neural-network

来源： https://stackoverflow.com/questions/47665417/mlp-with-partial-fit-performing-worse-than-with-fit-in-a-supervised-classifi

来源： https://stackoverflow.com/questions/55348647/weight-matrix-dimension-intuition-in-a-neural-network

来源： https://stackoverflow.com/questions/55348647/weight-matrix-dimension-intuition-in-a-neural-network

来源： https://stackoverflow.com/questions/59345934/1d-convolution-for-vector

问题 What I want to achieve is to compute the gradient of cross entropy with respect to input values x . In TensorFlow I had no troubles with that: ce_grad = tf.gradients(cross_entropy, x) But as my networks grew bigger and bigger I switched to Keras to build them faster. However, now I don't really know how to achieve the above? Is there a way to extract cross entropy and input tensor from model variable that store my whole model? Just for clarity my cross_entropy is: cross_entropy = tf.reduce

问题 I'm new to python and neural networks. I have a simple network written in Keras that can predict the next number in a linear sequence: import numpy as np from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM data = [[i for i in range(6)]]; data = np.array(data, dtype=int); target = [[i for i in range(10, 16)]]; target = np.array(target, dtype=int); model = Sequential(); model.add(Dense(1, input_dim=1)) model.add(Dense(1)); model.compile(loss='mean

问题 I'm working with RNN and using Pytorch & Torchtext. I've got a problem with building vocab in my RNN. My code is as follows: TEXT = Field(tokenize=tokenizer, lower=True) LABEL = LabelField(dtype=torch.float) trainds = TabularDataset( path='drive/{}'.format(TRAIN_PATH), format='tsv', fields=[ ('label_start', LABEL), ('label_end', None), ('title', None), ('symbol', None), ('text_content', TEXT), ]) testds = TabularDataset( path='drive/{}'.format(TEST_PATH), format='tsv', fields=[ ('text_content

问题 I'm working with RNN and using Pytorch & Torchtext. I've got a problem with building vocab in my RNN. My code is as follows: TEXT = Field(tokenize=tokenizer, lower=True) LABEL = LabelField(dtype=torch.float) trainds = TabularDataset( path='drive/{}'.format(TRAIN_PATH), format='tsv', fields=[ ('label_start', LABEL), ('label_end', None), ('title', None), ('symbol', None), ('text_content', TEXT), ]) testds = TabularDataset( path='drive/{}'.format(TEST_PATH), format='tsv', fields=[ ('text_content

问题 My team is training a CNN in Tensorflow for binary classification of damaged/acceptable parts. We created our code by modifying the cifar10 example code. In my prior experience with Neural Networks, I always trained until the loss was very close to 0 (well below 1). However, we are now evaluating our model with a validation set during training (on a separate GPU), and it seems like the precision stopped increasing after about 6.7k steps, while the loss is still dropping steadily after over

问题 My team is training a CNN in Tensorflow for binary classification of damaged/acceptable parts. We created our code by modifying the cifar10 example code. In my prior experience with Neural Networks, I always trained until the loss was very close to 0 (well below 1). However, we are now evaluating our model with a validation set during training (on a separate GPU), and it seems like the precision stopped increasing after about 6.7k steps, while the loss is still dropping steadily after over