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#Keras give your own name to a layer sequential model how to#
#Keras give your own name to a layer sequential model code#

#Keras give your own name to a layer sequential model code#

I have tried the following code which compiles but the output is not what I wanted: model = Sequential([ĭense(128, input_shape=(1,), activation='relu', name='date'),ĭense(128, input_shape=(1,), activation='relu', name='km'),ĭense(128, input_shape=(10,), activation='relu', name='consume'),ĭense(128, input_shape=(440,), activation='relu', name='type'), Below is my implementation of one lstm: def lstmmodel(nfeatures, nhiddenunit, learningrate, p, recurrentp): model keras.Sequential() model. import numpy as np np.ed (5) import os os.environ 'CUDAVISIBLEDEVICES' '-1' from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense from keras import regularizers from keras.optimizers. I would like to do something similar to this: Running a single hidden layer MLP on MNIST, I get extremly different results for Keras and sklearn.

type: the car's type (for example: "BMW-320", stored in one-hot encoded vector with 440 element ).

It provides a clean and clear way of creating Deep Learning models.

consume: the car's consume type (one-hot encoded vector with 10 element e.g. The cell abstraction, together with the generic class, make it very easy to implement custom RNN architectures for your research. It is a high-level neural network API that runs on the top of TensorFlow and Theano.

I tried reconstructing the model without some of the layers and I found the culprit to be these 2 layers constructed by the line xpreprocess.

#Keras give your own name to a layer sequential model how to#

date: the car's first registration date (int) How to add names to layers of Keras sequential model Ask Question Asked 3 years, 7 months ago Modified 3 years, 7 months ago Viewed 361 times 0 I use Keras in Tensorflow 2. predict () I get slightly different results everytime when using the exact same batch (with shuffleFalse in my dataset, and all the random seeds initialized).I stuck where I have to feed my data to my model. I am quite new to machine learning and I am currently working on a "car value predictor" application.