I want to train a model, and use it on another computer. Want to save the model. how I should save the model? this is enough? model.save(‘model.h5’) And when its training is complete, How I should use it on another computer? All of my systems are based on windows. Thank you. Source: Python..

#### Category : keras

I want to train a Keras model by using CrossValidation, but my data is dict of lists of the form: pairs = {‘0’: list1, ‘1’: list2, ‘2’: list3, ‘3’: list4, ‘4’: list5, ‘5’: list6, ‘6’: list7, ‘7’: list8, ‘8’: list9, ‘9’: list10, } I want 10 folds, so I want the subset of 10 % ..

I’m trying to learn by working on the example given on the the Keras website I have a solid grasp on basic seq2seq, but Im struggling to understand how to implement an attention mechanism. Source: Python..

While I was training my cnn I got this error about tensorflow.python.framework.errors_impl.InvalidArgumentError: Input to reshape is a tensor with 1622400 values, but the requested shape requires a multiple of 14400 [[node sequential/flatten/Reshape (defined at /train.py:147) ]] [Op:__inference_train_function_1558] Function call stack: train_function But I don’t really know which part is the error coming from. I think ..

I have trained a model with 7,000,000 parameters on a GPU with 12 GB memory and batch size 20. The problem is that now I want to train another model with 4,000,000 parameters and the same batch size but the GPU gives me OOM error. Why is this happening? Source: Python..

I created this model from tensorflow.keras.layers import Conv2D from tensorflow.keras.layers import MaxPooling2D, Input, Dense from tensorflow.keras.layers import Reshape, Flatten from tensorflow.keras import Model def create_DeepCAPCHA(input_shape=(28,28,1),n_prediction=1,n_class=10,optimizer=’adam’, show_summary=True): inputs = Input(input_shape) x = Conv2D(filters=32, kernel_size=3, activation=’relu’, padding=’same’)(inputs) x = MaxPooling2D(pool_size=2)(x) x = Conv2D(filters=48, kernel_size=3, activation=’relu’, padding=’same’)(x) x = MaxPooling2D(pool_size=2)(x) x = Conv2D(filters=64, kernel_size=3, activation=’relu’, padding=’same’)(x) x = ..

I am using version 3.8 of Python. I used the following function in code, which is the design of an auto-encoder. But I get the following error. Thank you for your help. def multiple_layer_autoencoder(X_train, X_test, activation = ‘linear’, batch_size = 100, nb_epoch = 100, last_dim = 64): nb_hidden_layers = [X_train.shape[1], 256, 128, last_dim] X_train_tmp = ..

I am currently working with the neuMF model from TensorFlow (you can find it here). I’ve already debugged the whole TensorFlow project. The parameter transfer is what I still find difficult. A parameter item_input is passed as well as a user_input and params are passed. I once took screenshots of it. item_input: user_input: params: I ..

I am running a notebook in colab. I apologise in advance – I am just beginning and very inexperienced Basically – until August I was using the model to train on a small dataset of images in it worked pretty well. After August the same model would no longer train – the discriminator would go ..

I have a Keras pre-trained model "model_keras" and I want to use it in a loss function. The input of model "model_keras" is an output of another Tensorflow model "model_tf" (a generative model). I’m trying to update the weights of "model_tf" by minimizing the loss. During the optimization, "model_kears" is only used for inference and ..

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