keras-layer

问题 I am currently trying to recreate the Unet. At the "upconvolution" part where the outputs of two layers needs to be merged I got the mentioned error. (TypeError: init () got multiple values for argument 'axis') Keras Version: 2.0.6 Tensorflow-gpu: 1.2.1 Code snippet: import gzip import os from six.moves import urllib import tensorflow as tf import numpy as np from keras.models import Sequential, Model from keras.layers import Input, Dropout, Flatten, Concatenate from keras.layers import

问题 I am currently trying to recreate the Unet. At the "upconvolution" part where the outputs of two layers needs to be merged I got the mentioned error. (TypeError: init () got multiple values for argument 'axis') Keras Version: 2.0.6 Tensorflow-gpu: 1.2.1 Code snippet: import gzip import os from six.moves import urllib import tensorflow as tf import numpy as np from keras.models import Sequential, Model from keras.layers import Input, Dropout, Flatten, Concatenate from keras.layers import

问题 I am new in using tensorflow. I am trying to train my network with images of shape (16*16). I have divided 3 grayscale images of 512*512 into 16*16 and appended all. so i have 3072*16*16. while training I am getting error. I am using jupyter notebook.Can anyone please help me? Here is the code import tensorflow as tf import numpy as np from numpy import newaxis import glob import os from PIL import Image,ImageOps import random from os.path import join import matplotlib.pyplot as plt from

问题 can anyone tell me how is backpropagation done in Keras? I read that it is really easy in Torch and complex in Caffe, but I can't find anything about doing it with Keras. I am implementing my own layers in Keras (A very beginner) and would like to know how to do the backward propagation. Thank you in advance 回答1: You simply don't. (Late edit: except when you are creating custom training loops, only for advanced uses) Keras does backpropagation automatically. There's absolutely nothing you

问题 can anyone tell me how is backpropagation done in Keras? I read that it is really easy in Torch and complex in Caffe, but I can't find anything about doing it with Keras. I am implementing my own layers in Keras (A very beginner) and would like to know how to do the backward propagation. Thank you in advance 回答1: You simply don't. (Late edit: except when you are creating custom training loops, only for advanced uses) Keras does backpropagation automatically. There's absolutely nothing you

问题 I'm learning tensorflow 2.0 from its older versions. I found tensorflow model is changed Sequential-base from Class-base. But I want to use Class-base model because it is easy to read for me. I want to try translate : https://www.tensorflow.org/beta/tutorials/keras/basic_text_classification_with_tfhub embedding = 'https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1' hub_layer = hub.KerasLayer(embedding, input_shape=[], dtype=tf.string, trainable=True) # hub_layer(train_example_batch[:3]

问题 I'm learning tensorflow 2.0 from its older versions. I found tensorflow model is changed Sequential-base from Class-base. But I want to use Class-base model because it is easy to read for me. I want to try translate : https://www.tensorflow.org/beta/tutorials/keras/basic_text_classification_with_tfhub embedding = 'https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1' hub_layer = hub.KerasLayer(embedding, input_shape=[], dtype=tf.string, trainable=True) # hub_layer(train_example_batch[:3]

问题 I have the following two models, where model_A is trained first, then the output of model_A is used to train the model_C : import keras from keras.layers import Input, Dense from keras.models import Model inputs = Input(shape=(12,)) # --------------------------------------- # model_A x = Dense(64, activation='relu')(inputs) x = Dense(64, activation='relu')(x) predictions_A = Dense(3, activation='softmax')(x) model_A = Model(inputs=inputs, outputs=predictions_A) model_A.compile(optimizer=

问题 I'm building this model: inputs = model.inputs[:2] layer_output = model.get_layer('Encoder-12-FeedForward-Norm').output input_layer= keras.layers.Input(shape=(SEQ_LEN,768))(layer_output) conv_layer= keras.layers.Conv1D(100, kernel_size=3, activation='relu', data_format='channels_first')(input_layer) maxpool_layer = keras.layers.MaxPooling1D(pool_size=4)(conv_layer) flat_layer= keras.layers.Flatten()(maxpool_layer) outputs = keras.layers.Dense(units=3, activation='softmax')(flat_layer) model =

问题 I'm using the Mask_RCNN package from this repo: https://github.com/matterport/Mask_RCNN . I tried to train my own dataset using this package but it gives me an error at the beginning. 2020-11-30 12:13:16.577252: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1 2020-11-30 12:13:16.587017: E tensorflow/stream_executor/cuda/cuda_driver.cc:314] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected 2020-11-30