With only 3 line of code… scaler = StandardScaler() X_train = scaler.fit_transform(X_train.reshape(-1, X_train.shape[-1])).reshape(X_train.shape) X_test = scaler.transform(X_test.reshape(-1, X_test.shape[-1])).reshape(X_test.shape)
Pytorch doc for register_buffer() method reads This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm’s running_mean is not a parameter, but is part of the persistent state. As you already observed, model parameters are learned and updated using SGD during the training process. However, … Read more
What is the version of your Ubuntu install? Try this. In your Makefile.config try to append /usr/include/hdf5/serial/ to INCLUDE_DIRS: — INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include +++ INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/ and rename hdf5_hl and hdf5 to hdf5_serial_hl and hdf5_serial in the Makefile: — LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5 +++ LIBRARIES … Read more
This worked for me from keras import backend as K and after predicting my data i inserted this part of code then i had again loaded the model. K.clear_session() i faced this problem in production server, but in my pc it was running fine ……….. from keras import backend as K #Before prediction K.clear_session() #After … Read more
Using Dense(activation=softmax) is computationally equivalent to first add Dense and then add Activation(softmax). However there is one advantage of the second approach – you could retrieve the outputs of the last layer (before activation) out of such defined model. In the first approach – it’s impossible.
I got the same problem when using the 1.0 released and I could make it work without having to roll back on a previous version. The problem is caused by change in the api. That discussion helped me to find the solution: Google group > Recent API Changes in TensorFlow You just have to update … Read more
Regularizers that’ll work best will depend on your specific architecture, data, and problem; as usual, there isn’t a single cut to rule all, but there are do’s and (especially) don’t’s, as well as systematic means of determining what’ll work best – via careful introspection and evaluation. How does RNN regularization work? Perhaps the best approach … Read more
Why don’t you use the InfogainLoss layer to compensate for the imbalance in your training set? The Infogain loss is defined using a weight matrix H (in your case 2-by-2) The meaning of its entries are [cost of predicting 1 when gt is 0, cost of predicting 0 when gt is 0 cost of predicting … Read more
Quoting an answer mentioned in github, you need to specify the dimension ordering: Keras is a wrapper over Theano or Tensorflow libraries. Keras uses the setting variable image_dim_ordering to decide if the input layer is Theano or Tensorflow format. This setting can be specified in 2 ways – specify ‘tf’ or ‘th’ in ~/.keras/keras.json like … Read more
Your first one consists of a 100 neurons input layer connected to one single output neuron Your second one consists of a 100 neurons input layer, one hidden layer of 32 neurons and one output layer of one single neuron. You have to think of your first layer as your input layer (with the same … Read more