I have the following subclassed Keras model which I have already trained. I want to be able to call all the methods in B_frame_CNN (e.g., get_embedding()) on the loaded model. The following code works perfectly and does what I need when run in an ipython notebook. However, when I run it in a python script (.py), I get the following
Tag: keras
Tensorflow accuracy from model.predict does not match final epoch val_accuracy of model.fit
I am trying to match the accuracy of a model.predict call to the final val_accuracy of model.fit(). I am using tf dataset. The dataset setup for train_ds is similar. I prefetch both… Than I get the labels for the val_ds so I can use them later My model Compiles fine Seems to fit fine The last epoch output Epoch 10:
Input 0 is incompatible with layer model_2
i have a generator rev_generator that yields a tuple of two elements (numpyarray of shape (1279,300,1) , int value: 0 or 1) then i pass it to: and then a simple model but when i call fit it throws me an error: Answer If you are using the tf.data.Dataset API, you should set the batch size explicitly and not in
Create a ML model with tensorflow that predicts a values at any given time range at hourly intervals
I am pretty new to ML and completely new to creating my own models. I have went through tensorflows time-series forecasting tutorial and other LSTM time series examples on how to predict with multi-variate inputs. After trying multiple examples I think I realized that this is not what I want to achieve. My problem involves a dataset that is in
Keras and Tensorflow OS resource requirement
I keep getting F tensorflow/core/platform/default/env.cc:73] Check failed: ret == 0 (11 vs. 0)Thread tf_data_private_threadpool creation via pthread_create() failed. errors during training, although the machine is quite powerful: altogether 64 logical cores ulimit -s gives 32768, ulimit -u gives 1030608 I want to train the following network with a bunch of online generated 512*512 grayscale images along with two additional parameters
Keras, simple neural network Error Code (model.predict)
Do any of you know why I get the following error code? My Code : You can ignore the Integrator Part, I just want to know why the model.predict wont work. Here is the error: Answer The problem is with the lines: Here your model is setup to receive a rank 2 tensor as input, but you are only giving
Mask layer is not working with MLPs, how to add a custom layer with masking?
I’m using MLPs to forecast a time series, I implement a code that contain a mask layer to let the model skip the mask values. for instance, in my data, the time series has a lot of NaN values, I fill it by a ‘value = -999’. I don’t want to remove it, but I want the Keras masking to
layer.get_weights() is not equal in the same model layers
Why not all the layer weights equal: Here is the output: The a_weights == b_weights are not all the “True”. What’s the problem? Answer Notice that the only time a_weights == b_weights is True, is when you are referencing a layer, which does not have any weights. np.array_equal is returning False because you are actually comparing lists of arrays and
Tensorflow Fused conv implementation does not support grouped convolutions
I did a neural network machine learning on colored images (3 channels). It worked but now I want to try to do it in grayscale to see if I can improve accuracy. Here is the code: You can see that I have changed the input_shape to have 1 single channel for grayscale. I’m getting an error: Node: ‘sequential_26/conv2d_68/Relu’ Fused conv
Prediction with keras embedding leads to indices not in list
I have a model that I trained with For the embedding I use Glove as a pre-trained embedding dictionary. Where I first build the tokenizer and text sequence with: t = Tokenizer() t.fit_on_texts(all_text) and then I’m calculating the embedding matrix with: now I’m using a new dataset for the prediction. This leads to an error: Node: ‘model/synopsis_embedd/embedding_lookup’ indices[38666,63] = 136482