I had this problem running numpy on an ubuntu server. I got all of the following errors, depending on whether I tried to import numpy in a shell or running my django app: PyCapsule_Import could not import module “datetime” from numpy.core._multiarray_umath import ( OpenBLAS blas_thread_init: pthread_create failed for thread 25 of 32: Resource temporarily unavailable … Read more
Installing error in Google Cloud using CloudML library in R
you can change the factor parameter to change that. By default it is set to 3, but you can increase this number to get more than 2 epoch per trial see : docs The Hyperband tuning algorithm uses adaptive resource allocation and early-stopping to quickly converge on a high-performing model. This is done using a … Read more
I’ve figured it out myself: TL;DR: Make sure your loss magnitude is independent of your mini-batch size. The long explanation: In my case the issue was Keras-specific after all. Maybe the solution to this problem will be useful for someone at some point. It turns out that Keras divides the loss by the mini-batch size. … Read more
You can import Layer and InputSpec from TensorFlow as follows: from tensorflow.python.keras.layers import Layer, InputSpec UPDATE: 30/10/2019 from tensorflow.keras.layers import Layer, InputSpec
I solved the problem by clearing the keras session before each load from keras import backend as K for i in range(…): K.clear_session() model = load_model(…)
Use keras backend to fit the input tensor to 2D transpose convolution. Do not always use transpose operation for it will consume a lot of time. import keras.backend as K from keras.layers import Conv2DTranspose, Lambda def Conv1DTranspose(input_tensor, filters, kernel_size, strides=2, padding=’same’): “”” input_tensor: tensor, with the shape (batch_size, time_steps, dims) filters: int, output dimension, i.e. … Read more
It will shuffle your entire dataset (x, y and sample_weight together) first and then make batches according to the batch_size argument you passed to fit. Edit As @yuk pointed out in the comment, the code has been changed significantly since 2018. The documentation for the shuffle parameter now seems more clear on its own. You … Read more
You can get the output shape of a layer by layer.output_shape. for layer in model.layers: print(layer.output_shape) Gives you: (None, None, 64, 64, 40) (None, None, 64, 64, 40) (None, None, 64, 64, 40) (None, None, 64, 64, 40) (None, None, 64, 64, 40) (None, None, 64, 64, 40) (None, None, 64, 64, 40) (None, None, … Read more
Actually – there is no a good answer to your question. Most of the architectures are usually carefully designed and finetuned during many experiments. I could share with you some of the rules of thumbs one should apply when designing its own architecture: Avoid a dimension collapse in the first layer. Let’s assume that your … Read more