keras

问题 I am trying to use a custom loss-function which depends on some arguments that the model does not have. The model has two inputs ( mel_specs and pred_inp ) and expects a labels tensor for training: def to_keras_example(example): # Preparing inputs return (mel_specs, pred_inp), labels # Is a tf.train.Dataset for model.fit(train_data, ...) train_data = load_dataset(fp, 'train).map(to_keras_example).repeat() In my loss function I need to calculate the lengths of mel_specs and pred_inp . This

问题 I am trying to use a custom loss-function which depends on some arguments that the model does not have. The model has two inputs ( mel_specs and pred_inp ) and expects a labels tensor for training: def to_keras_example(example): # Preparing inputs return (mel_specs, pred_inp), labels # Is a tf.train.Dataset for model.fit(train_data, ...) train_data = load_dataset(fp, 'train).map(to_keras_example).repeat() In my loss function I need to calculate the lengths of mel_specs and pred_inp . This

问题 I have a keras 3D/2D model. In this model a 3D layer has a shape of [None, None, 4, 32]. I want to reshape this into [None, None, 128]. However, if I simply do the following: reshaped_layer = Reshape((-1, 128))(my_layer) my_layer has a shape of [None, 128] and therefore I cannot apply afterwards any 2D convolution, like: conv_x = Conv2D(16, (1,1))(reshaped_layer) I've tried to use tf.shape(my_layer) and tf.reshape, but I have not been able to compile the model since tf.reshape is not a Keras

问题 I fond the following code in (https://www.tensorflow.org/tutorials/eager/custom_layers) class MyDenseLayer(tf.keras.layers.Layer): def __init__(self, num_outputs): super(MyDenseLayer, self).__init__() self.num_outputs = num_outputs def build(self, input_shape): self.kernel = self.add_variable("kernel", shape=[int(input_shape[-1]), self.num_outputs]) def call(self, input): return tf.matmul(input, self.kernel) The last two lines is call method, while it does not like usual python class method

问题 I'm working in a project that isolate vocal parts from an audio. I'm using the DSD100 dataset, but for doing tests I'm using the DSD100subset dataset from I only use the mixtures and the vocals. I'm basing this work on this article First I process the audios to extract a spectrogram and put it on a list, with all the audios forming four lists (trainMixed, trainVocals, testMixed, testVocals). Like this: def to_spec(wav, n_fft=1024, hop_length=256): return librosa.stft(wav, n_fft=n_fft, hop

问题 I'm working in a project that isolate vocal parts from an audio. I'm using the DSD100 dataset, but for doing tests I'm using the DSD100subset dataset from I only use the mixtures and the vocals. I'm basing this work on this article First I process the audios to extract a spectrogram and put it on a list, with all the audios forming four lists (trainMixed, trainVocals, testMixed, testVocals). Like this: def to_spec(wav, n_fft=1024, hop_length=256): return librosa.stft(wav, n_fft=n_fft, hop

问题 I'm working in a project that isolate vocal parts from an audio. I'm using the DSD100 dataset, but for doing tests I'm using the DSD100subset dataset from I only use the mixtures and the vocals. I'm basing this work on this article First I process the audios to extract a spectrogram and put it on a list, with all the audios forming four lists (trainMixed, trainVocals, testMixed, testVocals). Like this: def to_spec(wav, n_fft=1024, hop_length=256): return librosa.stft(wav, n_fft=n_fft, hop

问题 I'm working in a project that isolate vocal parts from an audio. I'm using the DSD100 dataset, but for doing tests I'm using the DSD100subset dataset from I only use the mixtures and the vocals. I'm basing this work on this article First I process the audios to extract a spectrogram and put it on a list, with all the audios forming four lists (trainMixed, trainVocals, testMixed, testVocals). Like this: def to_spec(wav, n_fft=1024, hop_length=256): return librosa.stft(wav, n_fft=n_fft, hop

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