONHASHSEED'] = str(self.seed) random.seed(self.seed) np.rand...
no matter what shape it is. So in our case, you might be very tempted to writeloss=tf.nn.sigmoid_cross_entropy_with_logitsbased on all of the Keras tutorial you’ve seen, but it’s incorrect since it takes average of the cross-entropy losses of all pixels for each ...
fine-grained control over when the internal state of the LSTM is updated. Normally LSTM state is cleared at the end of each batch in Keras, but we can control it by making the LSTM stateful and callingmodel.reset_state()to manage this state manually. This will be needed in later ...
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 ...
pt-styletransfer: Neural style transfer as a class in PyTorch. OpenFacePytorch: PyTorch module to use OpenFace's nn4.small2.v1.t7 model img_classification_pk_pytorch: Quickly comparing your image classification models with the state-of-the-art models (such as DenseNet, ResNet, ...) ...
optimizers import SGD, Adam, RMSprop from keras.utils import np_utils import keras.backend as K from itertools import product # Custom loss function with costs def w_categorical_crossentropy(y_true, y_pred, weights): nb_cl = len(weights) final_mask = K.zeros_like(y_pred[:, 0]) y_...
add(keras.layers.Dense(1, activation='sigmoid')) return model GAN Loss function: We train D to maximize the probability of assigning the correct label to both training examples and samples from G. We simultaneously train G to minimize log(1 − D(G(z))). In other words, D and G ...