Here I’m answering to OP’s topic question rather than his exact problem. I’m doing this as the question shows up in the top when I google the topic problem. You can implement a custom metric in two ways. As mentioned in Keras docu. import keras.backend as K def mean_pred(y_true, y_pred): return K.mean(y_pred) model.compile(optimizer=”sgd”, loss=”binary_crossentropy”, metrics=[‘accuracy’, … Read more
Confusingly both FLOPs, floating point operations, and FLOPS, floating point operations per second, are used in reference to machine learning. FLOPs are often used to describe how many operations are required to run a single instance of a given model, like VGG19. This is the usage of FLOPs in both of the links you posted, … Read more
(tensorflow)$ pip install –upgrade pip # for Python 2.7 (tensorflow)$ pip3 install –upgrade pip # for Python 3.n (tensorflow)$ pip install –upgrade tensorflow # for Python 2.7 (tensorflow)$ pip3 install –upgrade tensorflow # for Python 3.n (tensorflow)$ pip install –upgrade tensorflow-gpu # for Python 2.7 and GPU (tensorflow)$ pip3 install –upgrade tensorflow-gpu # for Python … Read more
Have a look at metrics.py, there you can find definition of all available metrics including different types of accuracy. Accuracy is not printed unless you add it to the list of desired metrics when you compile your model. Regularizers are by definition added to the loss. For example, see add_loss method of the Layerclass. Update … Read more
I see most people confused about tf.shape(tensor) and tensor.get_shape() Let’s make it clear: tf.shape tf.shape is used for dynamic shape. If your tensor’s shape is changable, use it. An example: a input is an image with changable width and height, we want resize it to half of its size, then we can write something like: … Read more
The problem is from my training loop: it doesn’t detach or repackage the hidden state in between batches? If so, then loss.backward() is trying to back-propagate all the way through to the start of time, which works for the first batch but not for the second because the graph for the first batch has been … Read more
Models and layers have special method for that purpose: model.count_params() Also, to get a short summary of each layer dimensions and parameters, you might find useful the following method model.summary()
Since you have a pretty small dataset (~ 1000 samples), you would probably be safe using a batch size of 32, which is pretty standard. It won’t make a huge difference for your problem unless you’re training on hundreds of thousands or millions of observations. To answer your questions on Batch Size and Epochs: In … Read more
Models can be any way you want. If I understood it right, you just want to know how to create models with LSTM? Using LSTMs Well, first, you have to define what your encoded vector looks like. Suppose you want it to be an array of 20 elements, a 1-dimension vector. So, shape (None,20). The … Read more
If you want to implement dropout approach to measure uncertainty you should do the following: Implement function which applies dropout also during the test time: import keras.backend as K f = K.function([model.layers[0].input, K.learning_phase()], [model.layers[-1].output]) Use this function as uncertainty predictor e.g. in a following manner: def predict_with_uncertainty(f, x, n_iter=10): result = numpy.zeros((n_iter,) + x.shape) for … Read more