deep-learning

问题 I tried myself but couldn't reach the final point that's why posting here, please guide me. I am working in multi-label image classification and have slightly different scenarios. Actually I am confused, how we will map labels and their attribute with Id etc So we can use for training and testing. Here is code on which I am working import os import numpy as np import pandas as pd from keras.utils import to_categorical from collections import Counter from keras.callbacks import Callback from

问题 I tried myself but couldn't reach the final point that's why posting here, please guide me. I am working in multi-label image classification and have slightly different scenarios. Actually I am confused, how we will map labels and their attribute with Id etc So we can use for training and testing. Here is code on which I am working import os import numpy as np import pandas as pd from keras.utils import to_categorical from collections import Counter from keras.callbacks import Callback from

问题 I tried myself but couldn't reach the final point that's why posting here, please guide me. I am working in multi-label image classification and have slightly different scenarios. Actually I am confused, how we will map labels and their attribute with Id etc So we can use for training and testing. Here is code on which I am working import os import numpy as np import pandas as pd from keras.utils import to_categorical from collections import Counter from keras.callbacks import Callback from

问题 I have a Keras Sequential model taking inputs from csv files. When I run the model, its accuracy remains zero even after 20 epochs. I have gone through these two stackoverflow threads (zero-accuracy-training and why-is-the-accuracy-for-my-keras-model-always-0) but nothing solved my problem. As my model is binary classification, and I think it should not work like a regression model to make accuracy metric ineffective. Here is the Model def preprocess(*fields): return tf.stack(fields[:-1]), tf

问题 The Keras website has this article about exporting Keras models to core Tensorflow. However the step new_model = model_from_config(config) throws an error: Traceback (most recent call last): File "/home/hal9000/tf_serving_experiments/sndbx.py", line 38, in <module> new_model = model_from_config(config) File "/home/hal9000/keras2env/local/lib/python2.7/site-packages/keras/models.py", line 304, in model_from_config return layer_module.deserialize(config, custom_objects=custom_objects) File "

问题 I have been trying to implement the DCGan, the face book's paper, and blocked by below two issues almost for 2 weeks. Any suggestions would be appreciated. Thanks. Issue 1: DCGAN paper suggest to use BN(Batch Normalization) both the generator and discriminator. But, I couldn't get better result with BN rather than w/out BN. I copied DCGAN model I used which is exactly the same with a DCGAN paper. I don't think it is due to overfitting. Because (1) It keeps showing the noise the same with an

问题 I have been trying to implement the DCGan, the face book's paper, and blocked by below two issues almost for 2 weeks. Any suggestions would be appreciated. Thanks. Issue 1: DCGAN paper suggest to use BN(Batch Normalization) both the generator and discriminator. But, I couldn't get better result with BN rather than w/out BN. I copied DCGAN model I used which is exactly the same with a DCGAN paper. I don't think it is due to overfitting. Because (1) It keeps showing the noise the same with an

问题 I'm checking the output of summary function and don't understand all the printed values. For example, look on this simple code: x = [1, 2, 3, 4, 5] y = [1.2, 1.8, 3.5, 3.7, 5.3] model = Sequential() model.add(Dense(10, input_dim=1, activation='relu')) model.add(Dense(30, input_dim=1, activation='relu')) model.add(Dense(10, input_dim=1, activation='relu')) model.add(Dense(1)) model.summary() The output: Model: "sequential" _________________________________________________________________ Layer

问题 I am going through Andrew Ng’s tutorial from the CS230 Stanford course, and in every epoch of the training, evaluation is performed by calculating the metrics. But before calculating the metrics, they are sending the batches to CPU and converting them to numpy arrays (code here). # extract data from torch Variable, move to cpu, convert to numpy arrays output_batch = output_batch.data.cpu().numpy() labels_batch = labels_batch.data.cpu().numpy() # compute all metrics on this batch summary_batch

问题 In the end you can see that i have tried converting this into a numpy array but I don't understand why tensorflow dosen't support it? I have looked at the other related pages but none seemed to help. Is there some other format i have to do to the data in order to properly fit in model? this is what keras says: x Vector, matrix, or array of training data (or list if the model has multiple inputs). If all inputs in the model are named, you can also pass a list mapping input names to data. x can