If you want the output printed in a fancy way: model.summary() If you want the sizes in an accessible form for layer in model.layers: print(layer.get_output_at(0).get_shape().as_list()) There are probably better ways to access the shapes than this. Thanks to Daniel for the inspiration.
In keras, non-trainable parameters (as shown in model.summary()) means the number of weights that are not updated during training with backpropagation. There are mainly two types of non-trainable weights: The ones that you have chosen to keep constant when training. This means that keras won’t update these weights during training at all. The ones that … Read more
To get the gradients of model output with respect to weights using Keras you have to use the Keras backend module. I created this simple example to illustrate exactly what to do: from keras.models import Sequential from keras.layers import Dense, Activation from keras import backend as k model = Sequential() model.add(Dense(12, input_dim=8, init=”uniform”, activation=’relu’)) model.add(Dense(8, … Read more
This question was raised at the Keras github repository in Issue #4446: Quick Question: can a model be fit for multiple times? It was closed by François Chollet with the following statement: Yes, successive calls to fit will incrementally train the model. So, yes, you can call fit multiple times.
Create a .keras (note the . in front) folder in you home directory and put the keras.json file there. For example, /home/DaniPaniz/.keras/keras.json (or ~/.keras/keras.json in short) if you are on a UNIX like system (MacOS X, Linux, *BSD). On Windows you want to create the folder %USERPROFILE%/.keras and put the JSON file there. Alternatively, you … Read more
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 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
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
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