I agree with @Qualia ‘s comment, np.moveaxis(a, source, destination) is easier to understand. This does the job: x = np.zeros((12, 12, 3)) x.shape #yields: (12, 12, 3) x = np.moveaxis(x, -1, 0) x.shape #yields: (3, 12, 12)
The most likely reason is that the optimizer is not suited to your dataset. Here is a list of Keras optimizers from the documentation. I recommend you first try SGD with default parameter values. If it still doesn’t work, divide the learning rate by 10. Do that a few times if necessary. If your learning … Read more
The number of filters is the number of neurons, since each neuron performs a different convolution on the input to the layer (more precisely, the neurons’ input weights form convolution kernels). A feature map is the result of applying a filter (thus, you have as many feature maps as filters), and its size is a … Read more
Due to that you can’t calculate ROC&AUC by mini-batches, you can only calculate it on the end of one epoch. There is a solution from jamartinh, I patch the code below for convenience: from sklearn.metrics import roc_auc_score from keras.callbacks import Callback class RocCallback(Callback): def __init__(self,training_data,validation_data): self.x = training_data[0] self.y = training_data[1] self.x_val = validation_data[0] self.y_val … Read more
It is my solution for the same problem Install TDM GCC x64. Install Anaconda x64. Open the Anaconda prompt Run conda update conda Run conda update –all Run conda install mingw libpython Install the latest version of Theano, pip install git+git://github.com/Theano/Theano.git Run pip install git+git://github.com/fchollet/keras.git
To answer my own question and have a solution – I wrote a plain c++ solution called keras2cpp (its code available on github). In this solution you store network architecture (in json) and weights (in hdf5). Then you can dump a network to a plain text file with provided script. You can use obtained text … Read more
The number of parameters is 7850 because with every hidden unit you have 784 input weights and one weight of connection with bias. This means that every hidden unit gives you 785 parameters. You have 10 units so it sums up to 7850. The role of this additional bias term is really important. It significantly … Read more
You can find the answer at the Keras docs: https://keras.io/getting-started/faq/#how-can-i-obtain-reproducible-results-using-keras-during-development. In short, to be absolutely sure that you will get reproducible results with your python script on one computer’s/laptop’s CPU then you will have to do the following: Set the PYTHONHASHSEED environment variable at a fixed value Set the python built-in pseudo-random generator at a … Read more
model.predict() expects the first parameter to be a numpy array. You supply a list, which does not have the shape attribute a numpy array has. Otherwise your code looks fine, except that you are doing nothing with the prediction. Make sure you store it in a variable, for example like this: prediction = model.predict(np.array(tk.texts_to_sequences(text))) print(prediction)
Regression with neural networks is hard to get working because the output is unbounded, so you are especially prone to the exploding gradients problem (the likely cause of the nans). Historically, one key solution to exploding gradients was to reduce the learning rate, but with the advent of per-parameter adaptive learning rate algorithms like Adam, … Read more